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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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How To Write the Findings Section of a Research Paper

Posted by Rene Tetzner | Sep 2, 2021 | Paper Writing Advice | 0 |

How To Write the Findings Section of a Research Paper Each research project is unique, so it is natural for one researcher to make use of somewhat different strategies than another when it comes to designing and writing the section of a research paper dedicated to findings. The academic or scientific discipline of the research, the field of specialisation, the particular author or authors, the targeted journal or other publisher and the editor making the decisions about publication can all have a significant impact. The practical steps outlined below can be effectively applied to writing about the findings of most advanced research, however, and will prove especially helpful for early-career scholars who are preparing a research paper for a first publication.

Step 1 : Consult the guidelines or instructions that the targeted journal (or other publisher) provides for authors and read research papers it has already published, particularly ones similar in topic, methods or results to your own. The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches. Watch particularly for length limitations and restrictions on content. Interpretation, for instance, is usually reserved for a later discussion section, though not always – qualitative research papers often combine findings and interpretation. Background information and descriptions of methods, on the other hand, almost always appear in earlier sections of a research paper. In most cases it is appropriate in a findings section to offer basic comparisons between the results of your study and those of other studies, but knowing exactly what the journal wants in the report of research findings is essential. Learning as much as you can about the journal’s aims and scope as well as the interests of its readers is invaluable as well.

Step 2 : Reflect at some length on your research results in relation to the journal’s requirements while planning the findings section of your paper. Choose for particular focus experimental results and other research discoveries that are particularly relevant to your research questions and objectives, and include them even if they are unexpected or do not support your ideas and hypotheses. Streamline and clarify your report, especially if it is long and complex, by using subheadings that will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Consider appendices for raw data that might interest specialists but prove too long or distracting for other readers. The opening paragraph of a findings section often restates research questions or aims to refocus the reader’s attention, and it is always wise to summarise key findings at the end of the section, providing a smooth intellectual transition to the interpretation and discussion that follows in most research papers. There are many effective ways in which to organise research findings. The structure of your findings section might be determined by your research questions and hypotheses or match the arrangement of your methods section. A chronological order or hierarchy of importance or meaningful grouping of main themes or categories might prove effective. It may be best to present all the relevant findings and then explain them and your analysis of them, or explaining the results of each trial or test immediately after reporting it may render the material clearer and more comprehensible for your readers. Keep your audience, your most important evidence and your research goals in mind.

Step 3 : Design effective visual presentations of your research results to enhance the textual report of your findings. Tables of various styles and figures of all kinds such as graphs, maps and photos are used in reporting research findings, but do check the journal guidelines for instructions on the number of visual aids allowed, any required design elements and the preferred formats for numbering, labelling and placement in the manuscript. As a general rule, tables and figures should be numbered according to first mention in the main text of the paper, and each one should be clearly introduced and explained at least briefly in that text so that readers know what is presented and what they are expected to see in a particular visual element. Tables and figures should also be self-explanatory, however, so their design should include all definitions and other information necessary for a reader to understand the findings you intend to show without returning to your text. If you construct your tables and figures before drafting your findings section, they can serve as focal points to help you tell a clear and informative story about your findings and avoid unnecessary repetition. Some authors will even work on tables and figures before organising the findings section (Step 2), which can be an extremely effective approach, but it is important to remember that the textual report of findings remains primary. Visual aids can clarify and enrich the text, but they cannot take its place.

Step 4 : Write your findings section in a factual and objective manner. The goal is to communicate information – in some cases a great deal of complex information – as clearly, accurately and precisely as possible, so well-constructed sentences that maintain a simple structure will be far more effective than convoluted phrasing and expressions. The active voice is often recommended by publishers and the authors of writing manuals, and the past tense is appropriate because the research has already been done. Make sure your grammar, spelling and punctuation are correct and effective so that you are conveying the meaning you intend. Statements that are vague, imprecise or ambiguous will often confuse and mislead readers, and a verbose style will add little more than padding while wasting valuable words that might be put to far better use in clear and logical explanations. Some specialised terminology may be required when reporting findings, but anything potentially unclear or confusing that has not already been defined earlier in the paper should be clarified for readers, and the same principle applies to unusual or nonstandard abbreviations. Your readers will want to understand what you are reporting about your results, not waste time looking up terms simply to understand what you are saying. A logical approach to organising your findings section (Step 2) will help you tell a logical story about your research results as you explain, highlight, offer analysis and summarise the information necessary for readers to understand the discussion section that follows.

Step 5 : Review the draft of your findings section and edit and revise until it reports your key findings exactly as you would have them presented to your readers. Check for accuracy and consistency in data across the section as a whole and all its visual elements. Read your prose aloud to catch language errors, awkward phrases and abrupt transitions. Ensure that the order in which you have presented results is the best order for focussing readers on your research objectives and preparing them for the interpretations, speculations, recommendations and other elements of the discussion that you are planning. This will involve looking back over the paper’s introductory and background material as well as anticipating the discussion and conclusion sections, and this is precisely the right point in the process for reviewing and reflecting. Your research results have taken considerable time to obtain and analyse, so a little more time to stand back and take in the wider view from the research door you have opened is a wise investment. The opinions of any additional readers you can recruit, whether they are professional mentors and colleagues or family and friends, will often prove invaluable as well.

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How To Write the Findings Section of a Research Paper These five steps will help you write a clear & interesting findings section for a research paper

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From Data to Discovery: The Findings Section of a Research Paper

Discover the role of the findings section of a research paper here. Explore strategies and techniques to maximize your understanding.

Are you curious about the Findings section of a research paper? Did you know that this is a part where all the juicy results and discoveries are laid out for the world to see? Undoubtedly, the findings section of a research paper plays a critical role in presenting and interpreting the collected data. It serves as a comprehensive account of the study’s results and their implications.

Well, look no further because we’ve got you covered! In this article, we’re diving into the ins and outs of presenting and interpreting data in the findings section. We’ll be sharing tips and tricks on how to effectively present your findings, whether it’s through tables, graphs, or good old descriptive statistics.

Overview of the Findings Section of a Research Paper

The findings section of a research paper presents the results and outcomes of the study or investigation. It is a crucial part of the research paper where researchers interpret and analyze the data collected and draw conclusions based on their findings. This section aims to answer the research questions or hypotheses formulated earlier in the paper and provide evidence to support or refute them.

In the findings section, researchers typically present the data clearly and organized. They may use tables, graphs, charts, or other visual aids to illustrate the patterns, trends, or relationships observed in the data. The findings should be presented objectively, without any bias or personal opinions, and should be accompanied by appropriate statistical analyses or methods to ensure the validity and reliability of the results.

Organizing the Findings Section

The findings section of the research paper organizes and presents the results obtained from the study in a clear and logical manner. Here is a suggested structure for organizing the Findings section:

Introduction to the Findings

Start the section by providing a brief overview of the research objectives and the methodology employed. Recapitulate the research questions or hypotheses addressed in the study.

To learn more about methodology, read this article .

Descriptive Statistics and Data Presentation

Present the collected data using appropriate descriptive statistics. This may involve using tables, graphs, charts, or other visual representations to convey the information effectively. Remember: we can easily help you with that.

Data Analysis and Interpretation

Perform a thorough analysis of the data collected and describe the key findings. Present the results of statistical analyses or any other relevant methods used to analyze the data. 

Discussion of Findings

Analyze and interpret the findings in the context of existing literature or theoretical frameworks . Discuss any patterns, trends, or relationships observed in the data. Compare and contrast the results with prior studies, highlighting similarities and differences. 

Limitations and Constraints

Acknowledge and discuss any limitations or constraints that may have influenced the findings. This could include issues such as sample size, data collection methods, or potential biases. 

Summarize the main findings of the study and emphasize their significance. Revisit the research questions or hypotheses and discuss whether they have been supported or refuted by the findings.

Presenting Data in the Findings Section

There are several ways to present data in the findings section of a research paper. Here are some common methods:

• Tables : Tables are commonly used to present organized and structured data. They are particularly useful when presenting numerical data with multiple variables or categories. Tables allow readers to easily compare and interpret the information presented. Learn how to cite tables in research papers here .
• Graphs and Charts: Graphs and charts are effective visual tools for presenting data, especially when illustrating trends, patterns, or relationships. Common types include bar graphs, line graphs, scatter plots, pie charts, and histograms. Graphs and charts provide a visual representation of the data, making it easier for readers to comprehend and interpret.
• Figures and Images: Figures and images can be used to present data that requires visual representation, such as maps, diagrams, or experimental setups. They can enhance the understanding of complex data or provide visual evidence to support the research findings.
• Descriptive Statistics: Descriptive statistics provide summary measures of central tendency (e.g., mean, median, mode) and dispersion (e.g., standard deviation, range) for numerical data. These statistics can be included in the text or presented in tables or graphs to provide a concise summary of the data distribution.

How to Effectively Interpret Results

Interpreting the results is a crucial aspect of the findings section in a research paper. It involves analyzing the data collected and drawing meaningful conclusions based on the findings. Following are the guidelines on how to effectively interpret the results.

Step 1 – Begin with a Recap

Start by restating the research questions or hypotheses to provide context for the interpretation. Remind readers of the specific objectives of the study to help them understand the relevance of the findings.

Step 2 – Relate Findings to Research Questions

Clearly articulate how the results address the research questions or hypotheses. Discuss each finding in relation to the original objectives and explain how it contributes to answering the research questions or supporting/refuting the hypotheses.

Step 3 – Compare with Existing Literature

Compare and contrast the findings with previous studies or existing literature. Highlight similarities, differences, or discrepancies between your results and those of other researchers. Discuss any consistencies or contradictions and provide possible explanations for the observed variations.

Step 4 – Consider Limitations and Alternative Explanations

Acknowledge the limitations of the study and discuss how they may have influenced the results. Explore alternative explanations or factors that could potentially account for the findings. Evaluate the robustness of the results in light of the limitations and alternative interpretations.

Step 5 – Discuss Implications and Significance

Highlight any potential applications or areas where further research is needed based on the outcomes of the study.

Step 6 – Address Inconsistencies and Contradictions

If there are any inconsistencies or contradictions in the findings, address them directly. Discuss possible reasons for the discrepancies and consider their implications for the overall interpretation. Be transparent about any uncertainties or unresolved issues.

Step 7 – Be Objective and Data-Driven

Present the interpretation objectively, based on the evidence and data collected. Avoid personal biases or subjective opinions. Use logical reasoning and sound arguments to support your interpretations.

Reporting Statistical Significance

When reporting statistical significance in the findings section of a research paper, it is important to accurately convey the results of statistical analyses and their implications. Here are some guidelines on how to report statistical significance effectively:

• Clearly State the Statistical Test: Begin by clearly stating the specific statistical test or analysis used to determine statistical significance. For example, you might mention that a t-test, chi-square test, ANOVA, correlation analysis, or regression analysis was employed.
• Report the Test Statistic: Provide the value of the test statistic obtained from the analysis. This could be the t-value, F-value, chi-square value, correlation coefficient, or any other relevant statistic depending on the test used.
• State the Degrees of Freedom: Indicate the degrees of freedom associated with the statistical test. Degrees of freedom represent the number of independent pieces of information available for estimating a statistic. For example, in a t-test, degrees of freedom would be mentioned as (df = n1 + n2 – 2) for an independent samples test or (df = N – 2) for a paired samples test.
• Report the p-value: The p-value indicates the probability of obtaining results as extreme or more extreme than the observed results, assuming the null hypothesis is true. Report the p-value associated with the statistical test. For example, p < 0.05 denotes statistical significance at the conventional level of α = 0.05.
• Provide the Conclusion: Based on the p-value obtained, state whether the results are statistically significant or not. If the p-value is less than the predetermined threshold (e.g., p < 0.05), state that the results are statistically significant. If the p-value is greater than the threshold, state that the results are not statistically significant.
• Discuss the Interpretation: After reporting statistical significance, discuss the practical or theoretical implications of the finding. Explain what the significant result means in the context of your research questions or hypotheses. Address the effect size and practical significance of the findings, if applicable.
• Consider Effect Size Measures: Along with statistical significance, it is often important to report effect size measures. Effect size quantifies the magnitude of the relationship or difference observed in the data. Common effect size measures include Cohen’s d, eta-squared, or Pearson’s r. Reporting effect size provides additional meaningful information about the strength of the observed effects.
• Be Accurate and Transparent: Ensure that the reported statistical significance and associated values are accurate. Avoid misinterpreting or misrepresenting the results. Be transparent about the statistical tests conducted, any assumptions made, and potential limitations or caveats that may impact the interpretation of the significant results.

Conclusion of the Findings Section

The conclusion of the findings section in a research paper serves as a summary and synthesis of the key findings and their implications. It is an opportunity to tie together the results, discuss their significance, and address the research objectives. Here are some guidelines on how to write the conclusion of the Findings section:

Summarize the Key Findings

Begin by summarizing the main findings of the study. Provide a concise overview of the significant results, patterns, or relationships that emerged from the data analysis. Highlight the most important findings that directly address the research questions or hypotheses.

Revisit the Research Objectives

Remind the reader of the research objectives stated at the beginning of the paper. Discuss how the findings contribute to achieving those objectives and whether they support or challenge the initial research questions or hypotheses.

Suggest Future Directions

Identify areas for further research or future directions based on the findings. Discuss any unanswered questions, unresolved issues, or new avenues of inquiry that emerged during the study. Propose potential research opportunities that can build upon the current findings.

The Best Scientific Figures to Represent Your Findings 

Have you heard of any tool that helps you represent your findings through visuals like graphs, pie charts, and infographics? Well, if you haven’t, then here’s the tool you need to explore – Mind the Graph . It’s the tool that has the best scientific figures to represent your findings. Go, try it now, and make your research findings stand out!

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Home » Research Summary – Structure, Examples and Writing Guide

Research Summary – Structure, Examples and Writing Guide

Table of Contents

Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

• Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
• Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
• Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
• Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
• Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
• References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

• Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
• Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
• Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
• Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
• Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
• Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
• Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
• Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
• Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

• Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
• Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
• Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

• Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
• Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
• Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
• Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

• Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
• Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
• Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
• Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
• Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
• Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
• Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
• Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

• Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
• Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
• Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
• Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
• Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

• Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
• Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
• Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
• Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
• Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
• Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

• Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
• Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
• Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
• Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
• Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
• Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 8. The Discussion
• 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
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• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
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• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
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• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
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• Further Readings
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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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Communicating Research Findings

• First Online: 03 January 2022

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Research is a scholarship activity and a collective endeavor, and as such, its finding should be disseminated. Research findings, often called research outputs, can be disseminated in many forms including peer-reviewed journal articles (e.g., original research, case reports, and review articles) and conference presentations (oral and poster presentations). There are many other options, such as book chapters, educational materials, reports of teaching practices, curriculum description, videos, media (newspapers/radio/television), and websites. Irrespective of the approach that is chosen as the mode of communicating, all modes of communication entail some basic organizational aspects of dissemination processes that are common. These are to define research project objectives, map potential target audience(s), relay target messages, define mode of communication/engagement, and create a dissemination plan.

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Davidson, R., Makanjee, C. (2021). Communicating Research Findings. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_7

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired samples correlation, paired samples test.

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

How do I report quantitative findings?

The best way to present your quantitative findings is to structure them around the  research hypothesis  or  research questions  you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the  qualitative research  results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your  research question  or  hypothesis  should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the  discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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When writing your dissertation, an abstract serves as a deal maker or breaker. It can either motivate your readers to continue reading or discourage them.

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In This Article Expand or collapse the "in this article" section Reporting Research Findings

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• Reference Resources
• History and Trends
• Guidance on Reporting Quantitative Reports, Syntheses, and Meta-analyses
• Linguistic Analyses of Written Research Results
• Writing Review Articles
• Writing Qualitative Research
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Reporting Research Findings by James T. Austin LAST REVIEWED: 27 May 2020 LAST MODIFIED: 24 June 2020 DOI: 10.1093/obo/9780199846740-0032

Not all research culminates in publication. This updated article surveys themes in reporting research findings for scholars and students. As context, consider that investigations of organizational phenomena require a series of choices that are cast here as craft. Choices span primary, secondary, and synthesis designs across qualitative and quantitative traditions. Primary research is the traditional design, measurement, and analysis of collected data, while secondary research involves reanalysis of existing data sets (obtained from peers or repositories), and research synthesis involves narrative or quantitative aggregation of studies. This distinction also holds for the qualitative mode. Reporting research findings is important for dissemination and for synthesis and evidence-based management (EBM). Primarily, the importance lies in dissemination across conferences, journals, books, and increasingly digital media. Understanding and replication by outside scholars depend on complete and accurate reporting; this centrality to the research craft commands a learning-development focus. Within a communications paradigm, individuals or teams create or send a persuasive message and the reader or listener receives (or may choose not to receive) the message. Persuasion is targeted via rhetoric across writing and graphics. Although oral and written forms of dissemination dominate, data repositories are emerging. Two additional reasons for importance pertain to the accumulation of knowledge. One is research synthesis. Structuring knowledge through synthesis uses the results of individual studies as data, and the audience is scientists. Narrative and quantitative reviews depend on the completeness and accuracy of reported findings. A related source of importance pertains to evidence-based management at the interface of research and practice—translation of research findings into practices and bundles of practices that can be used by managers. Given that practicing managers appear to rely on obsolete knowledge (aka “fads, fashions, and folderol” as used by Dunnette), proponents of evidence-based management advocate that firms consider the adoption of evidence-based medicine (EBM). Communicating clearly and establishing a context of implementation to assist practitioners are essential for EBM (in parallel to research synthesis, for an audience of practitioners). This article organizes a range of resources on writing and reviewing articles across the taxonomy above. For completeness, this article includes citations for scientific graphics (tables, charts, figures, etc.) organized around conceptualizations of graphics and related guidance, research on perception of scientific graphics, and recent developments in computing technology. Especially relevant are software routines for interactive graphics based on “grammars.” While this article draws on work in management studies (organizational behavior and human resources), it necessarily searches beyond traditional boundaries for relevant insights.

There are sporadic specialized sources on reporting of research findings. On scholarly writing, Cummings and Frost 1995 is an influential analysis of the publishing system in the organizational sciences. Abelson 1995 defines rhetoric as styles of writing up results in psychology. Research synthesis writing is addressed comprehensively in Cooper, et al. 2009 (cited under Guidance on Reporting Quantitative Reports, Syntheses, and Meta-analyses ). There are two major standards available for research synthesis: Meta-Analysis Reporting Standards (MARS) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ).For graphics and quantitative studies, Tufte 2001 and Tukey 1977 are classics for guidance and perspective; others, including Cleveland 1985 , Kosslyn 2006 , Wainer 2000 (cited under History and Trends ), and Wilkinson 2005 , provide unique value. The work on maps in Börner 2015 is aptly named Atlas of Knowledge , while Grant 2019 provides a concise introduction to data visualization with a section on interactive graphics (a related instance is the class of data explorers used for large data sets as the Programme for International Student Assessment [PISA] and the National Assessment of Educational Progress [NAEP]—both large-scale testing programs). Sternberg and Sternberg 2010 is typical guidance offered to students and is not the only such resource. Many of these texts can be mined for dimensions to code the content and results of published organizational behavior and human resources research to facilitate critique A trio of books by Katy Börner ( Börner 2010 , Börner 2015 ) and colleagues ( Börner and Polley 2014 ) represents the newest in knowledge mapping. In addition, a rapidly emerging topic across science is the reproducibility and replicability of results—the consensus review published in 2019 by a committee of the National Academies of Science, Medicine, and Engineering provides an excellent overview.

Abelson, Robert P. Statistics as Principled Argument . Mahwah, NJ: Lawrence Erlbaum, 1995.

Describes magnitude-articulation-generality-interestingness-credibility (MAGIC) criteria to organize rhetoric in presenting research findings. Accepting statistics as an organizer of arguments using quantitative evidence allows identification of styles. Brash and stuffy are end points on a liberal-conservative style dimension. Management students and scholars could learn MAGIC for reporting quantitative findings; qualitative researchers might consider translation.

Börner, Katy. Atlas of Science: Visualizing What We Know . Cambridge, MA: Massachusetts Institute of Technology Press, 2010.

Books by Katy Börner show the potential and the practice of science and knowledge mapping. Atlas of Science (2010) presents three themes: power of maps (switching from geographic cartography to research-collaboration mapping), reference systems, and forecasts, as well as numerous examples.

Börner, Katy. Atlas of Knowledge: Anyone Can Map . Cambridge, MA: Massachusetts Institute of Technology Press, 2015.

Börner deftly gives readers principles for visualizing knowledge with more than forty large-scale and over a hundred small-scale color maps. Drives home the point that data literacy is as important as language literacy. She introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. Together with Börner 2010 and Börner and Polley 2014 , this trio provides levels of analysis from frameworks to workflow that support improved visualizations of science, knowledge, and interdisciplinary collaboration.

Börner, Katy, and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data . Cambridge, MA: Massachusetts Institute of Technology Press, 2014.

Along with Börner 2010 and Börner 2015 , a practical book by Börner and Polley based on the Information Visualization MOOC includes seven chapters—from a visualization framework through “when, where, what, and with whom” and dynamic visualizations—and concludes with chapters on case studies and discussion/outlook.

Cleveland, William S. The Elements of Graphing Data . Monterey, CA: Wadsworth Advanced Books and Software, 1985.

Cognitive science and statistical principles help dissect and improve graphics (a predecessor book from 1983 and articles that searched prestigious journals for common graphic errors are also useful). Based on extensive experience with AT&T data, the author distills and emphasizes procedural knowledge for constructing graphic displays.

Cummings, Larry L., and Peter J. Frost, eds. Publishing in the Organizational Sciences . 2d ed. Foundations of Organizational Science. Thousand Oaks, CA: SAGE, 1995.

This classic covers most aspects of publishing in organizational behavior and human resources (absent are emergent digital-technological issues). Organized into sections on perspectives on and realities of publishing, which are insightful for scholar and student alike. Benjamin Schneider’s ten propositions on “getting research published” end with practicing the skill of writing. This edition inaugurated the Foundations of Organizational Science series, and the 1985 edition is also useful.

Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis . Oakland, CA: Analytics, 2009.

Suggests that in a data-dense world, the human brain—and hence, visualization—is key to avoiding overload. Three sections, namely “Building Core Skills for Visual Analysis” and “Honing Skills,” each with six chapters plus a “Further Thoughts and Hopes” with eight promising trends, cover much ground. Based on quantitative preferences, the most substantive portion is contained in Part 2. The book ends with an excerpt from the poetry of T. S. Eliot.

Grant, Robert. Data Visualization: Charts, Maps and Interactive Graphics . Boca Raton, FL: CRC Press, 2019.

This author provides a vast range of examples of data visualization, mostly open source and with code available on a website . It provides a good mix of detail with sharing of tacit knowledge.

Kosslyn, Stephen M. Graph Design for the Eye and Mind . New York: Oxford University Press, 2006.

DOI: 10.1093/acprof:oso/9780195311846.001.0001

Based on sound cognitive science and ample research by the author, presents and elaborates eight principles of effective graph construction (summarized in pp. 5–20). Analyzes prominent guidance on graphics, Edward R. Tufte for example, and suggests flaws. that could lead to productive research.

Sternberg, Robert J., and Karin Sternberg The Psychologist’s Companion: A Guide to Writing Scientific Papers for Students and Researchers . 5th ed. Cambridge, UK: Cambridge University Press, 2010.

DOI: 10.1017/CBO9780511762024

Aligned to American Psychological Association (APA) style as a prototype of good practice in publishing; the author is a productive researcher and APA journal editor; thus tacit knowledge in this edition is well grounded and expressed. Represents a class of books on research communication. Some translation required to organizational behavior and human resources context. Comparable to Cooper 2010 (cited under Writing Review Articles ). Next edition will need to conform to the seventh edition of the Publication Manual of the American Psychological Association .

Tufte, Edward R. The Visual Display of Quantitative Information . 2d ed. Cheshire, CT: Graphics Press, 2001.

Revises a classic 1983 text in analytic design (Tufte’s preferred term); presents and expands on five core principles and coins numerous terms (“chartjunk” as well as “sparkline” and “data-ink ratios” are personal favorites). Critiqued for its advice, however, by other researchers on graphics ( Kosslyn 2006 ).

Tukey, John W. Exploratory Data Analysis . Reading, MA: Addison-Wesley, 1977.

A classic presenting Tukey’s data detective work rooted in his 1962 “The Future of Data Analysis” exposition ( Annals of Mathematical Statistics 33.1: 1–67). Premise is that exploratory data analysis (EDA) deserves status with confirmatory. Loaded with philosophy of EDA and tools—the stem leaf, box plot, and “five-number summary.” Graphic display and analysis are covered in the service of learning about data. A part of research craft to be honed post-schooling.

Wilkinson, Leland L. The Grammar of Graphics . 2d ed. New York: Springer-Verlag, 2005.

Cited by many, this conceptualization rooted in the work of Jacques Bertin extends work done with the Task Force on Statistical Reporting in 1999. Within an object-oriented design approach, the grammar consists of the rules and elements of graphics, for example, geoms, scales, and coordinates. Framework has been useful for deriving tools, such as Wilkinson’s GPL, Wickham’s ggplot2, and others.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

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• Research findings

On this Page

• Introduction
• Objectives of Establishing Research Findings

Step by step presentation of research findings

Significant research findings, implications of statistically significant research findings, what causes insignificant research findings, relationship between research findings and suggestion for further research, common mistake by postgraduate students when suggesting area for further research, how to correctly suggest for further research, importance of research findings, research findings; definition; objectives; step by step procedure; significance & importance, 1.1 definition.

Research finding is the outcome gotten from data analysis and represents the factual link or association between or amongst the variables being interrogated by the researcher. It is any results arrived at and portrays the true position of how two or more variables relate with one another.

1.2 Objectives of Establishing Research Findings

The researcher ends his/her research assignment when he/she establishes the research findings. It is the end that justifies the means. So, the specific objectives of establishing the research findings of any type of study are;

• To establish the answers to the research questions already in the mind of the researcher.
• To assess/evaluate the extent to which the set specific objective(s) have been achieved by the study.
• To establish the level of significance of the predictors influence on the dependent variable
• To prove or disapprove the claim(s) or suppositions of the researcher.
• To have a basis of suggesting further research.
• To justify why it is necessary to utilize the research resources allocated for the research assignment.

In a nutshell, research findings are the totality of outcome. But NOT conclusions or recommendations drawn from it. So, research findings are characterized by reporting PURELY the results as they are. At this point or stage, no suggestion or recommendations by the researcher. Next point then is, how can you appropriately prepare Research Findings? The following are step by step procedure to be followed;

Research findings section in most of the times domiciled in chapter four of the research project or thesis in almost all research institution project or thesis formats. There are basically 5 steps that govern how to write good research finding section. These steps are as explained below;

Step 1: Review the specific objectives of your research project

The specific objectives set during proposal development is always the basis or the theoretical foundation of research findings. That is, all aspects of research findings are anchored on the specific objectives . So, the first step is to align the research findings with the specific objectives in twofold ways;

i). Number of Specific Objectives -If there are FOUR specific objectives, there will be FOUR research findings (outcomes), whether significant or insignificant.

ii). Aim of the specific objective -the research findings end goal is to achieve the specific objectives set. This is true even if the specific objectives will be achieved significantly or not significantly.

Step 2: Focus on the actual results

In this step, portray the main research findings and the minor research findings. Main research findings mean the findings approving the researcher’s proposition or theory if the independent variable shows statistically significant relationship with the dependent variable. Minor research findings are those which do not comply with the researcher’s proposition or theory. The student or researcher in question should report the two perspectives of the research findings.

On the same breath, the researcher should report other past studies with either similar research findings so as to appreciate that in research one cannot re-invent the wheel and equally those past research findings with contrary results should be reported but the researcher has to disclose why there exists a significant difference.  

Step 3: Visual presentations

Visual presentations involve use of tools such as tables, figures, maps, graphs or photos to enhance reporting. The design used should be as per the respective institution’s design or the sponsor’s preferred approach. However, there are some set standards to be adhered to such as APA style of reporting which have specific requirements. Therefore, if one is reporting to a Journal, each of them have their specific preferences and therefore the researcher need to stick to the set demands.

Note the Following;

Table of Contents

The researcher needs to note that when presenting or incorporating the tables and figures in the table of contents section in the preliminary pages of the thesis or project, Tables are ranked number Four Heading and Figures are in Fifth Heading in that order. This is expressed as below;

Heading one: is the chapter title of the project or thesis.

Sub-heading two: is the sub-heading such as introduction of a particular concept of the project or Thesis.

Sub-heading three: is sub-sub heading of sub heading two of the project or Thesis.

Sub-heading four: is for Tables found in the project or Thesis.

Sub-heading five: is for Figures in the project or Thesis.

This is well elaborated in the article about table of contents  where description on how to prepare table of content is explained in details.

Labeling of Visual tools

The researcher has to number the tables using capitalization option in MS-word program if the table has a specific title describing it. For example, a table which describes the population size will be referred to as population frame, and will be indicated as;  

Table1.1: Population Frame” and NOT “1.1 table 1.1: Population Frame.”

A table describing the list of teachers who teach Geography will be indicated as

“Table 1.1: “List of Geography teachers ” and NOT “table 1.1: List of Geography teachers”

i). The name table should start with capital letter “T”

ii). APA style requirement of labeling

The researcher needs to note that for APA format, the title of the table is always on the top as indicated below in Table 1.1

Similarly, figures should be labeled using capitalization option in MS-word program such that for example, a figure with a title such as population frame, should be indicated as “Figure 1.1: Population Frame” and NOT “ figure 1.1: Population Frame .”

A figure on list of teachers who teach Geography should be indicated as “Figure 1.1: List of Geography teachers ” and NOT “figure 1.1: List of Geography teachers”

i). The name Figure should start with capital letter “F”

The researcher needs to note that for APA format, the title of the figure is always below as indicated herein

Figures are labeled from below as indicated in Figure 1.1

Relationship between Numbering of Tables and Chapters of the Research project/thesis

The numbering of the tables in the research project or thesis is based in a certain order as follows

Whereby digit 1 before the decimal point (1.) represents the chapter of the research project/thesis where the table appears. Such that in this case, the table is in chapter one.

Whereas, the digit after the decimal point (.1) which is also a 1 represents the n th number of tables in a particular chapter. Such that in this case, the table is the first one in chapter one of the research project or thesis.

In summary, Table 1.2 below portrays the order in which the tables should be arranged and numbered in the project/thesis.

Step 4: Write Research findings section

This is the stage of actual write up pertaining the results gotten. In this step, the researcher needs to capture the true picture of the research findings. To achieve this objective, he/she need to carefully transfer the report as it is portrayed in the tables, figures and maps amongst other tools which the researcher has visually utilized. It will be a grievous mistake to distort the research findings especially when reasonable care is not applied. Also, the language used should be simple and well-structured considering the right gramma to be put in place. The researcher should bank on Active voice for writing research-finding chapters. For example, from the table;

One should not say;

“From Table 4.4, 23% of the respondents feel... (It is not from the Table 4.4 that the

respondents feel or reason in a certain manner.)

One should say;

Table 4.4 reveals that 40% of the respondents feel... or reason in a certain manner.

 Edit the work to clean issues of punctuation, and spelling.

Step 5: Specific Objective Confirmation

In this step, the researcher needs to counter check whether the specific objectives have been achieved by the research findings.  This is because, they are the reason as to why the researcher had to go to the field to collect the relevant data. So, satisfaction of those objective(s) will imply that the changes witnessed in the dependent variable is not due to chance. Hence the results will portray statistically significant results.

Step 6: Review draft of the research findings section

In this stage it entails re-visiting the information captured after data analysis is over with the intention of ensuring that it is accurate and correct for the end users. This is achieved by comparing the information domiciled in the visual tools and what is recorded under research findings/specific objectives.

The expectation of any researcher, be it a professional or a postgraduate student, is that the research findings portray statistically significant results/outcome. In this case, two questions arise;

a). What are significant research findings?

b). What causes or makes research findings to be statistically significant?

Statistically significant research findings are research outcome where by the dependent or outcome variable commonly referred to as dependent variable is explained by a factor or factors where by there exists strong evidence in the form of an observed difference that is too large to be explained solely by chance.

The answer to the question “b” is as follows; that the endeavors the researcher need to undertake to realize statistically significant results, and I would also advise you as a scholar or professional researcher to follow suit is that; 

• If a suitable sampling technique is used to establish the correct sample size. If the researcher chooses the appropriate sampling technique, the right sample size which truly represents the characteristics of the population will be identified and this avoids invalidity.
• If the appropriate method of data analysis is used.
• If sufficient data is collected from the whole population or from a sample size which is a true representative of the entire population. If sufficient/enough data is available. i.e., collected, then, chances are high that the research findings will portray statistically significant results.

Statistically significant research findings means that the researcher’s claim or proposition is true.

i). The changes noticed in the dependent variable are not due to chance only but the responsible predictor variable under consideration has caused a significant influence as stated in answer “b” above.

ii). The conceptualized idea/concept is logical/ plausible/practical/theoretical and is underpinned by a theory.

iii). The relational link between or amongst the study variables is logical in its natural phenomenon. E.g., a dog bite is logical as compared to a man bite.

iv). The null hypothesis has been rejected (failed to accept the null hypothesis) and the alternative hypothesis has been accepted (failed to reject the alternative hypothesis)

Ok, in research, we say “research findings were “ not statistically significant ”. But we do not say research findings were “insignificant”. This is for the sake of you as a scholar adopting the right practice in research.

The following are some of the reasons which make results not to be statistically significant

• When the concept is not logical/not plausible e.g., if the researcher is for instance investigating a case of a man biting a dog.
• When available data is not enough. That is, if the data collected was from a sample size that was not a true representation of the population.
• When the wrong data analysis method is used.

Does your Research Findings have any connection with your Suggestions or areas for Further Research?

The answer is YES .

In an ideal situation, the research findings gotten from any research activity will either fit well in the research questions or the specific objectives already set. What do I mean by this?

The research findings will satisfactorily answer the research questions and number two; the research findings will fulfill the set objectives as supposedly.

However, ideal situations like the one aforementioned are rare. The research findings may either answer all the research questions as supposedly, or may partially answer the research questions. Whichever way, you should note that suggestion for further research is pegged on the research findings. Such that the suggested areas should be in line with the thematic issues stated in the specific objectives/research questions of the study. So, it should be noted that the researcher should not introduce new areas for further research. Therefore, in a nutshell, the relationship between research findings and suggestion for further research is manifold as stated below;

• Both aspects of research findings and suggestions for further research are aligned on a common thematic issue and the researcher should not make a mistake of introducing a new area of study.
• Both should be specifically stated in a manner that there is no confusion or ambiguity. That is, the manner in which specific objectives are SMART, both research findings and suggestion for further research should be specific

Most students in research and even some times the professional researchers ill-treat the research findings and suggestion for further research sections in their project or thesis. Especially postgraduate students carelessly and casually suggest just anything of their interest to be the suggestion for further research. The following are some of the common research mistakes. They include;

1). Deviation from thematic aspects of research objectives. Sometimes the researcher suggests further research areas which are not in line with the specific objectives. This is not correct for no one can re-invent the wheel.

Look at this…….

Let us assume that in this case, the topic of the study is as indicated below, the research findings as further shown and lastly the researcher’s wrong suggestion for further research.

Topic: “Factors Affecting Financial Performance of Firms Listed at Shanghai Stock Exchange.”

Specific objectives:

The specific objectives were as stated below;

Shanghai Stock Exchange 50 (SSE 50).

at Shanghai Stock Exchange 50 (SSE 50)

Corresponding Research Findings

• Results for Objective-1; Liquidity positively influenced firm financial performance but it was not statistically significant.
• Results for Objective-2; Asset utilization positively influenced firm financial performance which was statistically significant.
• Results for Objective-3; Leverage negatively influenced firm financial performance and was statistically significant.
• Results for Objective-4; Firm size had no statistically significant influence on firm financial performance.

Researcher’s wrong Thematic Suggestion for Further Research was as follows;

1.)Results for Objective-1; Liquidity positively influenced firm financial performance but it was not statistically significant.

SUGGESTION-1; The researcher suggested that since the scope of the study was limited to the top 50 listed companies in Shanghai stock exchange, further study should be done to include Small and Medium Enterprises (SMEs).  

2). Results for Objective-2; Asset utilization positively influenced firm financial performance which was statistically significant.

SUGGESTION-2; the researcher suggested that since annual data is the only best source that s available, and that the fact the Chinese economy continues to develop, we will expect to see more new data sets available, therefore further research should be pegged on the new data in the future.

3). Results for Objective-3; Leverage negatively influenced firm financial performance and was statistically significant.

SUGGESTION 3: The researcher suggested that since this study selected only four factors to test influence on financial performance of firms listed on Shanghai stock exchange 50, further study using economic factors such as Gross Domestic Product (GDP), Exchange rate and Inflation rate is important.

4). Results for Objective-4; Results for Objective-4; Firm size had no statistically significant influence on firm financial performance.

SUGGESTION 4: The researcher suggested that in future, other researchers to include qualitative component in designing the research. This would have provided more comprehensive insight into the boards’ accountability to all firms listed on Shanghai stock exchange.50.

Suggestion for further research ought to originate from the research findings which the researcher has realized. Whether with significant or without significant results. But may be much emphasis can be done on the cases with no statistical significance. Therefore, suggestion for further research for this study should be as follows

First research finding - liquidity positively but insignificantly affected firms’ financial performance in this study.

Using the same example above on the topic “ Factors Affecting Financial Performance of Firms Listed at Shanghai Stock Exchange ” the following are the correct way of suggesting further research;  

Suggestion for further research:

Results for Objective-1; The aspect of liquidity did not show statistical significance influence to financial performance. Hence, there is need to carry further research to find out if a different methodology of measuring liquidity perspective is used would result to significant outcome. This is because may be the context in which liquidity is based on in the current study is not plausible or logical to the users. 

Results for Objective-2; Asset utilization positively influenced firm financial performance which was statistically significant. Since asset utilization is an aspect of physical fixed assets and how they are effectively utilized, the researcher can suggest further area of research by preferring to incorporate Asset Tangibility viewpoint to investigate the extent to which this factor can influence financial performance of the of firms listed at Shanghai Stock Exchange 50 (SSE 50). 

Results for Objective-3; Leverage negatively influenced firm financial performance and was statistically significant. A correct suggestion for further research can be that, since leverage is a variable with capital structure implications, further research on the influence of other capital structure perspectives such as long term or short-term influence on financial performance can be necessary for the firms listed at Shanghai Stock Exchange 50 (SSE 50).

Results for Objective-4; S ince firm size did not significantly affect financial performance of the firms in question, further interrogation is necessary to establish whether the variable has any other conceptual role it plays in the natural/physical phenomenon such as being a moderator on the link between the selected determinants of financial performance instead of it being classified as a PURE predictor of financial performance.

• It is the source of new knowledge to be added in the already existing body of knowledge.
• It is the basis where the suggestions for further research is anchored on. That is, if the research findings fail to provide answers research questions as supposedly, then there will be suggestions made for areas of further research.
• Research finding guide in assessing the extent to which a hypothesis is proved to be true or false
• It is the basis that either the null hypothesis is accepted or rejected
• Increases the researchers in depth understanding of the research problem .
• Portrays the level of significance of undertaking the research activity at hand
• It is the research findings which guide the users of research to provide solutions to the problem at hand.

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Key findings about Americans and data privacy

Many Americans have endless digital tools at their fingertips. And each device, site or app collects, analyzes and uses personal data. What does this mean for Americans now that so much of their day-to-day life leaves a digital footprint?

Pew Research Center has a long record of studying Americans’ views of privacy and their personal data, as well as their online habits. This study sought to understand how people think about each of these things – and what, if anything, they do to manage their privacy online. ( Read the full report .)

This survey was conducted among 5,101 U.S. adults from May 15 to 21, 2023. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Here are nine takeaways from a new Pew Research Center report exploring these issues.

Americans, especially Republicans, are growing more concerned about how the government uses the data it collects about them. About seven-in-ten U.S. adults (71%) say they are very or somewhat concerned about this, up from 64% in 2019. Concern has grown among Republicans and those who lean Republican but has held steady among Democrats and Democratic leaners.

Many Americans have little trust in companies to use AI responsibly. Among those who have heard of artificial intelligence (AI):

• 70% say they have little to no trust in companies to make responsible decisions about how they use AI in their products.
•   81% say the information companies collect will be used in ways that people are not comfortable with
• 80% say it will be used in ways that were not originally intended.

Still, 62% of those who have heard of AI say companies using it to analyze personal details could make life easier.

Many trust themselves to make the right decisions but are skeptical their actions matter. About eight-in-ten (78%) say they trust themselves to make the right decisions to protect their personal information.

But a majority (61%) are skeptical anything they do will make much difference. And only about one-in-five are confident that those with access to their personal information will treat it responsibly.

More than half of Americans (56%) say they always, almost always or often click “agree” without reading privacy policies. Another 22% say they do this sometimes and 18% rarely or never do this.

People are also largely skeptical that privacy policies do what they’re intended to do. About six-in-ten Americans (61%) think they’re ineffective at explaining how companies use people’s data.

About seven-in-ten Americans are overwhelmed by the number of passwords they have to remember. And nearly half (45%) report feeling anxious about whether their passwords are strong and secure.

Despite these concerns, only half of adults say they typically choose passwords that are more secure, even if they are harder to remember. A slightly smaller share (46%) opts for passwords that are easier to remember, even if they are less secure.

Some Americans have been targets of data breaches and hacking. In the past 12 months:

• Roughly a quarter of Americans (26%) say someone put fraudulent charges on their debit or credit card.
• A smaller share say they have had someone take over their email or social media account without their permission (11%).
• And 7% have had someone attempt to open a line of credit or apply for a loan using their name.

In total, 34% of Americans have experienced at least one of these issues in the past year. However, Black Americans are more likely than members of other racial and ethnic groups to have faced this.

Americans have little faith that social media executives will protect user privacy. Some 77% of Americans have little or no trust in leaders of social media companies to publicly admit mistakes and take responsibility for data misuse.

They are no more optimistic about the government reining them in: 71% have little to no trust that tech leaders will be held accountable for their missteps.

There is bipartisan support for more regulation to protect personal information. Some 78% of Democrats and 68% of Republicans think there should be more government regulation of what companies can do with customers’ personal information.

These findings are largely similar to our 2019 survey , which also showed strong support for increased regulation across parties.

About nine-in-ten Americans (89%) are concerned about social media sites knowing personal information about children. Most Americans are also concerned about advertisers using data about children’s online activities to target ads to them (85%) and online games tracking children on the internet (84%).

When it comes to who should be responsible for protecting kids’ online privacy, a vast majority (85%) says parents should bear a great deal of the responsibility. Still, roughly six-in-ten say the same about technology companies, and just under half believe the government should have a great deal of responsibility.

Note: Here are the questions used for this analysis , along with responses, and its methodology .

• Artificial Intelligence
• Online Privacy & Security
• Privacy Rights
• Social Media

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

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A systematic approach to searching: an efficient and complete method to develop literature searches

Associated data.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

INTRODUCTION

Librarians and information specialists are often involved in the process of preparing and completing systematic reviews (SRs), where one of their main tasks is to identify relevant references to include in the review [ 1 ]. Although several recommendations for the process of searching have been published [ 2 – 6 ], none describe the development of a systematic search strategy from start to finish.

Traditional methods of SR search strategy development and execution are highly time consuming, reportedly requiring up to 100 hours or more [ 7 , 8 ]. The authors wanted to develop systematic and exhaustive search strategies more efficiently, while preserving the high sensitivity that SR search strategies necessitate. In this article, we describe the method developed at Erasmus University Medical Center (MC) and demonstrate its use through an example search. The efficiency of the search method and outcome of 73 searches that have resulted in published reviews are described in a separate article [ 9 ].

As we aimed to describe the creation of systematic searches in full detail, the method starts at a basic level with the analysis of the research question and the creation of search terms. Readers who are new to SR searching are advised to follow all steps described. More experienced searchers can consider the basic steps to be existing knowledge that will already be part of their normal workflow, although step 4 probably differs from general practice. Experienced searchers will gain the most from reading about the novelties in the method as described in steps 10–13 and comparing the examples given in the supplementary appendix to their own practice.

CREATING A SYSTEMATIC SEARCH STRATEGY

Our methodology for planning and creating a multi-database search strategy consists of the following steps:

• Determine a clear and focused question
• Describe the articles that can answer the question
• Decide which key concepts address the different elements of the question
• Decide which elements should be used for the best results
• Choose an appropriate database and interface to start with
• Document the search process in a text document
• Identify appropriate index terms in the thesaurus of the first database
• Identify synonyms in the thesaurus
• Add variations in search terms
• Use database-appropriate syntax, with parentheses, Boolean operators, and field codes
• Optimize the search
• Evaluate the initial results
• Check for errors
• Translate to other databases
• Test and reiterate

Each step in the process is reflected by an example search described in the supplementary appendix .

1. Determine a clear and focused question

A systematic search can best be applied to a well-defined and precise research or clinical question. Questions that are too broad or too vague cannot be answered easily in a systematic way and will generally result in an overwhelming number of search results. On the other hand, a question that is too specific will result into too few or even zero search results. Various papers describe this process in more detail [ 10 – 12 ].

2. Describe the articles that can answer the question

Although not all clinical or research questions can be answered in the literature, the next step is to presume that the answer can indeed be found in published studies. A good starting point for a search is hypothesizing what the research that can answer the question would look like. These hypothetical (when possible, combined with known) articles can be used as guidance for constructing the search strategy.

3. Decide which key concepts address the different elements of the question

Key concepts are the topics or components that the desired articles should address, such as diseases or conditions, actions, substances, settings, domains (e.g., therapy, diagnosis, etiology), or study types. Key concepts from the research question can be grouped to create elements in the search strategy.

Elements in a search strategy do not necessarily follow the patient, intervention, comparison, outcome (PICO) structure or any other related structure. Using the PICO or another similar framework as guidance can be helpful to consider, especially in the inclusion and exclusion review stage of the SR, but this is not necessary for good search strategy development [ 13 – 15 ]. Sometimes concepts from different parts of the PICO structure can be grouped together into one search element, such as when the desired outcome is frequently described in a certain study type.

4. Decide which elements should be used for the best results

Not all elements of a research question should necessarily be used in the search strategy. Some elements are less important than others or may unnecessarily complicate or restrict a search strategy. Adding an element to a search strategy increases the chance of missing relevant references. Therefore, the number of elements in a search strategy should remain as low as possible to optimize recall.

Using the schema in Figure 1 , elements can be ordered by their specificity and importance to determine the best search approach. Whether an element is more specific or more general can be measured objectively by the number of hits retrieved in a database when searching for a key term representing that element. Depending on the research question, certain elements are more important than others. If articles (hypothetically or known) exist that can answer the question but lack a certain element in their titles, abstracts, or keywords, that element is unimportant to the question. An element can also be unimportant because of expected bias or an overlap with another element.

Schema for determining the optimal order of elements

Bias in elements

The choice of elements in a search strategy can introduce bias through use of overly specific terminology or terms often associated with positive outcomes. For the question “does prolonged breastfeeding improve intelligence outcomes in children?,” searching specifically for the element of duration will introduce bias, as articles that find a positive effect of prolonged breastfeeding will be much more likely to mention time factors in their titles or abstracts.

Overlapping elements

Elements in a question sometimes overlap in their meaning. Sometimes certain therapies are interventions for one specific disease. The Lichtenstein technique, for example, is a repair method for inguinal hernias. There is no need to include an element of “inguinal hernias” to a search for the effectiveness of the Lichtenstein therapy. Likewise, sometimes certain diseases are only found in certain populations. Adding such an overlapping element could lead to missing relevant references.

The elements to use in a search strategy can be found in the plot of elements in Figure 1 , by following the top row from left to right. For this method, we recommend starting with the most important and specific elements. Then, continue with more general and important elements until the number of results is acceptable for screening. Determining how many results are acceptable for screening is often a matter of negotiation with the SR team.

5. Choose an appropriate database and interface to start with

Important factors for choosing databases to use are the coverage and the presence of a thesaurus. For medically oriented searches, the coverage and recall of Embase, which includes the MEDLINE database, are superior to those of MEDLINE [ 16 ]. Each of these two databases has its own thesaurus with its own unique definitions and structure. Because of the complexity of the Embase thesaurus, Emtree, which contains much more specific thesaurus terms than the MEDLINE Medical Subject Headings (MeSH) thesaurus, translation from Emtree to MeSH is easier than the other way around. Therefore, we recommend starting in Embase.

MEDLINE and Embase are available through many different vendors and interfaces. The choice of an interface and primary database is often determined by the searcher’s accessibility. For our method, an interface that allows searching with proximity operators is desirable, and full functionality of the thesaurus, including explosion of narrower terms, is crucial. We recommend developing a personal workflow that always starts with one specific database and interface.

6. Document the search process in a text document

We advise designing and creating the complete search strategies in a log document, instead of directly in the database itself, to register the steps taken and to make searches accountable and reproducible. The developed search strategies can be copied and pasted into the desired databases from the log document. This way, the searcher is in control of the whole process. Any change to the search strategy should be done in the log document, assuring that the search strategy in the log is always the most recent.

7. Identify appropriate index terms in the thesaurus of the first database

Searches should start by identifying appropriate thesaurus terms for the desired elements. The thesaurus of the database is searched for matching index terms for each key concept. We advise restricting the initial terms to the most important and most relevant terms. Later in the process, more general terms can be added in the optimization process, in which the effect on the number of hits, and thus the desirability of adding these terms, can be evaluated more easily.

Several factors can complicate the identification of thesaurus terms. Sometimes, one thesaurus term is found that exactly describes a specific element. In contrast, especially in more general elements, multiple thesaurus terms can be found to describe one element. If no relevant thesaurus terms have been found for an element, free-text terms can be used, and possible thesaurus terms found in the resulting references can be added later (step 11).

Sometimes, no distinct thesaurus term is available for a specific key concept that describes the concept in enough detail. In Emtree, one thesaurus term often combines two or more elements. The easiest solution for combining these terms for a sensitive search is to use such a thesaurus term in all elements where it is relevant. Examples are given in the supplementary appendix .

8. Identify synonyms in the thesaurus

Most thesauri offer a list of synonyms on their term details page (named Synonyms in Emtree and Entry Terms in MeSH). To create a sensitive search strategy for SRs, these terms need to be searched as free-text keywords in the title and abstract fields, in addition to searching their associated thesaurus terms.

The Emtree thesaurus contains more synonyms (300,000) than MeSH does (220,000) [ 17 ]. The difference in number of terms is even higher considering that many synonyms in MeSH are permuted terms (i.e., inversions of phrases using commas).

Thesaurus terms are ordered in a tree structure. When searching for a more general thesaurus term, the more specific (narrower) terms in the branches below that term will also be searched (this is frequently referred to as “exploding” a thesaurus term). However, to perform a sensitive search, all relevant variations of the narrower terms must be searched as free-text keywords in the title or abstract, in addition to relying on the exploded thesaurus term. Thus, all articles that describe a certain narrower topic in their titles and abstracts will already be retrieved before MeSH terms are added.

9. Add variations in search terms (e.g., truncation, spelling differences, abbreviations, opposites)

Truncation allows a searcher to search for words beginning with the same word stem. A search for therap* will, thus, retrieve therapy, therapies, therapeutic, and all other words starting with “therap.” Do not truncate a word stem that is too short. Also, limitations of interfaces should be taken into account, especially in PubMed, where the number of search term variations that can be found by truncation is limited to 600.

Databases contain references to articles using both standard British and American English spellings. Both need to be searched as free-text terms in the title and abstract. Alternatively, many interfaces offer a certain code to replace zero or one characters, allowing a search for “pediatric” or “paediatric” as “p?ediatric.” Table 1 provides a detailed description of the syntax for different interfaces.

Field codes in five most used interfaces for biomedical literature searching

Searching for abbreviations can identify extra, relevant references and retrieve more irrelevant ones. The search can be more focused by combining the abbreviation with an important word that is relevant to its meaning or by using the Boolean “NOT” to exclude frequently observed, clearly irrelevant results. We advise that searchers do not exclude all possible irrelevant meanings, as it is very time consuming to identify all the variations, it will result in unnecessarily complicated search strategies, and it may lead to erroneously narrowing the search and, thereby, reduce recall.

Searching partial abbreviations can be useful for retrieving relevant references. For example, it is very likely that an article would mention osteoarthritis (OA) early in the abstract, replacing all further occurrences of osteoarthritis with OA . Therefore, it may not contain the phrase “hip osteoarthritis” but only “hip oa.”

It is also important to search for the opposites of search terms to avoid bias. When searching for “disease recurrence,” articles about “disease free” may be relevant as well. When the desired outcome is survival , articles about mortality may be relevant.

10. Use database-appropriate syntax, with parentheses, Boolean operators, and field codes

Different interfaces require different syntaxes, the special set of rules and symbols unique to each database that define how a correctly constructed search operates. Common syntax components include the use of parentheses and Boolean operators such as “AND,” “OR,” and “NOT,” which are available in all major interfaces. An overview of different syntaxes for four major interfaces for bibliographic medical databases (PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest) is shown in Table 1 .

Creating the appropriate syntax for each database, in combination with the selected terms as described in steps 7–9, can be challenging. Following the method outlined below simplifies the process:

• Create single-line queries in a text document (not combining multiple record sets), which allows immediate checking of the relevance of retrieved references and efficient optimization.
• Type the syntax (Boolean operators, parentheses, and field codes) before adding terms, which reduces the chance that errors are made in the syntax, especially in the number of parentheses.
• Use predefined proximity structures including parentheses, such as (() ADJ3 ()) in Ovid, that can be reused in the query when necessary.
• Use thesaurus terms separately from free-text terms of each element. Start an element with all thesaurus terms (using “OR”) and follow with the free-text terms. This allows the unique optimization methods as described in step 11.
• When adding terms to an existing search strategy, pay close attention to the position of the cursor. Make sure to place it appropriately either in the thesaurus terms section, in the title/abstract section, or as an addition (broadening) to an existing proximity search.

The supplementary appendix explains the method of building a query in more detail, step by step for different interfaces: PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest. This method results in a basic search strategy designed to retrieve some relevant references upon which a more thorough search strategy can be built with optimization such as described in step 11.

11. Optimize the search

The most important question when performing a systematic search is whether all (or most) potentially relevant articles have been retrieved by the search strategy. This is also the most difficult question to answer, since it is unknown which and how many articles are relevant. It is, therefore, wise first to broaden the initial search strategy, making the search more sensitive, and then check if new relevant articles are found by comparing the set results (i.e., search for Strategy #2 NOT Strategy #1 to see the unique results).

A search strategy should be tested for completeness. Therefore, it is necessary to identify extra, possibly relevant search terms and add them to the test search in an OR relationship with the already used search terms. A good place to start, and a well-known strategy, is scanning the top retrieved articles when sorted by relevance, looking for additional relevant synonyms that could be added to the search strategy.

We have developed a unique optimization method that has not been described before in the literature. This method often adds valuable extra terms to our search strategy and, therefore, extra, relevant references to our search results. Extra synonyms can be found in articles that have been assigned a certain set of thesaurus terms but that lack synonyms in the title and/or abstract that are already present in the current search strategy. Searching for thesaurus terms NOT free-text terms will help identify missed free-text terms in the title or abstract. Searching for free-text terms NOT thesaurus terms will help identify missed thesaurus terms. If this is done repeatedly for each element, leaving the rest of the query unchanged, this method will help add numerous relevant terms to the query. These steps are explained in detail for five different search platforms in the supplementary appendix .

12. Evaluate the initial results

The results should now contain relevant references. If the interface allows relevance ranking, use that in the evaluation. If you know some relevant references that should be included in the research, search for those references specifically; for example, combine a specific (first) author name with a page number and the publication year. Check whether those references are retrieved by the search. If the known relevant references are not retrieved by the search, adapt the search so that they are. If it is unclear which element should be adapted to retrieve a certain article, combine that article with each element separately.

Different outcomes are desired for different types of research questions. For instance, in the case of clinical question answering, the researcher will not be satisfied with many references that contain a lot of irrelevant references. A clinical search should be rather specific and is allowed to miss a relevant reference. In the case of an SR, the researchers do not want to miss any relevant reference and are willing to handle many irrelevant references to do so. The search for references to include in an SR should be very sensitive: no included reference should be missed. A search that is too specific or too sensitive for the intended goal can be adapted to become more sensitive or specific. Steps to increase sensitivity or specificity of a search strategy can be found in the supplementary appendix .

13. Check for errors

Errors might not be easily detected. Sometimes clues can be found in the number of results, either when the number of results is much higher or lower than expected or when many retrieved references are not relevant. However, the number expected is often unknown, and very sensitive search strategies will always retrieve many irrelevant articles. Each query should, therefore, be checked for errors.

One of the most frequently occurring errors is missing the Boolean operator “OR.” When no “OR” is added between two search terms, many interfaces automatically add an “AND,” which unintentionally reduces the number of results and likely misses relevant references. One good strategy to identify missing “OR”s is to go to the web page containing the full search strategy, as translated by the database, and using Ctrl-F search for “AND.” Check whether the occurrences of the “AND” operator are deliberate.

Ideally, search strategies should be checked by other information specialists [ 18 ]. The Peer Review of Electronic Search Strategies (PRESS) checklist offers good guidance for this process [ 4 ]. Apart from the syntax (especially Boolean operators and field codes) of the search strategy, it is wise to have the search terms checked by the clinician or researcher familiar with the topic. At Erasmus MC, researchers and clinicians are involved during the complete process of structuring and optimizing the search strategy. Each word is added after the combined decision of the searcher and the researcher, with the possibility of directly comparing results with and without the new term.

14. Translate to other databases

To retrieve as many relevant references as possible, one has to search multiple databases. Translation of complex and exhaustive queries between different databases can be very time consuming and cumbersome. The single-line search strategy approach detailed above allows quick translations using the find and replace method in Microsoft Word (<Ctrl-H>).

At Erasmus MC, macros based on the find-and-replace method in Microsoft Word have been developed for easy and fast translation between the most used databases for biomedical and health sciences questions. The schema that is followed for the translation between databases is shown in Figure 2 . Most databases simply follow the structure set by the Embase.com search strategy. The translation from Emtree terms to MeSH terms for MEDLINE in Ovid often identifies new terms that need to be added to the Embase.com search strategy before the translation to other databases.

Schematic representation of translation between databases used at Erasmus University Medical Center

Dotted lines represent databases that are used in less than 80% of the searches.

Using five different macros, a thoroughly optimized query in Embase.com can be relatively quickly translated into eight major databases. Basic search strategies will be created to use in many, mostly smaller, databases, because such niche databases often do not have extensive thesauri or advanced syntax options. Also, there is not much need to use extensive syntax because the number of hits and, therefore, the amount of noise in these databases is generally low. In MEDLINE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCOhost), the thesaurus terms must be adapted manually, as each database has its own custom thesaurus. These macros and instructions for their installation, use, and adaptation are available at bit.ly/databasemacros.

15. Test and reiterate

Ideally, exhaustive search strategies should retrieve all references that are covered in a specific database. For SR search strategies, checking searches for their recall is advised. This can be done after included references have been determined by the authors of the systematic review. If additional papers have been identified through other non-database methods (i.e., checking references in included studies), results that were not identified by the database searches should be examined. If these results were available in the databases but not located by the search strategy, the search strategy should be adapted to try to retrieve these results, as they may contain terms that were omitted in the original search strategies. This may enable the identification of additional relevant results.

A methodology for creating exhaustive search strategies has been created that describes all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Many of the steps described are not new, but together, they form a strong method creating high-quality, robust searches in a relatively short time frame.

Our methodology is intended to create thoroughness for literature searches. The optimization method, as described in step 11, will identify missed synonyms or thesaurus terms, unlike any other method that largely depends on predetermined keywords and synonyms. Using this method results in a much quicker search process, compared to traditional methods, especially because of the easier translation between databases and interfaces (step 13). The method is not a guarantee for speed, since speed depends on many factors, including experience. However, by following the steps and using the tools as described above, searchers can gain confidence first and increase speed through practice.

What is new?

This method encourages searchers to start their search development process using empty syntax first and later adding the thesaurus terms and free-text synonyms. We feel this helps the searcher to focus on the search terms, instead of on the structure of the search query. The optimization method in which new terms are found in the already retrieved articles is used in some other institutes as well but has to our knowledge not been described in the literature. The macros to translate search strategies between interfaces are unique in this method.

What is different compared to common practice?

Traditionally, librarians and information specialists have focused on creating complex, multi-line (also called line-by-line) search strategies, consisting of multiple record sets, and this method is frequently advised in the literature and handbooks [ 2 , 19 – 21 ]. Our method, instead, uses single-line searches, which is critical to its success. Single-line search strategies can be easily adapted by adding or dropping a term without having to recode numbers of record sets, which would be necessary in multi-line searches. They can easily be saved in a text document and repeated by copying and pasting for search updates. Single-line search strategies also allow easy translation to other syntaxes using find-and-replace technology to update field codes and other syntax elements or using macros (step 13).

When constructing a search strategy, the searcher might experience that certain parentheses in the syntax are unnecessary, such as parentheses around all search terms in the title/abstract portion, if there is only one such term, there are double parentheses in the proximity statement, or one of the word groups exists for only one word. One might be tempted to omit those parentheses for ease of reading and management. However, during the optimization process, the searcher is likely to find extra synonyms that might consist of one word. To add those terms to the first query (with reduced parentheses) requires adding extra parentheses (meticulously placing and counting them), whereas, in the latter search, it only requires proper placement of those terms.

Many search methods highly depend on the PICO framework. Research states that often PICO or PICOS is not suitable for every question [ 22 , 23 ]. There are other acronyms than PICO—such as sample, phenomenon of interest, design, evaluation, research type (SPIDER) [ 24 ]—but each is just a variant. In our method, the most important and specific elements of a question are being analyzed for building the best search strategy.

Though it is generally recommended that searchers search both MEDLINE and Embase, most use MEDLINE as the starting point. It is considered the gold standard for biomedical searching, partially due to historical reasons, since it was the first of its kind, and more so now that it is freely available via the PubMed interface. Our method can be used with any database as a starting point, but we use Embase instead of MEDLINE or another database for a number of reasons. First, Embase provides both unique content and the complete content of MEDLINE. Therefore, searching Embase will be, by definition, more complete than searching MEDLINE only. Second, the number of terms in Emtree (the Embase thesaurus) is three times as high as that of MeSH (the MEDLINE thesaurus). It is easier to find MeSH terms after all relevant Emtree terms have been identified than to start with MeSH and translate to Emtree.

At Erasmus MC, the researchers sit next to the information specialist during most of the search strategy design process. This way, the researchers can deliver immediate feedback on the relevance of proposed search terms and retrieved references. The search team then combines knowledge about databases with knowledge about the research topic, which is an important condition to create the highest quality searches.

Limitations of the method

One disadvantage of single-line searches compared to multi-line search strategies is that errors are harder to recognize. However, with the methods for optimization as described (step 11), errors are recognized easily because missed synonyms and spelling errors will be identified during the process. Also problematic is that more parentheses are needed, making it more difficult for the searcher and others to assess the logic of the search strategy. However, as parentheses and field codes are typed before the search terms are added (step 10), errors in parentheses can be prevented.

Our methodology works best if used in an interface that allows proximity searching. It is recommended that searchers with access to an interface with proximity searching capabilities select one of those as the initial database to develop and optimize the search strategy. Because the PubMed interface does not allow proximity searches, phrases or Boolean “AND” combinations are required. Phrase searching complicates the process and is more specific, with the higher risk of missing relevant articles, and using Boolean “AND” combinations increases sensitivity but at an often high loss of specificity. Due to some searchers’ lack of access to expensive databases or interfaces, the freely available PubMed interface may be necessary to use, though it should never be the sole database used for an SR [ 2 , 16 , 25 ]. A limitation of our method is that it works best with subscription-based and licensed resources.

Another limitation is the customization of the macros to a specific institution’s resources. The macros for the translation between different database interfaces only work between the interfaces as described. To mitigate this, we recommend using the find-and-replace functionality of text editors like Microsoft Word to ease the translation of syntaxes between other databases. Depending on one’s institutional resources, custom macros can be developed using similar methods.

Results of the method

Whether this method results in exhaustive searches where no important article is missed is difficult to determine, because the number of relevant articles is unknown for any topic. A comparison of several parameters of 73 published reviews that were based on a search developed with this method to 258 reviews that acknowledged information specialists from other Dutch academic hospitals shows that the performance of the searches following our method is comparable to those performed in other institutes but that the time needed to develop the search strategies was much shorter than the time reported for the other reviews [ 9 ].

CONCLUSIONS

With the described method, searchers can gain confidence in their search strategies by finding many relevant words and creating exhaustive search strategies quickly. The approach can be used when performing SR searches or for other purposes such as answering clinical questions, with different expectations of the search’s precision and recall. This method, with practice, provides a stepwise approach that facilitates the search strategy development process from question clarification to final iteration and beyond.

SUPPLEMENTAL FILE

Acknowledgments.

We highly appreciate the work that was done by our former colleague Louis Volkers, who in his twenty years as an information specialist in Erasmus MC laid the basis for our method. We thank Professor Oscar Franco for reviewing earlier drafts of this article.

Researchers find that accelerated aging biology in the placenta contributes to a rare form of pregnancy-related heart failure

A form of heart failure that occurs during late pregnancy or early postpartum, peripartum cardiomyopathy (ppcm) is a major cause of maternal death.

A form of heart failure that occurs during late pregnancy or early postpartum, peripartum cardiomyopathy (PPCM) is a major cause of maternal death.

New research led by investigators from Massachusetts General Hospital, a founding member of the Mass General Brigham healthcare system, reveals new insights into the mechanisms behind PPCM's development and points to potential new strategies for therapeutic development. The results are published in Science Translational Medicine .

"Even though heart disease now represents the leading cause of maternal death in the US, our understanding of the biology driving many of these diseases is still very limited," said co-lead author Jason Roh, MD, MHS, a cardiologist who runs a cardiovascular aging laboratory in the Massachusetts General Hospital Cardiovascular Research Center. "Our study identifies some underlying aging-related biology that contributes to the development of maternal heart failure in pregnancy and provides evidence from both patients and animal models."

Roh and his colleagues work began with an unexpected finding. While studying the role of senescent (or aged) cells in older adults with heart failure, they were surprised to find that proteins secreted by these aged cells were being detected at even higher levels in the blood of young pregnant women with heart failure.

Based on these initial findings, the researchers conducted experiments to see whether these senescence proteins might be contributing to the development of PPCM as well as preeclampsia, a hypertensive disorder of pregnancy that is a leading risk factor for PPCM and postpartum heart failure.

Their reasoning was based on prior work showing that the placenta, a hybrid maternal-fetal organ unique to pregnancy, manifests markers of increased senescence towards the end of pregnancy.

When the team assessed placentas from women with preeclampsia, they found that they displayed multiple markers of amplified senescence and tissue aging, as well as increased expression of many of the senescence proteins that were detected in the blood of women with preeclampsia or PPCM.

The most highly expressed cellular senescence protein in these placentas was activin A, and higher levels of this protein were linked to either more severe heart dysfunction or heart failure in women with preeclampsia or PPCM.

"While the placenta undergoes a normal physiological process of aging (or senescence) throughout pregnancy, this seems to be further amplified in those who develop heart failure during pregnancy," said Roh. "We believe this causes it to secrete various factors into the mom's blood that can negatively impact the function of the heart."

In experiments conducted in mice, the placentas of mice with PPCM showed similarly increased expression of cellular senescence-associated proteins. Treating these mice with fisetin, a drug that can selectively clear highly senescent cells, during mid to late pregnancy partially reduced placental senescence and improved heart function. Treatment with an antibody directed against the receptor for activin A, after pregnancy, had similar effects in these animals.

"Although we are still in the very early stages of understanding how amplified placental senescence can affect the function of the mom's heart, we believe our findings answer some fundamental questions about the biology underlying heart failure in pregnancy," said Roh. "It is important to note that placental senescence is a normal part of pregnancy. Fully understanding why this process becomes perturbed in pregnancy-related heart disease and rigorously determining how to safely regulate it are critical next steps before translating these findings."

• Heart Disease
• Pregnancy and Childbirth
• Stroke Prevention
• Birth Defects
• Diseases and Conditions
• Cholesterol
• Chronic Illness
• Heart failure
• Biochemistry
• Heat shock protein
• Umbilical cord
• Ischaemic heart disease

Story Source:

Materials provided by Massachusetts General Hospital . Note: Content may be edited for style and length.

Journal Reference :

• Jason D. Roh, Claire Castro, Andy Yu, Sarosh Rana, Sajid Shahul, Kathryn J. Gray, Michael C. Honigberg, Melanie Ricke-Hoch, Yoshiko Iwamoto, Ashish Yeri, Robert Kitchen, Justin Baldovino Guerra, Ryan Hobson, Vinita Chaudhari, Bliss Chang, Amy Sarma, Carolin Lerchenmüller, Zeina R. Al Sayed, Carmen Diaz Verdugo, Peng Xia, Niv Skarbianskis, Amit Zeisel, Johann Bauersachs, James L. Kirkland, S. Ananth Karumanchi, John Gorcsan, Masataka Sugahara, Julie Damp, Karen Hanley-Yanez, Patrick T. Ellinor, Zoltan Arany, Dennis M. McNamara, Denise Hilfiker-Kleiner, Anthony Rosenzweig, James D. Fett, Jessica Pisarcik, Charles McTiernan, Erik Schelbert, Rami Alharethi, Kismet Rasmusson, Kim Brunisholz, Amy Butler, Deborah Budge, A. G. Kfoury, Benjamin Horne, Joe Tuinei, Heather Brown, Allen J. Naftilan, Jill Russell, Darla Freehardt, Eileen Hsich, Cynthia Oblak, Greg Ewald, Donna Whitehead, Jean Flanagan, Anne Platts, Uri Elkayam, Jorge Caro, Stephanie Mullin, Michael M. Givertz, M. Susan Anello, Navin Rajagopalan, David Booth, Tiffany Sandlin, Wendy Wijesiri, Leslie T. Cooper, Lori A. Blauwet, Joann Brunner, Mary Phelps, Ruth Kempf, Kalgi Modi, Tracy Norwood, Joan Briller, Decebal Sorin Griza, G. Michael Felker, Robb Kociol, Patricia Adams, Gretchen Wells, Vinay Thohan, Deborah Wesley-Farrington, Sandra Soots, Richard Sheppard, Caroline Michel, Nathalie Lapointe, Heather Nathaniel, Angela Kealey, Marc Semigran, Maureen Daher, John Boehmer, David Silber, Eric Popjes, Patricia Frey, Todd Nicklas, Jeffrey Alexis, Lori Caufield, John W. Thornton, Mindy Gentry, Vincent J. B. Robinson, Gyanendra K. Sharma, Joan Holloway, Maria Powell, David Markham, Mark Drazner, Lynn Fernandez, Mark Zucker, David A. Baran, Martin L. Gimovsky, Natalia Hochbaum, Bharati Patel, Laura Adams, Gautam Ramani, Stephen Gottlieb, Shawn Robinson, Stacy Fisher, Joanne Marshall, Jennifer Haythe, Donna Mancini, Rachel Bijou, Maryjane Farr, Marybeth Marks, Henry Arango, Biykem Bozkurt, Mariana Bolos, Paul Mather, Sharon Rubin, Raphael Bonita, Susan Eberwine, Hal Skopicki, Kathleen Stergiopoulos, Ellen McCathy-Santoro, Jennifer Intravaia, Elizabeth Maas, Jordan Safirstein, Audrey Kleet, Nancy Martinez, Christine Corpoin, Donna Hesari, Sandra Chaparro, Laura J. Hudson, Jalal K. Ghali, Zora Injic, Ilan S. Wittstein. Placental senescence pathophysiology is shared between peripartum cardiomyopathy and preeclampsia in mouse and human . Science Translational Medicine , 2024; 16 (743) DOI: 10.1126/scitranslmed.adi0077

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Undergraduate Research Key to Finding Future Career Interests

For many undergraduate students in the School of Engineering, research is an integral part of their time as Flyers. Many students choose to work alongside faculty researchers on their personal projects or even research sponsored by national organizations and the military. 

Two students currently conducting research with associate professor Dr. Brad Ratliff in his Applied Sensing Lab have found a future career interest in research thanks to the experience. 

Michael Taylor

Senior electrical engineering student Michael Taylor just started research work with Dr. Ratliff this semester, but he’s already secured a paid graduate assistantship in the Applied Sensing Lab. 

Taylor, from Fairborn, is eager to continue studying for his master’s degree in electrical engineering at UD while also learning more about image and signal processing.

“I found out about Dr. Ratliff’s lab after I mentioned to a friend who was working with him that I was interested in image and signal processing,” Taylor said. “I emailed Dr. Ratliff then met with him and he took me right in. It never occurred to me that finding research opportunities would be that easy.”

Taylor will be working on an Army sponsored project that utilizes object detection. Right now, he’s mainly learning more about the field before he starts working with hands-on applications this summer.

“With research, I like that I kind of can guide it myself,” Taylor said. “I can say, ‘This is something I'm interested in’, and then go do it. I don't feel limited.”

Allison Schafer

For Allison Schafer, an electrical engineering student from Beavercreek, Ohio, UD, electrical engineering — and even research — is a family affair. 

Her brother and father are also electrical engineers, her siblings are Flyers and now she is working in Dr. Brad Ratliff’s lab alongside her brother, Austin, who introduced her to Dr. Ratliff during her freshman year and now continues research as a graduate student.

“The research that I am working on has to do with machine learning and image processing,” Schafer said. “My brother and I are taking scans of a parking lot scene Dr. Ratliff made, and we are trying to teach the software to be able to decipher between what in the scan is a car, and what is not.”

Schafer enjoys her research because it provides her an opportunity to learn more about electrical engineering outside of her classes.

“As an undergrad, we have to take some classes that we may not be interested in,” Schafer said. “Doing this research really just helps guide you on the right path with what elective classes you may want to take here, and sparked my interest to learn more and take certain classes dealing with image and signal processing.”

After graduation, Schafer is looking forward to continuing into a master’s program and a career in research.

“I very much enjoy the research aspect of this job because I enjoy furthering my knowledge every day with what I do,” Schafer said.

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Coral reefs can't keep up with climate change. So scientists are speeding up evolution

Lauren Sommer

Ryan Kellman

Record levels of heat in the ocean are causing a worldwide mass bleaching event on coral reefs, as seen here on the Great Barrier Reef. Scientists are working on creating more heat-resistant coral to help restore reefs. Veronique Mocellin /AIMS hide caption

Record levels of heat in the ocean are causing a worldwide mass bleaching event on coral reefs. It's the second one this decade, where the delicate skeletons of corals turn a ghostly white.

With mass bleaching only expected to get worse as the climate keeps warming, coral scientists are urgently searching for ways to help reefs endure. Bleaching can kill corals, putting some of the most diverse ecosystems in the world at risk. So scientists are homing in on how bleaching happens.

It boils down to relationship drama between corals and a tiny organism that's too small to see.

Scientists estimate that a quarter of all marine species depend on coral reefs. Biologists say that's a best guess and it's very likely there are species yet to be discovered. Ryan Kellman/NPR hide caption

Corals are the builders of reefs, their skeletons creating the vast infrastructure that tens of thousands of other species depend on. But corals are powered by the tiny algae that live in their tissue, which provide food for them.

"They're these microscopic, sort of nondescript algae," says Matthew Nitschke, research scientist at the Australian Institute of Marine Science, as he magnifies a few under a microscope, revealing golden-brown circles.

Scientists are breeding 'super corals.' Can they withstand climate change?

"People are like: why are you so interested in them?" he says. "And it's because they, for me, are really at the foundation of the ecosystem."

The tiny algae and coral make up one of the most productive roommate relationships on the planet. But as the climate gets hotter, that relationship is increasingly going bad. When ocean temperatures rise, corals get stressed and their algae get expelled. Without their roommates, corals can starve and eventually die.

Studies show that if climate change continues at the same pace, 99% of the world's coral reefs are likely to die off by the end of the century. To buy reefs a little extra time, scientists are breeding both algae and corals to withstand more heat, speeding up the natural process of evolution. But with oceans heating up more rapidly than expected, they're racing against the clock.

"I think anyone who wasn't worried, needs to be worried now," says Kate Quigley, coral biologist at James Cook University in Australia and the Minderoo Foundation. "Nature has time to make mistakes and then adjust. We don't have that time."

"There just doesn't seem be enough time," says Kate Quigley, coral biologist at James Cook University in Australia and the Minderoo Foundation. "We're going from one bleaching event to the next." Ryan Kellman/NPR hide caption

Natural selection in a bottle

The tanks at the Australian Institute of Marine Science, just outside of Townsville in Queensland, are full of delicate branching corals in a vast array of colors. Another lab there is somewhat less eye-catching – full of scientific flasks with clouds of brown algae in them. They're zooxanthellae, the algae that live in coral, but these have been isolated from their coral homes (the algae can live in the ocean without the coral, but coral can't live without algae).

"If you look at a coral, they look bright, they look colorful," Nitschke says. "They're actually mostly translucent and a lot of the color of the coral that you see comes from the algae."

The algae in Nitschke's lab have been grown over hundreds of generations, subjected to an accelerated version of survival-of-the-fittest. They've been exposed to heat, singling out those best able to handle higher temperatures, which then go to produce future algal generations.

"What we're really doing is natural selection in a bottle," he says. "We're really excited about the possibility for that to help corals persist into the future."

Algae in Nitschke's lab, grown over hundreds of generations. They've been exposed to heat, singling out those best able to handle higher temperatures, which then go to produce more heat-tolerant algae. Ryan Kellman/NPR hide caption

Scientists are still trying to tease out exactly what happens between a coral and its algae when temperatures get hot. They depend on a carefully-balanced living arrangement. The algae get a comfy home and nutrients they need from the coral. In return, they do photosynthesis, using sunlight to produce energy for the coral.

But when the ocean heats up, that balance gets upset. Scientists believe one reason is that the warmer water stresses the coral, upsetting the nutrient exchange between the coral and algae. Another reason could be that the hotter water impairs how cells function, causing them to release too much of certain chemicals. The result is that most algae get the boot, leaving the coral without its main food supply.

"They begin to starve," Nitschke says. "That primary energy source – the loss of that during a heat stress event is potentially catastrophic for an individual coral. They are now in a race against time."

Corals bleach, turning ghostly white, when they're under stress from hotter temperatures. If the heat subsides, they can recover. But long periods of heat and repeated marine heat waves cause corals to die, wreaking havoc on one of the most biodiverse ecosystems on the planet. Veronique Mocellin /AIMS hide caption

Buying time for coral

If the heat subsides, corals can recover, slowly building back their algae population. But if the heat persists, or if there are too many marine heat waves back-to-back, the corals die.

Bleaching events are becoming more frequent, putting corals on a path for a mass die-off by the end of the century if the planet warms more than 2 degrees Celsius (3.6 degrees Fahrenheit). The effects could be devastating for marine biodiversity and for human communities. Hundreds of millions of people worldwide live near coral reefs, relying on them for food and coastal protection, since reefs can reduce flooding by absorbing wave energy.

It's why Nitschke and his colleagues have focused on breeding algae. They're in the process of testing them, giving them to tiny brain corals the size of walnuts. In trials, they've found corals inoculated with the heat-tolerant algae seem to resist bleaching for longer .

Corals the size of walnuts have been inoculated with heat resistant algae by Matthew Nitschke and his colleagues at the Australian Institute of Marine Science. They've found corals inoculated with heat-tolerant algae seem to resist bleaching for longer. Ryan Kellman/NPR hide caption

Researchers are also breeding corals themselves to be more heat-tolerant, in the hope that a combination of both a "super coral" and "super algae" can be used to restore reefs someday. Both are "assisted evolution" – a technique to speed up the natural process organisms use to adapt to their environment.

"Assisted evolution is an umbrella term for many things we've been doing in many other systems: agriculture, for pets." Nitschke says. "We're really only just starting to understand what we can do in the coral space."

Research aquarist Andrea Severati peers at large sheets over which coral larvae were released to settle. Once corals have picked their spot, each will be assessed for coral growth and survival. Ryan Kellman/NPR hide caption

Not a "get out of jail free" card

Still, in nature, there is no free lunch. Heat-tolerant algae may not share as many nutrients with their coral hosts, which means corals grow more slowly and reproduce later than they would otherwise. That could hamper their ability to restore reefs impacted by climate change. A key step will be testing the corals and algae on the Great Barrier Reef itself to see how they do.

"The last thing we want to do is make things worse," says Line Bay, a research program director at the Australian Institute of Marine Science. "We don't want to produce lab-adapted corals and then put them out in the real world where they don't do well."

Even if the heat-tolerant corals prove to be successful, the number of coral needed to restore impaired reefs could be enormous. The Great Barrier Reef is more than 1,000 miles long. And regulators will need to assess if the corals pose any risk to wild populations or the ecosystem as a whole.

The corals developed at AIMS are placed by divers on the Great Barrier reef. They are being tested in the ocean, as part of a large field trial.

Credit: AIMS

"Coral reefs are magical places," Bay says. "I think we need to be brave and we should use all the tools at our disposal in a humble and sensitive manner."

Coral scientists are clear about one aspect of the work: it's not a long-term solution. At best, it only buys coral reefs extra time until the effects of climate change become too much.

"It's not our 'get out of jail free' card," Quigley says. "Maybe that gets us to 2030, 2050 for a very few number of species that we can work with. If we don't have an ocean to put them back in that's healthy, no amount of incredible technology or money is worth it."

The hope is that giving coral reefs a few extra years, or even decades, will be enough time for humans to slow the pace of climate change. That means cutting heat-trapping emissions from the largest source – burning fossil fuels – and switching to alternative energy sources like solar and wind.

"We could all be despondent and be hopeless if there weren't great solutions on the table to turn climate change problems around," Quigley says. "We just need to get it on, now, really."

• coral reefs
• Great Barrier Reef
• conservation
• climate change

Microsoft Research Blog

Sammo: a general-purpose framework for prompt optimization.

Published April 18, 2024

By Tobias Schnabel , Senior Researcher Jennifer Neville , Partner Research Manager

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Large language models (LLMs) have revolutionized a wide range of tasks and applications that were previously reliant on manually crafted machine learning (ML) solutions, streamlining through automation. However, despite these advances, a notable challenge persists: the need for extensive prompt engineering to adapt these models to new tasks. New generations of language models like GPT-4 and Mixtral 8x7B advance the capability to process long input texts. This progress enables the use of longer inputs, providing richer context and detailed instructions to language models. A common technique that uses this enhanced capacity is the Retrieval Augmented Generation (RAG) approach. RAG dynamically incorporates information into the prompt based on the specific input example. This process is illustrated in Figure 1, which shows a RAG prompt designed to translate user queries into a domain-specific language (DSL), also known as semantic parsing. 

The example in Figure 1 combines three distinct structures to construct the final prompt. The first structure, the task description, remains static and independent of the input as a result of conventional prompt optimization techniques. However, RAG contains two input-specific structures: the example retriever and the input text itself. These introduce numerous optimization opportunities that surpass the scope of most traditional approaches. Despite previous efforts in prompt optimization, the evolution towards more complex prompt structures has rendered many older strategies ineffective in this new context. 

SAMMO: A prompt optimization approach 

• Download SAMMO 

To address these challenges, we developed the Structure-Aware Multi-objective Metaprompt Optimization (SAMMO) framework. SAMMO is a new open-source tool that streamlines the optimization of prompts, particularly those that combine different types of structural information like in the RAG example above. It can make structural changes, such as removing entire components or replacing them with different ones. These features enable AI practitioners and researchers to efficiently refine their prompts with little manual effort.

Central to SAMMO’s innovation is its approach to treating prompts not just as static text inputs but as dynamic, programmable entities— metaprompts . SAMMO represents these metaprompts as function graphs, where individual components and substructures can be modified to optimize performance, similar to the optimization process that occurs during traditional program compilation.

The following key features contribute to SAMMO’s effectiveness:

Structured optimization: Unlike current methods that focus on text-level changes, SAMMO focuses on optimizing the structure of metaprompts. This granular approach facilitates precise modifications and enables the straightforward integration of domain knowledge, for instance, through rewrite operations targeting specific stylistic objectives.    Multi-objective search: SAMMO’s flexibility enables it to simultaneously address multiple objectives, such as improving accuracy and computational efficiency. Our paper illustrates how SAMMO can be used to compress prompts without compromising their accuracy.

General purpose application: SAMMO has proven to deliver significant performance improvements across a variety of tasks, including instruction tuning, RAG, and prompt compression.

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Exploring SAMMO’s impact through use cases 

Use case 1: rag optimization .

A common application of LLMs involves translating natural user queries into domain-specific language (DSL) constructions, often to communicate with external APIs. For example, Figure 1 shows how an LLM can be used to map user queries about geography facts to a custom DSL.

In a realistic RAG scenario, SAMMO demonstrates significant performance improvements. To demonstrate this, we conducted experiments across three semantic parsing datasets of varying complexity: GeoQuery, SMCalFlow, and Overnight. Given the often limited availability of data in practical settings, we trained and tested the model on a subsampled dataset (training and retrieval set n=600, test set n=100). We compared SAMMO against a manually designed competitive baseline, using enumerative search within a search space of 24 configurations. This included variations in data formats, the number of few-shot examples, and DSL specifications.  

Evaluation  

As illustrated in Figure 2, SAMMO improved accuracy across different datasets and backend LLMs in almost all cases, with the most notable gains observed in older generation models. However, even newer models like GPT-4, SAMMO facilitated accuracy improvements exceeding 100 percent.

Use case 2: Instruction tuning 

Instruction tuning addresses the optimization of static instructions given to LLMs that provide the goal and constraints of a task. To show that SAMMO extends beyond many previous prompt tuning methods, we applied this conventional setting.

To align with previous research, we used eight zero-shot BigBench classification tasks where the baseline prompt for GPT-3.5 achieved an accuracy of less than 0.9. We compared it against Automatic Prompt Optimization (APO) and GrIPS, applying open-source models Mixtral 7x8B and Llama-2 70B, alongside GPT-3.5 as backend LLMs. We did not include GPT-4 due to minimal improvement potential identified in pilot experiments. The results, shown in Figure 3, demonstrate that SAMMO outperformed all baselines regardless of the backend model, proving its effectiveness with even more complex metaprompts.

Implications and looking forward

SAMMO introduces a new and flexible approach to optimize prompts for specific requirements. Its design works with any LLM, and it features versatile components and operators suitable for a broad range of applications.

We are excited to integrate and apply SAMMO to the components and pipelines behind AI-powered assistant technologies. We also hope to establish a user-driven community centered around SAMMO, where people can exchange best practices and patterns, and encourage the expansion of the existing set of search operators.

Related publications

Prompts as programs: a structure-aware approach to efficient compile-time prompt optimization, meet the authors.

Tobias Schnabel

Senior Researcher

Jennifer Neville

Partner Research Manager

Continue reading

Improving LLM understanding of structured data and exploring advanced prompting methods

Structured knowledge from LLMs improves prompt learning for visual language models

LLMLingua: Innovating LLM efficiency with prompt compression

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

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