5 parts of research paper methods

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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Writing Research Papers

• Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin 

The methods section of an APA format psychology paper provides the methods and procedures used in a research study or experiment . This part of an APA paper is critical because it allows other researchers to see exactly how you conducted your research.

Method refers to the procedure that was used in a research study. It included a precise description of how the experiments were performed and why particular procedures were selected. While the APA technically refers to this section as the 'method section,' it is also often known as a 'methods section.'

The methods section ensures the experiment's reproducibility and the assessment of alternative methods that might produce different results. It also allows researchers to replicate the experiment and judge the study's validity.

This article discusses how to write a methods section for a psychology paper, including important elements to include and tips that can help.

What to Include in a Method Section

So what exactly do you need to include when writing your method section? You should provide detailed information on the following:

• Research design
• Participants
• Participant behavior

The method section should provide enough information to allow other researchers to replicate your experiment or study.

Components of a Method Section

The method section should utilize subheadings to divide up different subsections. These subsections typically include participants, materials, design, and procedure.

Participants 

In this part of the method section, you should describe the participants in your experiment, including who they were (and any unique features that set them apart from the general population), how many there were, and how they were selected. If you utilized random selection to choose your participants, it should be noted here.

For example: "We randomly selected 100 children from elementary schools near the University of Arizona."

At the very minimum, this part of your method section must convey:

• Basic demographic characteristics of your participants (such as sex, age, ethnicity, or religion)
• The population from which your participants were drawn
• Any restrictions on your pool of participants
• How many participants were assigned to each condition and how they were assigned to each group (i.e., randomly assignment , another selection method, etc.)
• Why participants took part in your research (i.e., the study was advertised at a college or hospital, they received some type of incentive, etc.)

Information about participants helps other researchers understand how your study was performed, how generalizable the result might be, and allows other researchers to replicate the experiment with other populations to see if they might obtain the same results.

In this part of the method section, you should describe the materials, measures, equipment, or stimuli used in the experiment. This may include:

• Testing instruments
• Technical equipment
• Any psychological assessments that were used
• Any special equipment that was used

For example: "Two stories from Sullivan et al.'s (1994) second-order false belief attribution tasks were used to assess children's understanding of second-order beliefs."

For standard equipment such as computers, televisions, and videos, you can simply name the device and not provide further explanation.

Specialized equipment should be given greater detail, especially if it is complex or created for a niche purpose. In some instances, such as if you created a special material or apparatus for your study, you might need to include an illustration of the item in the appendix of your paper.

In this part of your method section, describe the type of design used in the experiment. Specify the variables as well as the levels of these variables. Identify:

• The independent variables
• Dependent variables
• Control variables
• Any extraneous variables that might influence your results.

Also, explain whether your experiment uses a  within-groups  or between-groups design.

For example: "The experiment used a 3x2 between-subjects design. The independent variables were age and understanding of second-order beliefs."

The next part of your method section should detail the procedures used in your experiment. Your procedures should explain:

• What the participants did
• How data was collected
• The order in which steps occurred

For example: "An examiner interviewed children individually at their school in one session that lasted 20 minutes on average. The examiner explained to each child that he or she would be told two short stories and that some questions would be asked after each story. All sessions were videotaped so the data could later be coded."

Keep this subsection concise yet detailed. Explain what you did and how you did it, but do not overwhelm your readers with too much information.

Tips for How to Write a Methods Section

In addition to following the basic structure of an APA method section, there are also certain things you should remember when writing this section of your paper. Consider the following tips when writing this section:

• Use the past tense : Always write the method section in the past tense.
• Be descriptive : Provide enough detail that another researcher could replicate your experiment, but focus on brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
• Use an academic tone : Use formal language and avoid slang or colloquial expressions. Word choice is also important. Refer to the people in your experiment or study as "participants" rather than "subjects."
• Use APA format : Keep a style guide on hand as you write your method section. The Publication Manual of the American Psychological Association is the official source for APA style.
• Make connections : Read through each section of your paper for agreement with other sections. If you mention procedures in the method section, these elements should be discussed in the results and discussion sections.
• Proofread : Check your paper for grammar, spelling, and punctuation errors.. typos, grammar problems, and spelling errors. Although a spell checker is a handy tool, there are some errors only you can catch.

After writing a draft of your method section, be sure to get a second opinion. You can often become too close to your work to see errors or lack of clarity. Take a rough draft of your method section to your university's writing lab for additional assistance.

A Word From Verywell

The method section is one of the most important components of your APA format paper. The goal of your paper should be to clearly detail what you did in your experiment. Provide enough detail that another researcher could replicate your study if they wanted.

Finally, if you are writing your paper for a class or for a specific publication, be sure to keep in mind any specific instructions provided by your instructor or by the journal editor. Your instructor may have certain requirements that you need to follow while writing your method section.

Frequently Asked Questions

While the subsections can vary, the three components that should be included are sections on the participants, the materials, and the procedures.

• Describe who the participants were in the study and how they were selected.
• Define and describe the materials that were used including any equipment, tests, or assessments
• Describe how the data was collected

To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded, left-aligned and in title case.

The purpose of the methods section is to describe what you did in your experiment. It should be brief, but include enough detail that someone could replicate your experiment based on this information. Your methods section should detail what you did to answer your research question. Describe how the study was conducted, the study design that was used and why it was chosen, and how you collected the data and analyzed the results.

Erdemir F. How to write a materials and methods section of a scientific article ? Turk J Urol . 2013;39(Suppl 1):10-5. doi:10.5152/tud.2013.047

Kallet RH. How to write the methods section of a research paper . Respir Care . 2004;49(10):1229-32. PMID: 15447808.

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

American Psychological Association. APA Style Journal Article Reporting Standards . Published 2020.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Scientific and Scholarly Writing

• Literature Searches
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Parts of a Scientific & Scholarly Paper

Introduction.

• Writing Effectively
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Different sections are needed in different types of scientific papers (lab reports, literature reviews, systematic reviews, methods papers, research papers, etc.). Projects that overlap with the social sciences or humanities may have different requirements. Generally, however, you'll need to include:

INTRODUCTION (Background)

METHODS SECTION (Materials and Methods)

What is a title

Titles have two functions: to identify the main topic or the message of the paper and to attract readers.

The title will be read by many people. Only a few will read the entire paper, therefore all words in the title should be chosen with care. Too short a title is not helpful to the potential reader. Too long a title can sometimes be even less meaningful. Remember a title is not an abstract. Neither is a title a sentence.

What makes a good title?

A good title is accurate, complete, and specific. Imagine searching for your paper in PubMed. What words would you use?

• Use the fewest possible words that describe the contents of the paper.
• Avoid waste words like "Studies on", or "Investigations on".
• Use specific terms rather than general.
• Use the same key terms in the title as the paper.
• Watch your word order and syntax.

The abstract is a miniature version of your paper. It should present the main story and a few essential details of the paper for readers who only look at the abstract and should serve as a clear preview for readers who read your whole paper. They are usually short (250 words or less).

The goal is to communicate:

•  What was done?
•  Why was it done?
•  How was it done?
•  What was found?

A good abstract is specific and selective. Try summarizing each of the sections of your paper in a sentence two. Do the abstract last, so you know exactly what you want to write.

• Use 1 or more well developed paragraphs.
• Use introduction/body/conclusion structure.
• Present purpose, results, conclusions and recommendations in that order.
• Make it understandable to a wide audience.
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How to write the methods section of a research paper

Affiliation.

• 1 Respiratory Care Services, San Francisco General Hospital, NH:GA-2, 1001 Potrero Avenue, San Francisco, CA 94110, USA. [email protected]
• PMID: 15447808

The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. Material in each section should be organized by topic from most to least important.

• Biomedical Research*
• Research Design
• Writing* / standards

How to Write a Research Paper: Parts of the Paper

• Choosing Your Topic
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• Critical Thinking
• Evaluating Information
• Parts of the Paper
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Parts of the Research Paper Papers should have a beginning, a middle, and an end. Your introductory paragraph should grab the reader's attention, state your main idea, and indicate how you will support it. The body of the paper should expand on what you have stated in the introduction. Finally, the conclusion restates the paper's thesis and should explain what you have learned, giving a wrap up of your main ideas.

1. The Title The title should be specific and indicate the theme of the research and what ideas it addresses. Use keywords that help explain your paper's topic to the reader. Try to avoid abbreviations and jargon. Think about keywords that people would use to search for your paper and include them in your title.

2. The Abstract The abstract is used by readers to get a quick overview of your paper. Typically, they are about 200 words in length (120 words minimum to  250 words maximum). The abstract should introduce the topic and thesis, and should provide a general statement about what you have found in your research. The abstract allows you to mention each major aspect of your topic and helps readers decide whether they want to read the rest of the paper. Because it is a summary of the entire research paper, it is often written last. 

3. The Introduction The introduction should be designed to attract the reader's attention and explain the focus of the research. You will introduce your overview of the topic,  your main points of information, and why this subject is important. You can introduce the current understanding and background information about the topic. Toward the end of the introduction, you add your thesis statement, and explain how you will provide information to support your research questions. This provides the purpose and focus for the rest of the paper.

4. Thesis Statement Most papers will have a thesis statement or main idea and supporting facts/ideas/arguments. State your main idea (something of interest or something to be proven or argued for or against) as your thesis statement, and then provide your supporting facts and arguments. A thesis statement is a declarative sentence that asserts the position a paper will be taking. It also points toward the paper's development. This statement should be both specific and arguable. Generally, the thesis statement will be placed at the end of the first paragraph of your paper. The remainder of your paper will support this thesis.

Students often learn to write a thesis as a first step in the writing process, but often, after research, a writer's viewpoint may change. Therefore a thesis statement may be one of the final steps in writing. 

Examples of Thesis Statements from Purdue OWL

5. The Literature Review The purpose of the literature review is to describe past important research and how it specifically relates to the research thesis. It should be a synthesis of the previous literature and the new idea being researched. The review should examine the major theories related to the topic to date and their contributors. It should include all relevant findings from credible sources, such as academic books and peer-reviewed journal articles. You will want  to:

• Explain how the literature helps the researcher understand the topic.
• Try to show connections and any disparities between the literature.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.

More about writing a literature review. . .

6. The Discussion ​The purpose of the discussion is to interpret and describe what you have learned from your research. Make the reader understand why your topic is important. The discussion should always demonstrate what you have learned from your readings (and viewings) and how that learning has made the topic evolve, especially from the short description of main points in the introduction.Explain any new understanding or insights you have had after reading your articles and/or books. Paragraphs should use transitioning sentences to develop how one paragraph idea leads to the next. The discussion will always connect to the introduction, your thesis statement, and the literature you reviewed, but it does not simply repeat or rearrange the introduction. You want to: 

• Demonstrate critical thinking, not just reporting back facts that you gathered.
• If possible, tell how the topic has evolved over the past and give it's implications for the future.
• Fully explain your main ideas with supporting information.
• Explain why your thesis is correct giving arguments to counter points.

7. The Conclusion A concluding paragraph is a brief summary of your main ideas and restates the paper's main thesis, giving the reader the sense that the stated goal of the paper has been accomplished. What have you learned by doing this research that you didn't know before? What conclusions have you drawn? You may also want to suggest further areas of study, improvement of research possibilities, etc. to demonstrate your critical thinking regarding your research.

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What are the 5 parts of the research paper

A regular research paper usually has five main parts, though the way it’s set up can change depending on what a specific assignment or academic journal wants. Here are the basic parts;

Introduction:  This part gives an overview of what the research is about, states the problem or question being studied, and explains why the study is important. It often includes background info, context, and a quick look at the research to show why this study is needed.

Literature Review:  In this part, the author looks at and summarizes existing research and writings on the chosen topic. This review helps spot gaps in what we already know and explains why a new study is necessary. It also sets up the theory and hypotheses for the research.

Methodology:  The methodology section describes how the research was done – the plan, methods, and steps used to collect and analyze data. It should be detailed enough for others to repeat the study.

Results:  This part shares what was found in the study based on the analyzed data. The results are often shown using tables, figures, and stats. It’s important to present the data accurately and without adding personal interpretations or discussions.

Discussion:  Here, the results are explained in the context of the research question and existing literature. The discussion looks at what the findings mean, acknowledges any limits to the study, and suggests where future research could go. This is where the researcher can analyze, critique, and connect the results.

Besides these main sections, research papers usually have other parts like a title page, abstract, acknowledgments, and references. The structure might change a bit depending on the subject or type of research, but these five parts are generally found in academic research papers.

What is the structure of a research paper

A research paper usually follows a set format, including these parts:

Title Page:  This page has the research paper’s title, the author’s name, where they’re affiliated (like a school), and often the date.

Abstract:  The abstract is a short summary of the whole research paper. It quickly talks about the research question, methods, results, and conclusions. It’s usually limited to a specific number of words.

Introduction:  This part introduces what the research is about. It states the main question, gives background info, and explains why the study is important. Often, it ends with a thesis statement or research hypothesis.

Literature Review:  In this section, the author looks at and talks about other research and writings on the same topic. It helps to place the study in the context of what we already know, finding gaps, and explaining why this new research is needed.

Methodology:  Here, the research plan is described. It explains how data was collected and analyzed, including details like who participated, what tools were used, and what statistical methods were applied. The goal is to provide enough info so others can do the same study.

Results:  The results section shows what was found in the study based on the analyzed data. Tables, figures, and stats often help present the data. This part should be objective and report the results without personal interpretations.

Discussion:  The discussion explains what the results mean in the context of the research question and existing literature. It looks at the implications of the findings, talks about any study limitations, and suggests where future research could go. This is where the author analyzes and connects the results.

Conclusion:  The conclusion sums up the key findings of the study and stresses their importance. It might also suggest practical uses and areas for further investigation.

References (or Bibliography):  This part lists all the sources cited in the paper, following a specific citation style like APA, MLA, or Chicago, as required by academic or publication guidelines.

Appendices:  Extra materials, like raw data, questionnaires, or added info, can be put in the appendices.

Remember, the requirements for each section can vary based on the guidelines given by the instructor, school, or the journal where the paper might be published. Always check the specific requirements for the research paper you’re working on.

What are the 10 common parts of a research paper list in proper order

Here are the ten main parts of a research paper, listed in the right order:

Title Page:  This page has the title of the research paper, the author’s name, where they’re affiliated (like a school), and the date.

Abstract:  The abstract gives a short summary of the research, covering the main question, methods, results, and conclusions.

Introduction:  This part introduces what the research is about. It states the main question, gives background info, and explains why the study is important.

Literature Review:  In this section, the author looks at and talks about other research and writings on the same topic. It helps place the study in the context of what we already know and explains why this new research is needed.

Methodology:  Here, the research plan is described. It explains how data was collected and analyzed, including details like who participated, what tools were used, and what statistical methods were applied.

Results:  The results section shows what was found in the study based on the analyzed data. This part should be objective and report the results without personal interpretations.

Discussion:  The discussion explains what the results mean in the context of the research question and existing literature. It looks at the implications of the findings, talks about any study limitations, and suggests where future research could go.

References (or Bibliography):  This part lists all the sources cited in the paper, following a specific citation style as required by academic or publication guidelines.

Always check the specific requirements and guidelines given for the research paper you’re working on, as they can vary based on the instructor, school, or the journal where the paper might be published.

How long should a research paper be

The length of a research paper can vary a lot, depending on factors like the academic level, the type of research, and the specific instructions from the instructor or the target journal. Here are some general guidelines;

Undergraduate Level:  Research papers at the undergraduate level, usually range from 10 to 20 pages, although this can change based on the requirements of the specific course.

Master’s Level:  Master’s level research papers are generally longer, often falling between 20 to 40 pages. However, the length can vary depending on the subject and the program.

Ph.D. Level:  Ph.D. dissertations or research papers are typically even longer, often going beyond 50 pages and sometimes reaching several hundred pages. The length is influenced by how deep and extensive the research is.

Journal Articles:  For research papers meant for academic journal publication, the length is usually specified by the journal’s guidelines. In many cases, journal articles range from 5,000 to 8,000 words, but this can differ.

It’s really important to stick to the specific guidelines given by the instructor or the target journal. If there aren’t specific guidelines, think about how complex your research is and how in-depth your analysis needs to be to properly address the research question.

Also, some instructors might specify the length in terms of word count instead of pages. In these cases, the word count can vary, but a common range might be 2,000 to 5,000 words for undergraduate papers, 5,000 to 10,000 words for master’s level papers, and 10,000 words or more for Ph.D. dissertations.

What are 3 formatting guidelines from APA

The American Psychological Association (APA) has special rules for how to set up your research paper. Here are three important rules;

Title Page:  Make a title page with the title of your paper, your name, and where you’re affiliated (like your school). Put the title in the middle, and your name and school below it in the middle too. In the top right corner, put a short version of the title and the page number.

In-Text Citations:  When you mention a source in your paper, use the author’s last name and the year of publication in brackets. For example, if you talk about a book by Smith from 2020, you write (Smith, 2020). If you quote directly, add the page number too, like this: (Smith, 2020, p. 45).

References Page:  Make a references page at the end listing all the sources you talked about in your paper. Arrange them alphabetically by the author’s last name. For books, use this format: Author, A. A. (Year of publication). Research Title: Capital letters also appear in the subheading. Publisher. For journal articles, it’s like this: Author, A. A. (Year of publication). Title of article. Title of Journal, volume number(issue number), page range. DOI or URL. Each entry should be indented right.

Remember, these are just a few important rules from APA. It’s crucial to check the official APA Publication Manual or the latest APA style guide for all the details and rules. Also, the rules might be a bit different for different types of sources, so pay attention to what APA says about each one.

What are the 4 major sections of a research paper

A research paper usually has four main parts;

Introduction:  This part gets things started. It talks about what the research is about, gives some background info, and states the main question or idea. It’s important to show why the study matters.

Methods (or Methodology):  The methods part explains how the research was done. It covers things like the plan, who took part, how data was collected, and how it was analyzed. The goal is to give enough detail so someone else could do the same study.

Results:  The results section shows what was found in the research. It includes the raw data, stats, and any other info needed to answer the main question. It should be objective and focused on just reporting what happened, without adding personal thoughts.

Discussion:  In the discussion part, the results are explained. It looks at what the findings mean in the context of the main question and other research. It talks about the impact of the results, mentions any study limits, and suggests where more research could go. This is where the researcher shares insights, makes conclusions, and talks about why the study is important.

Even though these four parts are common, the way they are set up can change. It depends on what the instructor, school, or journal wants. Always check the specific guidelines for the research paper you’re working on.

How do you write a reference page in APA format

In APA format, the reference page is super important in a research paper. It’s like a big list that shows all the sources mentioned in the paper. Here are the basic rules for making a reference page in APA format:

Heading:  At the top, center the title “References” without making it bold, italicized, underlined, or using quotation marks.

Format for Entries:  Each source follows a special format based on its type (like a book, journal article, or website). 

For a book, the setup is

• Author, A. A. (Year of publication). Title of work: Capital letter also for subtitle. Publisher.

For a journal article, it’s

• Author, A. A. (Year of publication). Title of article. Title of Journal, volume number(issue number), page range. DOI or URL

Alphabetical Order:  Organize the sources chronologically by the last name of the primary writer. If there’s no author, use the title for sorting, ignoring words like “A,” “An,” or “The.”

Hanging Indentation:  Each entry has a hanging indentation. This means the first line starts on the left, and the following lines are indented by 0.5 inches.

Italicize Titles:  Italicize the titles of bigger things like books and journals. For example:  Title of the Book  or  Title of the Journal .

Use Proper Capitalization:  Only capitalize the first word of the title, the first word after a colon in the subtitle, and any special names.

Remember these examples;

Book:  Author, A. A. (Year of publication). Title of work: Capital letter also for subheading. Publisher.

Journal Article:  Author, A. A. (Year of publication). Title of article. Title of Journal, volume number(issue number), page range. DOI or URL.

To make sure you get the latest information, check the APA rules.

What is the purpose of the Introduction section in a research paper

The Introduction section of a research paper serves several crucial purposes;

• Contextualization:  It provides background information to help readers understand the broader context of the research. This may include the historical development of the topic, relevant theoretical frameworks, or existing gaps in knowledge.
• Problem Statement:  The introduction outlines the specific problem or question that the research aims to address. It helps to articulate the gap in current knowledge or identify a need for further investigation.
• Justification and Significance:  The section explains why the research is important and how it contributes to the existing body of knowledge. It highlights the potential impact and significance of the study.
• Objectives or Hypothesis:  The introduction often states the research objectives or formulates a hypothesis, providing a clear roadmap for what the study aims to achieve or test.
• Scope and Limitations:  It defines the boundaries of the research, outlining what the study includes and excludes. This helps readers understand the context within which the research findings should be interpreted.
• Research Questions:  The introduction may pose specific questions that the research seeks to answer. These questions guide the reader in understanding the focus and purpose of the study.
• Overview of Methodology:  While detailed methods are typically discussed in a separate section, the introduction may provide a brief overview of the research design, methods, and data collection techniques.
• Thesis Statement:  In some cases, the introduction concludes with a concise thesis statement that encapsulates the main argument or purpose of the research paper.

Overall, the Introduction sets the stage for the research, engaging the reader’s interest, providing necessary context, and establishing the rationale for the study. It is a critical component that helps readers understand the importance of the research and motivates them to continue reading the paper.

How should the Literature Review be structured in a research paper

The structure of a literature review in a research paper typically follows a systematic and organized approach. Here’s a general guideline on how to structure a literature review;

Introduction

• Provide an overview of the topic and its significance.
• Clearly state the purpose of the literature review (e.g., identifying gaps, providing background).
• Mention the criteria used for including or excluding specific studies.

Organizing Themes or Categories

• Group relevant literature into themes or categories based on common themes, concepts, or methodologies.
• This could be chronological, thematic, methodological, or a combination, depending on the nature of the research.

Chronological Order  

• If your topic has a historical development, consider presenting studies chronologically to show the evolution of ideas or research in the field.

Thematic Organization

• Group studies based on common themes, concepts, or theoretical frameworks. Each theme could represent a section in your literature review.

Methodological Approach

• Discuss studies based on their research methods. This can be particularly relevant if your research involves comparing or contrasting different methodologies.

Critical Analysis

• Critically evaluate each study, discussing its strengths and weaknesses.
• Identify patterns, inconsistencies, or gaps in the existing literature.
• Highlight the significance of each study to your research question or topic.
• Summarize the key findings and insights from each study.
• Discuss how the studies relate to one another and contribute to the overall understanding of the topic.

Gaps and Limitations

• Identify gaps in the literature and areas where further research is needed.
• Discuss the limitations of existing studies.
• Summarize the main points of the literature review.
• Emphasize the contribution of the literature review to your research.
• Provide a smooth transition to the next section of your research paper.

Remember to use clear and concise language throughout the literature review. Each section should flow logically, with a clear connection between paragraphs. Additionally, ensure that you cite all relevant studies and sources using the appropriate citation style (e.g., APA, MLA).

What information should be included in the Methodology section of a research paper

The Methodology section of a research paper provides a detailed description of the procedures and techniques used to conduct the study. It should offer sufficient information for other researchers to replicate the study and verify the results. Here’s a comprehensive guide on what information should be included in the Methodology section;

Research Design

• Specify the overall design of the study (e.g., experimental, observational, survey, case study).
• Justify why the chosen design is appropriate for addressing the research question.

Participants or Subjects

• Clearly describe the characteristics of the participants (e.g., demographics, sample size).
• Explain the criteria for participant selection and recruitment.

Sampling Procedure

• Detail the sampling method used (e.g., random sampling, stratified sampling).
• Provide information on how participants were recruited and consented.
• Identify and define the independent and dependent variables.
• Describe any control variables or confounding factors.

Instrumentation or Materials

• Specify the tools, instruments, or materials used to collect data (e.g., surveys, questionnaires, equipment).
• Include information on the reliability and validity of instruments, if applicable.
• Outline the step-by-step process of data collection.
• Include details on the experimental setup, if applicable.
• Describe any pre-testing, training, or pilot studies conducted.

Data Collection

• Explain how data were collected, including the timeframe.
• Detail any procedures to ensure data accuracy and reliability.

Data Analysis

• Specify the statistical or analytical methods used to analyze the data.
• Justify the choice of statistical tests or analytical tools.

Ethical Considerations

• Discuss any ethical issues and how they were addressed (e.g., informed consent, confidentiality, institutional review board approval).
• State whether the study followed ethical guidelines and standards.

Validity and Reliability

• Talk about the measures undertaken to guarantee the reliability and accuracy of the research.
• Provide information on any measures taken to control extraneous variables.

Limitations:  Acknowledge any limitations of the study that may affect the generalizability of the results.

Statistical Significance:  If applicable, report the criteria used for determining statistical significance.

The Methodology section should be written in a clear and concise manner, providing enough detail for others to replicate the study. Additionally, it is crucial to adhere to the guidelines of the chosen citation style (e.g., APA, MLA) when documenting sources and references related to the methodology.

Why is the Results section important in scientific research papers

The Results section in scientific research papers is critical for several reasons;

• Presentation of Findings:  The Results section is where researchers present the outcomes of their study. It includes raw data, measurements, observations, and any other information gathered during the research process.
• Objectivity and Transparency:  By providing raw data and statistical analyses, the Results section ensures transparency and objectivity. Other researchers should be able to review the data and draw their own conclusions.
• Verification and Replicability:  Results allow other researchers to verify the study’s findings. Replicability is a fundamental principle in science, and a clear presentation of results facilitates the replication of experiments or studies by other researchers.
• Support or Refutation of Hypotheses:  The Results section is where researchers can determine whether their findings support or refute their initial hypotheses. This is a crucial step in the scientific method and contributes to the accumulation of knowledge in a particular field.
• Basis for Discussion and Interpretation:  The data presented in the Results section serve as the foundation for the subsequent Discussion section. Researchers interpret the results, discuss their implications, and relate them to existing literature. Without clear and accurate results, the discussion lacks a solid basis.
• Scientific Progress:  Reporting results allows the scientific community to advance. Other researchers can build upon the findings, either by confirming or challenging them, which contributes to the overall progress of scientific knowledge.
• Peer Review Process:  The Results section is a key component in the peer review process. Other experts in the field assess the validity and significance of the results before the paper is accepted for publication.
• Data Integrity and Research Ethics:  By presenting the raw data, researchers demonstrate the integrity of their work. It also allows for scrutiny regarding research ethics, ensuring that data collection and analysis were conducted ethically and rigorously.
• Support for Funding and Grants:  Clear and compelling results are often necessary when seeking funding or grants. Funding agencies and institutions need to see that the research is producing meaningful and impactful results.
• Communication of Findings to a Wider Audience:  The Results section, along with other parts of the research paper, contributes to the communication of findings to a broader audience, including scientists, educators, policymakers, and the general public.

In summary, the Results section is crucial because it is the primary means through which researchers communicate their findings to the scientific community and beyond. It plays a central role in the scientific method by providing a platform for the objective presentation and interpretation of data, fostering transparency, verification, and further research.

How do you properly format and present tables and figures in the Results section of the research paper

Properly formatting and presenting tables and figures in the Results section is essential for conveying information clearly and effectively. Here are some guidelines to follow;

Title and Numbering

• Provide a clear and concise title for each table.
• Number tables sequentially (e.g., Table 1, Table 2).

Headings and Subheadings

• Use clear and descriptive column and row headings.
• If the table is large, consider using subheadings to organize the data.

Alignment and Consistency

• Align text consistently within columns (e.g., left-align text, center numeric data).
• Maintain consistency in formatting throughout the table.
• Include footnotes to explain abbreviations, symbols, or provide additional context.
• Use superscript numbers or symbols for footnotes and explain them below the table.

Units of Measurement

• Clearly specify units of measurement for numerical data.
• Place units in the column or row headings or provide a separate row for units.

Formatting Numbers

• Use consistent decimal places and significant figures.
• Consider rounding numbers appropriately for clarity.

Empty Cells

• Avoid leaving empty cells; use dashes or other symbols to indicate missing data.
• Clearly state if a value is not applicable.

Reference in Text

• Reference each table in the text and briefly discuss key findings.
• Use the table number in parentheses (e.g., (Table 1)).

Caption and Numbering

• Provide a descriptive caption for each figure.
• Number figures sequentially (e.g., Figure 1, Figure 2).

Clarity of Graphics

• Ensure that the graphic is clear, legible, and appropriately sized.
• Use high-resolution images or create easily interpretable graphs.

Axes and Labels

• Clearly label all axes with the appropriate units.
• Use descriptive axis labels that convey the nature of the data.
• Include a legend if the figure includes different elements (e.g., lines, symbols).
• Ensure the legend is placed in a way that does not obscure the data.

Color and Contrast

• Use color strategically, considering accessibility for readers with color vision deficiencies.
• Ensure sufficient contrast for all elements in black-and-white printing.

Annotations

• If necessary, add annotations to highlight specific points or trends.
• Use arrows, labels, or other indicators for emphasis.

Consistent Style

• Maintain a consistent style across multiple figures within the same paper.
• Use similar fonts, colors, and scales for a cohesive presentation.
• Reference each figure in the text and briefly discuss key findings.
• Use the figure number in parentheses (e.g., (Figure 1)).

Remember, clarity and consistency are key. Ensure that tables and figures are easy to understand without the need for additional explanation. Additionally, follow the formatting guidelines of the specific journal or publication you are submitting to, as they may have specific requirements for tables and figures.

What is the significance of the Discussion section in a research paper

The Discussion section in a research paper holds significant importance as it allows researchers to interpret their findings, relate them to existing knowledge, and draw meaningful conclusions. Here are several key aspects highlighting the significance of the Discussion section;

• Interpretation of Results:  The Discussion section provides an opportunity to explain and interpret the results obtained in the study. Researchers can clarify the meaning of their findings and elaborate on their implications.
• Comparison with Previous Research:  Researchers can compare their results with existing literature to highlight similarities, differences, or advancements in knowledge. This contributes to the ongoing dialogue within the scientific community.
• Addressing Research Questions or Hypotheses:  The Discussion section allows researchers to address the initial research questions or hypotheses stated in the introduction. They can evaluate whether their findings support or refute the proposed hypotheses.
• Contextualizing Results:  Researchers can place their results in the broader context of the field. This involves discussing how the study contributes to existing knowledge and understanding, emphasizing its significance.
• Identification of Patterns and Trends:  Patterns and trends observed in the data can be explored and explained in the Discussion section. Researchers can discuss the reasons behind these patterns and their implications for the research question.
• Limitations and Potential Biases:  Acknowledging the limitations of the study is crucial in the Discussion section. Researchers can openly discuss any constraints, biases, or methodological issues that may have affected the results.
• Alternative Explanations:  Researchers should consider alternative explanations for their findings and discuss why these alternatives were ruled out or how they might impact the interpretation of the results.
• Implications for Future Research:  The Discussion section often includes suggestions for future research directions. Researchers can propose areas that need further exploration or recommend modifications to the study design for more robust investigations.
• Practical and Theoretical Implications:  Researchers can discuss the practical implications of their findings, addressing how the results may be applied in real-world situations. They can also explore the theoretical implications, contributing to the development or refinement of theoretical frameworks.
• Synthesis of Key Points:  The Discussion section serves as a synthesis of the key points of the paper, bringing together the results and their interpretation. It offers a cohesive and comprehensive understanding of the study’s outcomes.
• Contributions to the Field:  Researchers can articulate the unique contributions of their study to the field. This is important for demonstrating the value of the research within the broader scholarly context.

In essence, the Discussion section is where researchers engage in a thoughtful and critical analysis of their results, connecting them to the wider body of knowledge and providing insights that go beyond the raw data presented in the Results section. It is a crucial component that adds depth and context to the research paper, allowing readers to fully grasp the implications and significance of the study.

What elements should be included in the Conclusion of a research paper

The Conclusion section of a research paper serves to summarize the main findings, restate the significance of the study, and offer insights derived from the research. Here are the key elements that should be included in the Conclusion;

Summary of Key Findings

• Provide a concise recap of the main results obtained in the study.
• Highlight the most important and relevant findings that address the research question or hypothesis.

Restatement of Research Objectives or Hypotheses

• Remind the reader of the initial research objectives or hypotheses stated in the introduction.
• Discuss how the findings either support or challenge these objectives.

Significance of the Study

• Reinforce the importance and relevance of the research within the broader context of the field.
• Clearly articulate the contribution of the study to existing knowledge and its potential impact.

Implications for Practice

• Discuss any practical implications of the findings for real-world applications.
• Address how the results may inform decision-making or practices in relevant areas.

Implications for Future Research

• Suggest areas for further exploration and research based on the limitations or gaps identified in the current study.
• Provide recommendations for researchers interested in building on the current findings.

Integration with Existing Literature

• Connect the study’s results with existing literature and research in the field.
• Discuss how the findings either align with or challenge previous studies.

Limitations and Caveats

• Acknowledge and discuss the limitations of the study.
• Provide a balanced assessment of the study’s constraints and potential sources of bias.

Reflection on Methodology

• Reflect on the appropriateness and effectiveness of the research methodology.
• Discuss any challenges encountered during the research process and how they may have influenced the results.

Conclusion Statement

• Offer a conclusive statement summarizing the overall implications of the study.
• Clearly state the main takeaway or message that readers should derive from the research.

Closing Thoughts

• Conclude with any final thoughts, reflections, or remarks that enhance the overall understanding of the research.
• Consider leaving the reader with a thought-provoking statement or a call to action related to the study’s findings.

Avoid New Information:  The conclusion is not the place to introduce new information or data. It should focus on summarizing and synthesizing existing content.

Brevity and Clarity

• Keep the conclusion concise while ensuring clarity and coherence.
• Use straightforward language to communicate key points without unnecessary complexity.

So, the Conclusion section is the final opportunity to leave a lasting impression on the reader. It should effectively wrap up the research paper by summarizing the key elements and providing a sense of closure while encouraging further consideration of the study’s implications.

How do you write an effective Abstract that summarizes the key aspects of the research

Writing an effective abstract is crucial as it serves as a concise summary of your research, providing readers with a quick overview of the study’s key aspects. Here are some guidelines to help you write an impactful abstract;

• Understand the Purpose:  Recognize that the abstract is a standalone summary of your research, and readers may use it to decide whether to read the full paper. It should convey the main points and significance of your study.
• Follow Structure Guidelines:  Different journals and disciplines may have specific guidelines for abstracts. Ensure that you are aware of any required structure or word limit set by the journal or conference you are submitting to.
• Start with a Clear Context:  Begin your abstract by providing a brief context for your research. Clearly state the background or problem that your study addresses.
• State the Research Question or Objective:  Clearly articulate the research question, objective, or hypothesis that your study aims to address. Be concise but informative.
• Describe the Methods:  Briefly outline the research methods used in your study. Include key details such as study design, participants, materials, and procedures.
• Present Key Results:  Summarize the main findings of your research. Highlight the most important and relevant results that answer your research question.
• Include Quantitative Information:  If applicable, provide quantitative information such as effect sizes, statistical significance, or numerical data that convey the magnitude and importance of the results.
• Convey Interpretation and Significance:  Interpret the results briefly and discuss their significance. Explain how your findings contribute to the existing body of knowledge in the field.
• Highlight Key Conclusions:  Clearly state the conclusions drawn from your study. This is not the place for introducing new information; rather, it’s a summary of the primary outcomes.
• Avoid Abbreviations and Jargon:  Keep the abstract accessible to a broad audience by avoiding unnecessary abbreviations or discipline-specific jargon. Use language that can be easily understood by readers from diverse backgrounds.
• Be Concise and Specific:  Strive for brevity while ensuring that you cover all essential aspects of your research. Use specific and precise language to convey your points.
• Check for Clarity and Coherence:  Ensure that the abstract flows logically and that each sentence contributes to the overall understanding of your research. Check for clarity and coherence in your writing.
• Keywords:  Include relevant keywords in your abstract. These terms should capture the essential topics of your research and aid in the discoverability of your paper in databases and search engines.
• Proofread Carefully:  Eliminate grammatical errors, typos, or any unclear language. A well-written abstract demonstrates attention to detail and professionalism.
• Meet Word Limit Requirements:  If there is a word limit, adhere to it. Concision is crucial in abstract writing, and exceeding the word limit may result in important information being omitted.
• Review and Revise:  Once you have drafted your abstract, review it critically. Ask yourself if it effectively conveys the main points of your research and if it would pique the interest of potential readers.

The abstract is often the first (and sometimes only) part of your research paper that readers will see. Therefore, crafting a clear, concise, and compelling abstract is essential for drawing attention to your work and encouraging further exploration.

What is the difference between the Abstract and the Executive Summary in a research paper

The abstract and the executive summary serve similar purposes in providing a concise overview of a document, but they are typically used in different contexts and for different types of documents. Here are the key differences between an abstract and an executive summary;

Usage:  Commonly used in academic and scholarly writing, such as research papers, articles, and conference presentations.

• Summarizes the entire research paper, including background, methodology, results, and conclusions.
• Generally includes information about the research question, methods, key findings, and implications.
• Primarily aimed at an academic audience, including researchers, scholars, and students.
• Serves as a standalone summary for individuals seeking a quick understanding of the research without reading the entire paper.

Length:  Typically limited to a specific word count or length, often ranging from 150 to 250 words for academic papers.

Keywords:  May include keywords that highlight the main topics of the research for indexing and search purposes.

Location:  Positioned at the beginning of the research paper, providing readers with a preview of the study.

Executive Summary

Usage:  More commonly found in business and professional documents, such as business plans, proposals, and reports.

• Summarizes the key points of a longer document, focusing on the most critical information for decision-makers.
• Often includes an overview of the purpose, methodology, major findings, recommendations, and potential actions.
• Intended for a business or managerial audience, including executives, stakeholders, or decision-makers.
• Aids busy professionals in quickly grasping the main points of a document without delving into the details.

Length:  Can vary in length but is generally longer than an abstract, often spanning a page or more.

Keywords:  May not always include specific keywords for indexing, as the primary focus is on communicating essential information to decision-makers.

Location:  Typically placed at the beginning of a business document, allowing executives to quickly understand the document’s purpose and key recommendations.

In summary, while both the abstract and the executive summary serve the purpose of providing a brief overview, they are tailored to different audiences and contexts. The abstract is more common in academic settings, summarizing research papers, while the executive summary is often used in business and professional documents to distill key information for decision-makers.

How should citations and references be formatted in the References or Bibliography section

The formatting of citations and references in the References or Bibliography section of a research paper depends on the citation style specified by the journal, publication, or academic institution. Different disciplines and publications may have preferences for specific citation styles, such as APA (American Psychological Association), MLA (Modern Language Association), Chicago, Harvard, or others.

Here are general guidelines for formatting citations and references in common citation styles;

• Book:  Author, A. A. (Year of publication).  Title of work: C apital letters also appear in the subtitle. Publisher.
• Journal Article:  Author, A. A. (Year of publication). Title of article.  Title of Journal, volume number (issue number), page range. DOI or URL
• Webpage:  Author, A. A. (Year, Month Day of publication). Title of webpage. Website Name. URL
• Book:  Author’s Last Name, First Name.  Title of Book . Publisher, Publication Year.
• Journal Article:  Author’s Last Name, First Name. “Title of Article.”  Title of Journal , vol. number, no. number, Year, pages. Database name or URL.
• Webpage:  Author’s Last Name, First Name. “Title of Webpage.” Website Name, publication date, URL.

Chicago Style

• Book:  Author’s First Name Last Name.  Title of Book . Place of publication: Publisher, Year.
• Journal Article:  Author’s First Name Last Name. “Title of Article.”  Title of Journal  vol. number, no. number (Year): pages.
• Webpage:  Author’s First Name Last Name. “Title of Webpage.” Name of Website. URL

Harvard Style

• Book:  Author’s Last Name, First Initial(s). (Year)  Title of Book . Place of publication: Publisher.
• Journal Article:  Author’s Last Name, First Initial(s). (Year) ‘Title of Article.’  Title of Journal , Volume number (Issue number), Page range.
• Webpage:  Author’s Last Name, First Initial(s). (Year) ‘Title of Webpage.’ Available at: URL (Accessed: Day Month Year).

Always check the specific guidelines provided by the journal or publication you are submitting to, as they may have variations or preferences within a particular citation style. Additionally, consider using citation management tools like Zotero, EndNote, or Mendeley to streamline the citation process and ensure accuracy.

What is the role of the Acknowledgments section in a research paper

The Acknowledgments section in a research paper serves the purpose of expressing gratitude and recognizing individuals, institutions, or organizations that contributed to the research or the development of the paper. It is a way for the authors to acknowledge the support, assistance, and resources they received during the research process. Here are the key roles of the Acknowledgments section;

• Recognition of Contributions:  The Acknowledgments section provides an opportunity for authors to acknowledge the contributions of individuals who directly or indirectly supported the research. This can include colleagues, mentors, advisors, and peers.
• Expression of Gratitude:  Authors use this section to express gratitude for any assistance, guidance, or resources received. It is a way to show appreciation for the collaborative and supportive efforts of others.
• Mentioning Funding Sources:  If the research was funded by grants or scholarships, authors typically acknowledge the funding sources in this section. This includes government agencies, private foundations, or other organizations that provided financial support.
• Recognition of Technical Assistance:  Authors may acknowledge individuals or organizations that provided technical assistance, such as help with data analysis, laboratory techniques, or specialized equipment.
• Acknowledging Institutional Support:  Authors may express gratitude to their affiliated institutions for providing facilities, libraries, or other resources that facilitated the research.
• Thanking Reviewers or Editors:  In some cases, authors express appreciation for the feedback and constructive criticism received from peer reviewers during the publication process. This acknowledgment is often included in the Acknowledgments or sometimes in the opening of the paper.
• Acknowledging Personal Support:  Authors may use this section to acknowledge personal support from family members, friends, or anyone who has supported them during the research process.
• Maintaining Professional Courtesy:  Including an Acknowledgments section is also a matter of professional courtesy. It recognizes the collaborative and communal nature of research and emphasizes the importance of acknowledging those who contributed to the work.
• Ethical Considerations:  The Acknowledgments section can also serve as a platform for authors to clarify any potential conflicts of interest or ethical considerations related to the research.
• Humanizing the Research Process:  By acknowledging the human aspects of the research journey, the Acknowledgments section adds a personal touch to the paper, making it more relatable and emphasizing the collective effort involved in scholarly work.

It’s essential to strike a balance in the Acknowledgments section, being specific and genuine in expressing gratitude without making it overly lengthy. While it is a place to acknowledge various forms of support, it should remain focused on those contributions that directly impacted the research and its successful completion.

How do you determine the appropriate length for each section of a research paper

Determining the appropriate length for each section of a research paper involves considering several factors, including the type of paper, the guidelines provided by the target journal or publication, and the complexity of the research. While there are no fixed rules, the following general principles can help guide you;

• Follow Journal Guidelines:  Journals often provide specific guidelines on the preferred structure and length of each section. Always refer to the submission guidelines of the target journal to ensure that your paper adheres to their requirements.
• Consider the Type of Paper:  The length of each section can vary based on the type of paper. For example, a review article may have a more extensive literature review section compared to an original research paper. Understand the conventions for the type of paper you are writing.
• Adhere to Standard Structures:  Research papers typically follow standard structures such as Introduction, Literature Review, Methodology, Results, Discussion, and Conclusion. While the length of each section may vary, maintaining a coherent structure is important for readability and understanding.
• Prioritize Key Information:  Focus on presenting key information in each section. Avoid unnecessary details and ensure that the content is relevant to the research question or objective.
• Consider the Significance of Sections:  Sections like the Methods and Results, which present the core of your research, may require more detailed explanations. The Introduction and Conclusion, while important, may be more concise.
• Balance and Proportion:  Aim for a balanced distribution of content across sections. Avoid overemphasizing one section at the expense of others. Each section should contribute meaningfully to the overall narrative.
• Review Similar Publications:  Examine research papers published in the target journal or similar venues. Analyze the length of sections in these papers to get a sense of the expectations for your own paper.
• Be Mindful of Word Limits:  Some journals or conferences set word limits for articles. Be aware of these limits and allocate space accordingly. If there is a word limit, prioritize clarity and conciseness.
• Consider Reader Engagement:  Readers appreciate a clear and well-structured paper. Aim for sections that are informative without being overly detailed. Engage your readers and maintain their interest throughout the paper.
• Revise and Edit:  After drafting your paper, review and edit each section critically. Remove redundancies, unnecessary details, or content that does not directly contribute to the main message of each section.
• Seek Feedback:  Obtain feedback from peers, colleagues, or mentors. Others' perspectives can help identify areas where content could be expanded or condensed.

Note that the appropriate length for each section can vary based on the specific requirements of your research and the expectations of the target audience. Strive for clarity, coherence, and relevance in each section to ensure that your research paper effectively communicates its purpose and findings.

Should the title of a research paper be included in the Abstract

Yes, the title of a research paper is typically included in the abstract. The abstract serves as a concise summary of the entire research paper, providing readers with an overview of the study’s purpose, methods, results, and conclusions. Including the title in the abstract helps readers immediately identify the topic and focus of the research.

The standard structure of an abstract often includes the following elements;

• Title:  The title of the research paper is usually presented at the top of the abstract. It is written in the same way it appears in the full paper.
• Introduction or Background:  A brief statement that introduces the research question or problem addressed in the study.
• Methods:  A summary of the research methods employed, including the study design, participants, materials, and procedures.
• Results:  A concise presentation of the key findings of the study.
• Conclusion or Implications:  A discussion of the study’s conclusions, implications, or potential applications.

While the abstract aims to be succinct, it should still provide enough information for readers to understand the main components and contributions of the research. The inclusion of the title ensures that readers can quickly identify the specific topic of interest and decide whether the paper aligns with their interests or research needs.

What are the key components of the Introduction, Methods, Results, and Discussion (IMRAD) structure

The IMRAD structure is a commonly used format in scientific and academic writing, organizing research papers into distinct sections: Introduction, Methods, Results, and Discussion. Each section serves a specific purpose in presenting and communicating the research. Here are the key components of each section;

The Introduction section of a research paper typically includes the following components:

Background or Context

• Provides a brief overview of the research area, establishing the context for the study.
• Identifies the gap or problem in existing knowledge that the research aims to address.

Research Question or Hypothesis

• Clearly states the main research question or hypothesis that the study seeks to answer.
• Provides focus and direction for the research.

Objectives or Aims

• Outlines the specific objectives or aims of the study, detailing what the research intends to achieve.
• Explains the importance of the research and its potential contributions to the field.
• Highlights the relevance of addressing the identified gap or problem.

Review of Literature

• Summarizes relevant literature and previous studies related to the research topic.
• Provides the theoretical framework and context for the study.

The Methods section details the research design, participants, materials, and procedures used in the study;

Study Design

• Describes the overall design of the research (e.g., experimental, observational, survey).
• Justifies why the chosen design is appropriate for addressing the research question.
• Provides information about the participants or subjects involved in the study.
• Describes the criteria for participant selection and recruitment.
• Explains the method used for sampling and participant recruitment.
• Details how the sample represents the target population.
• Identifies and defines the independent and dependent variables.
• Describes any control variables or confounding factors.
• Specifies the tools, instruments, or materials used for data collection.
• Includes information on the reliability and validity of instruments.
• Outlines the step-by-step process of data collection.
• Includes any steps taken to ensure data accuracy and reliability.

The Results section presents the raw data and findings of the study:

Data Presentation

• Displays the gathered information in a structured and straightforward way.
• Utilizes tables, figures, and graphs to enhance data visualization.

Statistical Analyses

• Describes the statistical methods used to analyze the data.
• Presents statistical results, including significance levels.

Key Findings

• Summarizes the main findings of the study.
• Highlights any patterns, trends, or significant outcomes.

The Discussion section interprets the results, relates them to existing literature, and discusses their implications:

Interpretation of Results

• Offers a detailed interpretation of the study’s findings.
• Discusses how the results address the research question or hypothesis.

Comparison with Previous Research

• Compares the current findings with previous studies in the field.
• Discusses similarities, differences, or advancements in knowledge.

Limitations

• Acknowledges any limitations or constraints of the study.
• Addresses potential sources of bias or error.

Implications

• Discusses the broader implications of the findings.
• Explores the practical, theoretical, or policy implications.

Recommendations for Future Research

• Suggests directions for future research based on the study’s limitations or gaps identified.
• Provides guidance for researchers interested in building on the current findings.

The IMRAD structure is widely used because it provides a logical and organized framework for presenting research in a clear and systematic manner. Following this structure helps readers navigate the paper easily and understand the research process and outcomes.

How do you choose appropriate keywords for a research paper

Selecting appropriate keywords for a research paper is essential for enhancing the paper’s discoverability in databases and search engines. Here are steps to help you choose effective keywords;

• Identify Key Concepts:  Identify the main concepts and topics addressed in your research. These concepts should represent the core elements of your study.
• Use Specific Terms:  Choose keywords that are specific and closely related to your research. Avoid overly broad terms that may result in irrelevant search results.
• Consider Synonyms and Variations:  Think about synonyms, alternative terms, and variations of your key concepts. Different researchers and databases may use different terminology.
• Include Related Terms:  Consider terms that are closely related to your main concepts. This can include broader or narrower terms, related disciplines, or alternative phrasing.
• Review Existing Literature:  Look at relevant articles and papers in your field. Identify the keywords used in these papers, as they may be suitable for your own research.
• Check Subject Headings:  Explore the use of standardized subject headings or controlled vocabulary in the specific database or catalog you are using. These terms can help improve precision.
• Use Thesauruses and Databases:  Consult thesauruses or controlled vocabulary lists provided by databases like PubMed, ERIC, or PsycINFO. These tools can suggest standardized terms used in the literature.
• Think About Variations in Language:  Consider variations in language and spelling that may be used by researchers or authors in different regions or fields.
• Include Acronyms and Abbreviations:  If applicable, include acronyms or abbreviations commonly used in your field. This ensures that researchers using these terms can find your paper.
• Be Mindful of Trends:  Stay informed about emerging trends and terminology in your field. Include keywords that reflect the current discourse.
• Use a Mix of Broad and Specific Terms:  Include a mix of broad and specific terms to cater to different levels of search specificity.
• Think About Alternative Spellings:  Consider alternative spellings, particularly if certain terms may have multiple accepted spellings.
• Use Keywords Consistently:  Ensure consistency in the use of keywords throughout your paper, including the title, abstract, and body. This helps search engines and databases index your paper accurately.
• Test and Refine:  Test the effectiveness of your chosen keywords by conducting searches in relevant databases. If the results are too broad or narrow, adjust your keywords accordingly.
• Include Geographic and Temporal Keywords:  If relevant, include keywords related to geographic locations or time periods. This can be important for studies with a regional or historical focus.

Collaborate and Seek Feedback:  Discuss your chosen keywords with colleagues or mentors. They may offer valuable insights and suggestions.

Remember that the goal is to use keywords that accurately represent your research and align with the terminology used by others in your field. Using a combination of precise, specific terms and broader, related concepts ensures that your paper reaches a diverse audience interested in your research area.

When is it necessary to include a supplementary materials section in a research paper

A Supplementary Materials section in a research paper is included when there is additional information or content that is important for a comprehensive understanding of the research but is too extensive or detailed to be included in the main body of the paper. Here are situations when it is necessary or advisable to include a Supplementary Materials section;

• Extensive Data Sets:  When the dataset or raw data is extensive and detailed, it may be included as supplementary materials. This allows interested readers or researchers to access and analyze the data more thoroughly.
• Complex Methodology Details:  If the methodology used in the study is complex and detailed, providing additional explanations, schematics, or step-by-step procedures in the Supplementary Materials section can enhance clarity without overwhelming the main text.
• Additional Figures and Tables:  If there are numerous figures, tables, or other graphical elements that contribute to the study but may interrupt the flow of the main text, they can be placed in the Supplementary Materials.
• Extended Literature Reviews:  In cases where the literature review is extensive but not directly tied to the main narrative, an extended literature review or additional references can be placed in the Supplementary Materials.
• Code and Algorithms:  For studies involving computer code, algorithms, or detailed mathematical proofs, including these in the Supplementary Materials allows readers interested in the technical details to access and review them.
• Participant Details or Additional Experiments:  If there are extensive details about participants (e.g., demographics, characteristics) or additional experiments that are relevant but not critical to the main argument, they can be included in the Supplementary Materials.
• Supporting Information for Analyses:  Supporting information for statistical analyses, sensitivity analyses, or robustness checks can be included in the Supplementary Materials.
• Audio-Visual Material:  For studies involving audio-visual material (e.g., sound clips, video recordings), the Supplementary Materials section is an appropriate place to include these additional resources.
• Appendices:  Appendices that contain supplementary information, such as questionnaires, interview transcripts, or additional results, can be placed in the Supplementary Materials.
• Ethical Approvals and Permissions:  Copies of ethical approvals, permissions, or other documentation that may be required but are not integral to the main narrative can be included in the Supplementary Materials.
• Supplementary Text:  Additional explanations, derivations, or details that provide depth but might disrupt the main flow of the paper can be included in the Supplementary Materials.
• Additional Results or Analyses:  If there are secondary or exploratory analyses that are interesting but not crucial to the primary findings, they can be presented in the Supplementary Materials.

In general, the Supplementary Materials section is a flexible space that allows authors to include content that supports the main argument without overwhelming the main text. However, it’s crucial to ensure that the main paper remains coherent and self-contained, with the Supplementary Materials serving as supplementary, rather than essential, information. Authors should always check the specific guidelines of the journal they are submitting to regarding the inclusion of supplementary materials.

What is the difference between a research paper and a review article, and how does it affect the structure

A research paper and a review article serve different purposes in academic writing, and they differ in terms of their objectives, content, and structure.

Research Paper

Purpose: Objective Research Contribution:  A research paper presents the findings of original research or experimentation. It aims to contribute new knowledge to a specific field or address a research question or hypothesis.

Content: Empirical Data:  Research papers typically include detailed descriptions of the study’s methodology, data collection, and analysis. They present empirical data and discuss the implications of the results.

Structure: IMRAD Structure:  Research papers often follow the IMRAD structure (Introduction, Methods, Results, and Discussion), providing a systematic and organized presentation of the research process and outcomes.

Citations: Primary Literature:  Citations primarily include references to the original research, emphasizing the direct sources of data and information.

Audience: Specialized Audience:  Research papers are often written for a specialized audience, such as researchers, scholars, and professionals in the specific field of study.

Review Article

Purpose: Synthesis of Existing Literature:  A review article aims to summarize, evaluate, and synthesize existing literature on a specific topic. It provides an overview of the current state of knowledge in a particular area.

Content: Analysis and Evaluation:  Review articles analyze and evaluate the findings of multiple studies, offering a comprehensive perspective on the topic. They may include historical context, theoretical frameworks, and discussions of trends.

Structure: Varied Structure:  Review articles may have a more flexible structure compared to research papers. While they often include an introduction and conclusion, the body of the article may be organized thematically, chronologically, or by methodological approach.

Citations: Secondary Literature:  Citations in a review article primarily refer to existing literature, summarizing and citing multiple sources to provide a comprehensive overview.

Audience: Wider Audience:  Review articles are often written to appeal to a broader audience, including students, researchers, and professionals seeking a comprehensive understanding of a specific topic.

Structural Differences

• Introduction:  In a research paper, the introduction clearly defines the research question or hypothesis. In a review article, the introduction provides context for the broader topic, explaining why the review is important.
• Methods and Results:  Research papers include detailed sections on methods and results, describing the study design, data collection, and findings. Review articles do not typically have dedicated sections for methods and results but may include methodological considerations in the text.
• Discussion:  In a research paper, the discussion interprets the study’s results and discusses their implications. In a review article, the discussion synthesizes and interprets the findings from multiple studies, offering insights and identifying gaps in the existing literature.
• Conclusion:  The conclusion of a research paper summarizes the study’s main findings and their significance. In a review article, the conclusion often emphasizes the key themes, trends, or unresolved questions in the field.

While these distinctions are general, it’s important to note that the specific structure and requirements can vary based on the guidelines of the target journal or publication. Authors should always refer to the submission guidelines when preparing a research paper or a review article.

How do you write an effective thesis statement in the Introduction section

An effective thesis statement in the introduction serves as a concise and clear summary of the main point or claim of your research paper. It provides direction to the reader, outlining the purpose and focus of your study. Here are some guidelines on how to write an effective thesis statement in the introduction;

• Clarity and Conciseness:  Ensure that your thesis statement is clear, concise, and directly addresses the main point of your paper. Avoid vague or ambiguous language.
• Specificity:  Be specific about the topic or issue you are addressing. Clearly state the aspect of the subject that your paper will focus on.
• One Main Idea:  A thesis statement should convey one main idea or argument. Avoid trying to cover too many topics or issues in a single thesis statement.
• Declarative Statement:  Formulate your thesis as a declarative statement rather than a question. Your thesis should present a claim that you will support or argue throughout the paper.
• Position and Argument:  Clearly express your position on the topic and provide a brief overview of the argument you will make. This helps set the tone for the rest of the paper.
• Scope of the Paper:  Indicate the scope of your paper by mentioning the specific aspects, factors, or elements that your research will explore.
• Preview of Main Points:  If applicable, provide a brief preview of the main points or arguments that will be developed in the body of the paper. This helps to guide the reader through your paper.
• Avoid Ambiguity:  Steer clear of vague or general statements that could be interpreted in various ways. Your thesis should be straightforward and unambiguous.
• Relevance:  Take into account the prospective audience’s requirements and areas of interest. Your thesis statement should resonate with your readers and make them interested in your paper.
• Reflect Your Stance:  If your research involves taking a stance on an issue, make sure your thesis reflects your position clearly. This helps readers understand your perspective from the outset.
• Revise and Refine:  After drafting your thesis statement, review and refine it. Ensure that it accurately reflects the content and focus of your paper.
• Tailor to Your Paper’s Purpose:  Adjust your thesis statement based on the type of paper you are writing (e.g., argumentative, analytical, expository). Tailor it to suit the purpose of your paper.
• Consider Length:  While a thesis statement is typically a concise sentence or two, its length may vary depending on the complexity of your topic and the length of your paper. Aim for clarity and brevity.

Here’s an example to illustrate these principles;

In an essay about the impact of social media on mental health:

Weak Thesis Statement

“Social media has both positive and negative effects on mental health."

Strong Thesis Statement

“While social media provides a platform for communication and connection, its impact on mental health is a growing concern, as evidenced by the rise in anxiety and depression rates among frequent users."

The strong thesis statement is specific, takes a clear position, and provides a glimpse into the key points that will be explored in the paper.

What is the role of the Hypothesis in the Methods section, and when is it necessary

The hypothesis in the Methods section of a research paper serves as a clear and testable statement predicting the expected outcome of your study. It is typically included in studies that follow an experimental or quantitative research design. The role of the hypothesis is to guide the research process, facilitate the design of the study, and provide a basis for statistical analysis. Here’s when and how to include a hypothesis in the Methods section;

When is it Necessary

• Experimental or Quantitative Research:  Hypotheses are most commonly included in studies that involve experimental or quantitative research designs. These types of studies aim to measure, manipulate, or observe variables to test specific relationships.
• Testable Predictions:  If your research involves making specific, testable predictions about the relationship between variables, a hypothesis is necessary. It provides a clear expectation of what the study aims to demonstrate or investigate.
• Guidance for Study Design:  A hypothesis guides the design of the study by framing the research question in a way that can be empirically tested. It helps define the variables and conditions under investigation.
• Statistical Analysis:  In quantitative research, a hypothesis is essential for statistical analysis. It allows for the use of statistical tests to determine whether the observed results are consistent with the expected outcome stated in the hypothesis.

How to Include a Hypothesis in the Methods Section

• Placement:  The hypothesis is typically presented early in the Methods section, after the introduction of the research question or objective. It sets the stage for the reader to understand the specific aim of the study.
• Clear Statement:  State your hypothesis clearly and concisely. Use language that is unambiguous and directly addresses the relationship or effect you are investigating.
• Null and Alternative Hypotheses:  If applicable, include both null and alternative hypotheses. The null hypothesis represents the absence of an effect, while the alternative hypothesis states the expected effect.
• Directionality:  If your research involves a directional prediction (e.g., an increase or decrease in a variable), specify this in your hypothesis. If the prediction is non-directional, state it as such.
• Variables and Relationships:  Clearly define the variables involved in the hypothesis and the expected relationship between them. This helps readers understand the scope of your study.
• Testable:  Ensure that your hypothesis is testable. This means that it should be possible to collect data and perform statistical analyses to determine whether the observed results support or reject the hypothesis.

Research Question: Does a new drug reduce blood pressure in hypertensive patients?

Null Hypothesis (H0)

“The new medication had no apparent impact on blood pressure readings between those with hypertension receiving it and those receiving a placebo. "

Alternative Hypothesis (H1)

“Hypertensive patients who receive the new drug will show a significant reduction in blood pressure levels compared to those who receive a placebo."

Including a hypothesis in the Methods section provides a clear roadmap for the research, helping both researchers and readers understand the anticipated outcomes and objectives of the study. Keep in mind that not all studies require hypotheses, especially in qualitative or exploratory research where the emphasis may be on understanding phenomena rather than testing specific predictions.

How should limitations and future research directions be addressed in a research paper

Addressing limitations and proposing future research directions is an important aspect of the Discussion section in a research paper. These sections allow you to acknowledge the constraints of your study and suggest avenues for further investigation. Here are guidelines on how to effectively address limitations and future research directions;

Addressing Limitations

• Be Transparent and Honest:  Clearly and honestly acknowledge the limitations of your study. This demonstrates transparency and helps readers understand the scope of your research.
• Link to Methodology:  Connect limitations to specific aspects of your methodology. Discuss any constraints in data collection, sample size, experimental design, or other methodological considerations.
• Consider External Validity:  Address external validity by discussing the generalizability of your findings. Be explicit about the population to which your results can be applied and any potential limitations in generalizing the results to broader contexts.
• Recognize Data Limitations:  If there are limitations in the data used in your study, such as missing information or reliance on self-report measures, acknowledge these shortcomings and discuss their potential impact on the results.
• Discuss Sampling Issues:  If your study involves a specific sample that may not be fully representative of the broader population, discuss the implications of this limitation.
• Address Potential Biases:  Identify and discuss any biases that might have affected your study, whether they are selection biases, response biases, or other forms of bias. Be clear about the potential impact on the study’s validity.
• Account for Confounding Variables:  If there are confounding variables that could have influenced your results, acknowledge these and discuss how they may have affected the interpretation of your findings.
• Highlight Practical Constraints:  If your study faced practical constraints such as time, resources, or access to certain populations, discuss how these limitations might have influenced the study’s outcomes.

Proposing Future Research Directions

• Connect to Current Findings:  Tie your future research suggestions to the current findings of your study. Identify gaps in knowledge or areas where further investigation is needed based on your results.
• Specify Research Questions:  Clearly formulate specific research questions or hypotheses for future studies. This provides a roadmap for researchers interested in building on your work.
• Consider Different Methodologies:  Propose different methodologies or research designs that could address the limitations of your current study. This could involve using different data collection methods, expanding the sample size, or employing new experimental approaches.
• Explore Unanswered Questions:  Identify unanswered questions that arose during your study and propose ways to explore and answer them in future research.
• Extend to Different Populations:  Discuss how future research could extend your findings to different populations, contexts, or settings. Consider the external validity of your study and suggest ways to enhance it.
• Examine Long-Term Effects:  If your study was short-term or focused on immediate outcomes, suggest research directions that explore long-term effects or consequences.
• Address Cross-Cultural Perspectives:  If applicable, propose future research that explores cross-cultural perspectives or comparisons to enhance the generalizability of findings.
• Integrate Interdisciplinary Approaches:  Consider interdisciplinary approaches by proposing collaborations with researchers from other disciplines. This can enrich the scope and depth of future research.
• Highlight Emerging Technologies:  If relevant, discuss how emerging technologies or methodologies could be employed in future research to address limitations and enhance the study’s robustness.
• Encourage Replication:  Emphasize the importance of replication studies to validate and verify your findings. This contributes to the cumulative nature of scientific knowledge.

By effectively addressing limitations and proposing future research directions, you contribute to the ongoing scholarly conversation, guide fellow researchers, and demonstrate a nuanced understanding of the complexities within your field of study.

What is the meaning of a research paper outline

Types of research paper outlines

What is a research paper

What should be the length of a research paper

What is the best format to write a research paper

How to prepare a research paper outline

What are the steps for writing a research paper

How to incorporate data and statistics in research papers

What is a research paper with an example

How many pages should a research paper be

What can be the topics for a research paper

• Research Guides

BSCI 1510L Literature and Stats Guide: 3.2 Components of a scientific paper

• 1 What is a scientific paper?
• 2 Referencing and accessing papers
• 2.1 Literature Cited
• 2.2 Accessing Scientific Papers
• 2.3 Traversing the web of citations
• 2.4 Keyword Searches
• 3 Style of scientific writing
• 3.1 Specific details regarding scientific writing

3.2 Components of a scientific paper

• 4 For further information
• Appendix A: Calculation Final Concentrations
• 1 Formulas in Excel
• 2 Basic operations in Excel
• 3 Measurement and Variation
• 3.1 Describing Quantities and Their Variation
• 3.2 Samples Versus Populations
• 3.3 Calculating Descriptive Statistics using Excel
• 4 Variation and differences
• 5 Differences in Experimental Science
• 5.1 Aside: Commuting to Nashville
• 5.2 P and Detecting Differences in Variable Quantities
• 5.3 Statistical significance
• 5.4 A test for differences of sample means: 95% Confidence Intervals
• 5.5 Error bars in figures
• 5.6 Discussing statistics in your scientific writing
• 6 Scatter plot, trendline, and linear regression
• 7 The t-test of Means
• 8 Paired t-test
• 9 Two-Tailed and One-Tailed Tests
• 10 Variation on t-tests: ANOVA
• 11 Reporting the Results of a Statistical Test
• 12 Summary of statistical tests
• 1 Objectives
• 2 Project timeline
• 3 Background
• 4 Previous work in the BSCI 111 class
• 5 General notes about the project
• 6 About the paper
• 7 References

Nearly all journal articles are divided into the following major sections: abstract, introduction, methods, results, discussion, and references.  Usually the sections are labeled as such, although often the introduction (and sometimes the abstract) is not labeled.  Sometimes alternative section titles are used.  The abstract is sometimes called the "summary", the methods are sometimes called "materials and methods", and the discussion is sometimes called "conclusions".   Some journals also include the minor sections of "key words" following the abstract, and "acknowledgments" following the discussion.  In some journals, the sections may be divided into subsections that are given descriptive titles.  However, the general division into the six major sections is nearly universal.

3.2.1 Abstract

The abstract is a short summary (150-200 words or less) of the important points of the paper.  It does not generally include background information.  There may be a very brief statement of the rationale for conducting the study.  It describes what was done, but without details.  It also describes the results in a summarized way that usually includes whether or not the statistical tests were significant.  It usually concludes with a brief statement of the importance of the results.  Abstracts do not include references.  When writing a paper, the abstract is always the last part to be written.

The purpose of the abstract is to allow potential readers of a paper to find out the important points of the paper without having to actually read the paper.  It should be a self-contained unit capable of being understood without the benefit of the text of the article . It essentially serves as an "advertisement" for the paper that readers use to determine whether or not they actually want to wade through the entire paper or not.  Abstracts are generally freely available in electronic form and are often presented in the results of an electronic search.  If searchers do not have electronic access to the journal in which the article is published, the abstract is the only means that they have to decide whether to go through the effort (going to the library to look up the paper journal, requesting a reprint from the author, buying a copy of the article from a service, requesting the article by Interlibrary Loan) of acquiring the article.  Therefore it is important that the abstract accurately and succinctly presents the most important information in the article.

3.2.2 Introduction

The introduction provides the background information necessary to understand why the described experiment was conducted.  The introduction should describe previous research on the topic that has led to the unanswered questions being addressed by the experiment and should cite important previous papers that form the background for the experiment.  The introduction should also state in an organized fashion the goals of the research, i.e. the particular, specific questions that will be tested in the experiments.  There should be a one-to-one correspondence between questions raised in the introduction and points discussed in the conclusion section of the paper.  In other words, do not raise questions in the introduction unless you are going to have some kind of answer to the question that you intend to discuss at the end of the paper. 

You may have been told that every paper must have a hypothesis that can be clearly stated.  That is often true, but not always.  If your experiment involves a manipulation which tests a specific hypothesis, then you should clearly state that hypothesis.  On the other hand, if your experiment was primarily exploratory, descriptive, or measurative, then you probably did not have an a priori hypothesis, so don't pretend that you did and make one up.  (See the discussion in the introduction to Experiment 4 for more on this.)  If you state a hypothesis in the introduction, it should be a general hypothesis and not a null or alternative hypothesis for a statistical test.  If it is necessary to explain how a statistical test will help you evaluate your general hypothesis, explain that in the methods section. 

A good introduction should be fairly heavy with citations.  This indicates to the reader that the authors are informed about previous work on the topic and are not working in a vacuum.  Citations also provide jumping-off points to allow the reader to explore other tangents to the subject that are not directly addressed in the paper.  If the paper supports or refutes previous work, readers can look up the citations and make a comparison for themselves. 

"Do not get lost in reviewing background information. Remember that the Introduction is meant to introduce the reader to your research, not summarize and evaluate all past literature on the subject (which is the purpose of a review paper). Many of the other studies you may be tempted to discuss in your Introduction are better saved for the Discussion, where they become a powerful tool for comparing and interpreting your results. Include only enough background information to allow your reader to understand why you are asking the questions you are and why your hyptheses are reasonable ones. Often, a brief explanation of the theory involved is sufficient. …

Write this section in the past or present tense, never in the future. " (Steingraber et al. 1985)

3.2.3 Methods (taken verbatim from Steingraber et al. 1985)

The function of this section is to describe all experimental procedures, including controls. The description should be complete enough to enable someone else to repeat your work. If there is more than one part to the experiment, it is a good idea to describe your methods and present your results in the same order in each section. This may not be the same order in which the experiments were performed -it is up to you to decide what order of presentation will make the most sense to your reader.

1. Explain why each procedure was done, i.e., what variable were you measuring and why? Example:

Difficult to understand : First, I removed the frog muscle and then I poured Ringer’s solution on it. Next, I attached it to the kymograph.

Improved: I removed the frog muscle and poured Ringer’s solution on it to prevent it from drying out. I then attached the muscle to the kymograph in order to determine the minimum voltage required for contraction.

2. Experimental procedures and results are narrated in the past tense (what you did, what you found, etc.) whereas conclusions from your results are given in the present tense.

3. Mathematical equations and statistical tests are considered mathematical methods and should be described in this section along with the actual experimental work.

4. Use active rather than passive voice when possible.  [Note: see Section 3.1.4 for more about this.]  Always use the singular "I" rather than the plural "we" when you are the only author of the paper.  Throughout the paper, avoid contractions, e.g. did not vs. didn’t.

5. If any of your methods is fully described in a previous publication (yours or someone else’s), you can cite that instead of describing the procedure again.

Example: The chromosomes were counted at meiosis in the anthers with the standard acetocarmine technique of Snow (1955).

3.2.4 Results (with excerpts from Steingraber et al. 1985)

The function of this section is to summarize general trends in the data without comment, bias, or interpretation. The results of statistical tests applied to your data are reported in this section although conclusions about your original hypotheses are saved for the Discussion section.

Tables and figures should be used when they are a more efficient way to convey information than verbal description. They must be independent units, accompanied by explanatory captions that allow them to be understood by someone who has not read the text. Do not repeat in the text the information in tables and figures, but do cite them, with a summary statement when that is appropriate.  Example:

Incorrect: The results are given in Figure 1.

Correct: Temperature was directly proportional to metabolic rate (Fig. 1).

Please note that the entire word "Figure" is almost never written in an article.  It is nearly always abbreviated as "Fig." and capitalized.  Tables are cited in the same way, although Table is not abbreviated.

Whenever possible, use a figure instead of a table. Relationships between numbers are more readily grasped when they are presented graphically rather than as columns in a table.

Data may be presented in figures and tables, but this may not substitute for a verbal summary of the findings. The text should be understandable by someone who has not seen your figures and tables.

1. All results should be presented, including those that do not support the hypothesis.

2. Statements made in the text must be supported by the results contained in figures and tables.

3. The results of statistical tests can be presented in parentheses following a verbal description.

Example: Fruit size was significantly greater in trees growing alone (t = 3.65, df = 2, p < 0.05).

Simple results of statistical tests may be reported in the text as shown in the preceding example.  The results of multiple tests may be reported in a table if that increases clarity. (See Section 11 of the Statistics Manual for more details about reporting the results of statistical tests.)  It is not necessary to provide a citation for a simple t-test of means, paired t-test, or linear regression.  If you use other tests, you should cite the text or reference you followed to do the test.  In your materials and methods section, you should report how you did the test (e.g. using the statistical analysis package of Excel). 

It is NEVER appropriate to simply paste the results from statistical software into the results section of your paper.  The output generally reports more information than is required and it is not in an appropriate format for a paper.

3.2.4.1 Tables

• Do not repeat information in a table that you are depicting in a graph or histogram; include a table only if it presents new information.
• It is easier to compare numbers by reading down a column rather than across a row. Therefore, list sets of data you want your reader to compare in vertical form.
• Provide each table with a number (Table 1, Table 2, etc.) and a title. The numbered title is placed above the table .
• Please see Section 11 of the Excel Reference and Statistics Manual for further information on reporting the results of statistical tests.

3.2.4.2. Figures

• These comprise graphs, histograms, and illustrations, both drawings and photographs. Provide each figure with a number (Fig. 1, Fig. 2, etc.) and a caption (or "legend") that explains what the figure shows. The numbered caption is placed below the figure .  Figure legend = Figure caption.
• Figures submitted for publication must be "photo ready," i.e., they will appear just as you submit them, or photographically reduced. Therefore, when you graduate from student papers to publishable manuscripts, you must learn to prepare figures that will not embarrass you. At the present time, virtually all journals require manuscripts to be submitted electronically and it is generally assumed that all graphs and maps will be created using software rather than being created by hand.  Nearly all journals have specific guidelines for the file types, resolution, and physical widths required for figures.  Only in a few cases (e.g. sketched diagrams) would figures still be created by hand using ink and those figures would be scanned and labeled using graphics software.  Proportions must be the same as those of the page in the journal to which the paper will be submitted. 
• Graphs and Histograms: Both can be used to compare two variables. However, graphs show continuous change, whereas histograms show discrete variables only.  You can compare groups of data by plotting two or even three lines on one graph, but avoid cluttered graphs that are hard to read, and do not plot unrelated trends on the same graph. For both graphs, and histograms, plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Label both axes, including units of measurement except in the few cases where variables are unitless, such as absorbance.
• Drawings and Photographs: These are used to illustrate organisms, experimental apparatus, models of structures, cellular and subcellular structure, and results of procedures like electrophoresis. Preparing such figures well is a lot of work and can be very expensive, so each figure must add enough to justify its preparation and publication, but good figures can greatly enhance a professional article, as your reading in biological journals has already shown.

3.2.5 Discussion (taken from Steingraber et al. 1985)

The function of this section is to analyze the data and relate them to other studies. To "analyze" means to evaluate the meaning of your results in terms of the original question or hypothesis and point out their biological significance.

1. The Discussion should contain at least:

• the relationship between the results and the original hypothesis, i.e., whether they support the hypothesis, or cause it to be rejected or modified
• an integration of your results with those of previous studies in order to arrive at explanations for the observed phenomena
• possible explanations for unexpected results and observations, phrased as hypotheses that can be tested by realistic experimental procedures, which you should describe

2. Trends that are not statistically significant can still be discussed if they are suggestive or interesting, but cannot be made the basis for conclusions as if they were significant.

3. Avoid redundancy between the Results and the Discussion section. Do not repeat detailed descriptions of the data and results in the Discussion. In some journals, Results and Discussions are joined in a single section, in order to permit a single integrated treatment with minimal repetition. This is more appropriate for short, simple articles than for longer, more complicated ones.

4. End the Discussion with a summary of the principal points you want the reader to remember. This is also the appropriate place to propose specific further study if that will serve some purpose, but do not end with the tired cliché that "this problem needs more study." All problems in biology need more study. Do not close on what you wish you had done, rather finish stating your conclusions and contributions.

3.2.6 Title

The title of the paper should be the last thing that you write.  That is because it should distill the essence of the paper even more than the abstract (the next to last thing that you write). 

The title should contain three elements:

1. the name of the organism studied;

2. the particular aspect or system studied;

3. the variable(s) manipulated.

Do not be afraid to be grammatically creative. Here are some variations on a theme, all suitable as titles:

THE EFFECT OF TEMPERATURE ON GERMINATION OF ZEA MAYS

DOES TEMPERATURE AFFECT GERMINATION OF ZEA MAYS?

TEMPERATURE AND ZEA MAYS GERMINATION: IMPLICATIONS FOR AGRICULTURE

Sometimes it is possible to include the principal result or conclusion in the title:

HIGH TEMPERATURES REDUCE GERMINATION OF ZEA MAYS

Note for the BSCI 1510L class: to make your paper look more like a real paper, you can list all of the other group members as co-authors.  However, if you do that, you should list you name first so that we know that you wrote it.

3.2.7 Literature Cited

Please refer to section 2.1 of this guide.

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Writing the Five Principal Sections: Abstract, Introduction, Methods, Results and Discussion

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The five-part structure of research papers (Introduction, Method, Results and Discussion plus Abstract) serves as the conceptual basis for the content. The structure can be considered an “hourglass” or a “conversation” with predictable elements. Each section of the paper has its own common internal structure and linguistic features. The structures and features are explained with examples from published papers in a range of scientific disciplines. Exceptions to the four-part structure are also discussed.

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Cargill, M., & O’Connor, P. (2009). Writing scientific research articles . West Sussex: Wiley-Blackwell.

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Englander, K. (2014). Writing the Five Principal Sections: Abstract, Introduction, Methods, Results and Discussion. In: Writing and Publishing Science Research Papers in English. SpringerBriefs in Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7714-9_8

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Parts of a Research Paper

One of the most important aspects of science is ensuring that you get all the parts of the written research paper in the right order.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

You may have finished the best research project on earth but, if you do not write an interesting and well laid out paper, then nobody is going to take your findings seriously.

The main thing to remember with any research paper is that it is based on an hourglass structure. It begins with general information and undertaking a literature review , and becomes more specific as you nail down a research problem and hypothesis .

Finally, it again becomes more general as you try to apply your findings to the world at general.

Whilst there are a few differences between the various disciplines, with some fields placing more emphasis on certain parts than others, there is a basic underlying structure.

These steps are the building blocks of constructing a good research paper. This section outline how to lay out the parts of a research paper, including the various experimental methods and designs.

The principles for literature review and essays of all types follow the same basic principles.

Reference List

For many students, writing the introduction is the first part of the process, setting down the direction of the paper and laying out exactly what the research paper is trying to achieve.

For others, the introduction is the last thing written, acting as a quick summary of the paper. As long as you have planned a good structure for the parts of a research paper, both approaches are acceptable and it is a matter of preference.

A good introduction generally consists of three distinct parts:

• You should first give a general presentation of the research problem.
• You should then lay out exactly what you are trying to achieve with this particular research project.
• You should then state your own position.

Ideally, you should try to give each section its own paragraph, but this will vary given the overall length of the paper.

1) General Presentation

Look at the benefits to be gained by the research or why the problem has not been solved yet. Perhaps nobody has thought about it, or maybe previous research threw up some interesting leads that the previous researchers did not follow up.

Another researcher may have uncovered some interesting trends, but did not manage to reach the significance level , due to experimental error or small sample sizes .

2) Purpose of the Paper

The research problem does not have to be a statement, but must at least imply what you are trying to find.

Many writers prefer to place the thesis statement or hypothesis here, which is perfectly acceptable, but most include it in the last sentences of the introduction, to give the reader a fuller picture.

3) A Statement of Intent From the Writer

The idea is that somebody will be able to gain an overall view of the paper without needing to read the whole thing. Literature reviews are time-consuming enough, so give the reader a concise idea of your intention before they commit to wading through pages of background.

In this section, you look to give a context to the research, including any relevant information learned during your literature review. You are also trying to explain why you chose this area of research, attempting to highlight why it is necessary. The second part should state the purpose of the experiment and should include the research problem. The third part should give the reader a quick summary of the form that the parts of the research paper is going to take and should include a condensed version of the discussion.

This should be the easiest part of the paper to write, as it is a run-down of the exact design and methodology used to perform the research. Obviously, the exact methodology varies depending upon the exact field and type of experiment .

There is a big methodological difference between the apparatus based research of the physical sciences and the methods and observation methods of social sciences. However, the key is to ensure that another researcher would be able to replicate the experiment to match yours as closely as possible, but still keeping the section concise.

You can assume that anybody reading your paper is familiar with the basic methods, so try not to explain every last detail. For example, an organic chemist or biochemist will be familiar with chromatography, so you only need to highlight the type of equipment used rather than explaining the whole process in detail.

In the case of a survey , if you have too many questions to cover in the method, you can always include a copy of the questionnaire in the appendix . In this case, make sure that you refer to it.

This is probably the most variable part of any research paper, and depends on the results and aims of the experiment.

For quantitative research , it is a presentation of the numerical results and data, whereas for qualitative research it should be a broader discussion of trends, without going into too much detail.

For research generating a lot of results , then it is better to include tables or graphs of the analyzed data and leave the raw data in the appendix, so that a researcher can follow up and check your calculations.

A commentary is essential to linking the results together, rather than just displaying isolated and unconnected charts and figures.

It can be quite difficult to find a good balance between the results and the discussion section, because some findings, especially in a quantitative or descriptive experiment , will fall into a grey area. Try to avoid repeating yourself too often.

It is best to try to find a middle path, where you give a general overview of the data and then expand on it in the discussion - you should try to keep your own opinions and interpretations out of the results section, saving that for the discussion later on.

This is where you elaborate on your findings, and explain what you found, adding your own personal interpretations.

Ideally, you should link the discussion back to the introduction, addressing each point individually.

It’s important to make sure that every piece of information in your discussion is directly related to the thesis statement , or you risk cluttering your findings. In keeping with the hourglass principle, you can expand on the topic later in the conclusion .

The conclusion is where you build on your discussion and try to relate your findings to other research and to the world at large.

In a short research paper, it may be a paragraph or two, or even a few lines.

In a dissertation, it may well be the most important part of the entire paper - not only does it describe the results and discussion in detail, it emphasizes the importance of the results in the field, and ties it in with the previous research.

Some research papers require a recommendations section, postulating the further directions of the research, as well as highlighting how any flaws affected the results. In this case, you should suggest any improvements that could be made to the research design .

No paper is complete without a reference list , documenting all the sources that you used for your research. This should be laid out according to APA , MLA or other specified format, allowing any interested researcher to follow up on the research.

One habit that is becoming more common, especially with online papers, is to include a reference to your own paper on the final page. Lay this out in MLA, APA and Chicago format, allowing anybody referencing your paper to copy and paste it.

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Original research article, a community health worker led approach to cardiovascular disease prevention in the uk—spices-sussex (scaling-up packages of interventions for cardiovascular disease prevention in selected sites in europe and sub-saharan africa): an implementation research project.

• 1 Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, United Kingdom
• 2 Department of Disease Control and Environmental Health, Makerere University, Kampala, Central Region, Uganda
• 3 Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium

Background: This paper describes a UK-based study, SPICES-Sussex, which aimed to co-produce and implement a community-based cardiovascular disease (CVD) risk assessment and reduction intervention to support under-served populations at moderate risk of CVD. The objectives were to enhance stakeholder engagement; to implement the intervention in four research sites and to evaluate the use of Voluntary and Community and Social Enterprises (VCSE) and Community Health Worker (CHW) partnerships in health interventions.

Methods: A type three hybrid implementation study design was used with mixed methods data. This paper represents the process evaluation of the implementation of the SPICES-Sussex Project. The evaluation was conducted using the RE-AIM framework.

Results: Reach: 381 individuals took part in the risk profiling questionnaire and forty-one women, and five men participated in the coaching intervention. Effectiveness: quantitative results from intervention participants showed significant improvements in CVD behavioural risk factors across several measures. Qualitative data indicated high acceptability, with the holistic, personalised, and person-centred approach being valued by participants. Adoption: 50% of VCSEs approached took part in the SPICES programme, The CHWs felt empowered to deliver high-quality and mutually beneficial coaching within a strong project infrastructure that made use of VCSE partnerships. Implementation: Co-design meetings resulted in local adaptations being made to the intervention. 29 (63%) of participants completed the intervention. Practical issues concerned how to embed CHWs in a health service context, how to keep engaging participants, and tensions between research integrity and the needs and expectations of those in the voluntary sector. Maintenance: Several VCSEs expressed an interest in continuing the intervention after the end of the SPICES programme.

Conclusion: Community-engagement approaches have the potential to have positively impact the health and wellbeing of certain groups. Furthermore, VCSEs and CHWs represent a significant untapped resource in the UK. However, more work needs to be done to understand how links between the sectors can be bridged to deliver evidence-based effective alternative preventative healthcare. Reaching vulnerable populations remains a challenge despite partnerships with VCSEs which are embedded in the community. By showing what went well and what did not, this project can guide future work in community engagement for health.

1 Introduction

Cardiovascular disease (CVD) is among the most prevalent, costly to treat, and deadly medical issues in the world ( 1 ). As part of the continual effort to combat CVD, greater emphasis is being placed on prevention. This often takes the form of behavioural or lifestyle change, focusing on the reduction of risk factors (e.g., hypertension, poor diet, obesity). Reducing these risk factors using evidence-based interventions not only works to lower rates of CVD, but also impacts rates of a variety of other medical issues, including susceptibility to severe COVID-19 infection ( 2 ), many common Noncommunicable Diseases (NCDs) including Type 2 diabetes and a wide range of cancers ( 3 ). Furthermore these preventative interventions are less expensive than reactionary care and can lower the treatment burden on strained medical systems ( 4 ).

Community-Based Participatory Research (CBPR) and Community Engagement (CE) have grown increasingly popular as potential methods to engender sustainable, long-term change in communities—particularly those communities under-served by existing medical systems and/or those at heightened risk of CVD ( 5 ). One's behaviour is influenced by their environment and the community they live in, meaning that tapping into a community's resources can be effective in changing lifestyle behaviour as well as having impacts on the wider community ( 6 , 7 ). The use of community-based practices fits within the growing South-North collaboration that this project joins as part of an international collaboration known as “Scaling-up Packages of Interventions for Cardiovascular disease prevention in selected sites in Europe and Sub-Saharan Africa: An implementation research project” (SPICES). In the Low- and Middle-Income countries (LMIC) there is evidence for the successful implementation of evidence-based community-based interventions in increasing knowledge of, and changing behaviour related to, CVD ( 8 ) however their use in the Global North is less well tested or understood ( 9 ).

In the UK, the flagship intervention to address preventative health issues is the National Health Service's (NHS) Health Check initiative, which is free to individuals ages 40–76 and which assesses risk for long term health conditions including CVD ( 10 ). Following initial assessment by a health professional, patients are advised on a course of action which often includes some degree of preventative prescribing to address behavioural risk factors ( 11 ). Just under half of eligible individuals accepts a first health-checks appointment (44.2%)—it is associated with increased detection of CVD risk, but uptake is skewed by several demographic factors (principally, age, gender, and socio-demographics), and it has struggled to create change in underserved groups ( 12 , 13 ). Marginalised coastal communities in Sussex face overall below-average healthy-life expectancy ( 14 ). This, alongside heightening inequality and the impact of COVID-19, has left some communities in Sussex significantly deprived in terms of access and engagement with health services ( 15 ). People in these communities experience transgenerational poverty, precarity, and lifestyle behaviours ingrained into the communities that lead many to be at higher risk for CVD. CBPR and CE models have the potential to lead to improved health and health behaviours among disadvantaged populations if designed properly and implemented through effective community consultation and participation ( 16 ).

CBPR and CE offer the chance to bring lessons from effective programmes in the Global South and apply them to programmes in the Global North. Community-based strategies to promote evidence-based preventative health interventions using Community Health Workers (CHWs) are often more established in the Global South where more tightly knit communities and established community health programmes fulfil a range of public health needs ( 17 , 18 ). CHWs interventions are a form of “task-sharing” intervention in which responsibility and power is shared between professional health workers and communities which have been proposed to effectively manage non-communicable disease risk ( 19 ). Lay Community Health Workers are individuals who are trained to perform of health-related functions but lack a formal professional health education. They can provide links between local communities and health care institutions thereby building and on and developing the social capital that already exists in communities ( 20 ). Although there is plenty of evidence communicating the importance and usefulness of these methods (the “what”), there remains a lack of attention given to how to do it . This article joins the work and voices attempting to begin filling that lacuna.

Within the literature on CBPR and CE, a handful of common themes emerge. The first is a push for human-centred research design ( 21 , 22 ). Yardley et al. ( 23 ) focused on this idea in their “person-based” approach to digital health interventions, where they recommended a “focus on understanding and accommodating the perspectives of the people who will use the intervention” ( 24 ). Hopkins and Rippon's ( 25 ) “asset-based” approach to CE interventions recommends recognising and adapting to the need, wants, and strengths already present in the community. Particularly the strengths, or “assets” already present in the community provide an opportunity for projects to use those assets. Such an implementation approach requires flexibility and adaptability, as well as deep involvement with the community. The second theme builds on the first, with the idea that not only should project design be person-centred, but those participants and other stakeholders in the community should be involved at every level of project planning through co-design. Yardley et al. ( 23 ) included this as a key element of their paper, writing that people from the target population should be involved in project development as well as at every stage of the intervention. Similarly, Berrera et al. ( 26 ) emphasise the need to adapt all projects to the cultural context of the community. This insight speaks to the third theme, continuous evaluation ( 27 ). As the needs of the community will be ever-shifting, so must the project adapt to those needs continually. Instead of designated periods of evaluation, a shift to continual processes of qualitative evaluation is called for to identify and adjust to the needs of the community. These processes require elevated levels of trust and participation from the community, which has its own challenges. Trust especially takes significant time and resources to develop and is an under-studied area of community engagement ( 28 ).

The SPICES-Sussex project was carried out from January 2019 and aimed to answer the following overarching research question: How can Community Health Workers (CHW) CVD prevention interventions, that have been used in the Global South, be developed, and implemented in a Global North setting and what barriers and enablers exist to their implementation? The project began with a situational analysis which included an exploration of the views and experiences of the local community with regards to CVD health and Community Health Workers and early stakeholder mapping of the research sites which was carried out between 2019 and 2020 ( 29 , 30 ).

The primary aim of the current paper is to provide a comprehensive examination of the project's implementation including complementary mixed methods analyses according to the Reach Effectiveness-Adoption Implementation and Maintenance (RE-AIM) framework ( 31 ). The secondary aims of the project are to inform future CE projects what worked (and did not work) for our project and to tie insights from our project to broader discussions in the discipline. The project is based on a protocol published in 2020 prior to the onset of COVID and was conducted through the period of the COVID-19 pandemic ( 29 ). Subsequently, several aspects of the original protocol were adapted to make implementation feasible within the constraints of this period (see Supplementary Appendix 2 ).

2.1 Study design

The project uses a type 3 hybrid implementation design ( 29 ) meaning that the primary aim of the research was to determine utility of an implementation intervention/strategy whilst the secondary aim was to assess clinical outcomes associated with the implementation trial. This means that we focused on understanding what barriers and enablers existed for the project's implementation and the context within which it operated. Effectiveness of the intervention remained important, however we were primarily interested in how and why it did (or did not) work. The project was carried out at four geographic research sites within Sussex (see Section 2.3 ) and implementation was conducted on an iterative basis from research site to research site broadly following the Medical Research Council's (MRC) framework for the development and implementation of complex interventions ( 32 ). The research team developed and then began delivering the intervention at each site before moving onto the next. At each site the following stages were carried out: (1) Development: this included stakeholder mapping, formation of implementation partners, and codesign/local adaptation of the intervention [covered in the study's pre-implementation paper ( 30 )]; (2) Implementation: this included the delivery of the CHW intervention at the research sites and collection of mixed method data pertaining to effectiveness and stakeholder experiences, and (3) Evaluation: this included the analysis of the mixed method data in line with the MRC guidance on analysis complex interventions.

2.2 Research site and voluntary and community sector enterprise partner selection

Four study sites were selected across East Sussex by identifying Middle Layer Super Output Area (MSOA) postcodes with high levels of deprivation according to the Indices of Multiple Deprivation (IMD) ( 33 ). Selection of the research sites was based on the pre-implementation community mapping phase of the project ( 30 ). Following on from CBPR practices, VCSEs and Volunteer Coordinators (VCs) were recruited to co-design and deliver the implementation strategy at each of the research sites.

VCSEs organisations were recruited as partners at each research site. The intervention was primarily run through these organisations and a paid staff member was recruited at each organisation. Their responsibilities included, CHW management, and participant recruitment. They also had a role in local adaptation activities. VCSE organisations were eligible to take part in the organisations if they were based in the research site, if they had interests and existing activities that aligned with the project's goals (CVD risk reduction and community development), and if they had existing experience of volunteer recruitment and management.

2.3 Community health worker recruitment and training

The aim was for each site to recruit a pool of five to eight CHWs. As part of this each site was asked for input into local CHW recruitment flyers, which were shared on VCSE websites and social media pages and shared on social media via existing CHWs at the VCSEs. CHWs were recruited through intermediary organisation recruitment via the VCSE partner organisation. The project was also advertised at a Virtual Volunteer Fair. Local contacts and existing volunteer pools at the VCSEs meant that the target number of CHWs was rapidly recruited at each site. CHWs were eligible to take part in the intervention if they were over 18 years of age, if they lived within the research site (determined by postcode), and if they had some kind of pre-existing relationship with the VCSE partner organisation (i.e., as a volunteer).

Potential CHWs who expressed an interest in the project were invited to attend an induction to the project, and then the local adaptation co-design meeting. Those who decided they would like to become a CHW then went on to receive five online, group training sessions (each of which lasted for 2 h, 10 h in total): an introductory session, a session covering project policies, heart health and the structure of the intervention, and three sessions on behaviour change techniques. These training sessions were developed and delivered by an external organisation (National Centre for Behaviour Change) specifically for the project after a consultation and planning process with the research team. Before the onset of the intervention at each site CHWs made various recommendations in the local adaptation meetings on the design of the training programme. These included providing information on listening techniques, engaging, and managing resistance, providing simple health information, using accessible language, using different starting points depending on the CHW's background knowledge and experience, training on conducting coaching virtually, and providing a training handbook. A Volunteer coordinator (VC) was recruited at each site. This VC was a trained and experience health coach (KFS) and provided training support and guidance through monthly group training support sessions in addition to the initial 10 h training block the received prior to the intervention onset. These monthly training and support sessions were organised into specific themes and agendas that were set with the CHW participants.

2.4 Local adaptation

Elements of the evidence-based intervention were tailored to the individuals and their community in the stakeholder-mapping phase using qualitative interviews, workshops, and focus groups with a range of stakeholders across the study site ( 30 ). Further rounds of local adaptation were carried out with VCs and CHWs at each of the research sites to tailor to individuals and their community context through iterative co-design workshops ( 34 ). CHWs and VCSE also agreed on a “volunteer charter” during the co-design session. This was a list of principles, behaviours, and practices upon which guided interactions between research staff, CHWs, VCSE and participants. The charter was designed to ensure that the practices of the project aligned with the principles of the CHW and partner organisation.

2.5 Participant recruitment and screening questionnaire

Participants (who received coaching) were eligible to take part in the eligibility screening if they lived in, or adjacent to, the study site's postcode and if they were aged eighteen or older. Participant recruitment was also based on intermediary organisation recruitment, community outreach, paid social media advertisement (through Meta™), gatekeeper and snowball sampling. Gatekeeper recruitment was conducted when interacting with a relevant statutory or non-statutory service provider (i.e., a fitness/weight loss group leader) and involved asking them to recommend the intervention to their members or to recommend participants who may be interested in taking part. Snowball sampling involved asking participants who participated in the study to sending email invitations to their social group. A social media recruitment strategy was undertaken to recruit people from the local area to the risk profiling survey to supplement the community-based recruitment through the VCSE partners. Social media was conducted on Facebook via paid advertisement in four waves of recruitment which took place over 1–2 weeks at each site. The advert targeted people who were 35+ and over and to people with 5 km of each research site. Messages were changed regularly from a list of recruitment messages drafted with CHWs during co-design sessions. Additionally, CHWs and VCSE participants were asked to send recruitment emails to any social or professional networks they thought would be interested in taking part. We did not record where participants were recruited.

Screening and risk profiling for the CVD coaching was carried out using the validated non-laboratory based INTERHEART questionnaire, presented online, for all participants that expressed an interest in the study ( 35 ). This questionnaire assessed modifiable and non-modifiable CVD risk factors and categorised participants as either “Low,” “Moderate.” Or “High” risk. See the protocol paper for further information on the INTERHEART risk profiling; for more information on the screening questionnaire, see the study protocol paper ( 29 , 35 ). Questionnaire data were collected and managed using Research Electronic Data Capture (REDCap) electronic data capture tools hosted at the University of Antwerp ( 36 ). Participants were considered to be eligible for the intervention if they were aged eighteen or over, if they lived within the research site (determined by postcode), and if they were categorised as “Moderate” risk of CVD according to the INTERHEART questionnaire. High risk participants were not included as their needs were considered to be too high for a pilot study involving CHWs. Eligible participants were then emailed by the research team with an invitation to take part in the CVD coaching intervention. After recruitment for the intervention was closed for each site, an online questionnaire survey was sent to eligible participants to gather information the reasons for not accepting the invitation to the intervention. Open response questions were used which the research team later categorised into codes.

2.6 The CVD prevention coaching intervention

The coaching intervention was based on motivational interviewing techniques which are promoted by the European commission on cardiovascular disease prevention in clinical practice ( 37 ) and which include techniques such as Open questions, Affirmation, Reflective listening and Summary reflections (OARS) ( 38 , 39 ). The use of these Behaviour Change Techniques (BCTs) used during the intervention were based on five target behaviours highlighted by the World Health Organisation including: reduce/cease smoking, increase moderate physical activity, reduce the fat, salt, and sugar content of the diet, increase fibre, oily fish (or alternatives), fruit, and vegetable content of the diet, reduce sedentary hours. The intervention involved six, one-hour long coaching sessions between participants and CHWs which were delivered every two weeks. Participants were also considered to have completed the intervention if they only completed three sessions and then notified the team of their withdrawal from the intervention.

The study team included two participant co-ordinators (PCs) who managed the participant journey through the intervention, sending welcome emails, questionnaires, and invitations to post-intervention interviews, and co-ordination between participants and CHWs to book coaching sessions. Reminders of appointments were also sent to CHWs and participants one week and two days before the session. Participants and CHWs were matched, based on gender preference and availability, and supported throughout the coaching intervention the PCs. CHWs were provided with guidance, resources, and signposting information throughout the intervention but were also given the flexibility to deliver the coaching in a way that suited them and their participant(s). Initially, counselling and goalsetting were based on their individual item INTERHEART assessment scores. Participants and CHWs were then encouraged to create an action plan with appropriate goal setting for the behaviours they wanted to change (e.g., diet, exercise habits). The goals were set in relation to when, where, and how they would undertake the behaviour, e.g., when the physical activity will be performed, where it will be performed, how often it will be performed (i.e., in a group or using specific equipment). CHWs helped participants to analyse any factors which might influence their ability to achieve the goals and to generate strategies which could help them overcome these barriers using problem solving. Full details of the participant journey through the intervention are given in Supplementary Appendix 2 in the Supplementary Material . All coaching was conducted virtually using Zoom™ to host and monitor coaching sessions and Microsoft OneDrive to store, recruit, and communicate written and visual resources with CHWs and participants. Monitoring in Zoom calls was called out by the PCs who checked whether both the participant began and ended the coaching session. If either the participants or CHW did not join, the PC could join the call to help the attendee. Feedback was obtained from the participant about the coaching session through emails after the session and by inviting participants to a follow-up interview after the intervention (see qualitative evaluation).

2.7 Evaluation

The evaluation was underpinned by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework ( 31 ) which allows for an understanding of the multifaceted and interactive effects of personal, social, and environmental factors that determine behaviour; and for identifying behavioural and organisational leverage points and intermediaries for health promotion within organisations and communities. RE-AIM has been used to evaluate programs and setting in public health and community settings and is thought to be particularly useful when evaluate interventions in “real-world” settings ( 40 , 41 ). It has also been used to evaluate public health interventions which make use of community health workers in community-based setting ( 42 – 44 ). Results are made up of quantitative measures from the participant questionnaires, qualitative interviews with the participants, the CHWs, VCSE partners, and the research team. Primary quantitative outcome measures included implementation measures such as uptake and engagement and the pre/post changes to the self-report CVD behavioural questions which included the following three questionnaires: (1) the INTERHEART CVD risk questionnaire collected during the screening process was used as the baseline and collected again after completion of the intervention. (2) Physical activity levels were measured using the International Physical Activity Questionnaire (IPAQ) ( 45 ). The IPAQ is an internationally validated instrument to capture information about weekly physical activity habits, behaviours, and routines. (3) Diet was assessed using a 20-item questionnaire based on a modified version of the UK Diet and Diabetes Questionnaire ( 46 ), a brief food frequency questionnaire designed to assess conformity to healthy eating guidelines, and to assist in the setting of dietary goals. It was used to estimate the number of portions eaten daily or weekly of fruit and vegetables, oily fish (or alternative), and foods high in fat, salt, and sugar, what proportion of the time wholegrain cereal products were chosen, weekly units of alcohol consumed and the frequency of binge drinking. Due to the small sample sizes and non-parametric data used in this study, Wilcoxon Sign test was used to evaluate for differences in continuous variables whilst McNemar's test was used for binary categorical data. The pre-intervention assessment of the primary outcome measures was sent to participants before they participated in the intervention (no participant could begin the intervention without completing the baseline measures). Post intervention primary outcome measures were collected after their participant in the intervention was completed.

Focus groups and one-to-one interviews were conducted with four groups of stakeholders: (1) VCSE partners; (2) CHWs; (3) members of the research team, (4) participants in the intervention. Individual interviews were conducted with VCs, members of the research team, and participants, while data from the CHWs was collected in focus groups. Discussion guides for VCs, CHWs and members of the research team all included questions on the respondents' role within the project, the process of community engagement, barriers, and facilitators the implementation process, recommendations for the future and sustainability. Discussion guides for participant interviews included questions on how and why participants became involved in the project, their experience of the health coaching, and their views on the impact and usefulness of the project. Interviews and focus groups were conducted online using Zoom or MS Teams. The analysis was conducted by TGJ, IR, and RD and using qualitative framework analysis based on the components of the RE-AIM framework. Following data collection interviews and focus groups were transcribed by a professional transcription service and TGJ, IR, and RD familiarised themselves with the full set of data. They then undertook line-by-line coding of the data in NVivo using descriptive primary codes which were then interlinked with secondary codes. These secondary codes were then organised under the five elements of the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance). The analysis was interpreted, findings were synthesised with reference to the stakeholder group and theme descriptions were produced with supplementary illustrative quotes.

The Reach of the intervention was assessed through recruitment rates for the VCSE partners, CHWs and Intervention participants and qualitative data collected from the VCSE partners, and the research team was used to understand barriers and facilitators to recruitment. Effectiveness was assessed during the primary outcome measures and barriers and facilitators to effectiveness were assessed through qualitative interviews with the participants and CHWs. Adoption was at the setting level was determined through assessment of the retention of VCSE partners and qualitatively through interviews with VCSE partners and the research team. At the individual level, Adoption was assessed through CHW retention rates and qualitatively assessed through interviews with the research team and the CHWs. Implementation was assessed qualitatively through interviews with the intervention participants focusing on intervention fidelity. Maintenance was assessed at the setting level qualitatively through interviews with VCSE partners and the research team and through a report of the status of the intervention after 6 months. No individual level maintenance data is reported. A description of the data sources which contributed to each component of the RE-AIM framework is listed in Table 1 .

Table 1 . Description of data source used to evaluate the SPICES-Sussex intervention for each of the RE-AIM components.

Ethical approval for this research was obtained from Brighton and Sussex Medical School's Research Governance and Ethics Committee (R-GEC) (application reference: ER/DG241/17BSMS9E3G/1). This ethics review covered the methods described herein, key research materials, and recruitment and consent protocols for both intervention participants and staff/CHW interviews. Due to the changes imposed on the project by COVID-19 (see Supplementary Appendix 2 ) and because of minor adaptations from research site to research sites; several minor amendments were made (final application reference: ER/BSMS9E3G/6).

Informed consent was obtained in three ways from study participants depending on the nature of their participation. (1) Online screening questionnaire: these participants were presented with an approved information sheet on the first page of the online screening questionnaire, they were then provided with an Informed Consent Form (ICF) which they had to sign with a digital signature. (2) Intervention participants: just prior to participation and data collection participants met with a research staff member to review the information sheet and to sign the ICF if they agreed to participate, consent was sought again for those intervention participants who took part in a post-intervention interview. (3) CHW and research staff members: participants were sent the information sheet and consent form several days before their interview and were asked to sign and return the ICF prior to their interview appointment.

3.1 Participant characteristics

Risk profiling data was collected from 381 participants (Females: 310, Males: 71; mean (SD) age = 58 (12.39) years. Forty-Six participants began the intervention (39 Females, 7 Males; age = 58 (11.94) years. Sixteen participants took part in one-to-one interviews at the end of the intervention (thirteen females and two males, aged 32–67 years). Seven members of the research team (6 females, 1 male), and four VCSE partners (3 females and 1 male) took part in the research team interviews. Four focus groups with a total of thirteen participants (10 females and 3 males) were conducted with CHWs from each of the research sites. Thirteen participants (no gender data collected) took part in the post-intervention questionnaire for non-participants.

3.2 Analytical framework

The remainder of these findings are organised into RE-AIM dimensions with various quantitative and qualitative methods used to evidence each dimension, see Table 1 for a description of each of the data sources. Table 2 summarises concordance and discordance with expectations of the intervention [as described in the study protocol ( 29 )] in line with the RE-AIM framework. Supplementary Appendix 3 summarises changes to the study design from the published study protocol . Throughout this section participant codes are used to attribute quotations and references to specific terminology to a respondent. The codes identify the respondent as either a member of the Research team (RT), VCSE partners (VCSE), Community Health Worker (CHW) or Participants (PP). For VCSEs, CHWs and PPs references to their sites are also made (EB, HA, NH, HG). All codes refer to gender and (F/M), and their number within each respondent category.

Table 2 . Description of concordance/discordance with our pre-implementation expectations of the SPICES intervention for each of the RE-AIM components ( 29 ).

4.1 Recruitment of voluntary and community sector enterprise partners

A community-mapping exercise was carried out during the pre-implementation phase of the project ( 30 ) in which three partner organisations were identified across three research sites in East Sussex (Hastings, East Brighton, and Newhaven). All these organisations were volunteer based community organisations with a focus on local community development and improving health, with the Hastings organisations being focused on health and wellbeing. During the intervention set up phase, the East Brighton organisations dropped out of the study due to the impact of Covid-19 whilst the Hastings, and Newhaven organisations were carried forward to deliver the intervention. The East Brighton organisations helped the research team to develop links with a health and wellbeing organisation that was associated with a local General Practice (GP) clinic in East Brighton. Finally, a fourth research site was identified in West Hove and a final VCSE partner was identified. This organisation was a local community development organisation for the area. In total four VCSE organisations were partnered with across four research sites. In each site a VC was recruited from the partner organisation to deliver the intervention with the research team.

4.2 Community health worker recruitment

The research team and VCSE partners recruited 38 individuals who attended the introductory CHW meetings (Gender: 27 females and 11 males, NH n  = 7, EB n  = 13, HG n  = 10, HA n  = 8). Twenty-seven of these individuals completed the full training for CHWs (20 females and 7 males; NH n  = 5, EB n  = 9, HG n  = 7, HA n  = 6).

4.3 Participant recruitment

Social media recruitment had a wider reach to potential participants compared with gatekeeper recruitment, however, several participants did not complete the REDCap screening questions, had a poor understanding of the study, or were not part of the study's target population. VCSE gatekeepers yielded poor recruitment results apart from when a newsletter with a particularly large reach was used. Social media was the primary strategy for recruiting participants to the study. In total the messages reached 13,086 individuals across four waves of recruitment and of these 472 (3.6%) engaged with post by clicking on the survey link. Of those who clicked on the link 80% were female and 20% were male.

The INTERHEART screening data is shown in Figure 1 and Supplementary Appendix 1 for all those who completed the screening questionnaire ( N  = 381), participants who started the intervention and then withdrew ( N  = 17), and participants who completed the intervention and on whom we have full data ( n  = 27). Of the CVD risk factors measured by the INTERHEART screening tool, the two most prevalent were stress (reported by 61% of those screened, 56% of those who started the intervention, and 78% of those who went on to complete), and physical inactivity (reported by 55%, 81% and 64% respectively).

Figure 1 . Primary outcome measures for the “Reach” and “Effectiveness” components of the SPICES-Sussex intervention. ( A ) The proportion of “Low”, “Medium”, and “High” risk participants identified during the Interheart risk profiling questionnaire; ( B ) the mean Interheart score pre and post intervention for those who completed the intervention, p value from paired t -tests; ( C ) shows the % change regularly of dietary behaviours from pre/post intervention UKDDQ score, within-group t -tests; ( D ) the change in the % of intervention participants classified as having either low or medium/high activity levels pre and post intervention, p value from McNemar's test.

Forty-six participants took part in the CVD coaching intervention across the four research sites, all of whom completed the pre-intervention quantitative questionnaires. Sixty-three percent completed the full coaching intervention, and one participant withdrew from the project after three months. We had full data for twenty-seven of twenty-nine participants who completed the full 6-month coaching intervention (note: these participants have been removed from Supplementary Appendix 1 , n  = 2), Participants' characteristics are summarised in Table 3 . Several participants withdrew (37%), reasons given for withdrawing were: ill health/poor mental health/ill health in the family (13%); the intervention was considered a poor fit for the participant/did not meet their expectations/they did not need the intervention (9%); other commitments got in the way/they were too busy with their normal lives (7%); repeated non-attendance at planned coaching sessions from the CHW (4%); did not get on well with CHW (2%), language issues (2%).

Table 3 . Facilitators and barriers to reach and recruitment.

Due to low initial recruitment rates, the recruitment areas were expanded and included more affluent adjacent areas. The proportion of those completing the screening questionnaire and of those who went on to start the intervention who were in the target population (i.e., had an address with a postcode and IMD in the most deprived three deciles) was 30% in both cases. Despite recruitment being gender neutral and without gender/sex related parameters on social media our risk profiling questionnaire recruited far more women than men (77% female, 23% male, see Supplementary Appendix 1 ). This issue was carried forward to the main intervention in which only five of the forty-six who initially took part in the study were male.

4.4 Reasons for non-participation

Reasons given for not participating included missing or not receiving an invitation to take part ( n  = 4), lack of time due to responsibilities and commitments ( n  = 4), not feeling like the intervention was a good fit for them and their circumstances ( n  = 2), not being happy with the CHW allocated to them ( n  = 2), being reluctant to take part in online activities due to a lack of privacy at home ( n  = 1). When asked what would have made them more likely to participate the most common response was more clarity/detail on what was involved ( n  = 3).

4.5 Facilitators and barriers to reach

Intervention participants referred to several intervention components that functioned as facilitators or barriers to the reach of the intervention. These barriers and facilitators were organised into themes which include: (1) Experience of CHW recruitment; (2) The value of community partnerships; (3) The experience of the risk profiling questionnaire; (4) Impacts of COVID-19. These barriers and facilitators are described in more detail in Table 3 and illustrative quotes are provided.

5 Effectiveness

5.1 primary outcomes measures.

For those participants who completed the intervention, the before and after measures of cardiovascular risk, diet, physical activity, and readiness to change were compared (see Figure 1 and Supplementary Appendix 1 ). Mean INTERHEART score fell significantly from 11.7 to 9.9, taking the mean to within the low-risk range. There were also significant improvements in the self-reported dietary measures including: an increase in the proportion of time wholegrain foods were chosen, and the daily portions of fruit and vegetables eaten, and decreases in the consumption of fatty, salty, and sugary food. No changes were observed in the consumption of oily fish. Self-reported levels of physical inactivity also dropped over the course of the intervention with the proportion of those classified in the “low” physical activity category falling from 40% to 7%. Additionally, the self-reported levels of participants' “readiness to change” during the intervention increased from 3.6 to 4.5, which indicates increased levels of motivation as a result of the intervention.

5.2 Participant reported facilitators and barriers to the effectiveness

Intervention participants referred to several intervention components that functioned as facilitators or barriers to the effectiveness of the intervention. These barriers and facilitators were organised into themes which include: (1) accountability—the ways CHWs kept participants accountable about their health behaviours; (2) connection and community—the importance of making human connections with the CHWs and feelings of community togetherness; (3) judgement-free—the importance of a judgement-free intervention experience; (4) motivation and support—the coaching role that the intervention took in the lives of participants; (5) personalisation—the feeling that the intervention was adapted to their own needs and experiences; (6) reflection—the value of reflecting on experiences during the coaching intervention; (7) self-efficacy—the ways in which CHWs made participants feel in control of their health behaviours; (8) gradual or modest impact—the feeling that the intervention largely lead to modest impacts (9) generic or inappropriate advice—the feeling that the information provided during the coaching was too generic, obvious, or inappropriate to their needs. These barriers and facilitators are described in more detail in Table 4 and illustrative quotes are provided.

Table 4 . Facilitators and barriers to intervention effectiveness.

6.1 Retention of voluntary and community sector enterprise partnerships (setting level)

Of the six VCSE organisation engaged with during the pre-implementation phase of the project, four went on to be VCSE partner organisations during the implementation phase. Disruption and staff pressures resulting from the COVID-19 pandemic were a significant barrier to recruiting partner VCSEs, with two organisations who had been involved in initial discussions deciding not to proceed for this reason. Furthermore, interruptions to communication caused by COVID-19 and research team changes led to a loss of trust and engagement in some cases. One organisation which had a group of people ready to volunteer at the beginning of the project later withdrew as this group had fragmented due to COVID-19-related delays and substantial staffing changes that took place just prior to the implementation phase between 2019 and 2020. Other factors impacting on VCSE recruitment included the availability of funding, and issues with recruiting staff to the VC role. After one of the VCSE partners dropped out of the study just prior to the implementation phase, the same organisation linked the research team with another organisation who eventually functioned as VCSE partners for the implementation phase. The need to develop trust, and having the time to achieve this, was stated by several members of the research team as being key to recruiting partner VCSEs. Quality of communication was also felt to be especially important.

6.2 Facilitators of voluntary and community sector enterprise partnerships (setting level)

VCSES and research team members referred to several intervention components that functioned as facilitators or barriers to setting level adoption. These barriers and facilitators were organised into themes which include: (1) Trust—the importance of developing trust with community- partners; (2) Local Knowledge—the value of local knowledge and to delivering appropriate community care; (3) Local Skills—the value of the skills and experiences in local communities to delivering the intervention. These barriers and facilitators are described in more detail in Table 5 and illustrative quotes are provided.

Table 5 . Facilitators and barriers to intervention setting level adoption.

6.3 Retention of community health workers (individual level)

Of the twenty-seven CHWs who were recruited and trained to be a part of the intervention, twenty-one went on to deliver one or more session as an active CHW (Gender: 15 females and 6 males NH n  = 5, EB n  = 6 HG n  = 5, HA n  = 5). Each of these CHWs completed the intervention with at least one participant and the maximum number of participants who completed the intervention with one CHW was three.

6.4 Community health worker training needs feedback (individual level)

After training sessions in our first site, a short questionnaire was conducted with CHWs who attended the training in the formof one-to-one discussions with the training coordinator and the research team. Questions were asked about the anticipated barriers that CHWs thought they would face during the coaching as well as key training needs. Anticipated barriers and challenges during the project included: a sense of mistrust amongst participants, issues of poverty and deprivation, triggers, and sensitivities to the experiences of participants (i.e., trauma or addition triggers). The key training needs identified included: the sharing of personal stories to empower participants, how to set achievable health goals, preparing CHWs with tools to challenge the participant in a supportive way, improving CHW confidence, and advice on how to communicate CVD risk to participants in a straightforward way.

6.5 Community health worker facilitators and barriers to adoption (individual level)

CHWs referred to several intervention components that functioned as facilitators or barriers to the adoption of the intervention at the individual CHW level. These barriers and facilitators were organised into themes which include: (1) Local adaptation and Codesign Sessions; (2) CHW motivation for participating; (3) CHW experiences of the training; (4) CHW experience of the support provided to them. These barriers and facilitators are described in more detail in Table 6 and illustrative quotes are provided.

Table 6 . Facilitators and barriers to intervention individual level adoption.

7 Implementation

7.1 participant retention and fidelity.

Overall, 48% ( n  = 51) of those eligible ( n  = 106) to take part in the intervention agreed to do so and provided consent, of those 90% ( n  = 46) attended their first CHW coaching session and completed the baseline questionnaire. Of those who completed their first session 63% ( n  = 29) completed three sessions and 45% completed six sessions. For the 46 participants that began the intervention there were 276 planned program contacts of which 183 (66%) were completed. Retention and attendance data are summarised in Supplementary Appendix 2 . No data was collected on the amount of time each participant spent in their coaching session.

7.2 Participant facilitators and barriers to implementation

Intervention participants referred to several intervention components that functioned as facilitators or barriers to the implementation of the intervention. These barriers and facilitators were organised into themes which include: (1) expectations of the coaching intervention, (2) the virtual coaching sessions; (3) holistic and flexible, (4) length of the coaching session, (5) administrative support, (6) past experiences, (6) mental health. These barriers and facilitators are described in more detail in Table 7 and illustrative quotes are provided.

Table 7 . Facilitators and barriers to intervention implementation .

8 Maintenance

8.1 status of the intervention after six months.

Six months after the intervention's funding period ended the program was being continued at two of the sites. One of the sites continued it as volunteer opportunity and peer support program which was covered by their existing funding for peer support programs. A second site was awarded funding from the National Health Service to continue the intervention. The latter's findings will be reported as a program evaluation in the future.

8.2 Facilitators and barriers to maintenance

Interviewees referred to several intervention components that functioned as facilitators or barriers to the maintenance of the intervention at the setting level. These barriers and facilitators were organised into themes which include: (1) continuity of the intervention; (2) funding; (3) infrastructure These barriers and facilitators are described in more detail in Table 8 and illustrative quotes are provided.

Table 8 . Facilitators and barriers to maintenance.

9 Discussion

SPICES Sussex developed strategies to implement effective community-based CVD risk reduction interventions based on behaviour change coaching with CHWs by partnering with and leveraging the experience and influence of VCSE in four underserved communities in East Sussex, UK. Despite issues with recruitment and challenges associated with COVID-19 as well as other logistic, management, and research design challenges, the project showed clear markers of success. Participants experienced the interventions positively and many made gradual, and sometimes substantial, lifestyle changes. The quantitative results showed significant reduction in participants' CVD risk after taking part in the interventions. We think these successes were due to implementing our interventions in a flexible, personalised, and holistic way, which empowered CHWs to use their skills and experiences to aid participants. These results demonstrate how CHWs-led and community-based preventative CVD interventions could be implemented, such as those seen widely across the Global South ( 17 , 18 ). They also support a “person-based” and “asset-based” approach to community-based implementation design ( 23 , 25 ) in which the strengths and assets of communities and their members are used to promote health and wellbeing.

9.1 Intervention design

The SPICES-Sussex project used community-engagement and community health worker approaches to improve CVD health that are based on practices developed and tested in Kampala, Uganda ( 47 ). As part of the SPICES consortium these practices were adapted to several global north (UK, France, Belgium) and global south settings (South Africa). In the global south social public health approaches have long advocated for the decentralisation of healthcare to community partners and for a greater focus on prevention ( 48 ). Community-based public health practices such as task-sharing are often utilised in low-resource health systems in low-and middle income countries by recruiting and training community health workers to deliver low-intensity health intervention such as health coaching and signposting ( 49 ). In global south SPICES settings, there was greater buy-in to community-based interventions from governments and much of the trust building, and infrastructure for community health workers already exists ( 50 ). These settings, including the SPICES sites that influenced the Sussex site, often rely on voluntary or unpaid volunteers to conduct public health work in order to lower cost and to make use of existing social networks.

In resource-rich global north settings, healthcare is far more institutionalised and focused on secondary care and the infrastructure for community-based and participatory interventions is far less well developed. In the UK, most health interventions must adhere to the institutional demands of the National Health Service which presents a range of resource intensive training, recruitment, safeguarding, and management practices. There is much less history of CHWs in the UK; the role of these workers is not well understood or well defined outside of the third/voluntary sector despite recent calls for their use during the COVID-19 pandemic ( 51 ). This squeezed landscape for community-based intervention and the lack of familiarity with the role makes the development and implementation of these interventions challenging. In the global north there are increasing challenges to the volunteer nature of CHWs with researchers calling for compensation, capacity building, or payment of members of the public involved in intervention delivery of research and health interventions on moral and efficacy grounds ( 52 , 53 ). In our study, the decision not to pay CHW was made as a result of us following the SPICES approach developed in the Global South ( 17 ) and because the VCSE organisations we partnered with all had existing unpaid community volunteer programs. In our post-intervention qualitative evaluation interviews, participants and CHWs both discussed the value of paying CHWs. Furthermore, the drop in CHWs and the small number of participants they were able to take on implies that the lack of payment impacted the degree to which CHWs were able to engage with the project and therefore impacted the intervention's effectiveness and sustainability. In the UK, the NIHR now recommends that members of the public who are involved in research are properly reimbursed for their involvement and provide frequently updated guidelines on how to do so ( 54 ). In the future we argue that public health intervention that make use of CHWs should reimburse and pay them in some way for their involvement.

Community volunteers with low levels of training (10 h core training plus ongoing support), such as those used during this study, are not well-suited to complex cases or acute needs that required specialised support. In our findings, participants complained of generic, inappropriate, or obvious advice from the CHWs. Participants did not seem to prioritize the knowledge or expertise of the CHWs and instead valued the personalised, holistic, and supportive relationships that were offered by the CHWs. Participants in the intervention reported having good knowledge of what they needed to do to improve their health but struggled to do it in practice. Therefore, this kind of intervention may be well-suited to providing emotional and social support to people at risk of CVD who know what they “should” do but need a support and judgement free support mechanism to make changes.

Interviews with participants revealed a tension in the study linked to the use of an individualistic lifestyle change intervention situated within a community-based and participatory study. The study design did not address community-level, socio-economic, or environmental issues known to be vital when addressing CVD health ( 55 ). Tengland ( 56 ) argues that an individualistic lens of behavioural change can limit understandings of a person's CVD health. The result can be too narrow, as the “secondary” effects of their wider environmental conditions (i.e., powerlessness, lack of control, or lack of hope), are not considered. They further suggest that interventions should focus more on the attainment of instrumental goals, such as increased real opportunities in life. For community-based projects to grow further, they should seek to become multi-faceted by combining individualistic interventions with environment/community activities such as community education ( 57 ).

The frequency with which mental health issues were raised in discussions was notable. Those who took part in the screening reported high levels of stress and depression, and rates were even higher amongst participants taking part in the programme, furthermore, participants reported lower levels of stress and depression at the end. This may show that this type of intervention is particularly well suited to people with mental health concerns for whom talking to someone can make a real difference. This was also observed in the SPICES consortium partner sites including Brest (France) ( 58 ), and Antwerp (Belgium) ( 59 ). Most non-specialist or non-clinical people do not think of their health siloed into CVD, mental health, digestive health etc ( 60 ). Instead, one's health is perceived holistically, and mental health is often the most prominent barrier and facilitator to behaviour change.

9.2 Implementation strategy

We adopted a type 3 hybrid implementation study which focused primarily on implementation factors rather than evaluation, dropping the randomisation approach and embracing flexible more emergent iterative development and growth perspective, co-design, and contextual/place-based factors. A rigid evaluation linear approach as required for a type 1/2 design, which was initially planned, caused tensions with the community-based, participatory, and “emergent” aspects of the project and (2) the pressures imposed on our voluntary sector partners by the pandemic meant that adhering to a rigid randomisation approach was less realistic ( 7 ). The planned approach placed power in the hands of the research team which negatively affected our stakeholder relationships, and a rigid adherence to study protocols would have meant we could not effectively adapt strategies or interventions to context.

Instead we adopted a type 3 approach, which has been used to assess a wide range of preventative health and eHealth interventions which operate in communities based on participatory principles ( 61 ). In their systematic review of such strategies to implement interventions, Haldane et al. ( 62 ) highlight the importance of building mutually beneficial and trust-based relationships particularly with marginalised stakeholders, and stress the importance of developing strategies and interventions contextually whilst reporting and acting on lessons learnt throughout the project. Wildman et al. ( 63 ) argue that successful community-based projects require extensive community input, learning and adaptation captured from existing programmes to facilitate the replicability of programmes in other community contexts. With the more flexible type three approach we were able to make local adaptation to meet the need and priorities of the local community and local VCSE partner organisation thereby listening to the voices of those who are involved. This iterative approach to intervention design is similar to the “scaling-out” approach suggested by Aaron et al. ( 64 ) which advocates iterative roll-out and local adaptation in place of simply “copy and pasting” interventions across context. In reality, during SPICES-Sussex the local adaption became less flexible as the intervention became more well-developed as the internal factors became more institutionalised within the research team. However, the principle of meeting the needs and priorities of the local VCSE organisation were maintained from site to site and the team sought input from local organisations where possible.

We do not know whether the changes observed will be maintained due to the short follow up period, both at an individual level or a setting level ( 65 ), and the research lacks an economic appraisal. The short follow-up period was forced on the research team because of delays to the project caused by COVID-19 which meant our funding period was not long enough to conduct a follow up assessment. An economic appraisal was not considered appropriate because the development approach taken during the study meant that any economic appraisal was not likely to reflect real-world roll-out. In the future we would advocate for greater scaling-out to include a larger sample and an economic appraisal.

9.3 Recruitment and retention

The impacts of the restrictions placed on the people, organisations, and communities involved in this research due to COVID-19 were extensive and wide-ranging. The per-implementation phase of the research began in January of 2020 with the recruitment of an implementation team and participant recruitment was due to begin in April 2020. Following the outbreak of COVID-19 in the UK, recruitment was stopped from 16 March 2020 to 1 October 2020. By June 2020, a decision was made to fully move to remote delivery of the coaching intervention using video conferencing services.

Research recruitment and retention were near constant challenges, and all activities were significantly impacted by the Covid restrictions. We believe that the use of the INTERHEART tool, presented on the REDCap platform, acted as a barrier to recruitment as evidenced through the follow sources: (1) Over 650 participants attempted to begin the screening questionnaire and our records show those who did not complete it stopped towards the beginning or mid-way through the questions, particularly when they were asked to measure their waist/hip circumference, (2) of the 380 participants who completed the survey only approximately 100 were eligible for the intervention meaning we were selecting from a very limited pool of participants, (3) many of the participants in the per implementation interviews mentioned finding the screening tool to be “clunky” or “annoying” to use. Its overly “medical” focus, as a basis for lifestyle discussions may not have been engaging for the target audience.

Our initial recruitment strategy was to rely heavily on our VCSE partners to act as gatekeepers for recruitment, a practice commonly seen in participatory research methods ( 66 ). Whilst the VCSE partners were adept in the recruitment and management of CHWs and in the development of practices and policies, they did not seem to have the reach or access for the recruitment of large numbers of potential participants. Our experience aligns with that of Williams ( 67 ), who states that VCSE and end users' relationships are often smaller in number but deep, based on trust and protection, and covered by a range of risk related policies. Instead, we relied heavily on the use of paid for social media adverts for recruitment due to our ethics restrictions. Much like the experience of other researchers who used these tools, we found that they were low cost and reached large numbers of people but engagement with the screening and risk profiling and participant recruitment was low ( 68 ). In future studies, it may be more suitable to use social media as an adjunct to mixed recruitment strategies which make use of community outreach, primary care recruitment, and media outreach ( 69 , 70 ).

The study sample was heavily skewed towards middle-aged females and much of the sample was not considered to be from vulnerable or low socio-economic groups. Furthermore, males are under-represented in both the risk profiling and intervention samples which represents a divergence with our planned recruitment targets in which we aimed for a more representative sample. The difficulties in recruiting men and vulnerable and other “seldom heard” populations to life style interventions are well-recognised ( 71 , 72 ). Recommended strategies to improve male participation in community-based interventions include engaging with male-friendly spaces, workplace-based interventions, and incorporating activity-based programming, social-support, and group activities ( 73 , 74 ). Some of these elements were suggested during the planning phase of SPICES but were not feasible due to COVID restrictions ( 30 ).

9.4 Project infrastructure

We made the key decision to bring VCSE organisations into the research team with paid roles to foster stronger community/research partnerships as promoted by CBPR researchers ( 75 ) and the NHS's PPIE (Public and Patient Involvement and Engagement) initiatives ( 76 ). Our research shows that the VCSE sector is an untapped resource within primary and community care that has a great deal of expertise, compassion, and enthusiasm to offer health provision ( 77 ). To facilitate this community-based project, we focused on the concept of trust building throughout the intervention as described by Christopher et al. ( 78 ).

VCSE partnerships brought knowledge and expertise of their local communities, policies/practices of volunteer management and, critically, perspectives of the motivations and drivers for CHWs and communities. CHWs were empowered to bring their own skills and abilities to the intervention through an asset based and flexible project development which included them in the co-design of the project ( 79 ). The strategies we used to implement the interventions were not prescriptive and did not force CHWs to follow a set of strict guidelines. This led to a highly personalised, flexible, and reflective experience for CHWs. However, our experience highlights potential problems with relying on unpaid volunteers to deliver complex interventions, including issues with volunteer commitment, attendance and drop out.

Our research highlights the importance of infrastructure when managing CHWs and partnering with VCSE sector organisations. We developed a bespoke behaviour change training course for CHWs, a range of CHW risk appraisal and mitigation policies with our VCSE partners, and a dedicated team of participant and CHW support and management coordinators. Clear protocols were developed and followed for the recruitment, onboarding, matching, and hosting of participant coaching sessions whilst CHWs were provided with multiple channels of regular communication and continuous training and feedback opportunities. We support calls for project managers, VCSEs, primary care providers, and community members to be more explicitly involved in the design and development of interventions which affect and include communities ( 80 ).

In this study, the research team also experienced issues of positionality throughout the project whereby the lines between implementor, community worker, and evaluator were blurred. Coulter et al. ( 81 ) have pointed out that research that includes CHWs in the design and delivery of interventions commonly experience a tension between fidelity of the intervention protocol and community expectations, needs, and norms. We also experienced differing goals between academic and community partners (including CHWs), where academic partners prioritized data collection and community partners prioritized funding, sustainability, and policy. This can be likened to the experience of Furman et al. ( 82 ) who discussed how community partners were hesitant to endorse their research due to conflicts with on-the-ground realities of the community members they served.

9.5 Recommendations

During this project the research team, VCSE partners, and CHWs constantly learnt lessons and were quick to make adaptations to their approach based on feedback from a range of stakeholders and capturing all of these in this paper would be an impossible task. However, several key insights can be drawn from our collective experience and evaluation of the project. They include:

1. Environmental issues are larger and more complex than any coaching intervention based on individualistic changes can hope to remedy.

2. The voluntary and community sector has a range of strengths and assets based on local experience and knowledge developed over significant periods of time that can be used for CVD prevention. However, the sector is highly under-resourced and spread thinly across a wide range of priorities. Individual VCSE partner organisations do not always have enough reach to facilitate recruitment.

3. Community engagement works best if it is built into a project early on through co-design and resources and time should be allocated to this activity.

4. CHWs bring significant advantages during the delivery of community-based interventions. They are trusted peers, they bring their own skills and experience, and they can benefit from the intervention alongside the participants.

5. Strategies to encourage the participation of men should be specifically considered during the planning phase.

6. Virtual coaching interventions are acceptable to participants, and in many cases preferable to participants, due to their flexibility and ease of use.

7. The issue of mental health must be addressed even when working with unrelated health public conditions.

8. A strong project infrastructure, made up of well-trained support/administrative staff, is essential when delivering community-based interventions.

9. CHWs should be paid or reimbursed for their involvement in research and public health interventions. Falling to do so is looked down on my stakeholders and has impacts on sustainability and effectiveness.

10. The Global North can look to innovations in the Global South for examples of success for community-based interventions, however, proper contextual or situational analyses must be conducted to understand the needs and priorities of target communities.

9.6 Conclusion

This study demonstrates the feasibility of a CHW-led preventative health interventions could be implemented with overseen and unheard communities in the UK. It highlights the wealth of untapped resources that exist with VCSE and CHWs and suggests how a beneficial community-based service could be set up to run alongside and support NHS Health Checks, to reduce the incidence of CVD. The aim was to empower CHWs to discuss health with people in their communities based on behaviour change principles. We have set out what worked well and what did not, to facilitate development of future community-based interventions in the Global North. We believe that the community-based approach need not be restricted to CVD risk reduction, and that it could easily be applied to low level mental health conditions, diabetes, or other preventable NCDs. If CHWs are confident, well supported, and well-trained, they will have the skills and ability to contribute to improving the health and wellbeing of people in their communities. The benefits do not only extend to patients but also to CHWs and to the VCSE partners involved. We believe our project shows how these interventions can become a supplementary tool that links primary care services with the VCSE sector.

Data availability statement

The datasets presented in this study can be found in the University of Sussex's data repository through the following link: https://sussex.figshare.com/ ; (doi: 10.25377/sussex.25569084).

Ethics statement

The studies involving humans were approved by Brighton and Sussex Medical School Research Governance and Ethics Committee (RGEC). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

First Author (First Authorship): Thomas Grice-Jackson Second Authors (Equal Contribution): Imogen Rogers, Elizabeth Ford, Robert Dickson Third Authors (Equal Contribution): Kat-Frere Smith, Katie Goddard, Linda Silver, Catherine Topham Fourth Author (Equal Contribution): Papreen Nahar, Geofrey Musinguzi, Hilde Bastiens. Senior Author (Senior Authorship): Harm Van Marwijk. All authors contributed to the article and approved the submitted version.

This project was funded as part of an EU Commission Horizon. CORDIS (The Community Research and Development Information Service (CORDIS) Grant agreement number: 733356.

Acknowledgments

We thank the following voluntary and community sector organisations for their partnerships whilst designing and delivering this project: Active Hastings, Wellsbourne Healthcare Community Interest Company, Sussex Community Development Association, the Crew Club, and the Hangleton and Knoll project. We thank the National Centre for Behaviour Change for their contribution to the development and delivery of the Community Health Workers training. We thank all members of the SPICES consortium and European Commission who provide consultation and advice throughout the project. Finally, we thank all our Community Health Workers for giving up their time for this project. They were central to every part of this work and their contribution is greatly appreciated. We would also like to thank the editorial and reviewer team assigned to this manuscript. Their contributions improved the quality of our manuscript presentation, structure, and discussion.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frhs.2024.1152410/full#supplementary-material

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Keywords: community based participatory research, implementation research, RE-AIM (reach, effectiveness, adoption, implementation and maintenance), cardiovascular disease, community health workers (CHW)

Citation: Grice-Jackson T, Rogers I, Ford E, Dickinson R, Frere-Smith K, Goddard K, Silver L, Topham C, Nahar P, Musinguzi G, Bastiaens H and Van Marwijk H (2024) A community health worker led approach to cardiovascular disease prevention in the UK—SPICES-Sussex (scaling-up packages of interventions for cardiovascular disease prevention in selected sites in Europe and Sub-saharan Africa): an implementation research project. Front. Health Serv. 4:1152410. doi: 10.3389/frhs.2024.1152410

Received: 27 January 2023; Accepted: 20 March 2024; Published: 7 May 2024.

Reviewed by:

© 2024 Grice-Jackson, Rogers, Ford, Dickinson, Frere-Smith, Goddard, Silver, Topham, Nahar, Musinguzi, Bastiaens and Van Marwijk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Thomas Grice-Jackson [email protected]

This article is part of the Research Topic

Hybrid Effectiveness-Implementation Trial Designs: Critical Assessments, Innovative Applications, and Proposed Advancements

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• Published: 30 April 2024

Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

• Taylor J. Bell   ORCID: orcid.org/0000-0003-4177-2149 1 , 2 ,
• Nicolas Crouzet   ORCID: orcid.org/0000-0001-7866-8738 3 ,
• Patricio E. Cubillos 4 , 5 ,
• Laura Kreidberg 6 ,
• Anjali A. A. Piette   ORCID: orcid.org/0000-0002-4487-5533 7 ,
• Michael T. Roman   ORCID: orcid.org/0000-0001-8206-2165 8 , 9 ,
• Joanna K. Barstow   ORCID: orcid.org/0000-0003-3726-5419 10 ,
• Jasmina Blecic 11 , 12 ,
• Ludmila Carone   ORCID: orcid.org/0000-0001-9355-3752 5 ,
• Louis-Philippe Coulombe   ORCID: orcid.org/0000-0002-2195-735X 13 ,
• Elsa Ducrot   ORCID: orcid.org/0000-0002-7008-6888 14 ,
• Mark Hammond 15 ,
• João M. Mendonça 16 ,
• Julianne I. Moses   ORCID: orcid.org/0000-0002-8837-0035 17 ,
• Vivien Parmentier 18 ,
• Kevin B. Stevenson   ORCID: orcid.org/0000-0002-7352-7941 19 ,
• Lucas Teinturier   ORCID: orcid.org/0000-0002-0797-5746 20 , 21 ,
• Michael Zhang 22 ,
• Natalie M. Batalha 23 ,
• Jacob L. Bean 22 ,
• Björn Benneke   ORCID: orcid.org/0000-0001-5578-1498 13 ,
• Benjamin Charnay 20 ,
• Katy L. Chubb 24 ,
• Brice-Olivier Demory 25 , 26 ,
• Peter Gao 7 ,
• Elspeth K. H. Lee 25 ,
• Mercedes López-Morales 27 ,
• Giuseppe Morello   ORCID: orcid.org/0000-0002-4262-5661 28 , 29 , 30 ,
• Emily Rauscher   ORCID: orcid.org/0000-0003-3963-9672 31 ,
• David K. Sing   ORCID: orcid.org/0000-0001-6050-7645 32 , 33 ,
• Xianyu Tan 15 , 34 , 35 ,
• Olivia Venot   ORCID: orcid.org/0000-0003-2854-765X 36 ,
• Hannah R. Wakeford   ORCID: orcid.org/0000-0003-4328-3867 37 ,
• Keshav Aggarwal   ORCID: orcid.org/0000-0002-7004-8670 38 ,
• Eva-Maria Ahrer 39 , 40 ,
• Munazza K. Alam   ORCID: orcid.org/0000-0003-4157-832X 7 ,
• Robin Baeyens   ORCID: orcid.org/0000-0001-7578-969X 41 ,
• David Barrado   ORCID: orcid.org/0000-0002-5971-9242 42 ,
• Claudio Caceres   ORCID: orcid.org/0000-0002-6617-3823 43 , 44 , 45 ,
• Aarynn L. Carter 23 ,
• Sarah L. Casewell 8 ,
• Ryan C. Challener 31 ,
• Ian J. M. Crossfield 46 ,
• Leen Decin   ORCID: orcid.org/0000-0002-5342-8612 47 ,
• Jean-Michel Désert 41 ,
• Ian Dobbs-Dixon 11 ,
• Achrène Dyrek 14 ,
• Néstor Espinoza 33 , 48 ,
• Adina D. Feinstein 22 , 49 ,
• Neale P. Gibson 50 ,
• Joseph Harrington   ORCID: orcid.org/0000-0002-8955-8531 51 ,
• Christiane Helling 5 ,
• Renyu Hu   ORCID: orcid.org/0000-0003-2215-8485 52 , 53 ,
• Nicolas Iro 54 ,
• Eliza M.-R. Kempton   ORCID: orcid.org/0000-0002-1337-9051 55 ,
• Sarah Kendrew 56 ,
• Thaddeus D. Komacek   ORCID: orcid.org/0000-0002-9258-5311 55 ,
• Jessica Krick 57 ,
• Pierre-Olivier Lagage 14 ,
• Jérémy Leconte 58 ,
• Monika Lendl   ORCID: orcid.org/0000-0001-9699-1459 59 ,
• Neil T. Lewis 60 ,
• Joshua D. Lothringer 61 ,
• Isaac Malsky   ORCID: orcid.org/0000-0003-0217-3880 31 ,
• Luigi Mancini   ORCID: orcid.org/0000-0002-9428-8732 6 , 62 , 63 ,
• Megan Mansfield   ORCID: orcid.org/0000-0003-4241-7413 64 ,
• Nathan J. Mayne   ORCID: orcid.org/0000-0001-6707-4563 65 ,
• Thomas M. Evans-Soma   ORCID: orcid.org/0000-0001-5442-1300 6 , 66 ,
• Karan Molaverdikhani   ORCID: orcid.org/0000-0002-0502-0428 67 , 68 ,
• Nikolay K. Nikolov   ORCID: orcid.org/0000-0002-6500-3574 48 ,
• Matthew C. Nixon   ORCID: orcid.org/0000-0001-8236-5553 55 ,
• Enric Palle   ORCID: orcid.org/0000-0003-0987-1593 28 ,
• Dominique J. M. Petit dit de la Roche 59 ,
• Caroline Piaulet   ORCID: orcid.org/0000-0002-2875-917X 13 ,
• Diana Powell 27 ,
• Benjamin V. Rackham   ORCID: orcid.org/0000-0002-3627-1676 69 , 70 ,
• Aaron D. Schneider   ORCID: orcid.org/0000-0002-1448-0303 47 , 71 ,
• Maria E. Steinrueck   ORCID: orcid.org/0000-0001-8342-1895 6 ,
• Jake Taylor 13 , 15 ,
• Luis Welbanks   ORCID: orcid.org/0000-0003-0156-4564 72 ,
• Sergei N. Yurchenko   ORCID: orcid.org/0000-0001-9286-9501 73 ,
• Xi Zhang   ORCID: orcid.org/0000-0002-8706-6963 74 &
• Sebastian Zieba   ORCID: orcid.org/0000-0003-0562-6750 3 , 6  

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Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5 μm to 12 μm with the JWST’s Mid-Infrared Instrument. The spectra reveal a large day–night temperature contrast (with average brightness temperatures of 1,524 ± 35 K and 863 ± 23 K, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase-curve shape and emission spectra strongly suggest the presence of nightside clouds that become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2 σ upper limit of 1–6 ppm, depending on model assumptions). Our results provide strong evidence that the atmosphere of WASP-43b is shaped by disequilibrium processes and provide new insights into the properties of the planet’s nightside clouds. However, the remaining discrepancies between our observations and our predictive atmospheric models emphasize the importance of further exploring the effects of clouds and disequilibrium chemistry in numerical models.

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Hot Jupiters are tidally synchronized to their host stars, with vast differences in irradiation between the dayside and nightside. Previous observations with the Hubble Space Telescope (HST) and the Spitzer Space Telescope show that these planets have cooler nightsides and weaker hotspot offsets than expected from cloud-free three-dimensional models 1 , 2 , 3 , 4 , 5 . The main mechanism believed to be responsible for this behaviour is the presence of nightside clouds, which would hide the thermal flux of the planet and lead to a sharp longitudinal gradient in brightness temperature 3 , 4 , 6 , 7 , 8 , 9 , 10 . Other mechanisms have been proposed, such as the presence of atmospheric drag due to hydrodynamic instabilities or magnetic coupling 11 , 12 , 13 , super-stellar atmospheric metallicity 14 , 15 , or interaction between the deep winds and the photosphere 16 , but these mechanisms are less universal than the cloud hypothesis 17 , 18 .

WASP-43b, a hot Jupiter with an orbital period of just 19.5 h (ref. 19 ), is an ideal target for thermal phase-curve observations. Its host star is a K7 main-sequence star 87 pc away with metallicity close to solar and weak variability 20 . Previous measurements of the planet’s orbital phase curve in the near-infrared have revealed a large temperature contrast between the dayside and nightside hemispheres, broadly consistent with the presence of nightside clouds 3 , 21 , 22 , which could be composed of magnesium silicates (Mg 2 SiO 4 /MgSiO 2 ) and other minerals (for example, MnS, Na 2 S, metal oxides) 23 , 24 . Owing to the low nightside flux, the exact temperature and cloud properties were challenging to determine from previous observations 4 , 25 , 26 . With the mid-infrared capabilities of the JWST, we have the opportunity to measure the phase-resolved thermal spectrum with unprecedented sensitivity, particularly on the cold nightside. We observed a full orbit of WASP-43b in the 5–12 μm range with the JWST’s Mid-Infrared Instrument (MIRI) 27 in low-resolution spectroscopy (LRS) 28 slitless mode on 1 and 2 December 2022, as part of the Transiting Exoplanet Community Early Release Science Program (JWST-ERS-1366). This continuous observation lasted 26.5 h at a cadence of 10.34 s (9,216 integrations) and included a full phase curve with one transit and two eclipses.

We performed multiple independent reductions and fits to these observations (see ‘Data reduction pipelines’ and ‘Light-curve fitting’ in Methods ) to ensure robust conclusions. Our analyses all identified a strong systematic noise feature from 10.6 μm to 11.8 μm, the source of which is still unclear, and we were unable to adequately detrend these 10.6–11.8 μm data (see ‘Shadowed region effect’ in Methods ). As shown in Extended Data Fig. 1 , we also found that larger wavelength bins were required to accurately estimate our final spectral uncertainties (see ‘Spectral binning’ in Methods ). As a result, our final analyses consider only the 5–10.5 μm data, which we split into 11 channels with a constant 0.5 μm wavelength spacing. Similar to the MIRI commissioning time-series observations, our data show a strong downwards exponential ramp in the first ~60 min and a weaker ramp throughout the observation 29 (Extended Data Fig. 2 ). To minimize correlations with the phase variations, we removed the initial strong ramp by excluding the first 779 integrations (134.2 min) and then fitted a single exponential ramp model to the remaining data. A single ramp effectively removed the systematic noise, with the broadband light curve showing scatter ~1.25× the expected photon noise, while the spectroscopic light curves reach as low as ~1.1× the photon limit, probably due to improved decorrelation of wavelength-dependent systematics. Figure 1 shows the spectral light curves, broadband light curve, dayside spectra and nightside spectra from our fiducial reduction and fit.

a , The observed spectroscopic light curves binned to a 0.5 μm wavelength resolution and after systematic noise removal, following the Eureka! v1 methods. The first 779 integrations have been removed from this figure and our fits as they were impacted by strongly decreasing flux. Wavelengths longer than 10.5 μm marked with a hatched region were affected by the ‘shadowed region effect’ ( Methods ) and could not be reliably reduced. b , The observed band-integrated light curve after systematic noise removal (grey points) and binned data with a cadence of 15 min (black points, with error bars smaller than the point sizes), compared with the best-fitting astrophysical model (red line). c , d , The measured dayside ( c ) and nightside ( d ) emission spectra are shown with black points and 1 σ error bars, and black-body curves (dotted line denoted as ‘BB’, assuming a PHOENIX 74 , 75 , 76 model for the star) are shown to emphasize planetary spectral features with black-body temperatures estimated by eye to match the continuum flux levels. Wavelengths longer than 10.5 μm were affected by the shadowed region effect and are unreliable.

From our Eureka! v1 analysis ( Methods ), we measure a broadband (5–10.5 μm) peak-to-trough phase variation of 4,180 ± 33 ppm with an eclipse depth of 5,752 ± 19 ppm and a nightside flux of 1,636 ± 37 ppm. Assuming a PHOENIX stellar model and marginalizing over the published stellar and system parameters 30 , the broadband dayside brightness temperature is 1,524 ± 35 K while the nightside is 863 ± 23 K. This corresponds to a day–night brightness temperature contrast of 659 ± 19 K, in agreement with the large contrasts previously observed 4 , 21 , 22 , 25 . The phase variations are well fitted by a sum of two sinusoids (the first and second harmonics), with two sinusoids preferred over a single sinusoid at 16 σ (see ‘Determining the number of sinusoid harmonics’ in Methods ) for the broadband light curve. The peak brightness of the broadband phase curve occurs at 7.34 ± 0.38° E from the substellar point (although individual reductions find offsets ranging from 7.34° E to 9.60° E), while previous studies have found offsets of 12.3 ± 1.0° E for HST Wide Field Camera 3’s (WFC3) 1.1–1.7 μm bandpass 21 , offsets ranging from 4.4° E to 12.2° E for Spitzer InfraRed Array Camera’s (IRAC) 3.6 μm filter 22 , 25 and offsets ranging from 10.4° E to 21.1° E for Spitzer/IRAC’s 4.5 μm filter 4 , 22 , 25 , 26 , 31 , 32 . Overall, these broadband data represent roughly an order of magnitude in improved precision on the eclipse depth (6×), phase-curve amplitude (6×) and phase-curve offset (10×) over individual Spitzer/IRAC 4.5 μm observations of the system 22 , 26 , 32 ; this improvement is largely driven by the JWST’s larger mirror (45×), about 12× less pointing jitter (per axis), about 4× improved stability in the width of the point spread function (PSF) along each axis and MIRI’s much broader bandpass.

Model interpretation

To interpret the measurements, we compared the observations with synthetic phase curves and emission spectra derived from general circulation models (GCMs). Simulations were gathered from five different modelling groups, amounting to 31 separate GCM realizations exploring a range of approaches and assumptions. Notably, in addition to cloud-free simulations, the majority of the GCMs modelled clouds with spatial distributions that were either fully predicted 5 , 26 , 33 or simply limited to the planet’s nightside 4 . For the predictive cloud models, simulations favoured warmer, clearer daysides with cooler, cloudier nightsides, but the precise distributions varied with assumptions regarding cloud physics and compositions. In general, models with smaller cloud particles or extended vertical distributions tended to produce thicker clouds at the pressures sensed by the observations. Details of the different models are provided in Methods .

Despite fundamental differences in the models and the parameterizations they employ, simulated phase curves derived from models that include cloud opacity on the planet’s nightside provide a better match to the observed nightside flux compared with the clear simulations (Fig. 2 ). In contrast, the observed dayside fluxes (180° orbital phase) were matched similarly well by models with and without clouds. This implies the presence of widespread clouds preferentially on the planet’s nightside with cloud optical thicknesses sufficient to suppress thermal emission and cool the thermal photosphere. Specifically, models with integrated mid-infrared cloud opacities of roughly 2–4 above the 300 mbar level (that is, blocking ~87–98% of the underlying emission), best match the observed nightside flux.

The black points show the temporally binned broadband light curve. The solid lines represent modelled phase curves derived from the 31 GCM simulations, integrated over the same wavelength range as the data, and separated into two groups based on the inclusion of clouds. The cloudless GCMs (red lines) simulated completely cloud-free skies, whereas the cloudy GCMs (blue lines) included at least some clouds on the nightside of the planet. The red and blue shaded areas span the range of all the cloudless and cloudy simulations, respectively, with the spread of values owing to differences in the various model assumptions and parameterizations. On average, the cloudless GCM phase curves have a maximum planet-to-star flux ratio of 5,703 ppm and a minimum of 2,681 ppm. This matches the observed maximum of the phase curve well but does not match its observed minimum at 1,636 ± 37 ppm. On average, the cloudy GCM phase curves have a maximum of 5,866 ppm and a minimum of 1,201 ppm, in better agreement with the observed nightside emission, but their spread of maximum values is much larger than the cloudless simulations. The cloudy models are able to suppress the nightside emission and better match the data; however, not all cloud models fit equally well and those with the optically thickest nightside clouds suppress too much emission. The models do not include the eclipse signals (phases −0.5 and 0.5) or transit signal (phase 0.0).

Including nightside clouds also improved the agreement with the measured hotspot offset (7.34± 0.38° E). While cloudless models all produced eastward offsets greater than 16.6° (25.5° on average), simulations with clouds had offsets as low as 7.6° (with a mean of 16.4°). These reduced offsets were associated with decreases in the eastwards jet speeds of up to several kilometres per second, with maximum winds of roughly 2.0–2.5 km s −1 providing the best match (see Extended Data Table 1 for further details). This modelled jet-speed reduction is probably due to a disruption in the equatorwards momentum transport 34 brought about by nightside clouds 4 , 35 , 36 . However, the resulting range of offsets seen in the suite of models suggests that this mechanism is quite sensitive to the details of cloud models, and other modelling factors (for example, atmospheric drag 11 , 12 , 16 , radiative timescales 14 , 15 , 37 ) probably still play an important role.

A comparison of the observed and modelled emission spectra further suggests that the majority of the cloud thermal opacity must be confined to pressures greater than ~10–100 mbar, because the presence of substantial cloud opacity at lower pressures dampens the modelled spectral signature amplitude below what is observed (Fig. 3 ). No distinct spectral signatures indicative of the cloud composition were evident in the observations. While no single GCM can match the emission spectra at all phases, spectra corresponding to nightside, morning and evening terminators appear qualitatively similar to GCM results that are intermediate between clear and cloudy simulations. In contrast, the absorption features indicative of water vapour (between ~5 μm and 8.5 μm) seen in the dayside emission spectrum are more consistent with an absence of cloud opacity at these mid-infrared wavelengths. Altogether, these findings represent new constraints on the spatial distribution and opacity of WASP-43b’s clouds.

a – c , The observed emission spectrum with 1 σ error bars at phases 0.0 ( a ), 0.25 ( b ), 0.5 ( c ) and 0.75 ( d ), along with select modelled spectra derived from different cloudy and cloudless GCMs (described in Methods and listed in Extended Data Table 1 ). Although absolute brightness temperatures differ appreciably between models owing to various GCM assumptions, differences in the relative shape of the spectra are strongly dependent on the cloud and temperature structure found in the GCMs (Extended Data Fig. 7 ). Models with more isothermal profiles (like RM-GCM) or thick clouds at pressures of ≲ 10–100 mbar (like THOR cloudy, Generic PCM with 0.1 μm cloud particles) produce flatter spectra, while clearer skies yield stronger absorption features. The observed spectra from the nightside and terminators appear muted compared with the clear-model spectra, suggesting the presence of at least some clouds or weak vertical temperature gradients at pressures of ≲ 10–100 mbar. In contrast, the spectral structure produced by water vapour opacity (indicated by the purple shading) appears more consistent with models lacking clouds at these low pressures on the dayside. Under equilibrium chemistry, methane would also show an absorption feature at ~7.5–8.5 μm (shaded pink) for the colder models at phases 0.0 and 0.75. Finally, the median retrieved spectrum and 1 σ contours from the HyDRA retrieval are shown in grey.

We further characterized the chemical composition of WASP-43b’s atmosphere by applying a suite of atmospheric retrieval frameworks to the phase-resolved emission spectra. The retrievals spanned a broad range of model assumptions, including free chemical abundances versus equilibrium chemistry, different temperature profile parameterizations and different cloud models (see ‘Atmospheric retrieval models’ in Methods ). Despite these differences, the retrievals yielded consistent results for both the chemical and thermal constraints. We detected water vapour across the dayside, nightside, morning and evening hemispheres, with detection significances of up to ~3–4 σ (Extended Data Fig. 3 and Extended Data Tables 2 and 3 ). The retrieved abundances of H 2 O largely lie in the 10–10 5  ppm range for all four phases and for all the retrieval frameworks (Fig. 4 and Extended Data Fig. 4 ), broadly consistent with the value expected for a solar composition (500 ppm) as well as previous observations 22 .

a , Temperature profile contours (68% confidence) constrained by the retrievals at each orbital phase (see legends). All frameworks produced consistent non-inverted thermal profiles that are consistent with two-dimensional radiative–convective equilibrium and photochemical models along the equator 23 (black curves) over the range of pressures probed by the observations (black bars). b , H 2 O abundance posterior distributions (volume mixing ratios). The shaded areas denote the span of the 68% confidence intervals. The green and blue bars on each panel denote the abundances predicted by equilibrium and disequilibrium chemistry solar-abundance models 23 , respectively, at the pressures probed by the observations (1–10 −3  bar, approximately). c , The same as in b but for CH 4 . The retrieved water abundances are consistent with either equilibrium or disequilibrium chemistry estimations for solar composition (500 ppm), whereas the retrieved upper limits to the CH 4 abundance are more consistent with disequilibrium chemistry predictions.

We also searched for signatures of disequilibrium chemistry in the atmosphere of WASP-43b. While CH 4 is expected to be present on the nightside under thermochemical equilibrium conditions, we did not detect CH 4 at any phase (Fig. 4 ). In the pressure range probed by the nightside spectrum (1–10 −3  bar; Extended Data Fig. 5 ), the equilibrium abundance of CH 4 is expected to vary between ~1 ppm and 100 ppm for a solar C/O ratio 23 , compared with our 95% upper limits of 1–6 ppm (Extended Data Table 2 ). The upper limits we place on the nightside CH 4 abundance are more consistent with disequilibrium models that account for vertical and horizontal transport 23 , 24 , 38 . In particular, two-dimensional photochemical models and GCMs predict the strongest depletion of CH 4 on the nightside due to strong zonal winds (>1 km s −1 ) transporting gas-phase constituents around the planet faster than the chemical reactions can maintain thermochemical equilibrium, thus ‘quenching’ and homogenizing the global composition at values more representative of dayside conditions (see also refs. 39 , 40 , 41 , 42 ). We note, however, that a low atmospheric C/O ratio and/or clouds at photospheric pressures could also lead to a non-detection of CH 4 . We also searched for signatures of NH 3 , which is predicted to have a volume mixing ratio less than 0.1–1 ppm in both equilibrium and disequilibrium chemistry models, and find that the results are inconclusive and model-dependent with the current retrieval frameworks.

Given the strong evidence for clouds from comparison with GCMs, we also searched for signatures of clouds in the atmospheric retrieval. Formally, the retrievals do not detect clouds with statistical significance, indicating that strong spectral features uniquely attributable to condensates are not visible in the data (see ‘Atmospheric retrieval models’ in Methods and Extended Data Fig. 6 ). However, the retrievals may mimic the effects of cloud opacity with a more isothermal temperature profile, as both tend to decrease the amplitude of spectral features, but the cloud-free, more isothermal temperature profile requires fewer free parameters and is therefore statistically favoured. Indeed, while the retrieved temperature profiles on the dayside and evening hemispheres agree well with the hemispherically averaged temperature profiles from the GCMs, they are more isothermal than the GCM predictions for the nightside and morning hemispheres (Extended Data Fig. 7 ). This discrepancy may hint at the presence of clouds on the nightside and morning hemispheres, consistent with the locations of clouds found in the GCMs.

Taken together, our results highlight the unique capabilities of JWST/MIRI for exoplanet atmosphere characterization. Combined with a range of atmospheric models, the observed phase curve and emission spectra provide strong evidence that the atmospheric chemistry of WASP-43b is shaped by complex disequilibrium processes and provide new constraints on the optical thickness and pressure of nightside clouds. However, while cloudy GCM predictions match the data better than cloud-free models, none of the simulations simultaneously reproduced the observed phase curve and spectra within measured uncertainties. These remaining discrepancies underscore the importance of further exploring the effects of clouds and disequilibrium chemistry in numerical models, as JWST continues to place unprecedented observational constraints on smaller and cooler planets.

Observations and quality of the data

We observed a full orbit of WASP-43b with the JWST MIRI LRS slitless mode as a part of JWST-ERS-1366. We performed target acquisition with the F1500W filter and used the SLITLESSPRISM subarray for the science observation. The science observation was taken between 1 December 2022 at 00:54:30 UT and 2 December 2022 at 03:23:36 UT, for a total of 26.5 h. We acquired 9,216 integrations, which were split into 3 exposures and 10 segments per exposure. Each integration lasts 10.34 s and is composed of 64 groups, with 1 frame per group. The LRS slitless mode reads an array of 416 × 72 pixels on the detector (the SLITLESSPRISM subarray) and uses the FASTR1 readout mode, which introduces an additional reset between integrations.

Owing to the long duration of the observation, two high-gain antenna moves occurred 8.828 h and 17.661 h after the start of the science observation. They affect only a couple of integrations that we removed from the light curves. A cross-shaped artefact is present on the two-dimensional images at the short-wavelength end due to light scattered by detector pixels 43 . It is stable over the duration of the observation but it contaminates the background and the spectral trace up to ~6 μm. This ‘cruciform’ artefact is observed in all MIRI LRS observations; a dedicated analysis is underway to estimate and mitigate its impact.

In the broadband light curve, the flux decays by ~0.1% during the first 60 min and continues to decay throughout the observation. This ramp is well modelled with 1 or 2 exponential functions after trimming the initial ~780 integrations. Without trimming any data, at least two ramps are needed. In addition, a downwards linear trend in flux is observed over the whole observation with a slope of −39 ppm per hour. These two types of drift also appear in the spectroscopic light curves. The exponential ramp amplitude in the first 60 min changes with wavelength from −0.67% in the 5–5.5 μm bin (downwards ramp) to +0.26% in the 10–10.5 μm bin (upwards ramp). The ramp becomes upwards at wavelengths longer than 7.5 μm and its timescale increases to more than 1 h at wavelengths longer than 10.5 μm. The slopes as a function of wavelength vary from −16 ppm to −52 ppm, all downwards. Such drifts (initial ramp and linear or polynomial trend) are also observed in other MIRI LRS time-series observations 29 but the strength of the trends differ for each observation. In these WASP-43b observations, we note that their characteristic parameters vary smoothly with wavelength, which may help identify their cause and build correction functions.

Over the course of the observation, the position of the spectral trace on the detector varies by 0.0036 pixels RMS (0.027 pixels peak to peak) in the spatial direction, and the Gaussian standard deviation of the spatial PSF varies by 0.00069 pixels RMS (root mean square; 0.0084 pixels peak to peak) following a sharp increase by 0.022 pixels during the first 600 integrations. Depending on the wavelength bin, that spatial drift causes noise at the level of 7–156 ppm, while variations in the PSF width cause noise at the level of 4–54 ppm (these numbers are obtained from a linear decorrelation). Overall, the MIRI instrument used in LRS slitless mode remains remarkably stable over this 26.5-h-long continuous observation and the data are of exquisite quality.

The noise in the light curve increases sharply at wavelengths beyond 10.5 μm and the transit depths obtained at these long wavelengths by different reduction pipelines are discrepant. These wavelengths were not used in the retrieval analyses and the final broadband light curve. The cause is unknown but it might be related to the fact that this region of the detector receives different illumination before the observation 44 (see ‘Shadowed region effect’ below for more details).

Data reduction pipelines

Eureka v1 reduction.

The Eureka! v1 reduction made use of version 0.9 of the Eureka! pipeline 45 , CRDS version 11.16.16 and context 1018, and jwst package version 1.8.3 46 . The gain value of 5.5 electrons per data number obtained from these CRDS reference files is known to be incorrect, and the actual gain is estimated to be ~3.1 electrons per data number although the gain may be wavelength dependent (S. Kendrew, private communication). A new reference file reflecting the updated gain is under development at STScI, which will improve the accuracy of photon-noise calculations. For the rest of this analysis, we assume a constant gain of 3.1 electrons per data number. The Eureka! control files and Eureka! parameter files files used in these analyses are available for download ( https://doi.org/10.5281/zenodo.10525170 ) and are summarized below.

Eureka! makes use of the jwst pipeline for stages 1 and 2, and both stages were run with their default settings, with the exception of increasing the stage 1 jump step’s rejection threshold to 8.0 and skipping the photom step in stage 2 because it is not necessary and can introduce additional noise for relative time-series observations. In stage 3 of Eureka!, we then rotated the MIRI/LRS slitless spectra 90° anticlockwise so that wavelength increases from left to right like the other JWST instruments to allow for easier reuse of Eureka! functions. We then extracted pixels 11–61 in the new y direction (the spatial direction) and 140–393 in the new x direction (spectral direction); pixels outside of these ranges primarily contain noise that is not useful for our reduction. Pixels marked as ‘DO_NOT_USE’ in the DQ array were then masked as were any other unflagged NaN or inf pixels. A centroid was then fit to each integration by summing along the spectral direction and fitting the resulting one-dimensional profile with a Gaussian function; the centroid from the first integration was used for determining aperture locations, while the centroids and PSF widths from all integrations were saved to be used as covariates when fitting the observations.

Our background subtraction method is tailored to mitigate several systematic effects unique to the MIRI instrument. First, MIRI/LRS observations exhibit a ‘cruciform artefact’ 43 at short wavelengths caused by scattered light within the optics; this causes bright rays of scattered light which must be sigma-clipped to avoid over-subtracting the background. In addition, MIRI/LRS observations show periodic noise in the background flux, which drifts with time 29 as well as 1/ f noise 47 , which leads to correlated noise in the cross-dispersion direction; as a result, background subtraction must be performed independently for each integration and column (row in MIRI’s rotated reference frame). Furthermore, in both these observations and the dedicated background calibration observations from JWST-COM/MIRI-1053, we found that there was a linear trend in the background flux, with the background flux increasing with increasing row index (column index in MIRI’s rotated reference frame). To robustly remove this feature, we found that it was important to either (1) use the mean from an equal number of pixels on either side of the spectral trace for each column and integration, or (2) use a linear background model for each column and integration; we adopted the former as it resulted in less noisy light curves. To summarize, for each column in each integration we subtracted the mean of the pixels separated by ≥11 pixels from the centre of the spectral trace after first masking 5 σ outliers in that column.

To compute the spatial profile for the optimal extraction of the source flux, we calculated a median frame, sigma-clipping 5 σ outliers along the time axis and smoothing along the spectral direction using a 7-pixel-wide boxcar filter. During optimal extraction, we only used the pixels within 5 pixels of the fitted centroid and masked pixels that were 10 σ discrepant with the spatial profile. Background exclusion regions ranging from 9 to 13 pixels and source aperture regions ranging from 4 to 6 pixels were considered, but our values of 11 and 5 pixels were selected as they produced the lowest median absolute deviation light curves before fitting.

Eureka! v2 reduction

The Eureka! v2 reduction followed the same procedure as the Eureka! v1 reduction except for the following differences. First, this reduction made use of version 1.8.1 of the jwst pipeline. For stage 1, we instead used a cosmic ray detection threshold of 5 and used a uniform ramp fitting weighting. For stage 2, we performed background subtraction using columns away from the trace on the left and on the right and subtracted the background for each integration 29 . Stage 3 was identical to Eureka! v1 reduction.

TEATRO reduction

We processed the data using the Transiting Exoplanet Atmosphere Tool for Reduction of Observations (TEATRO) that runs the jwst package, extracts and cleans the stellar spectra and light curves, and runs light-curve fits. We used the jwst package version 1.8.4, CRDS version 11.16.14 and context 1019. We started from the ‘uncal’ files and ran stages 1 and 2 of the pipeline. For stage 1, we set a jump rejection threshold of 6, turned off the ‘jump.flag_4_neighbors’ parameter and used the default values for all other parameters. For stage 2, we ran only the ‘AssignWcsStep’, ‘FlatFieldStep’ and ‘SourceTypeStep’; no photometric calibration was applied. The next steps were made using our own routines. We computed the background using two rectangular regions, one on each side of the spectral trace, between pixels 13 and 27 and between pixels 53 and 72 in the spatial direction, respectively. We computed the background value for each row (rows are along the spatial direction) in each region using a biweight location, averaged the two values and subtracted it from the full row. This background subtraction was done for each integration. Then, we extracted the stellar flux using aperture photometry by averaging pixels between 33 and 42 in each row to obtain the stellar spectrum at each integration. We also averaged pixels between 33 and 42 in the spatial direction and between 5 μm and 10.5 μm in the spectral direction to obtain the broadband flux. We averaged the spectra in 11 0.5-μm-wide wavelength channels. For each channel and for the broadband light curve, we normalized the light curve using the second eclipse as a reference flux, computed a running median filter using a 100-point window size, and rejected points that were more than 3 σ away from that median using a 5-iteration sigma-clipping. To limit the impact of the initial ramp on the fitting, we trim the first 779 integrations from the broadband light curve and a similar number of integrations for each channel (the exact number depends on the channel). Finally, we subtracted 1 from the normalized light curves to have the secondary eclipse flux centred on 0. These cleaned light curves were used for phase curve, eclipse and transit fits.

SPARTA reduction

We reduced the data with the open-source Simple Planetary Atmosphere Reduction Tool for Anyone (SPARTA), first introduced in ref. 48 to analyse the MIRI LRS phase curve of GJ 1214b. We started from the uncalibrated data and proceeded all the way to the final results without using any code from the jwst or Eureka! pipelines. In stage 1, we started by discarding the first five groups as well as the last group, because these groups show anomalies due to the reset switch charge decay and the last-frame effect. We fitted a slope to the up-the-ramp reads in every pixel of every integration in every exposure. We calculated the residuals of these linear fits, and for every pixel, we computed a median residual for every group across all integrations. This ‘median residual’ array has dimensions N grp  ×  N rows  ×  N cols . This array was subtracted from the original uncalibrated data and the up-the-ramp fit was redone, this time without discarding any groups except those that were more than 5 σ away from the best-fit line. Such outliers, which may be due to cosmic rays, were discarded and the fit recomputed until convergence. This procedure straightens out any nonlinearity in the up-the-ramp reads that is consistent across integrations, such as the reset switch charge decay, the last-frame effect or inaccuracies in the nonlinearity coefficients. After up-the-ramp fitting, we removed the background by removing the mean of columns 10–24 and 47–61 (inclusive, zero-indexed) for every row of every integration. As these two regions are of equal size and equally distant from the trace, any linear spatial trend in the background is naturally removed.

In the next step, we computed a pixel-wise median image over all integrations. This median image was used as a template to determine the position of the trace in each integration, by shifting and scaling the template until it matched the integration (and minimizes the χ 2 ). It was also used as the point spread profile for optimal extraction, after shifting in the spatial direction by the amount calculated in the previous step. Outliers more than 5 σ discrepant from the model image (which may be cosmic rays) were masked, and the optimal extraction was repeated until convergence. The z -scores image (image minus model image all divided by expected error, including photon noise and read noise) have a typical standard deviation of 0.88, compared with a theoretical minimum value of 1, indicating that the errors are being overestimated.

After optimal extraction, we gathered all the spectra and positions into one file. To reject outliers, we created a broadband light curve, detrended it by subtracting a median filter with a width 100 times less than the total data length and rejected integrations greater than 4 σ away from 0 (which may be cosmic rays). Sometimes only certain wavelengths of an integration are bad, not the entire integration. We repaired these by detrending the light curve at each wavelength, identifying 4 σ outliers and replacing them with the average of their two immediate temporal neighbours.

Spectral binning

To investigate the effects of spectral binning, we utilized the MIRI time-series commissioning observations of the transit of L168-9b (JWST-COM/MIRI-1033; ref. 29 ). L168-9b was chosen to have a clear transit signal while also having no detectable atmospheric signatures expected in its mid-infrared transmission spectrum; as a result, the observed scatter in the transmission spectrum can be used as an independent measurement of the uncertainties in the transit depths. The same procedure cannot be done on our WASP-43b science observations as there may be detectable atmospheric signatures.

Following the Eureka! reduction methods described by ref. 29 , we tried binning the L168-9b spectroscopic light curves at different resolutions and compared the observed standard deviation of the transmission spectrum with the median of the transit depth uncertainties estimated from fitting the spectral light curves. As shown in Extended Data Fig. 1 , the uncertainties in the pixel-level light curves underestimate the scatter in the transmission spectrum by a factor of about two. Because pairs of rows (in MIRI’s rotated reference frame) are reset together, it is reasonable to assume that there could be odd–even effects that would average out if combining pairs of rows; indeed, there do appear to be differences in the amplitude of the initial exponential ramp feature between odd and even rows. However, combining pairs of rows still leads to appreciable underestimation of the scatter in the transmission spectrum. Interestingly, the underestimation of the uncertainties appears to decrease with decreasing wavelength resolution. This is likely explained by wavelength-correlated noise that gets averaged out with coarse binning. A likely culprit for this wavelength-correlated noise may be the 390 Hz periodic noise observed in several MIRI subarrays, which causes clearly structured noise with a period of ~9 rows 29 (M. Ressler, private communication); this noise source is believed to be caused by MIRI’s electronics and possible mitigation strategies are still under investigation. Until the source of the excess wavelength-correlated noise is definitively determined and a noise mitigation method is developed, we recommend that MIRI/LRS observations should be binned to a fairly coarse spectral resolution as this gives better estimates of the uncertainties and also gives spectra that are closer to the photon-limited noise regime. However, we caution against quantitative extrapolations of the uncertainty underestimation to other datasets; because we do not know the source of the excess noise, we do not know how it might change with different parameters such as groups per integration or stellar magnitude.

Ultimately, for each reduction method, we binned the spectra down to a constant 0.50-μm-wavelength grid spanning 5–12 μm, giving a total of 14 spectral channels. However, as is described below, we only end up using the 11 spectral channels spanning 5–10.5 μm for science. This 0.5-μm-binning scheme combines 7 wavelengths for the shortest bin and 25 wavelengths for the longest bin, which has the added benefit of binning down the noise at longer wavelengths where there are fewer photons. However, even for this coarse of a binning scheme, we do expect there to be some additional noise beyond our estimated uncertainties on the spectrum of WASP-43b (Extended Data Fig. 1 ). As the structure of this noise source is not well understood nor is the extent to which our error bars are underestimated, our best course of action was to consider error inflation when performing spectroscopic inferences (described in more detail below).

Light-curve fitting

Detrending the initial exponential ramp.

As with other MIRI/LRS observations 29 , our spectroscopic light curves showed a strong exponential ramp at the start of the observations. As shown in Extended Data Fig. 2 , the strength and sign of the ramp varies with wavelength, changing from a strong downwards ramp at 5 μm to a nearly flat trend around 8 μm, and then becoming an upwards ramp towards longer wavelengths. From 10.6 μm to 11.8 μm, the ramp timescale became much longer and the amplitude of the ramp became much stronger; this region of the detector was previously discussed 44 and is discussed in more detail below. In general, most of the ramp’s strength had decayed within ~30–60 min, but at the precision of our data, the residual ramp signature still had an important impact on our nightside flux measurements due to the similarity of the ramp timescale with the orbital period. Unlike in the case of the MIRI/LRS commissioning observations of L168-9b 29 , we were not able to safely fit the entire dataset with a small number of exponential ramps. When fitting the entire dataset, we found that non-trivial choices about the priors for the ramp amplitudes and timescales resulted in significantly different spectra at phases 0.75 (morning hemisphere) and especially 0.0 (nightside); because the dayside spectrum is measured again near the end of the observations, it was less affected by this systematic noise.

Ultimately, we decided to conservatively discard the first 779 integrations (134.2 min), leaving only one transit duration of baseline before the first eclipse ingress began. After removing the initial 779 integrations, we found that a single exponential ramp model with broad priors that varied freely with wavelength was adequate to remove the signature. In particular, after removing the first 779 integrations we found that our dayside and nightside emission spectra were not significantly affected by (1) fitting two exponential ramps instead of one, (2) adjusting our priors on the ramp timescale to exclude rapidly decaying ramps with timescales >15 d −1 instead of >100 d −1 , (3) putting a uniform prior on the inverse timescale instead of the timescale, or (4) altering the functional form of the ramp by fitting for an exponential to which the time was raised. After removing the first ~2 h, we also found that the ramp amplitude and timescale did not vary strongly with wavelength (excluding the ‘shadowed region’ described below), although fixing these parameters to those fitted to the broadband light curve affected several points in the nightside spectrum by more than 1 σ ; we ultimately decided to leave the timescale and amplitude to vary freely with wavelength as there is no a priori reason to assume that they should be equal. With careful crafting of priors, it appeared possible to get results similar to our final spectra while removing only the first few integrations, but trimming more integrations and only using a single exponential ramp model required fewer carefully tuned prior assumptions for which we have little physical motivation.

Shadowed region effect

As was described in ref. 44 , we also identified a strong discontinuity in the spectroscopic light curves spanning pixel rows 156–220 (10.6–11.8 μm) in these observations. In this range, the temporal behaviour of the detector abruptly changes to a large-amplitude, long-timescale, upwards ramp that appears to slightly overshoot before decaying back down and approaching an equilibrium. These pixels coincide with a region of the detector between the Lyot coronagraph region and the four-quadrant phase mask region, which is unilluminated except when the dispersive element is in the optical path; as a result, we have taken to calling this unusual behaviour as the ‘shadowed region effect’. Strangely, not all MIRI/LRS observations show this behaviour, with the MIRI/LRS commissioning time-series observations 29 and the GJ 1214b phase-curve observations 48 showing no such effect. In fact, we know of only two other observations that show similar behaviour: the observation of the transit of WASP-80b (JWST-GTO-1177; T. Bell, private communication) and the observation of the phase curve of GJ 367b (JWST-GO-2508; M. Zhang, private communication). Informatively, the eclipse observation of WASP-80b taken 36 h after the WASP-80b transit using the same observing procedure (JWST-GTO-1177; T. Bell, private communication) did not show the same shadowed region effect, indicating that the effect is unlikely to be caused by stray light from nearby stars or any other factors that stayed the same between those two observations. Our best guess at this point is that the effect is related to the illumination history of the detector and the filter used by the previous MIRI observation (because the detector is illuminated at all times, even when it is not in use), but this is still under investigation and at present there is no way of predicting whether or not an observation will be impacted by the shadowed region effect. It is important to note, however, that from our limited knowledge at present that the shadowed region effect appears to be either present or not, with observations either strongly affected or seemingly completely unaffected.

Using the general methods described in the Eureka! v1 fit, we attempted to model the shadowed region effect with a combination of different ramp models, but nothing we tried was able to cleanly separate the effect from the phase variations, and there was always some clear structure left behind in the residuals of the fit. Another diagnostic that our detrending attempts were unsuccessful was that the phase offset as a function of wavelength smoothly varied around ~10° E in the unaffected region of the detector, but in the shadowed region, the phase offset would abruptly change to ~5° W; such a sharp change in a suspect region of the detector seems highly unlikely to be astrophysical in nature. As a result, we ultimately chose to exclude the three spectral bins spanning 10.5–12 μm from our retrieval efforts.

Determining the number of sinusoid harmonics

To determine the complexity of the phase-curve model required to fit the data, we used the Eureka! v1 reduction and most of the Eureka! v1 fitting methods described below, with the exception of using the dynesty 49 nested sampling algorithm (which computes the Bayesian evidence, \({{{\mathcal{Z}}}}\) ) and a batman transit and eclipse model. Within dynesty, we used 256 live points, ‘multi’ bounds, ‘rwalk’ sampling, and ran until the estimated \(d\ln ({{{\mathcal{Z}}}})\) reached 0.1. We then evaluated first-, second- and fourth-order models for the broadband light curve, excluding all third-order sinusoidal terms from the fourth-order model as these terms are not likely to be produced by the planet’s thermal radiation 50 , 51 . We then compared the Bayesian evidences of the different models following refs. 52 , 53 and found that the second-order model was significantly preferred over the first-order model at 16 σ ( \({{\Delta }}\ln ({{{\mathcal{Z}}}})=128\) ), while the second-order model was insignificantly preferred over the fourth-order model at 2.2 σ ( \({{\Delta }}\ln ({{{\mathcal{Z}}}})=1.3\) ). This is also confirmed by eye where the first-order model leaves clear phase-variation signatures in the residuals, while the residuals from the second-order model leave no noticeable phase variations behind. Finally, we also compared the phase-resolved spectra obtained from different order phase-curve models; we found that our spectra significantly changed going from a first- to second-order model (altering one or more spectral points by >1 σ ), but the fourth-order model did not significantly change the resulting phase-resolved spectra compared with the second-order. As a result, the final fits from all reductions used a second-order model. The broadband light curves obtained from the four reductions and the associated phase-curve models are shown in Supplementary Fig. 1 .

Eureka! v1 fitting methods

We first sigma-clipped any data points that were 4 σ discrepant from a smoothed version of the data (made using a boxcar filter with a width of 20 integrations) to remove any obviously errant data points while preserving the astrophysical signals like the transit.

Our astrophysical model consisted of a starry 54 transit and eclipse model, as well as a second-order sinusoidal phase-variation model. The complete astrophysical model had the form

where t is the time, F * is the received stellar flux (and includes the starry transit model), F day is the planetary flux at mid-eclipse, E ( t ) is the starry eclipse model (neglecting eclipse mapping signals for the purposes of this paper), and Ψ ( ϕ ) is the phase-variation model of the form

where ϕ is the orbital phase in radians with respect to eclipse, and AmpCos1, AmpSin1, AmpCos2 and AmpSin2 are all fitted coefficients. The second-order phase-variation terms allow for thermal variations across the face of the planet that are more gradual or steep than a simple first-order sinusoid would allow. We numerically computed dayside, morning, nightside and evening spectra using the above Ψ ( ϕ ) function at ϕ  = 0, π/2, π and 3π/2, respectively. To allow the starry eclipse function to account for light travel time, we used a stellar radius ( R * ) of 0.667  R ⊙ (ref. 55 ) to convert the fitted a / R * (the scaled semi-major axis) to physical units. For our transit model, we used a reparameterized version of the quadratic limb-darkening model 56 with coefficients u 1 and u 2 uniformly constrained between 0 and 1, and used a minimally informative prior on the planet-to-star radius ratio ( R p / R * ).

Our systematics model consisted of a single exponential ramp in time to account for the idle-recovery drift documented for MIRI/LRS time-series observations 29 , a linear trend in time, and a linear trend with the spatial position and PSF width. The full systematics model can be written as

The linear trend in time is modelled as

where t l is the time with respect to the mid-point of the observations and where c 0 and c 1 are coefficients. The exponential ramp is modelled as

where t r is the time with respect to the first integration and where r 0 and r 1 are coefficients. The linear trends as a function of spatial position, y , are PSF width s y are modelled as

where f and g are coefficients. The linear trends as a function of spatial position and PSF width are with respect to the mean-subtracted spatial position and PSF width. Finally, we also fitted a multiplier (scatter mult ) to the estimated Poisson noise level for each integration to allow us to account for any noise above the photon limit as well as an incorrect value for the gain applied in stage 3.

With an initial fit to the broadband light curve (5–10.5 μm), we assumed a zero eccentricity and placed a Gaussian prior on the planet’s orbital parameters (period, P ; linear ephemeris, t 0 ; inclination, i ; and scaled semi-major axis, a / R * ) based on previously published values for the planet 30 . For the fits to the spectroscopic phase curves, we then fixed these orbital parameters to the estimated best fit from the broadband light curve fit to avoid variations in these wavelength-independent values causing spurious features in the final spectra. We fitted the observations using the No U-Turns Sampler (NUTS) from PyMC3 57 with 3 chains, each taking 6,000 tuning steps and 6,000 production draws with a target acceptance rate of 0.95. We used the Gelman–Rubin statistic 58 to ensure the chains had converged. We then used the 16th, 50th and 84th percentiles from the PyMC3 samples to estimate the best-fit values and their uncertainties.

Eureka! v2 fitting methods

For the second fit made with Eureka!, we proceeded very similarly to the Eureka! v1 fit. We clipped the light curves using a boxcar filter of 20 integrations wide with a maximum of 20 iterations and a rejection threshold of 4 σ to reject these outliers. We also modelled the phase curve using a second-order sinusoidal function, but we modelled the transit and eclipse using batman 59 instead of starry. Like in the Eureka! v1 fit, we modelled instrumental systematics with a linear polynomial model in time (equation ( 4 )), an exponential ramp (equation ( 5 )), a first-order polynomial in y position (equation ( 6 )) and a first-order polynomial in PSF width in the s y direction (equation ( 7 )).

We fitted the data using the emcee sampler 60 instead of NUTS, with 500 walkers and 1,500 steps. The jump parameters that we used were the same as in the Eureka! v1 fit: R p / R * , F day , u 1 , u 2 , AmpCos1, AmpSin1, AmpCos2, AmpSin2, c 0 , c 1 , r 0 , r 1 , f , g and scatter mult (multiplier to the estimated Poisson noise level for each integration like in the Eureka! v1 fit). We used uniform priors on u 1 and u 2 from 0 to 1, uniform priors on AmpCos1, AmpSin1, AmpCos2, AmpSin2 from −1.5 to 1.5 and broad normal priors and all other jump parameters. Convergence, mean values and uncertainties were computed in the same way as for the Eureka! v1 fit.

TEATRO fitting methods

To measure the planet’s emission as a function of longitude, we modelled the light curves with a phase-variation model, an eclipse model, a transit model and an instrument systematics model. The phase-curve model, Ψ ( t ), consists of two sinusoids: one at the planet’s orbital period, P , and one at P /2 to account for second-order variations. The eclipse model, E ( t ), and transit model, T ( t ), are computed with the exoplanet 61 package that uses the starry package 54 . We save the eclipse depth, δ e , and normalize E ( t ) to a value of 0 during the eclipse and 1 out of the eclipse, which we then call E N ( t ). We used published transit ephemerides 62 , a null eccentricity and published stellar parameters 63 . The planet-to-star radius ratio, R p / R * , impact parameter, b , and mid-transit time, t 0 , are obtained from a fit to the broadband light curve. The systematics model, S ( t ), is composed of a linear function to account for a downwards trend and an exponential function to account for the initial ramp. The full model is expressed as:

where Ψ ( t e ) is the value of Ψ at the mid-eclipse time, t e .

We fit our model to the data using a Markov chain Monte Carlo (MCMC) procedure based on the PyMC3 package 57 and gradient-based inference methods as implemented in the exoplanet package 61 . We set normal priors for t 0 , R p / R * , the stellar density ( ρ * ), a Ψ , b Ψ , c S and d S with mean values obtained from an initial nonlinear least-squares fit, a normal prior for a S with a zero mean, uniform priors for the surface brightness ratio between the planet’s dayside and the star ( s ), b , c Ψ and d Ψ , uninformative priors for the quadratic limb-darkening parameters 56 , and allowed for wide search ranges. We ran two MCMC chains with 5,000 tuning steps and 100,000 posterior samples. Convergence was obtained for all parameters (except in one case where a S was negligible and b S was unconstrained). We merged the posterior distributions of both chains and used their median and standard deviation to infer final values and uncertainties for the parameters. We also verified that the values obtained from each chain were consistent.

For the spectroscopic light-curve fits, we fixed all physical parameters to those obtained from the broadband light-curve fit except the surface brightness ratio, s , that sets the eclipse depth, we masked the transit part of the light curve, and used a similar procedure. After the fits, we calculated the eclipse depth, δ e , as s  × ( R p / R * ) 2 , and computed Ψ ( t ) for the final parameters, Ψ f ( t ). The planetary flux is Ψ f ( t ) −  Ψ f ( t e ) +  δ e . We computed the uncertainty on the eclipse depth in two different ways: from the standard deviation of the posterior distribution of s  × ( R p / R * ) 2 , and from the standard deviation of the in-eclipse points divided by \(\sqrt{{N}_{{\mathrm{e}}}}\) , where N e is the number of in-eclipse points, and took the maximum of the two. To estimate the uncertainty on the planet’s flux, we computed the 1 σ interval of Ψ ( t ) based on the posterior distributions of its parameters, computed the 1 σ uncertainty of d S , and added them in quadrature to the uncertainty on the eclipse depth to obtain more conservative uncertainties.

The spectra presented in this paper and used in the combined spectra are based on system parameters that were derived from a broadband light curve obtained in the 5–12 μm range, a transit fit in which the stellar mass and radius were fixed, a simpler additive model in which the phase curve was not turned off during the eclipse, and an MCMC run that consisted in two chains of 10,000 tuning steps and 10,000 posterior draws. Updated spectra based on system parameters derived from the broadband light curve obtained in the 5–10.5 μm range, a transit fit that has the stellar density as a free parameter, the light-curve model shown in equation ( 8 ), and two MCMC chains of 5,000 tuning steps and 100,000 posterior draws are consistent within 1 σ at every point with those shown here. As we average four reductions and inflate the uncertainties during the retrievals, the impact of these updates on our results are expected to be marginal.

SPARTA fitting methods

We use emcee 60 to fit a broadband light curve with the transit time, eclipse time, eclipse depth, four phase-curve parameters ( C 1 and D 1 for the first-order, and C 2 and D 2 for the second-order sinusoids), transit depth, a / R * , b , flux normalization, error-inflation factor, instrumental ramp amplitude ( A ) and 1/timescale ( τ ), linear slope in time ( m ) with respect to the mean of the integration times \((\overline{t})\) , and linear slope with trace y position ( c y ) as free parameters. The best-fit transit and eclipse times, a / R * and b are fixed for the spectroscopic light curves.

For the spectroscopic fits, we then use emcee to fit the free parameters: the eclipse depth, four phase-curve parameters, error-inflation factor, flux normalization, instrumental ramp amplitude and 1/timescale, linear slope with time, and linear slope with trace y position. All parameters are given uniform priors. 1/timescale is given a prior of 5–100 d −1 , but the other priors are unconstraining. In summary, the instrumental model is:

while the planetary flux model is:

where E is the eclipse depth and ω = 2π/ P is the planet’s orbital angular frequency. Note that the phase variations were set to be zero during eclipse.

Combining independent spectra

Comparing the phase-resolved spectra from each reduction (Supplementary Fig. 2 ), we see that for wavelengths below 10.5 μm, the spectra are typically consistent, while larger differences arise in the >10.5 μm region affected by the shadowed region effect. For our final, fiducial spectrum, we decided to use the median spectrum and inflated our uncertainties to account for disagreements between different reductions. The median phase-resolved spectra were computed by taking the median F p / F * per wavelength. The uncertainties were computed by taking the median uncertainty per wavelength, and then adding in quadrature the RMS between the individual reductions and the median spectrum; this minimally affects the uncertainties where there is minimal disagreement and appreciably increases the uncertainties where the larger disagreements arise.

Each reduction also computed a transmission spectrum (Supplementary Fig. 2 ), which appears quite flat (within uncertainties) with minimal differences between reductions. WASP-43b is not an excellent target for transmission spectroscopy, however, and these transmission spectra are not expected to be overly constraining.

Atmospheric forward models

GCMs were used to simulate atmospheric conditions, from which synthetic phase curves and emission spectra were forward modelled and compared with the observations. The GCMs used in this study are listed in Supplementary Table 1 , and details of each simulation are provided in Extended Data Table 1 and the following sections.

Generic PCM

The Generic Planetary Climate Model (Generic PCM) is a three-dimensional global climate model designed for modelling the atmosphere of exoplanets and for palaeoclimatic studies. The model has been used for the study of planetary atmospheres of the Solar System 64 , 65 , 66 , terrestrial exoplanets 67 , mini-Neptunes 68 and hot Jupiters 69 . The dynamical core solves the primitive equations of meteorology on a Arakawa C grid. The horizontal resolution is 64 × 48 (that is, 5.625 × 3.75°) with 40 vertical levels, equally spaced in logarithmic scale between 10 Pa and 800 bars. Along with the various parameterizations of physical processes described in refs. 64 , 65 , 66 , 67 , 68 , the Generic PCM treats clouds as radiatively active tracers of fixed radii.

The model is initialized using temperature profiles from the radiative–convective one-dimensional model Exo-REM 70 . The radiative data are computed offline using the out-of-equilibrium chemical profiles of the Exo-REM run. We use 27 frequency bins in the stellar channel (0.261–10.4 μm) and 26 in the planetary channel (0.625–324 μm), all bins including 16 k -coefficients. We start the model from a rest state (no winds), with a horizontally homogeneous temperature profile. Models are integrated for 2,000 days, which is long enough to complete the spin-up phase of the simulation above the photosphere. We do not include Rayleigh drag in our models. The simulations are performed including clouds of Mg 2 SiO 4 , with varying cloud radii (0.1, 0.5, 1, 3 and 5 μm). We also computed cloudless and Mg 2 SiO 4 models with a 10× solar metallicity and the same radii for the cloud particles. Regardless of the composition and size of the clouds, our model clearly indicates that there is no cloud formation on the dayside. Asymmetric limbs are a natural result of our model, with the eastern terminator being warmer while the western limb is cloudier and cooler. Spectral phase curves were produced using the Pytmosph3R code 71 .

SPARC/MITgcm with radiative transfer post-processing by gCMCRT

SPARC/MITgcm couples a state-of-the-art non-grey, radiative-transfer code with the MITgcm 33 . The MITgcm solves the primitive equations of dynamical meteorology on a cubed-sphere grid 72 . It is coupled to the non-grey radiative-transfer scheme based on the plane-parallel radiative-transfer code of ref. 73 . The stellar irradiation incident on WASP-43b is computed with a PHOENIX model 74 , 75 , 76 . We use previously published opacities 77 , including more recent updates 78 , 79 , and the molecular abundances are calculated assuming local chemical equilibrium 80 . In the GCM simulations, the radiative-transfer calculations are performed on 11 frequency bins ranging from 0.26 μm to 300 μm, with 8 k -coefficients per bin statistically representing the complex line-by-line opacities 3 . The strong visible absorbers TiO and VO are excluded in our k tables similar to our previous GCMs of WASP-43b 3 , 23 that best match the observed dayside emission spectrum and photometry.

Clouds in the GCM are modelled as tracers that are advected by the flow 81 and can settle under gravity. Their formation and evaporation are subjected to chemical equilibrium predictions, that is, the condensation curves of various minerals described in ref. 80 . The conversion between the condensable ‘vapour’ and clouds is treated as a simple linear relaxation over a short relaxation timescale of 100 s. The scattering and absorption of the spatial- and time-dependent clouds are included in both the thermal and visible wavelengths of the radiative transfer. A similar dynamics–cloud–radiative coupling has been developed in our previous GCMs with simplified radiative transfer and has been used to study the atmospheric dynamics of brown dwarfs 9 , 82 and ultrahot Jupiters 83 . Clouds are assumed to follow a log-normal size distribution 84 , which is described by the reference radius r 0 and a non-dimensional deviation σ : \(n(r)=\frac{{{{\mathcal{N}}}}}{\sqrt{2\uppi }\sigma r}\exp \left(-\frac{{[\ln (r/{r}_{0})]}^{2}}{2{\sigma }^{2}}\right)\) , where n ( r ) is the number density per radius bin of r and \({{{\mathcal{N}}}}\) is the total number density. σ and r 0 are free parameters and the local \({{{\mathcal{N}}}}\) is obtained from the local mass mixing ratio of clouds. The size distribution is held fixed throughout the model and is the same for all types of cloud.

Our GCMs do not explicitly impose a uniform radiative heat flux at the bottom boundary but rather relax the temperature of the lowest model layer (that is, the highest pressure layer) to a certain value over a short timescale of 100 s. This assumes that the deep GCM layer reaches the convective zone and the temperature there is set by the interior convection that ties to the interior structure of the planet. This lowest-layer temperature is in principle informed by internal structure models of WASP-43b, which are run by MESA hot Jupiter evolution modules 12 to match the present radius of WASP-43b. In most models, this lowest-layer temperature is about 2,509 K at about 510 bars. The horizontal resolution of our GCMs is typically C48, equivalent to about 1.88° per grid cell. The vertical domain is from 2 × 10 −4  bar at the top to 700 bars at the bottom and is discretized to 53 vertical layers. We typically run the simulation for over 1,200 days and average all physical quantities over the last 100 days of the simulations.

All our GCMs assume a solar composition. We performed a baseline cloudless model and one case with only MnS and Na 2 S clouds with r 0  = 3 μm, and then a few cases with MnS, Na 2 S and MgSiO 3 clouds with r 0  = 1, 1.5, 2 and 3 μm. The σ is held fixed at 0.5 in all our cloudy GCMs.

We post-process our GCM simulations with the state-of-the-art gCMCRT code, which is a publicly available hybrid Monte Carlo radiative transfer (MCRT) and ray-tracing radiative-transfer code. The model is described in detail in ref. 85 and has been applied to a range of exoplanet atmospheres 83 , 86 . gCMCRT can natively compute albedo, transmission and emission spectra at both low and high spectral resolution. gCMCRT uses custom k tables, which take cross-section data from both HELIOS-K 87 and EXOPLINES 88 . Here, we apply gCMCRT to compute low-resolution emission spectra and phase curves at R  ≈ 300 from our GCM simulations. We use the three-dimensional temperature and condensate cloud tracer mixing ratio from the time-averaged end-state of each case. We assume the same cloud particle size distribution as our GCMs.

expeRT/MITgcm

The GCM expeRT/MITgcm uses the same dynamical core as SPARC/MITgcm and solves the hydrostatic primitive equations on a C32 cubed-sphere grid 72 . It resolves the atmosphere above 100 bar on 41 log-spaced cells between 1 × 10 −5  bar and 100 bar. Below 100 bar, 6 linearly spaced grid cells between 100 bar and 700 bar are added. The model expeRT/MITgcm thus resolves deep dynamics in non-inflated hot Jupiters like WASP-43b 16 , 89 .

The GCM is coupled to a non-grey radiative-transfer scheme based on petitRADTRANS 90 . Fluxes are recalculated every fourth dynamical time step. Stellar irradiation is described by the spectral fluxes from the PHOENIX model atmosphere suite 74 , 75 , 76 . The GCM operates on a precalculated grid of correlated k -binned opacities. Opacities from the ExoMol database 91 are precalculated offline on a grid of 1,000 logarithmically spaced temperature points between 100 K and 4,000 K for every vertical layer. We further include the same species as shown in ref. 89 except TiO and VO to avoid the formation of a temperature inversion in the upper atmosphere. These are: H 2 O (ref. 92 ), CH 4 (ref. 93 ), CO 2 (ref. 94 ), NH 3 (ref. 95 ), CO (ref. 96 ), H 2 S (ref. 97 ), HCN (ref. 98 ), PH 3 (ref. 99 ), FeH (ref. 100 ), Na (refs. 74 , 101 ) and K (refs. 74 , 101 ). For Rayleigh scattering, the opacities are H 2 (ref. 102 ) and He (ref. 103 ), and we add the following collision-induced absorption (CIA) opacities: H 2 –H 2 (ref. 104 ) and H 2 –He (ref. 104 ). We use for radiative-transfer calculations in the GCM the same wavelength resolution as SPARC/MITgcm (S1), but incorporate 16 instead of 8 k -coefficients. Two cloud-free WASP-43b GCM simulations were performed, one with solar and one with 10× solar element abundances. Each simulation ran for 1,500 days to ensure that the deep wind jet has fully developed. The GCM results used in this paper were time averaged over the last 100 simulation days.

Spectra and phase curves are produced from our GCM results in post-processing with petitRADTRANS 90 and prt_phasecurve 89 using a spectral resolution of R  = 100 for both the phase curve and the spectra.

Originally adapted from the GCM of ref. 105 by refs. 106 , 107 , 108 , the RM-GCM model has been applied to numerous investigations of hot Jupiters and mini-Neptunes 35 , 109 , 110 , 111 . The GCM’s dynamical core solves the primitive equations of meteorology using a spectral representation of the domain, and it is coupled to a two-stream, double-grey radiative-transfer scheme based on ref. 112 . Recent updates have added aerosol scattering 35 with radiative feedback 8 , 36 . Following ref. 8 , aerosols are representative of condensate clouds and are treated as purely temperature-dependent sources of opacity, with constant mixing ratios set by the assumed solar elemental abundances. The optical thicknesses of the clouds are determined by converting the relative molecular abundances (or partial pressures) of each species into particles with prescribed densities and radii 8 . The model includes up to 13 different cloud species of various condensation temperatures, abundances and scattering properties. Places where clouds overlap have mixed properties, weighted by the optical thickness of each species.

Simulations from this GCM included a clear atmosphere and two sets of cloudy simulations. Following ref. 8 , one set of cases included 13 different species: KCl, ZnS, Na 2 S, MnS, Cr 2 O 3 , SiO 2 , Mg 2 SiO 4 , VO, Ni, Fe, Ca 2 SiO 4 , CaTiO 2 and Al 2 O 3 ; the other set omitted ZnS, Na 2 S, MnS, Fe and Ni, based on considerations of nucleation efficiency 113 . For both cloud composition scenarios, the models explored the observational consequences of variations in the cloud deck’s vertical thickness through a series of simulations with clouds tops truncated over a range of heights at 5-layer intervals (roughly a scale height), ranging from 5 to 45 layers of the 50-layer model. This effectively mimics a range of vertical mixing strengths. From the complete set published in ref. 26 , we selected a subset, with clouds of maximum vertical extents between two and nine scale heights from each of the two cloud composition scenarios.

Simulations were initialized with clear skies, no winds and no horizontal temperature gradients. We ran the simulations for over 3,500 planetary orbits, assuming tidal synchronization. Resulting temperature, wind and cloud fields of the GCM were then post-processed 114 , 115 to yield corresponding emission phase curves.

THOR 116 , 117 is an open-source GCM developed to study the atmospheres and climates of exoplanets, free from Earth- or Solar System-centric tunings. The core that solves the fluid flow equations, the dynamical core, solves the non-hydrostatic compressible Euler equations on an icosahedral grid 116 , 118 . THOR has been validated and used to simulate the atmosphere of Earth 116 , 119 , Solar System planets 120 , 121 and exoplanets 116 , 117 , 122 .

For this work, THOR used the same configuration as with previously published simulations to study the atmospheric temperature structure, cloud cover and chemistry of WASP-43b 4 , 38 , 123 . Two simulations were conducted, one with a clear atmosphere and another with a cloud structure on the nightside of the planet. To represent the radiative processes, THOR uses a simple two-band formulation calibrated to reproduce the results from more complex non-grey models on WASP-43b 3 , 124 . A simple cloud distribution on the nightside of the planet and optical cloud properties are parameterized 4 and adapted to reproduce previous HST 21 and Spitzer 4 , 22 observations. These simulations on WASP-43b with THOR have also been used to test the performance of future Ariel phase-curve observations 125 .

Both simulations, with clear and cloudy atmospheres, started with isothermal atmospheres (1,440 K, equilibrium temperature) and integrated for roughly 9,400 planetary orbits (assuming a tidally locked configuration) until a statistically steady state of the deep atmosphere thermal structure was reached. The long integration avoids biasing the results towards the set initial conditions 120 .

The multiwavelength spectra are obtained from post-processing the three-dimensional simulations with a multiwavelength radiative-transfer model 126 . The disk-averaged planet spectrum is calculated at each orbital phase by projecting the outgoing intensity for each geographical location of the observed hemisphere. The spectra include cross-sections of the main absorbers in the infrared, drawn from the ExoMOL (H 2 O (ref. 92 ), CH 4 (ref. 127 ), NH 3 (ref. 128 ), HCN (ref. 129 ) and H 2 S (ref. 97 )), HITEMP 130 (CO 2 and CO) and HITRAN 131 (C 2 H 2 ) databases. The Na and K resonance lines 132 are also added, as were H 2 –H 2 and H 2 –He CIA 104 . The atmospheric bulk composition was assumed to have solar abundance (consistent with HST/WFC3 spectrum observations), and each chemical species concentration was calculated with the FastChem model 133 . The PHOENIX models 74 , 75 , 76 were used for the WASP-43 star spectrum.

Atmospheric retrieval models

We perform atmospheric retrievals on the phase-resolved emission spectra using six different retrieval frameworks, each described in turn below. The chemical constraints from these analyses are summarized in Extended Data Tables 2 and 3 , and the spectral fits obtained are shown in Extended Data Fig. 3 . Across the six retrieval analyses, we use an error-inflation parameter to account for the effects of unknown data and/or model uncertainties. This free parameter is wavelength independent and multiplies the 1 σ error bars in the calculation of the likelihood function in the Bayesian sampling algorithm.

HyDRA retrieval framework

The HyDRA atmospheric retrieval framework 134 consists of a parametric atmospheric forward model coupled to PyMultiNest 135 , 136 , a nested sampling Bayesian parameter estimation algorithm 137 . HyDRA has been applied to hydrogen-rich atmospheres 138 , 139 , and further adapted for secondary atmospheres 140 and high-resolution spectroscopy in both one and two dimensions 141 , 142 . The input parameters for the atmospheric forward model include constant-with-depth abundances for each of the chemical species considered, six temperature profile parameters corresponding to the temperature profile model of ref. 143 , and a constant-with-wavelength multiplicative error-inflation parameter to account for model uncertainties. We additionally include a dilution parameter, A HS , for the dayside, morning and evening hemispheres, which multiplies the emission spectrum by a constant factor <1 and accounts for temperature inhomogeneities in each hemisphere 144 .

We consider opacity contributions from the chemical species that are expected to be present in hot Jupiter atmospheres and that have opacity in the MIRI LRS wavelength range: H 2 O (ref. 130 ), CH 4 (refs. 127 , 145 ), NH 3 (ref. 128 ), HCN (refs. 98 , 129 ), CO (ref. 130 ), CO 2 (ref. 130 ), C 2 H 2 (refs. 131 , 146 ), SO 2 (ref. 147 ), H 2 S (refs. 97 , 148 ) and CIA due to H 2 –H 2 and H 2 –He (ref. 104 ). The line-by-line absorption cross-sections for these species are calculated following the methods described in ref. 134 , using data from each of the references listed. We further explore retrievals with a simple silicate cloud model, which includes the modal particle size, cloud particle abundance, cloud base pressure and a pressure exponent for the drop-off of cloud particle number density with decreasing pressure. The opacity structure of the cloud is calculated using the absorption cross-sections of ref. 149 .

Given the input chemical abundances, temperature profile and cloud parameters, the forward model calculates line-by-line radiative transfer to produce the thermal emission spectrum at a resolution of R  ≈ 15,000. The spectrum is then convolved to a resolution of 100, binned to the same wavelength bins as the observations and compared with the observed spectrum to calculate the likelihood of the model instance. The nested sampling algorithm explores the parameter space using 2,000 live points, and further calculates the Bayesian evidence of the retrieval model, which can be used to compare different models 52 . In particular, we calculate the detection significance of a particular chemical species by comparing retrievals that include/exclude that species, fixing the value of the error-inflation parameter to be the median retrieved value found with the full retrieval model.

Across the four phases, the only chemical species detected with statistical significance ( ≳ 3 σ ) is H 2 O. The retrieved H 2 O abundances are in the range ~30–10 4  ppm (1 σ uncertainties), with detection significances varying between ~3 σ and ~4 σ (Extended Data Table 2 ). We do not detect CH 4 at any phase, and place an upper limit of 16 ppm on the nightside CH 4 abundance, potentially indicating disequilibrium chemistry processes as described in the main text. We do not detect NH 3 at any phase either, consistent with the very low NH 3 abundances predicted by both chemical equilibrium and disequilibrium models 23 . The retrievals do not favour cloudy models over clear models with statistical significance, with extremely weak preferences of <1 σ at all phases. In addition, the posterior probability distributions for the cloud parameters are unconstrained. Extended Data Fig. 5 shows the pressure ranges of the atmospheric model probed by the observations.

PyratBay retrieval framework

PyratBay is an open-source framework that enables atmospheric modelling, spectral synthesis, and atmospheric retrievals of exoplanet observations 150 . The atmospheric model consists of parametric temperature, composition and altitude profiles as a function of pressure, for which emission and transmission spectra can be generated. The radiative-transfer module considers opacity from alkali lines 151 , Rayleigh scattering 152 , 153 , Exomol and HITEMP molecular line lists 130 , 154 pre-processed with the REPACK package 155 to extract the dominant line transitions, CIA 156 and cloud opacities. The PyratBay retrieval framework has the ability to stagger model complexity and explore a hierarchy of different model assumptions. Temperature models range from an isothermal profile to physically motivated parameterized models 143 , 157 . Composition profiles range from the simpler constant-with-altitude ‘free abundance’ to the more complex ‘chemically consistent’ retrievals, the latter accomplished via the TEA code 158 ; while cloud condensate prescriptions range from the classic ‘power law + grey’ to a ‘single-particle-size’ haze profile, a partial-coverage factor ‘patchy clouds’ 159 , and the complex parameterized Mie-scattering thermal stability cloud (TSC) model (J.B. et al., manuscript in preparation). The TSC cloud prescription, initially inspired by refs. 84 , 160 , has additional flexibility in the location of the cloud base and was further improved for this analysis (see below). The formulation utilizes a parameterized cloud shape, effective particle size and gas number density below the cloud deck, while the atmospheric mixing and settling are wrapped up inside the cloud extent and the condensate mole fraction as free parameters. This cloud model was applied to WASP-43b JWST/MIRI phase-curve simulations 23 , generated during the JWST preparatory phase, in anticipation of the actual WASP-43b JWST/MIRI observations. We showed that the TSC model has the ability to distinguish between MgSiO 3 and MnS clouds on the nightside of the planet.

For this analysis, we conducted a detailed investigation using various model assumptions. We started by exploring simple temperature prescriptions and gradually moved towards more complex ones. Initially, we considered opacity contributions from all chemical species expected to be observed in the MIRI wavelength range (H 2 O, CH 4 , NH 3 , HCN, CO, CO 2 , C 2 H 2 , SO 2 , H 2 S), but eventually focused on only those that are fit by the data. We also implemented the dilution parameter 144 and an error-inflation factor, which account for some additional model and data uncertainties. The constraints on H 2 O (together with the detection significance 161 ) and the upper limit for CH 4 for all phases are given in Extended Data Table 2 . The abundances of these species across all phases were largely model independent. However, the tentative constraints on NH 3 , which we saw in multiple phases, were strongly model dependent, and were completely erased with the inclusion of the dilution parameter and the error inflation, thus we do not report them here. WASP-43b emission spectra were computed at a resolution of R  ≈ 15,000 utilizing opacity sampling of high-resolution pre-computed cross-sections ( R  ≈ 10 6 ) of considered species. Furthermore, we thoroughly examined the possibility of detecting clouds in each of the four-quadrant phases, with a special emphasis on the nightside of the planet. To do this, we employed the TSC model, as in our previous analysis 23 , and explored a range of cloud species, MgSiO 3 , MnS, ZnS and KCl, that would condense under the temperature regimes expected for WASP-43b 162 (Extended Data Fig. 6 , left). We also introduced the effective standard deviation of the log-normal distribution 84 as a free parameter ( σ log ), allowing for even more flexibility in our cloud model (Extended Data Fig. 6 , right, last subpanel). To thoroughly explore the parameter space, we used two Bayesian samplers, the differential-evolution MCMC algorithm 163 , implemented following ref. 164 , and the nested sampling algorithm, implemented through PyMultiNest 135 , 136 , utilizing 15 million models and 2,000 live points, respectively. Our investigation did not provide constraints on any of the cloud parameters for any of the explored cloud condensates at any of the planetary phases, indicating the absence of detectable spectral features from clouds in the observations (Extended Data Fig. 6 , right).

NEMESIS retrieval framework

NEMESIS 165 , 166 is a free retrieval framework that uses a fast correlated- k 167 forward model, combined with either an optimal estimation or nested sampling retrieval algorithm. It has been used to perform retrievals on spectra of numerous planetary targets, both inside and outside the Solar System 168 , 169 . In this work, we use the PyMultiNest sampler 136 with 500 live points. The retrieval model presented includes four spectrally active gases, H 2 O (ref. 92 ), CO (ref. 96 ), CH 4 (ref. 93 ) and NH 3 (ref. 95 ), with k tables calculated as in ref. 91 ; we did not include CO 2 or H 2 S after initial tests indicated these were not required to fit the spectrum. All gases are assumed to be well mixed in altitude. CIA from H 2 and He is taken from refs. 156 , 170 . The spectrum is calculated at the resolution of the observation, using optimized channel integrated k tables generated from original k tables with a resolving power R  = 1,000. The temperature profile is modelled as a three-parameter Guillot profile, after ref. 157 , with free parameters κ , γ and β ( α is fixed to be zero). We include a well-mixed, spectrally grey cloud with a scalable total optical depth with a cloud top at 12.5 mbar. The other retrieved parameters are a hotspot dilution factor for phases 0.25, 0.5 and 0.75, following ref. 144 , and an error-inflation term.

To calculate the detection significance for H 2 O, we run the retrieval with and without H 2 O, with all other aspects of the run identical. We then take the difference of the PyMultiNest global log-evidence values for the two scenarios, and convert from log(Bayesian evidence) to sigma following ref. 52 . The 99% upper limit for CH 4 is calculated from the equally weighted posterior distribution. We also attempt to retrieve CO and NH 3 abundances. CO is generally poorly constrained, and NH 3 is unconstrained for phases 0 and 0.75; for log(NH 3 ), we recover a 99% upper limit of −2.2 at phase 0.25 and −3.9 at phase 0.5. The cloud opacity is also generally unconstrained, with the total optical depth able to span several orders of magnitude. We stress that this model is very crude as it has only one variable cloud parameter, and further exploration of suitable cloud models for mid-infrared phase curves is warranted in future work.

SCARLET retrieval framework

We perform atmospheric retrievals on the four phase-resolved spectra using the SCARLET framework 160 , 171 . The planetary disk-integrated thermal emission, F p , is modelled for a given set of atomic/molecular abundances, temperature–pressure profile and cloud properties. We compare our model spectra with the observations by normalizing the thermal emission F p using a PHOENIX 74 , 75 , 76 stellar model spectrum with effective temperature T eff  = 4,300 K and surface gravity log  g  = 4.50. The model spectra are computed at a resolving power of R  = 15,625, convolved to the resolving power of MIRI/LRS and then binned to the 11 spectral bins (<10.5 μm) considered in the analysis, assuming the throughput to be uniform over a single bin.

The atmospheric analysis is performed considering thermochemical equilibrium, where the metallicity [M/H] ( \({{{\mathcal{U}}}}[-3,3]\) ) and carbon-to-oxygen ratio ( \({{{\mathcal{U}}}}[0,3]\) ) are free parameters that dictate the overall atmospheric composition. We use a free parameterization of the temperature–pressure profile 172 by fitting for N  = 4 temperature points ( \({{{\mathcal{U}}}}[100,4400]\,{\mathrm{K}}\) ) with a constant spacing in log-pressure. The temperature–pressure profile is interpolated to the 50 layers ( P  = 10 2 –10 −6  bar) considered in the model using a spline function to produce a smooth profile. We use a grid of chemical equilibrium abundances produced with FastChem2 173 to interpolate the abundance of species as a function of temperature and pressure for given values of [M/H] and C/O. The species considered in the equilibrium chemistry are H, H − (refs. 174 , 175 ), H 2 , He, H 2 O (ref. 92 ), OH (ref. 130 ), CH 4 (ref. 127 ), C 2 H 2 (ref. 176 ), CO (ref. 130 ), CO 2 (ref. 130 ), NH 3 (ref. 95 ), HCN (ref. 98 ), PH 3 (ref. 99 ), TiO (ref. 177 ) and VO (ref. 178 ). All opacities for these species are considered when computing the thermal emission. We account for potential spatial atmospheric inhomogeneities in the planetary disk that are observed at a given phase by including an area fraction parameter A HS ( \({{{\mathcal{U}}}}[0,1]\) ), which is meant to represent the possibility of a fraction of the disk contributing to most of the observed thermal emission 144 . This parameter is considered for all phases with the exception of the nightside, which is expected to be relatively uniform. Finally, we fit for an error-inflation parameter k σ ( \({{{\mathcal{U}}}}[0.1,10]\) ) to account for potential model and data uncertainty, which results in a total of 8 (7 for the nightside) free parameters. We consider 8 walkers per free parameter for the retrievals which are run for 30,000 steps. The first 18,000 steps are discarded when producing the posterior distributions of the free parameters.

PLATON retrieval framework

PLATON 179 , Planetary Atmosphere Tool for Observer Noobs, is a Bayesian retrieval tool that assumes equilibrium chemistry. We adopt the temperature–pressure profile parameterization of ref. 180 , and use the dynesty nested sampler 49 to retrieve the following free parameters: stellar radius; stellar temperature; the log metallicity, log( Z ); C/O; 5 temperature–pressure parameters (log( κ th ), log( γ ), log( γ 2 ), α , β ); and an error multiplier. The stellar radius and temperature are given Gaussian priors with means and standard deviations set by the measurements in ref. 55 : 4,400 ± 200 K and 0.667 ± 0.011  R ⊙ , respectively. The combination of the two have a similar effect to the dilution parameter of other retrieval codes, which multiplies the emission spectrum by a constant. For phase 0.0, we obtain a significantly better fit when methane opacity is set to zero (thus removing all spectral features from methane). We therefore adopt this as the fiducial model, whereas for other phases, we do not zero out any opacities.

For all retrievals, we use nested sampling with 1,000 live points. The opacities (computed at R  = 10,000) and the line lists used to compute them are listed in ref. 179 . We include all 31 species in retrieval, notably including H 2 O, CO, CO 2 , CH 4 (except on the nightside), H 2 S and NH 3 .

ARCiS retrieval framework

ARCiS (Artful modelling code for exoplanet science) is an atmospheric modelling and Bayesian retrieval code 181 , 182 that utilizes the MULTINEST 135 Monte Carlo nested sampling algorithm. The code was used in previous retrievals of the atmosphere of WASP-43b in transmission 183 , using the observations of ref. 184 , and in phase-resolved emission 185 , using the observations of refs. 21 , 22 , 25 , 186 . Reference 183 found some evidence that AlO improves the fit of the transmission spectra of WASP-43b in the 1.1–1.6 μm region. We therefore include in our models for this work the following set of molecules in our free molecular retrievals: H 2 O (ref. 92 ), CO (ref. 96 ), CO 2 (ref. 94 ), NH 3 (ref. 95 ), CH 4 (ref. 93 ) and AlO (ref. 187 ). The molecular line lists are from the ExoMol 154 , 188 or HITEMP 130 databases as specified, and k tables from the ExoMolOP opacity database 91 . CIA for H 2 and He are taken from refs. 156 , 170 . We explore the inclusion of a variety of additional molecules that have available line list data with spectral features in the region of our observations, including HCN (ref. 98 ), SiO (ref. 189 ) and N 2 O (ref. 130 ). We use the Bayes factor, which is the difference between the nested sampling global log-evidence (log  E ) between two models, to assess whether the inclusion of a particular parameter is statistically significant. For this, we run a retrieval with the base set of species only and another with the base set plus the molecule being assessed. The difference in log  E between the two models is converted to a significance in terms of σ using the metric of ref. 52 . We explore the inclusion of a simple grey, patchy cloud model, which parameterizes cloud top pressure and degree of cloud coverage (from 0 for completely clear to 1 for completely covered). We use 1,000 live points and a sampling efficiency of 0.3 in MULTINEST for all retrievals.

We run retrievals both including and not including a retrieved error-inflation parameter. The error-inflation parameter is implemented as per ref. 190 to account for underestimated uncertainties and/or unknown missing forward model parameters. All phases apart from 0.0 retrieved a parameter that increases the observational error bars by two to three times their original values. The pressure–temperature profile parameterization of ref. 191 is used in all cases. We find evidence for the inclusion of H 2 O for all four phases, although this evidence goes from strong to weak when error inflation is included for the morning phase (0.75). We find no strong evidence for CH 4 at any phase, with 95% confidence upper limits on the log of the volume mixing ratio (VMR) of −4.9, −2.9, −3.2 and −2.2 for phases 0.0, 0.25, 0.5 and 0.75, respectively. We find some model-dependent hints of moderate evidence (based on the metric of ref. 52 ) of 4.4 σ for NH 3 at phase 0.5 (constrained to \(\log{{\mathrm{VMR}}}={-4.5}_{-0.5}^{+0.7}\) ), 3.1 σ for CO at phase 0.5 ( \(\log{{\mathrm{VMR}}}={-1.7}_{-0.7}^{+0.5}\) ) and 2.6 σ for CO at phase 0.25 ( \(\log{{\mathrm{VMR}}}={-4.0}_{-0.4}^{+0.3}\) ). However, these disappear when the error-inflation parameter is introduced. We are not able to constrain any of the cloud parameters for any phase, and so do not find a statistical reason to include our simple cloud parameterization in the models to better fit the observations.

Data availability

The data used in this paper are associated with JWST DD-ERS programme 1366 (principal investigators N.M.B., J.L.B. and K.B.S.; observation 11) and are publicly available from the Mikulski Archive for Space Telescopes ( https://mast.stsci.edu ). Additional intermediate and final results from this work are archived on Zenodo at https://doi.org/10.5281/zenodo.10525170 (ref. 192 ).

Code availability

We used the following codes to process, extract, reduce and analyse the data: STScI’s JWST Calibration pipeline 46 , Eureka! 45 , TEATRO, SPARTA 48 , Generic PCM 64 , 65 , 66 , 67 , 68 , SPARC/MITgcm 9 , 33 , 81 , 82 , expeRT/GCM 16 , 72 , 89 , RM-GCM 8 , 35 , 106 , 107 , 108 , THOR 4 , 116 , 117 , 124 , 126 , 193 , HyDRA 134 , PyratBay 150 , NEMESIS 165 , 166 , SCARLET 160 , 171 , PLATON 179 , starry 54 , exoplanet 61 , PyMC3 57 , emcee 60 , dynesty 49 , numpy 194 , astropy 195 , 196 and matplotlib 197 .

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Acknowledgements

T.J.B. acknowledges funding support from the NASA Next Generation Space Telescope Flight Investigations program (now JWST) via WBS 411672.07.05.05.03.02. J.K.B. is supported by a UKRI/STFC Ernest Rutherford Fellowship (grant ST/T004479/1). J.B. acknowledges the support received in part from the NYUAD IT High Performance Computing resources, services, and staff expertise. E.D. acknowledges funding as a Paris Region Fellow through the Marie Sklodowska-Curie Action. M.Z. and B.V.R. acknowledge funding from the 51 Pegasi b Fellowship. A.D.F. acknowledges support from the NSF Graduate Research Fellowship Program. M.M., D.P. and L.W. acknowledge funding from the NHFP Sagan Fellowship Program. P.E.C. is funded by the Austrian Science Fund (FWF) Erwin Schroedinger Fellowship program J4595-N. K.L.C. acknowledges funding from STFC, under project number ST/V000861/1. L.D. acknowledges funding from the KU Leuven Interdisciplinary Grant (IDN/19/028), the European Union H2020-MSCA-ITN-2019 under grant no. 860470 (CHAMELEON) and the FWO research grant G086217N. O.V. acknowledges funding from the ANR project ‘EXACT’ (ANR-21-CE49-0008-01) and from the Centre National d’Études Spatiales (CNES). L.T. and B.C. acknowledge access to the HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR-10-EQPX-29-01) of the programme Investissements d’Avenir supervised by the Agence Nationale pour la Recherche.

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Taylor J. Bell

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Space Research Institute, Austrian Academy of Sciences, Graz, Austria

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Max Planck Institute for Astronomy, Heidelberg, Germany

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Louis-Philippe Coulombe, Björn Benneke, Caroline Piaulet & Jake Taylor

Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, Gif-sur-Yvette, France

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Space Science Institute, Boulder, CO, USA

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Center for Space and Habitability, University of Bern, Bern, Switzerland

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INAF- Palermo Astronomical Observatory, Piazza del Parlamento, Palermo, Italy

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Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden

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Emily Rauscher, Ryan C. Challener & Isaac Malsky

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Olivia Venot

School of Physics, University of Bristol, Bristol, UK

Hannah R. Wakeford

Indian Institute of Technology, Indore, India

Keshav Aggarwal

Centre for Exoplanets and Habitability, University of Warwick, Coventry, UK

Eva-Maria Ahrer

Department of Physics, University of Warwick, Coventry, UK

Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands

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Centro de Astrofisica y Tecnologias Afines (CATA), Santiago, Chile

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Ian J. M. Crossfield

Institute of Astronomy, Department of Physics and Astronomy, KU Leuven, Leuven, Belgium

Leen Decin & Aaron D. Schneider

Space Telescope Science Institute, Baltimore, MD, USA

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Department of Astrophysical and Planetary Sciences, University of Colorado Boulder, Boulder, CO, USA

Adina D. Feinstein

School of Physics, Trinity College Dublin, Dublin, Ireland

Neale P. Gibson

Planetary Sciences Group, Department of Physics and Florida Space Institute, University of Central Florida, Orlando, FL, USA

Joseph Harrington

Astrophysics Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

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Institute of Planetary Research—Extrasolar Planets And Atmospheres, German Aerospace Center (DLR), Berlin, Germany

Nicolas Iro

Department of Astronomy, University of Maryland, College Park, MD, USA

Eliza M.-R. Kempton, Thaddeus D. Komacek & Matthew C. Nixon

European Space Agency, Space Telescope Science Institute, Baltimore, MD, USA

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Jessica Krick

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Département d’Astronomie, Université de Genève, Sauverny, Switzerland

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Department of Mathematics and Statistics, University of Exeter, Exeter, UK

Neil T. Lewis

Department of Physics, Utah Valley University, Orem, UT, USA

Joshua D. Lothringer

Department of Physics, University of Rome “Tor Vergata”, Rome, Italy

Luigi Mancini

INAF - Turin Astrophysical Observatory, Turin, Italy

Steward Observatory, University of Arizona, Tucson, AZ, USA

Megan Mansfield

Department of Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

Nathan J. Mayne

School of Information and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia

Thomas M. Evans-Soma

Universitäts-Sternwarte, Ludwig-Maximilians-Universität München, Munich, Germany

Karan Molaverdikhani

Exzellenzcluster Origins, Garching, Germany

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Benjamin V. Rackham

Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA

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School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

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Department of Earth and Planetary Sciences, University of California, Santa Cruz, Santa Cruz, CA, USA

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Contributions

All authors played an appreciable role in one or more of the following: development of the original proposal, management of the project, definition of the target list and observation plan, analysis of the data, theoretical modelling, and preparation of this paper. Some specific contributions are listed as follows. N.M.B., J.L.B. and K.B.S. provided overall programme leadership and management. L.K. and N.C. coordinated the MIRI working group. L.K., V.P., K.B.S., D.K.S., E.M.-R.K., O.V. and P.E.C. made substantial contributions to the design of the programme and the observing proposal. K.B.S. generated the observing plan with input from the team. A.D., P.-O.L., R.C.C., A.L.C., G.M. and M.M. led or co-led working groups and/or contributed to important strategic planning efforts like the design and implementation of the pre-launch data challenges. P.E.C., D.K.S., R.C.C., P.-O.L. and J.B. generated simulated data for pre-launch testing of methods. L.K., T.J.B., M.T.R., N.C., V.P., A.A.A.P. and J.I.M. contributed substantially to the writing of this paper. T.J.B., N.C., M.Z. and E.D. contributed to the development of data analysis pipelines and/or provided the data analysis products used in this analysis that is, reduced the data, modelled the light curves, and/or produced the planetary spectrum. A.A.A.P. coordinated the atmospheric retrieval analysis with contributions from J.K.B., J.B., L.-P.C., M.Z., and K.L.C. M.T.R. coordinated the GCM results and interpretation with contributions from X.T., L.T., L.C., J.M.M. and I.M. T.J.B., N.C., P.E.C., J.B., L.-P.C. and M.H. generated figures for this paper. M.C.N., X.Z., B.V.R., J.K., M.L.-M., B.C., S.L.C. and R.H. provided substantial feedback to the paper, and G.M. and K.L.C. coordinated comments from all authors.

Corresponding author

Correspondence to Taylor J. Bell .

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Extended data

Extended data fig. 1 the underestimation of uncertainties as a function of spectral binning for the l168-9b commissioning observations..

a , The observed L168-9b transmission spectrum with 1 σ error bars for spectrally unbinned data (grey circles), 0.15 μ m bins (black squares), 0.5 μ m bins (large red circles), and a 5-12 μ m broadband bin (horizontal blue shaded region). The spectrum for wavelength pairs is not shown to avoid excessive clutter. b , The median of the transit depth uncertainties are shown with blue squares, while the observed scatter in the transmission spectrum is shown with orange circles. For unbinned data, the transmission spectrum shows about 2.5 × the scatter predicted by the fits to the individual light curves. Binning pairs of wavelengths reduces the level of underestimation of the scatter in the transmission spectrum, but considerable excess noise remains. Coarser binning schemes like the constant 0.15 μ m bins used in the MIRI time-series observation commissioning paper 29 or the 0.5 μ m bins we use in this work further reduce the level of uncertainty underestimation.

Extended Data Fig. 2 A model-independent demonstration of the initial changes in flux for the WASP-43b observations.

a , The first 120 minutes of three of our spectroscopically binned light curves of WASP-43b (with 1 σ uncertainties) showing the initial settling behaviour as a function of wavelength. A teal dashed line shows the amplitude of a -0.25% change in flux compared to the values around 120 minutes, and a magenta dotted line shows a +0.25% change. b , A summary of the ramp amplitudes, signs, and timescales for each of our wavelength bins (with 1 σ uncertainties). The teal and magenta horizontal lines are the same as those in panel a to aid in translating between the two figures. At short wavelengths, the flux sharply drops by about 0.5% within the first 30 minutes and then largely settles but does continue to decrease with time. With increasing wavelength, the strength of this initial ramp decreases and eventually changes sign, becoming an upwards ramp. Within the ‘shadowed region’ (marked in red), the light curves show a very strong upwards ramp that takes much longer (greater than about 60 minutes) to appreciably decay. It is important to note that the data in this figure also includes a small amount of astrophysical phase variations which should result in a small increase in flux of less than 0.05% per hour.

Extended Data Fig. 3 Retrieved spectra from the six retrievals.

a , Median retrieved nightside spectra for the HyDRA (dark blue line), NEMESIS (dash-dotted gold line), and PyratBay (dashed magenta line) and their 1 σ contour. The regions of higher water opacity are indicated by the purple shading at the top of the panel, with the observed rise in flux at 6.3 μ m being caused by a drop in opacity. b , c , and d , Same as panel a for the evening terminator, dayside, and morning terminator respectively. e , f , g , and h , Same as panels a, b, c, and d, for the SCARLET (dashed red line), PLATON (blue line), and ARCiS (dash-dotted green line) retrievals.

Extended Data Fig. 4 Chemically-consistent atmospheric retrievals.

Same as Figure 4 but for retrievals assuming thermochemical-equilibrium abundances consistent with the pressure-temperature profiles. a , 1 σ credible interval contours of the temperature profiles. The black curves show the predicted temperature profile from a 2D radiative-transport model46. The vertical bars show the range of pressures probed by the observations. b and c , probability posterior distributions for H 2 O and CH 4 abundances, respectively. The shaded area for each curve denotes the 1 σ credible interval of each posterior. The green and blue bars denote the abundances predicted by equilibrium and disequilibrium-chemistry models with solar abundances, respectively, at the pressures probed by the observations. Compared to the free-chemistry retrievals, the thermochemical-equilibrium retrievals on the nightside spectra produced worse fits, this is driven particularly by the higher amount of methane expected under equilibrium chemistry.

Extended Data Fig. 5 Retrieval contribution functions.

Contribution functions integrated over the data point spectral bins, at each phase ( a-d ), and for each retrieval framework. These curves show the range of pressures probed by the observation according to the atmospheric models. The enhanced opacity from the water band around 7-9 μ m makes these wavelengths probe lower pressures and hence colder temperatures, whereas the rest of the observing window probes higher pressures and higher temperatures.

Extended Data Fig. 6 PyratBay clouds exploration.

a , Cloud species that condense in the temperature regime expected for the WASP-43b nightside. Dashed lines represent vapour pressure curves 162 for each species assuming solar composition, while the coloured ranges denote the corresponding extent of the vapour pressure curves assuming 100 × sub- and super-solar atmospheric composition. The extent of the retrieved nightside contribution functions is shown in grey, and the extent of the retrieved temperature uncertainties is shown in light purple. The intersection between the contribution function and temperature ranges indicates the pressures at which we could observe cloud condensation and potentially detect their spectral features, if present in the observations. b , Panels display the retrieved posterior density plots for the explored cloud parameters of the TSC model (cloud number density, q*; effective particle size, r eff ; and the standard deviation of the log-normal distribution, \({\sigma }_{\log }\) ) for the MnS clouds. The black vertical line denotes the parameter’s median value, while the extent of the purple region denotes the 1 σ uncertainties, both given at the top left corner of the panel. Similar, fully non-constrained posteriors are retrieved for other explored cloud species, MgSiO 3 , ZnS, and KCl, suggesting the lack of observable spectral characteristics from clouds in the observed data.

Extended Data Fig. 7 A comparison of the retrieved temperature-pressure profiles to the GCM simulations.

Each of a-d shows the temperature profile retrieved by HyDRA, compared to the GCM simulations highlighted in Figure 3 and listed in Extended Data Table 1. The GCM temperature profiles are calculated at phases 0.0, 0.25, 0.5, and 0.75 by averaging over the visible hemisphere by viewing angle, to produce a one-dimensional profile that is comparable to the retrieved profile. The GCM simulations are generally warmer on the nightside than the retrieved temperatures; cloudy simulations emit from lower pressures and so match the observed lower brightness temperatures better (see the contribution functions in Extended Data Fig. 5).

Supplementary information

Supplementary information.

Supplementary Table 1 and Figs. 1 and 2.

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Bell, T.J., Crouzet, N., Cubillos, P.E. et al. Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b. Nat Astron (2024). https://doi.org/10.1038/s41550-024-02230-x

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Received : 01 November 2023

Accepted : 22 February 2024

Published : 30 April 2024

DOI : https://doi.org/10.1038/s41550-024-02230-x

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