a research paper should be objective. what does it mean

Research Objectives: The Compass of Your Study

Table of contents

• 1 Definition and Purpose of Setting Clear Research Objectives
• 2 How Research Objectives Fit into the Overall Research Framework
• 3 Types of Research Objectives
• 4 Aligning Objectives with Research Questions and Hypotheses
• 5 Role of Research Objectives in Various Research Phases
• 6.1 Key characteristics of well-defined research objectives
• 6.2 Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives
• 6.3 How to Know When Your Objectives Need Refinement
• 7 Research Objectives Examples in Different Fields
• 8 Conclusion

Embarking on a research journey without clear objectives is like navigating the sea without a compass. This article delves into the essence of establishing precise research objectives, serving as the guiding star for your scholarly exploration.

We will unfold the layers of how the objective of study not only defines the scope of your research but also directs every phase of the research process, from formulating research questions to interpreting research findings. By bridging theory with practical examples, we aim to illuminate the path to crafting effective research objectives that are both ambitious and attainable. Let’s chart the course to a successful research voyage, exploring the significance, types, and formulation of research paper objectives.

Definition and Purpose of Setting Clear Research Objectives

Defining the research objectives includes which two tasks? Research objectives are clear and concise statements that outline what you aim to achieve through your study. They are the foundation for determining your research scope, guiding your data collection methods, and shaping your analysis. The purpose of research proposal and setting clear objectives in it is to ensure that your research efforts are focused and efficient, and to provide a roadmap that keeps your study aligned with its intended outcomes.

To define the research objective at the outset, researchers can avoid the pitfalls of scope creep, where the study’s focus gradually broadens beyond its initial boundaries, leading to wasted resources and time. Clear objectives facilitate communication with stakeholders, such as funding bodies, academic supervisors, and the broader academic community, by succinctly conveying the study’s goals and significance. Furthermore, they help in the formulation of precise research questions and hypotheses, making the research process more systematic and organized. Yet, it is not always easy. For this reason, PapersOwl is always ready to help. Lastly, clear research objectives enable the researcher to critically assess the study’s progress and outcomes against predefined benchmarks, ensuring the research stays on track and delivers meaningful results.

How Research Objectives Fit into the Overall Research Framework

Research objectives are integral to the research framework as the nexus between the research problem, questions, and hypotheses. They translate the broad goals of your study into actionable steps, ensuring every aspect of your research is purposefully aligned towards addressing the research problem. This alignment helps in structuring the research design and methodology, ensuring that each component of the study is geared towards answering the core questions derived from the objectives. Creating such a difficult piece may take a lot of time. If you need it to be accurate yet fast delivered, consider getting professional research paper writing help whenever the time comes. It also aids in the identification and justification of the research methods and tools used for data collection and analysis, aligning them with the objectives to enhance the validity and reliability of the findings.

Furthermore, by setting clear objectives, researchers can more effectively evaluate the impact and significance of their work in contributing to existing knowledge. Additionally, research objectives guide literature review, enabling researchers to focus their examination on relevant studies and theoretical frameworks that directly inform their research goals.

Types of Research Objectives

In the landscape of research, setting objectives is akin to laying down the tracks for a train’s journey, guiding it towards its destination. Constructing these tracks involves defining two main types of objectives: general and specific. Each serves a unique purpose in guiding the research towards its ultimate goals, with general objectives providing the broad vision and specific objectives outlining the concrete steps needed to fulfill that vision. Together, they form a cohesive blueprint that directs the focus of the study, ensuring that every effort contributes meaningfully to the overarching research aims.

• General objectives articulate the overarching goals of your study. They are broad, setting the direction for your research without delving into specifics. These objectives capture what you wish to explore or contribute to existing knowledge.
• Specific objectives break down the general objectives into measurable outcomes. They are precise, detailing the steps needed to achieve the broader goals of your study. They often correspond to different aspects of your research question , ensuring a comprehensive approach to your study.

To illustrate, consider a research project on the impact of digital marketing on consumer behavior. A general objective might be “to explore the influence of digital marketing on consumer purchasing decisions.” Specific objectives could include “to assess the effectiveness of social media advertising in enhancing brand awareness” and “to evaluate the impact of email marketing on customer loyalty.”

Aligning Objectives with Research Questions and Hypotheses

The harmony between what research objectives should be, questions, and hypotheses is critical. Objectives define what you aim to achieve; research questions specify what you seek to understand, and hypotheses predict the expected outcomes.

This alignment ensures a coherent and focused research endeavor. Achieving it necessitates a thoughtful consideration of how each component interrelates, ensuring that the objectives are not only ambitious but also directly answerable through the research questions and testable via the hypotheses. This interconnectedness facilitates a streamlined approach to the research process, enabling researchers to systematically address each aspect of their study in a logical sequence. Moreover, it enhances the clarity and precision of the research, making it easier for peers and stakeholders to grasp the study’s direction and potential contributions.

Role of Research Objectives in Various Research Phases

Throughout the research process, objectives guide your choices and strategies – from selecting the appropriate research design and methods to analyzing data and interpreting results. They are the criteria against which you measure the success of your study. In the initial stages, research objectives inform the selection of a topic, helping to narrow down a broad area of interest into a focused question that can be explored in depth. During the methodology phase, they dictate the type of data needed and the best methods for obtaining that data, ensuring that every step taken is purposeful and aligned with the study’s goals. As the research progresses, objectives provide a framework for analyzing the collected data, guiding the researcher in identifying patterns, drawing conclusions, and making informed decisions.

Crafting Effective Research Objectives

The effective objective of research is pivotal in laying the groundwork for a successful investigation. These objectives clarify the focus of your study and determine its direction and scope. Ensuring that your objectives are well-defined and aligned with the SMART criteria is crucial for setting a strong foundation for your research.

Key characteristics of well-defined research objectives

Well-defined research objectives are characterized by the SMART criteria – Specific, Measurable, Achievable, Relevant, and Time-bound. Specific objectives clearly define what you plan to achieve, eliminating any ambiguity. Measurable objectives allow you to track progress and assess the outcome. Achievable objectives are realistic, considering the research sources and time available. Relevant objectives align with the broader goals of your field or research question. Finally, Time-bound objectives have a clear timeline for completion, adding urgency and a schedule to your work.

Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives

So lets get to the part, how to write research objectives properly?

• Understand the issue or gap in existing knowledge your study aims to address.
• Gain insights into how similar challenges have been approached to refine your objectives.
• Articulate the broad goal of research based on your understanding of the problem.
• Detail the specific aspects of your research, ensuring they are actionable and measurable.

How to Know When Your Objectives Need Refinement

Your objectives of research may require refinement if they lack clarity, feasibility, or alignment with the research problem. If you find yourself struggling to design experiments or methods that directly address your objectives, or if the objectives seem too broad or not directly related to your research question, it’s likely time for refinement. Additionally, objectives in research proposal that do not facilitate a clear measurement of success indicate a need for a more precise definition. Refinement involves ensuring that each objective is specific, measurable, achievable, relevant, and time-bound, enhancing your research’s overall focus and impact.

Research Objectives Examples in Different Fields

The application of research objectives spans various academic disciplines, each with its unique focus and methodologies. To illustrate how the objectives of the study guide a research paper across different fields, here are some research objective examples:

• In Health Sciences , a research aim may be to “determine the efficacy of a new vaccine in reducing the incidence of a specific disease among a target population within one year.” This objective is specific (efficacy of a new vaccine), measurable (reduction in disease incidence), achievable (with the right study design and sample size), relevant (to public health), and time-bound (within one year).
• In Environmental Studies , the study objectives could be “to assess the impact of air pollution on urban biodiversity over a decade.” This reflects a commitment to understanding the long-term effects of human activities on urban ecosystems, emphasizing the need for sustainable urban planning.
• In Economics , an example objective of a study might be “to analyze the relationship between fiscal policies and unemployment rates in developing countries over the past twenty years.” This seeks to explore macroeconomic trends and inform policymaking, highlighting the role of economic research study in societal development.

These examples of research objectives describe the versatility and significance of research objectives in guiding scholarly inquiry across different domains. By setting clear, well-defined objectives, researchers can ensure their studies are focused and impactful and contribute valuable knowledge to their respective fields.

Defining research studies objectives and problem statement is not just a preliminary step, but a continuous guiding force throughout the research journey. These goals of research illuminate the path forward and ensure that every stride taken is meaningful and aligned with the ultimate goals of the inquiry. Whether through the meticulous application of the SMART criteria or the strategic alignment with research questions and hypotheses, the rigor in crafting and refining these objectives underscores the integrity and relevance of the research. As scholars venture into the vast terrains of knowledge, the clarity, and precision of their objectives serve as beacons of light, steering their explorations toward discoveries that advance academic discourse and resonate with the broader societal needs.

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Home » Research Objectives – Types, Examples and Writing Guide

Research Objectives – Types, Examples and Writing Guide

Table of Contents

Research Objectives

Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research . The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

Types of Research Objectives

Here are the different types of research objectives in research:

• Exploratory Objectives: These objectives are used to explore a topic, issue, or phenomenon that has not been studied in-depth before. The aim of exploratory research is to gain a better understanding of the subject matter and generate new ideas and hypotheses .
• Descriptive Objectives: These objectives aim to describe the characteristics, features, or attributes of a particular population, group, or phenomenon. Descriptive research answers the “what” questions and provides a snapshot of the subject matter.
• Explanatory Objectives : These objectives aim to explain the relationships between variables or factors. Explanatory research seeks to identify the cause-and-effect relationships between different phenomena.
• Predictive Objectives: These objectives aim to predict future events or outcomes based on existing data or trends. Predictive research uses statistical models to forecast future trends or outcomes.
• Evaluative Objectives : These objectives aim to evaluate the effectiveness or impact of a program, intervention, or policy. Evaluative research seeks to assess the outcomes or results of a particular intervention or program.
• Prescriptive Objectives: These objectives aim to provide recommendations or solutions to a particular problem or issue. Prescriptive research identifies the best course of action based on the results of the study.
• Diagnostic Objectives : These objectives aim to identify the causes or factors contributing to a particular problem or issue. Diagnostic research seeks to uncover the underlying reasons for a particular phenomenon.
• Comparative Objectives: These objectives aim to compare two or more groups, populations, or phenomena to identify similarities and differences. Comparative research is used to determine which group or approach is more effective or has better outcomes.
• Historical Objectives: These objectives aim to examine past events, trends, or phenomena to gain a better understanding of their significance and impact. Historical research uses archival data, documents, and records to study past events.
• Ethnographic Objectives : These objectives aim to understand the culture, beliefs, and practices of a particular group or community. Ethnographic research involves immersive fieldwork and observation to gain an insider’s perspective of the group being studied.
• Action-oriented Objectives: These objectives aim to bring about social or organizational change. Action-oriented research seeks to identify practical solutions to social problems and to promote positive change in society.
• Conceptual Objectives: These objectives aim to develop new theories, models, or frameworks to explain a particular phenomenon or set of phenomena. Conceptual research seeks to provide a deeper understanding of the subject matter by developing new theoretical perspectives.
• Methodological Objectives: These objectives aim to develop and improve research methods and techniques. Methodological research seeks to advance the field of research by improving the validity, reliability, and accuracy of research methods and tools.
• Theoretical Objectives : These objectives aim to test and refine existing theories or to develop new theoretical perspectives. Theoretical research seeks to advance the field of knowledge by testing and refining existing theories or by developing new theoretical frameworks.
• Measurement Objectives : These objectives aim to develop and validate measurement instruments, such as surveys, questionnaires, and tests. Measurement research seeks to improve the quality and reliability of data collection and analysis by developing and testing new measurement tools.
• Design Objectives : These objectives aim to develop and refine research designs, such as experimental, quasi-experimental, and observational designs. Design research seeks to improve the quality and validity of research by developing and testing new research designs.
• Sampling Objectives: These objectives aim to develop and refine sampling techniques, such as probability and non-probability sampling methods. Sampling research seeks to improve the representativeness and generalizability of research findings by developing and testing new sampling techniques.

How to Write Research Objectives

Writing clear and concise research objectives is an important part of any research project, as it helps to guide the study and ensure that it is focused and relevant. Here are some steps to follow when writing research objectives:

• Identify the research problem : Before you can write research objectives, you need to identify the research problem you are trying to address. This should be a clear and specific problem that can be addressed through research.
• Define the research questions : Based on the research problem, define the research questions you want to answer. These questions should be specific and should guide the research process.
• Identify the variables : Identify the key variables that you will be studying in your research. These are the factors that you will be measuring, manipulating, or analyzing to answer your research questions.
• Write specific objectives: Write specific, measurable objectives that will help you answer your research questions. These objectives should be clear and concise and should indicate what you hope to achieve through your research.
• Use the SMART criteria: To ensure that your research objectives are well-defined and achievable, use the SMART criteria. This means that your objectives should be Specific, Measurable, Achievable, Relevant, and Time-bound.
• Revise and refine: Once you have written your research objectives, revise and refine them to ensure that they are clear, concise, and achievable. Make sure that they align with your research questions and variables, and that they will help you answer your research problem.

Example of Research Objectives

Examples of research objectives Could be:

Research Objectives for the topic of “The Impact of Artificial Intelligence on Employment”:

• To investigate the effects of the adoption of AI on employment trends across various industries and occupations.
• To explore the potential for AI to create new job opportunities and transform existing roles in the workforce.
• To examine the social and economic implications of the widespread use of AI for employment, including issues such as income inequality and access to education and training.
• To identify the skills and competencies that will be required for individuals to thrive in an AI-driven workplace, and to explore the role of education and training in developing these skills.
• To evaluate the ethical and legal considerations surrounding the use of AI for employment, including issues such as bias, privacy, and the responsibility of employers and policymakers to protect workers’ rights.

When to Write Research Objectives

• At the beginning of a research project : Research objectives should be identified and written down before starting a research project. This helps to ensure that the project is focused and that data collection and analysis efforts are aligned with the intended purpose of the research.
• When refining research questions: Writing research objectives can help to clarify and refine research questions. Objectives provide a more concrete and specific framework for addressing research questions, which can improve the overall quality and direction of a research project.
• After conducting a literature review : Conducting a literature review can help to identify gaps in knowledge and areas that require further research. Writing research objectives can help to define and focus the research effort in these areas.
• When developing a research proposal: Research objectives are an important component of a research proposal. They help to articulate the purpose and scope of the research, and provide a clear and concise summary of the expected outcomes and contributions of the research.
• When seeking funding for research: Funding agencies often require a detailed description of research objectives as part of a funding proposal. Writing clear and specific research objectives can help to demonstrate the significance and potential impact of a research project, and increase the chances of securing funding.
• When designing a research study : Research objectives guide the design and implementation of a research study. They help to identify the appropriate research methods, sampling strategies, data collection and analysis techniques, and other relevant aspects of the study design.
• When communicating research findings: Research objectives provide a clear and concise summary of the main research questions and outcomes. They are often included in research reports and publications, and can help to ensure that the research findings are communicated effectively and accurately to a wide range of audiences.
• When evaluating research outcomes : Research objectives provide a basis for evaluating the success of a research project. They help to measure the degree to which research questions have been answered and the extent to which research outcomes have been achieved.
• When conducting research in a team : Writing research objectives can facilitate communication and collaboration within a research team. Objectives provide a shared understanding of the research purpose and goals, and can help to ensure that team members are working towards a common objective.

Purpose of Research Objectives

Some of the main purposes of research objectives include:

• To clarify the research question or problem : Research objectives help to define the specific aspects of the research question or problem that the study aims to address. This makes it easier to design a study that is focused and relevant.
• To guide the research design: Research objectives help to determine the research design, including the research methods, data collection techniques, and sampling strategy. This ensures that the study is structured and efficient.
• To measure progress : Research objectives provide a way to measure progress throughout the research process. They help the researcher to evaluate whether they are on track and meeting their goals.
• To communicate the research goals : Research objectives provide a clear and concise description of the research goals. This helps to communicate the purpose of the study to other researchers, stakeholders, and the general public.

Advantages of Research Objectives

Here are some advantages of having well-defined research objectives:

• Focus : Research objectives help to focus the research effort on specific areas of inquiry. By identifying clear research questions, the researcher can narrow down the scope of the study and avoid getting sidetracked by irrelevant information.
• Clarity : Clearly stated research objectives provide a roadmap for the research study. They provide a clear direction for the research, making it easier for the researcher to stay on track and achieve their goals.
• Measurability : Well-defined research objectives provide measurable outcomes that can be used to evaluate the success of the research project. This helps to ensure that the research is effective and that the research goals are achieved.
• Feasibility : Research objectives help to ensure that the research project is feasible. By clearly defining the research goals, the researcher can identify the resources required to achieve those goals and determine whether those resources are available.
• Relevance : Research objectives help to ensure that the research study is relevant and meaningful. By identifying specific research questions, the researcher can ensure that the study addresses important issues and contributes to the existing body of knowledge.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Frequently asked questions

How do i write a research objective.

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

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Crafting Clear Pathways: Writing Objectives in Research Papers

Struggling to write research objectives? Follow our easy steps to learn how to craft effective and compelling objectives in research papers.

Are you struggling to define the goals and direction of your research? Are you losing yourself while doing research and tend to go astray from the intended research topic? Fear not, as many face the same problem and it is quite understandable to overcome this, a concept called research objective comes into play here.

In this article, we’ll delve into the world of the objectives in research papers and why they are essential for a successful study. We will be studying what they are and how they are used in research.

What is a Research Objective?

A research objective is a clear and specific goal that a researcher aims to achieve through a research study. It serves as a roadmap for the research, providing direction and focus. Research objectives are formulated based on the research questions or hypotheses, and they help in defining the scope of the study and guiding the research design and methodology. They also assist in evaluating the success and outcomes of the research.

Types of Research Objectives

There are typically three main types of objectives in a research paper:

• Exploratory Objectives: These objectives are focused on gaining a deeper understanding of a particular phenomenon, topic, or issue. Exploratory research objectives aim to explore and identify new ideas, insights, or patterns that were previously unknown or poorly understood. This type of objective is commonly used in preliminary or qualitative studies.
• Descriptive Objectives: Descriptive objectives seek to describe and document the characteristics, behaviors, or attributes of a specific population, event, or phenomenon. The purpose is to provide a comprehensive and accurate account of the subject of study. Descriptive research objectives often involve collecting and analyzing data through surveys, observations, or archival research.
• Explanatory or Causal Objectives: Explanatory objectives aim to establish a cause-and-effect relationship between variables or factors. These objectives focus on understanding why certain events or phenomena occur and how they are related to each other. 

Also Read: What are the types of research?

Steps for Writing Objectives in Research Paper

1. identify the research topic:.

Clearly define the subject or topic of your research. This will provide a broad context for developing specific research objectives.

2. Conduct a Literature Review

Review existing literature and research related to your topic. This will help you understand the current state of knowledge, identify any research gaps, and refine your research objectives accordingly.

3. Identify the Research Questions or Hypotheses

Formulate specific research questions or hypotheses that you want to address in your study. These questions should be directly related to your research topic and guide the development of your research objectives.

4. Focus on Specific Goals

Break down the broader research questions or hypothesis into specific goals or objectives. Each objective should focus on a particular aspect of your research topic and be achievable within the scope of your study.

5. Use Clear and Measurable Language

Write your research objectives using clear and precise language. Avoid vague terms and use specific and measurable terms that can be observed, analyzed, or measured.

6. Consider Feasibility

Ensure that your research objectives are feasible within the available resources, time constraints, and ethical considerations. They should be realistic and attainable given the limitations of your study.

7. Prioritize Objectives

If you have multiple research objectives, prioritize them based on their importance and relevance to your overall research goals. This will help you allocate resources and focus your efforts accordingly.

8. Review and Refine

Review your research objectives to ensure they align with your research questions or hypotheses, and revise them if necessary. Seek feedback from peers or advisors to ensure clarity and coherence.

Tips for Writing Objectives in Research Paper

1. be clear and specific.

Clearly state what you intend to achieve with your research. Use specific language that leaves no room for ambiguity or confusion. This ensures that your objectives are well-defined and focused.

2. Use Action Verbs

Begin each research objective with an action verb that describes a measurable action or outcome. This helps make your objectives more actionable and measurable.

3. Align with Research Questions or Hypotheses

Your research objectives should directly address the research questions or hypotheses you have formulated. Ensure there is a clear connection between them to maintain coherence in your study.

4. Be Realistic and Feasible

Set research objectives that are attainable within the constraints of your study, including available resources, time, and ethical considerations. Unrealistic objectives may undermine the validity and reliability of your research.

5. Consider Relevance and Significance

Your research objectives should be relevant to your research topic and contribute to the broader field of study. Consider the potential impact and significance of achieving the objectives.

SMART Goals for Writing Research Objectives

To ensure that your research objectives are well-defined and effectively guide your study, you can apply the SMART framework. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. Here’s how you can make your research objectives SMART:

• Specific : Clearly state what you want to achieve in a precise and specific manner. Avoid vague or generalized language. Specify the population, variables, or phenomena of interest.
• Measurable : Ensure that your research objectives can be quantified or observed in a measurable way. This allows for objective evaluation and assessment of progress.
• Achievable : Set research objectives that are realistic and attainable within the available resources, time, and scope of your study. Consider the feasibility of conducting the research and collecting the necessary data.
• Relevant : Ensure that your research objectives are directly relevant to your research topic and contribute to the broader knowledge or understanding of the field. They should align with the purpose and significance of your study.
• Time-bound : Set a specific timeframe or deadline for achieving your research objectives. This helps create a sense of urgency and provides a clear timeline for your study.

Examples of Research Objectives

Here are some examples of research objectives from various fields of study:

• To examine the relationship between social media usage and self-esteem among young adults aged 18-25 in order to understand the potential impact on mental well-being.
• To assess the effectiveness of a mindfulness-based intervention in reducing stress levels and improving coping mechanisms among individuals diagnosed with anxiety disorders.
• To investigate the factors influencing consumer purchasing decisions in the e-commerce industry, with a focus on the role of online reviews and social media influencers.
• To analyze the effects of climate change on the biodiversity of coral reefs in a specific region, using remote sensing techniques and field surveys.

Importance of Research Objectives

Research objectives play a crucial role in the research process and hold significant importance for several reasons:

• Guiding the Research Process: Research objectives provide a clear roadmap for the entire research process. They help researchers stay focused and on track, ensuring that the study remains purposeful and relevant. 
• Defining the Scope of the Study: Research objectives help in determining the boundaries and scope of the study. They clarify what aspects of the research topic will be explored and what will be excluded. 
• Providing Direction for Data Collection and Analysis: Research objectives assist in identifying the type of data to be collected and the methods of data collection. They also guide the selection of appropriate data analysis techniques. 
• Evaluating the Success of the Study: Research objectives serve as benchmarks for evaluating the success and outcomes of the research. They provide measurable criteria against which the researcher can assess whether the objectives have been met or not. 
• Enhancing Communication and Collaboration: Clearly defined research objectives facilitate effective communication and collaboration among researchers, advisors, and stakeholders. 

Common Mistakes to Avoid While Writing Research Objectives

When writing research objectives, it’s important to be aware of common mistakes and pitfalls that can undermine the effectiveness and clarity of your objectives. Here are some common mistakes to avoid:

• Vague or Ambiguous Language: One of the key mistakes is using vague or ambiguous language that lacks specificity. Ensure that your research objectives are clearly and precisely stated, leaving no room for misinterpretation or confusion.
• Lack of Measurability: Research objectives should be measurable, meaning that they should allow for the collection of data or evidence that can be quantified or observed. Avoid setting objectives that cannot be measured or assessed objectively.
• Lack of Alignment with Research Questions or Hypotheses: Your research objectives should directly align with the research questions or hypotheses you have formulated. Make sure there is a clear connection between them to maintain coherence in your study.
• Overgeneralization : Avoid writing research objectives that are too broad or encompass too many variables or phenomena. Overgeneralized objectives may lead to a lack of focus or feasibility in conducting the research.
• Unrealistic or Unattainable Objectives: Ensure that your research objectives are realistic and attainable within the available resources, time, and scope of your study. Setting unrealistic objectives may compromise the validity and reliability of your research.

In conclusion, research objectives are integral to the success and effectiveness of any research study. They provide a clear direction, focus, and purpose, guiding the entire research process from start to finish. By formulating specific, measurable, achievable, relevant, and time-bound objectives, researchers can define the scope of their study, guide data collection and analysis, and evaluate the outcomes of their research.

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Objectivity for the research worker

Noah van dongen.

1 University of Amsterdam, Amsterdam, The Netherlands

Michał Sikorski

2 University of Gdańsk, Gdańsk, Poland

3 Warsaw University of Technology, Warsaw, Poland

In the last decade, many problematic cases of scientific conduct have been diagnosed; some of which involve outright fraud (e.g., Stapel, 2012) others are more subtle (e.g., supposed evidence of extrasensory perception; Bem, 2011). These and similar problems can be interpreted as caused by lack of scientific objectivity. The current philosophical theories of objectivity do not provide scientists with conceptualizations that can be effectively put into practice in remedying these issues. We propose a novel way of thinking about objectivity for individual scientists; a negative and dynamic approach.We provide a philosophical conceptualization of objectivity that is informed by empirical research. In particular, it is our intention to take the first steps in providing an empirically and methodologically informed inventory of factors that impair the scientific practice. The inventory will be compiled into a negative conceptualization (i.e., what is not objective), which could in principle be used by individual scientists to assess (deviations from) objectivity of scientific practice. We propose a preliminary outline of a usable and testable instrument for indicating the objectivity of scientific practice.

The first principle is that you must not fool yourself, and you are the easiest person to fool. Richard Feynman (Cargo Cult Science, 1974 )

Introduction: a story about a scientist

Despite its undeniable success (e.g., electricity, space flights, etc.), science seems to be in a difficult position today. In the last few years, many problematic cases of scientific conduct were diagnosed, some of which involve outright fraud (e.g., Stapel, 2012 ) while others are more subtle (e.g., supposed evidence for precognition; Bem, 2011 ). These particular issues and the general lack of replicability of scientific findings (e.g., Open Science Collaboration, 2015 ) have contributed to what has become known as the replication crisis (e.g., Harris, 2017 ). In addition, the general public has become aware of these problems, which has shaken the general trust in science (e.g., Lilienfeld, 2012 , Pashler & Wagenmakers, 2012 , Anvari & Lakens, 2018 ).

Let us imagine a scientist, Dr. Jane Summers. Dr. Summers 1 does research in cognitive psychology. One day, she reads about the low replicablity rate of the results of psychological studies (e.g., Open Science Collaboration, 2015 ). She becomes very concerned about the value of scientific results in general and her own research in particular. As a result, she is resolved to investigate to what extent her work is at risk of irreplicability and to ensure that her current and future work is as resilient as possible against such a fate. She decides that, apart from ensuring the accuracy and precision of her measurements, the methods she employs should not be significantly influenced by her feelings, values, biases and other idiosyncrasies. To her, this would mean that they are objective (Hawkins & Nosek, 2012 ; Ziman, 1996 ; Stegenga, 2011 ). 2 Objectivity can be attributed, among others, to scientific measurements, tools for development/improvement of scientific theories, and/or to true-to-nature explanations. It ensures that study outcomes are not biased (e.g., over estimation of drug efficacy, under estimation of risk; Goldacre, 2014 ), positive research results are not false-positives (to a larger proportion than is allowed by the statistical method; Simmons et al., 2011 ), and are independently reproducible by other scientists (Simons, 2014 ; Lindsay, 2015 ; Altmejd et al., 2019 ; van Bavel et al., 2016 ). Dr. Summers considers objectivity to be essential to science 3 and its absence to be a cause of the crisis that threatens the foundations of her research field. In short, Dr. Summers considers the assessment and safeguarding of scientific objectivity as being of vital importance. Plausibly, such sentiment toward objectivity is common among actual scientists. For example, we can easily imagine Prof. Bem wanting to present results as solid and close to incontrovertible evidence in favor of precognition as possible (Bem, 2011 ). Specifically, ensuring objectivity of his experiments would ensure that his claims are on solid ground.

It is therefore somewhat puzzling to Dr. Summers that a proper explication of objectivity appears to be lacking in science. She is unable to find tools for the qualitative and/or quantitative assessment of objectivity. Methodological reforms are inspired by problematic cases, for instance, measurement or impossible results (e.g., precognition; Bennett et al., 2010 ) or failures to reproduce established experimental results (e.g., Klein et al., 2018 ), rather than a clear understanding of objectivity. She could attempt to replicate her own work, have it replicated by others, and/or review her publication with respect to guidelines of statistical methods (see for instance Gervais, 2017 , Carney, 2016 ) and, as a result, declare a lack of confidence in her own work (see for instance Rohrer et al., 2018 ), but nothing more systematic is available. Similarly, Prof. Bem would have a hard time providing an objectivity assessment of his precognition experiments with the currently available tools. Thus, Dr. Summers realizes that science could greatly benefit from having a definition of ‘objectivity’ that can be explicated in a quantitative or qualitative assessment of scientific practice.

Dr. Summers has a hunch that philosophy might be of assistance in defining objectivity. After a short review of the philosophical literature, she does not manage to find a notion of objectivity ready for use in scientific practice. Typically, such proposals are descriptive and therefore lacks a guiding force, because they are not supported by normative considerations. Other proposals are difficult or impossible to test, thus prohibiting scientists from assessing objectivity (Section  2 ). In effect, Dr. Summers becomes disheartened and contemplates quitting her quest for objectivity.

It is our opinion that we, philosophers, should not disappoint scientists like Dr. Summers in this respect and that philosophy can and should do better. We believe that the philosophical literature currently lacks a scientifically useful conceptualization of objectivity and we intend to fill this gap. In this article, we present a conceptualization of objectivity of scientific practice that is practicable by the individual scientist. We understand scientific practice as pertaining to empirical research, which include all activities done by scientist essential for this endeavor. These include study design, data collection and measurement, data analysis, result reporting etc. 4 We recognize that the social and cultural conditions play a role in, for instance, determining what kind of research gets funded, and recognize the value of social epistemology and literature on non-epistemic values (e.g., Biddle, 2007 , Bueter, 2015 , Longino, 1990 , Elliott and McKaughan, 2009 ). However, much of this is beyond the purview of what an individual researcher can control and therefore beyond the scope of our paper. 5

Our aim is to provide a scientifically useful notion of objectivity. In order to be useful such a conceptualization must be both based on normative considerations and testable. For if it is not based on normative and reliable methodological results it is not clear if it possesses any guiding force and if it is not testable, it cannot be used to assess the objectivity of a given practice. In the next section, we will briefly discuss several philosophical views on objectivity and highlight where there is room for improvement. In the third section, we present a novel version of a negative approach to scientific objectivity and provide a testable conceptualization of objectivity that is based on robust empirical results and methodological considerations. Finally, we defend the fruitfulness of our notion by demonstrating how it can be used in scientific practice (Section  4.2 ) and provide a sketch of a tool for assessing objectivity inspired by our new conceptualization (Appendix  A ).

Philosophy on objectivity

In philosophy of science, scientific objectivity is a well discussed notion. Following Reiss and Sprenger ( 2017 ), we can list three main ways of conceptualizing it. Firstly, objectivity can be understood as a faithfulness to facts. Secondly, something can be understood as objective when it is free from value commitments. Thirdly, objectivity can be understood as being free from scientists’ personal biases. Recently, proposals which have gained much popularity are pluralist notions of objectivity (e.g., Douglas, 2004 , Megill, 1994 , Wright, 2018 ). Such notions encompass some or all of mentioned individual notions (e.g., the value-free objectivity, value neutral objectivity, procedural objectivity etc.). Finally, there are negative conceptions of objectivity (e.g., Koskinen, 2020 , Daston & Galison, 2010 , Hacking, 2015 ) which claim that the objectivity consist of the absence of certain factors. In the case of (Daston & Galison, 2010 ), these are factors of scientific subjectivity which are recognized by the scientific community as particularly troubling or important in a given time period. In the case of Koskinen ( 2020 ), these factors are epistemic risks which arise from the imperfections of epistemic agents.

Despite this effort, it seems that a conceptualization of scientific objectivity that can be easily used by scientists has not yet been proposed. The literature is comprised of proposals that were not designed to fulfill such a practical role. Instead, they were designed to describe how the concept is used. Following Searle ( 1975 ) we will understand the difference between descriptive and normative discourse in terms of the direction of fit. The descriptive claims aim at describing reality (e.g., ‘there is no poverty in the world’). In contrast, normative claims are not descriptions of how things are, they are intended to describe how the word should be (e.g, ‘there should not be poverty in the world’). In other words, descriptive claims have language-to-reality direction of fit while normative claims have a reality-to-language direction of fit . Consequently, a descriptive theory of a given concept describes in precise terms (the meaning of) the concept, which is actually used by natural language speakers (or some sub-group of them). Such theories can be assessed empirically by comparing it with the intuitions of a target group. The examples of such theories are the semantics for conditionals (see e.g., Douven et al., 2018 ). On the other hand, a normative theory of a given concept presents (a meaning of) the concept which, when used, will be beneficial for the hypothetical users. For example, some of the formal theories on truth offer replacements for the concept of ‘truth’ used in natural language (see e.g., Scharp, 2013 , Tarski, 1936 ). Authors of these proposals argue that new concepts are superior to the concept present in natural language, because, for example, they are not susceptible to notorious semantic paradoxes. In our article, we are interested in a normative theory of objectivity. Hence, we are less concerned with how the new conceptualization corresponds to how objectivity is used in natural language and more concerned with how the conceptualization promotes the methodological quality of science (e.g., replicability, lack of bias, etc.) and its results (e.g., approximately true / highly corroborated theories, theories with high predictive accuracy).

In light of the conflicting intuitions and conceptual confusion surrounding objectivity, the descriptive conceptualizations of objectivity are clearly useful. However, it is distinct from our aim of formalizing a notion that is normatively useful. Due to their descriptive aim, it is not clear if these theories can fulfill the normative task of guiding scientific practice and it would not be fair to assess them in this context. Some authors are explicit about the descriptive nature of their proposals. For example, the aims of Heather Douglas ( 2004 ) famous article seems to be primarily 6 descriptive:

In this paper, I will lay out a complex mapping of the senses of objectivity. This mapping will make two contributions to current discussions. First, it will dissect objectivity along operationally distinct modes.[...] Second, the mapping will allow me to cogently argue that the different meanings of objectivity I explore here are not logically reducible to one core meaning. (Douglas, 2004 , p. 454-455)

Similarly, Koskinen ( 2020 ) is explicit about the descriptive aim of her proposal:

In this article I defend a risk account of scientific objectivity. The account is meant to be a largely descriptive or even a semantic one; my aim is to draw together ideas presented in recent discussions, and to clarify what we philosophers of science do when we identify distinct, applicable senses of objectivity or call something objective. (Koskinen, 2020 , p.1)

These quotes indicate that Douglas ( 2004 ) collects applicable notions of objectivity ( procedural objectivity, value free objectivity , etc.) while Koskinen unifies those distinct meanings. Their aims are descriptive. In the case of other proposals, it is clear that they are descriptive due to their methodological approach. For instance, Datson and Galison’s ( 2010 ) historical methodology makes it a descriptive proposal.

Secondly, some of the proposed normative conceptualizations of objectivity are not suitable to be used by scientists. Such notions need to be testable. Otherwise, how can we assess if given scientific practice is objective or not? An example of a notion that fails in this respect is value-free objectivity . Value-free objectivity is based on a more general value-free ideal . The value-free ideal claims that scientists should not use their non-epistemic values, like ‘equality’ of ‘fairness’, when they justify their claims (e.g., Betz, 2013 ). This conception of objectivity claims that a scientific justification is objective as long as it is not influenced by non-epistemic values. There might be reasons to believe that value-free ideal should be followed (e.g., Betz, 2013 , Sober, 2007 ) or that the corresponding notion of objectivity is compelling. However, many problems of value-free objectivity have been diagnosed. For instance, (Douglas, 2004 ), after (Rudner, 1953 ), argued that value free-ideal is unrealizable. Similarly, (Longino, 1996 ) claims that the distinction between epistemic and non-epistemic values, on which value-free objectivity is based, is ill-defined, making this conceptualization of objectivity problematic. Additionally, there are clear difficulties in assessing the value-free objectivity of scientific practice. Most glaringly, we do not have access to scientists’ intentions, thus we cannot judge what motivated their decisions and actions. Therefore, we cannot use a theory which defines objectivity in terms of values used by scientist to, for example, assess the objectivity of a procedure or research result. In short, the rich and fruitful discussion concerning the role of values in science (see e.g., Douglas, 2009 , Steel, 2010 , Hicks, 2014 , Brown, 2013 , Longino, 1990 ) and other notions of objectivity inspired by it (see e.g., Douglas, 2009 , Longino, 2004 ) are not directly applicable to our problem. 7

A detailed discussion of the practical usability of other notions of objectivity presented in literature is beyond the scope of our paper. However, we expect that this cursory sketch provides an overview of the problems with using these notions and motivates the value of a new conceptualization of objectivity of scientific practice.

To see it from the other side: problems in science and the via-negativa approach to objectivity

There is no generally accepted positive definition of ‘health’ in health care and the medical sciences. 8 Fortunately, this does not prevent doctors from healing ailments and researchers from developing new drugs and technologies. A positive definition of health is unnecessary, when the instances that reduce or endanger health can be defined and addressed. In brief, health is what remains when the particular infirmities are removed. 9 Health care and medical science appears to be successful, even in the face of changing definitions, diagnostics, and disagreements about ailments. 10 We believe that this via-negativa approach can also be applied to the concept of scientific objectivity.

Our negative approach resembles other negative proposals in philosophy (e.g., Koskinen, 2020 , Daston & Galison, 2010 ). Just like these approaches, we conceptualize objectivity as what remains in the absence of certain factors, however, our aim and identified factors are different. Specifically, the purpose of our notion is to be testable and practicable by scientists. Hence, we base our conceptualization on empirical research. We postulate that non-objectivity consists of factors that have been empirically or methodologically identified as making scientific practice susceptible to the actions and decisions of scientist which can inadvertently or intentionally influence research results. Such practices have the propensity to reduce reliability, validity, and replication rates of the results (e.g., Simmons et al., 2011 ). We assert that the factors constituting non-objectivity translate to a conceptualization of objectivity, which not only preserves some of our intuitions about objectivity but also and more importantly, can be put into practice by scientists (Section  4.2 ).

The general sense of how the objectivity of scientific practice can be compromised is as follows. Researchers make certain decisions when they design their study and collect, process, and analyze their data. The possibility of choosing between two or more options in these instances are called researchers’ degrees of freedom (Simmons et al., 2011 ; Wicherts et al., 2016 ). The misuse of which can result in biased 11 and/or irreproducible outcomes. Kinds of such misuse are identified, for instance, by questioning scientists on their behavior and the behavior of others (e.g., Kerr, 1998 , John et al., 2012 ) or case studies (e.g., Schimmack, 2020 ) in comparison to data simulation or principles of statistical analysis. As an example of misuse identification, Schimmack ( 2020 ) reanalyzed the data of Bem’s Feeling the Future experiments (Bem, 2011 ) and uncovered that effects of precognition were very large if only the data of the first few included participants were analyzed, but decreased raptly to just above the statistical significance threshold towards the end of participant inclusion. Schimmack showed that such a pattern is produced by starting many studies on a non-existing effect and discontinuing all but those that show ‘promise’ (i.e., an initial strong positive effect).

The ways in which scientists can misuse this freedom can be grouped into two categories. Firstly, a scientist can make a priori decisions concerning the research design and data collection, which can preclude certain outcomes or make them more/less likely (i.e. introduce systematic bias in a certain direction). Secondly, a scientist has to make decisions on how to process and analyze the data, which allows her to try all possible combinations of decisions until a positive/desired result is found. In this section, we first focus on problematic practices taking place before or during conducting the study (Section  3.1 ). Then, we discuss problematic data management and analysis practices (Section  3.2 ). The section is concluded with a testable conceptualization of objectivity as resilience to such problematic practices.

Problems before and during research: design, data collection, and measurements

During the early stages of a scientific experiment (e.g., designing the observational study or experiment, sampling, measurement, etc.), a scientist has to make several decisions, which could influence the final result. In some cases, a scientist might make such choices with the aim of obtaining a specific result in mind. Such decisions introduce bias (e.g., Fanelli et al., 2017 ).

In science, biased research seems to results from the influence of beliefs or prejudices of the scientist on her methodological decisions. For example, scientists make methodological choices that increase the likelihood of getting results that align with the preferences of those that provide the research funds (this is known as ’funding bias’ Nelson, 2014 , Jones & Sugden, 2001 ). Similarly, a scientist can adjust the design of her experiment or observational study, consciously or subconsciously, in order to increase the probability that the results will support her prior beliefs. Typically, biased outcomes(s) only require(s) a single decision or a small number of decisions during the experiment design phase of the research. Taking pharmaceutical research as an illustration, positive results for tested medication is boosted by, for instance, selecting only unrepresentative ‘ideal’ patients, comparing the drug to an ineffective alternative, or using different effective doses for the treatment and control group (e.g., Rothwell, 2005 , Travers et al., 2007 , Safer, 2002 ). As another example, the biased studies presented in Wilholt ( 2008 ) involve scientists choosing a specific strain of experimental animals, which made the experiments significantly less likely to show the toxicity of the tested substance (in line with the preference of the funding institution). Next to sample selection, bias can be introduced through many of the other decision that a scientist has to make when designing and conducting research, specifically:

• Which measurement (outcome measure) to use?
• Which kind of independent variable (experimental manipulation) to use?
• Which sample to select and how?
• Setting of the experiment or observational study (when and where)?
• How and to what extent do researcher and research subject interact?
• How to perform the measurement (e.g., blinded or unblinded)?

Recognition of features that can introduce bias is reflected in proposals concerning how to counter it. For example, Wilholt ( 2008 ) proposed establishing conventions which regulate the way scientist should conduct their studies as a remedy to funding bias. In the case of choosing insensitive animals, he proposed to adopt the following convention:

Because of clear species and strain differences in sensitivity, animal model selection should be based on responsiveness to endocrine active agents of concern (i.e. responsive to positive controls), not on convenience and familiarity. (US Department of Health and Human Services, 2001 , p.vii)

Different conventions are and can be implemented in order to impose methodological restrictions on scientists. Some of them force scientists to measure the direct outcome of interest instead of a proxy, use standardized tests or measurements, use random sampling from the population, use random allocations of participants to conditions, use equal group treatment, use blind or double blind design (experimental studies), and/or use data collectors that are blind to the research aim (observational studies). All of these conventions restrict the range of biasing decisions a scientist can make. In addition, these conventions can be empirically tested with respect to prohibiting potentially biasing actions by the scientists and reducing bias in research outcomes.

Problems after experiments or observations: data management, analysis specification, and result reporting

After a researcher has run the experiment and the data have been collected, several decisions have to be made. For instance, the data need to be processed (e.g., removing outliers, combining variables, binning variable values, etc.), the statistical model needs to be specified (e.g., linear model, multilevel model, structural equation model, etc.), and finally the dependent and predictor variables for the model need to be selected. The assumption is that for each step only one (and the most appropriate) of the possible options is selected. However, research has shown that the general rate of false-positive results 12 is increased when, instead of taking a single option for each step, several possible combinations of options are explored and only the combinations that culminate in positive results are reported (e.g., Simmons et al., 2011 , Wicherts et al., 2016 , John et al., 2012 , Szucs, 2016 ). These behind-the-scenes practices that covertly influence results go by the name of questionable research practices . The causes of these practices may be the scientists’ (sub)conscious beliefs or preferences, the ambiguity or ignorance about how the methods works and what the statistics are/mean, or the desire to find/see associations and structure in what is being studied. Concretely, at least the following decisions need to be made by a researchers when dealing with quantitative data and performing statistical analyses (this incomplete list is adapted from: Bakker et al., 2012 , Nelson et al., 2018 , Simmons et al., 2011 , Wicherts et al., 2016 , Kass et al., 2016 ):

• How to handle incomplete or missing data?
• How to pre-processes data (e.g., cleaning, normalizing, etc.)?
• How to process data, deal with violations of statistical assumptions (e.g., normality, homoscedasticity, etc.)?
• How to deal with outliers?
• Which measured construct to select as primary outcome?
• Which variable to select as dependent variable out of several that measure the same construct?
• How to score, bin, recode the chosen dependent variable?
• Which variables to select as predictors out of the set of measured variables?
• How to recode or restructure these predictors (e.g., combining variables, combining levels of a variable, etc.)?
• If and which variables to additionally include as covariates, mediators, or moderators?
• Which statistical model to use?
• Which estimation method and computation of standard errors to use?
• If and which correction for multiple testing to use?
• Which inference criteria to use (e.g., p-values and alpha level, Bayes factor, etc.)?

Note that, if such decisions needs to be made and how many option the scientists has to choose from depends on how the study was designed and the structure and amount of data that were collected.

Currently, there are already some potential strategies for restricting uses of questionable research practices (i.e., ad hoc decision making in order to get positive results, also known as p-hacking). For instance, a) preregistration of the study from design to analysis (e.g., Chambers, 2013 , Wicherts et al., 2016 , Nosek et al., 2018 ); b) data and analysis blinding (e.g., MacCoun and Perlmutter, 2015 ); and c) running several/all of the (theoretically) possible tests in a multiverse analysis (Steegen et al., 2016 ). 13 The effectiveness of these strategies can be empirically verified by researching their effects in, for instance, replication studies. It should be noted that such strategies are not mutually exclusive and that combinations are possible, because they all restrict researcher’s degrees of freedom without introducing new ones. For instance, not all decisions can be made in advance, precluding their preregistration. In such a case, some of these can be caught by data blinding, because the scientist might not know what the data will look like in advance, though has an analysis plan that can be communicated for the independent data analysis. In addition, the multiverse analysis can be employed for those elements of the research that have an exploratory nature that do not allow for data blinding and handing the analysis to someone else.

To Sum up: a conceptualization of objectivity

Our negative version of conceptualizing objectivity ties it to scientific problems that result from the decisions and actions of individual scientists. These problems are notoriously hard to detect. For instance, a report of a study during which questionable research practices were used can be indistinguishable from a report of a study where scientists actively tried to avoid influencing the results. If objectivity is just absence of these problems, then testing it is extremely difficult to impossible. On the other hand, we can easily tell if precautions against such problems (e.g., preregistration) are present and thus how resilient a given practice is. Therefore, we state that a scientific practice becomes more objective when it becomes demonstrably more resilient to actions and decisions that have the potential to influence its outcome; concretely, when:

• the study design and data collection becomes demonstrably more resilient to the scientists’ influence on the data;
• and the data processing and analysis become demonstrably more resilient to ad hoc decision making and selective reporting of positive results.

In the limit, a practice is objective when it is impervious to biasing influences and precludes ad hoc decisions and actions.

Our approach has two clear advantages. 1) It is empirically verifiable. 2) It does not require universally agreement about factors that reduce objectivity nor does the procedure for identifying these factors need to be objective. Our notion, in opposition to traditional conceptualization (e.g., value-free objectivity), ties objectivity to features of scientific practice, the existence of which can be empirically tested (e.g., was the study preregistered or not). 14 These features can, for instance, be collected in a form of a checklist (see the  Appendix for a first setup). Concretely, such a checklist could in principle be used by reviewers to evaluate submitted manuscripts on the precautions taken against biasing influences; or by readers of published papers who want to assess their trustworthiness; or by reviewers (writers) of grant applications to evaluate (show) that future results will be as insulated as possible against biasing effects. 15 In addition, objectivity according to this conceptualization can be verified by assessing the extent of systematic bias and inflated false-positive rates in a body of literature. The presence of objectivity promoting features like preregistration decreases the chance of a given study being a false positive. Therefore we can indirectly test the objectivity of studies, for instance, by testing consistency of results between preregistered experiments in comparison to consistency of results between non-preregistered experiments.

The second advantage follows from the first. We do not claim that the list of objectivity reducing factors on which our conceptualization is based is exhaustive. Moreover, some factors might be considered controversial as objectivity reducing or it may not be objective how factors are included, while other are not. This is not problematic for our proposal, because a) the identification and inclusion of factors is based on robust empirical results and methodological considerations; and b) their impact on the quality of the study, as explained in the previous paragraph, can be empirically verified.

In this paper, we have offered a novel and practicable conceptualization of scientific objectivity. We have argued that many of the popular philosophical attempts at defining objectivity are not practicable and are likely to be impossible to implement by individual scientists. As we have argued, some of the theories aim at reconstructing the way the philosophers or scientists understand objectivity rather than proposing a normatively compelling notion. Secondly, some of the normative proposals define objectivity in terms of features that are prohibitively difficult to test empirically and hence use in practice. For example, testing conceptualizations that define objectivity in terms of the intentions of scientists, like value-free objectivity, would require real-time access to the mind of scientists during research.

In our approach, we have used findings from empirical research and methodological considerations to identify features of scientific practice considered to be problematic (i.e., potential causes of bias and inflated false-positive rates). We postulate that resilience to these features constitute objectivity. Given these features, scientific practice approaches objectivity when it becomes less vulnerable to decisions and actions of scientists that can influence its outcome.

In this section, we discuss the limitations and implications of our conceptualization. In the appendix, we present a draft for a tool that can be used to assess the objectivity of scientific endeavours (e.g., published papers, submitted manuscripts, proposed research in grant applications, etc.). In addition, we suggest investigations into a tool such as ours to test and improve its validity and reliability. We close this paper with a detailed illustration of how such a tool could be usefully implemented.

Limitations

Incompleteness . Plausibly, in our paper we do not reach a complete list of ways in which scientific practice can be compromised. Therefore, it is most likely that we did not reach a complete definition of objectivity, though rather a number of currently identified necessary conditions. However, our approach does provide a framework for learning from empirical research and methodological developments when, where, and how particular factors compromise scientific objectivity. Even with this limitation, we believe that our conceptualization is an improvement over previous attempts of conceptualizing objectivity and can still be used in a fruitful way (Sections  3.3 and  4.2 ).

Ritualization . Some might argue that restricting researchers in the proposed way will actually reduce objectivity. For instance, the (faulty) use of the Null Hypothesis Significance Testing procedure (NHST) has been described as a restrictive ritual; a practice that discourages informed reasoning and prescribes certain actions and decisions. The NHST ritual has been considered to be the main cause of the inflated number of false-positive results in science (Gigerenzer, 2004 ; Stark & Saltelli, 2018 ; Ioannidis, 2005 ), which is the opposite of what an objective method should achieve. However, the NHST ritual only appears to restrict researchers and provides just the illusion of objectivity. In particular, apart from inference criterion (i.e., an observed statistic lower than a conventional threshold), this ritual does not restrict (mis)use of degrees of freedom (mentioned in Section  3.2 ) at any point during the research process. Specifically, and in contrast to recommendations of our proposal, ad hoc decision-making in data management, analysis, and result reporting are not prohibited in the NHST ritual. It might even be considered that this partial formalization enshrines a false sense of objectivity that is actually harmful to the quality of scientific results (e.g., Gigerenzer, 2004 , Simmons et al., 2011 ). In other words, if the ritual had been restrictive in ruling out questionable research practices, it would actually promote objectivity. Our conceptualization does recommend these additional restrictions. Also, in contrast to the conservative nature of a ritual, our conceptualization is (meant to be) adaptive; developed in accordance with novel discoveries concerning problematic scientific practices and methodological changes in science.

Restricted . Our conceptualization is restricted to practice of quantifiable or countable research, which precludes qualitative research and non-empirical practices. Qualitative research is currently omitted from our definition, because, to our knowledge, empirical research and methodological considerations on the particulars of systematic bias and false-positive rate inflation in the use of qualitative methods are currently absent in the academic literature. It remains an open question if our or a analogous notion can be applied to qualitative research.

Scientist-independent problems . In some cases, a source of negative influence on research results is independent of the decisions of a scientist (e.g., Biddle, 2007 , Bueter, 2015 , Harding, 2015 , Leuschner, 2012 , Longino, 1990 ). For example, a scientist may be restricted in access to particular instruments, samples, or treatments of research subjects for external reasons (e.g. ethical, political, financial, practical, etc.). Therefore, the results can be compromised, though not because the scientist misused degrees of freedom. It is also possible that some internal features of a research field or used methodology cause the results to be systematically biased. In such a case, the culture and conventions of a particular area of research may restrict individual scientists to particular measurement instruments and research subjects, which could produce spurious and biased findings. For instance, culture and politics can influence which research projects get funded and thus carried out (e.g., Bueter, 2015 , Elliott & McKaughan, 2009 ). These factors might also influence which research results get published (i.e., publication bias). Specifically, at the moment it seems that most scientific journals prefer to publish articles describing experiments with positive results and/or scientists submit only positive results to these journals. This bias against negative results precludes some research from entering the scientific literature, which inflates the rate of published false-positive results. Consequently, even if the scientific practice of each individual scientist is (as) objective (as possible), the false-positive rate will still be inflated to an unknown degree. Publication bias (e.g., Malički & Marušić, 2014 ) and other similar scientist-independent problems (e.g., Leuschner, 2012 , Biddle, 2007 ) are discussed extensively in the literature and some solutions were proposed (see e.g., Carroll et al., 2017 , Longino, 1990 , Harding, 2015 ). These problems are larger than the individual scientists and thus the proposed solutions typically involve changing the social arrangement of science rather than practices and procedures used by individual scientists. For example, (Biddle, 2007 ) proposes to implement a system of institutionalized criticism to counter the corrupting effect of financial stakes on the integrity of research, another major scientist-independent problem. However, the two types of problems are distinct and therefore they require different solutions. Misuse of degrees of freedom requires the improvement in objectivity as understood in a way we have described above. On the other hand, external limitation requires improvement in the social structure of science and possibly general improvements in scientific methodology. Thus, we acknowledge the existence of these social, cultural, political, and technical problems and are in favor of programs addressing these issues. Additionally, the solutions on both social and individual level problems are complementary and might be combined into a more complete proposal.

Exploratory research and serendipitous discoveries . Many (if not most of the) famous scientific breakthroughs have been serendipitous discoveries. These discoveries were most likely the product of exploratory research that were neither done by unbiased scientist nor completely free from practices that would now be labeled as ’questionable’. It should be noted that we do not object to these practices and even see them as a vital part of science. However, when it comes to verifying these findings and integrating them in the rest of science, we firmly believe that these discoveries should be tested with a practice that is as objective as possible.

Too demanding. Clearly, our conceptualization is very exacting. Not many or maybe even no scientific practice, past or present, is objective in this sense. This is a criticism that has also been leveled at procedural objectivity (Jukola, 2017 ). 16 Be that as it may, this does not prevent our notion from being useful. As we will demonstrate in the next section (Section  4.2 ) we can compare the relative objectivity of two methods even if neither of them is fully objective according to our conceptualization. Secondly, the notions give us a clear idea of which modifications of a given practice increase its objectivity. In light of this, we believe that the usefulness of our conceptualization is not impaired because it is hard to satisfy. It is something to strive for, not necessarily something to reach.

Objective research does not guarantee true nor trustworthy results . Even if the work of a scientist did not suffer from anything that could jeopardize the research’s objectivity, it is still possible that the results are not true (i.e., do not reflect or represent reality). It could be as innocent as a false-positive or it might be that the measurement instrument is not adequate for investigating the phenomenon at hand. Either way, we should be clear that objectivity of a practice cannot be equated with scientific truth generation. Similarly, even when scientific practice is (as close to) objective (as possible), it may still suffer from low reliability (i.e., noisy measurement) or lacks validity (i.e., does not measure what it is supposed to measure). In other words, validity and reliability might be necessary to guarantee the quality and trustworthiness of results. Furthermore, the possibility of trustworthy results without procedural objectivity has been leveled as a criticism against this type of objectivity (Jukola, 2017 ). However, according to our conceptualization, perfect/high reliability, validity and thus trustworthiness are neither necessary nor sufficient conditions for the objectivity of the scientific practice that produced the results. That being said, we should still care about objectivity, because validity and reliability are promoted by it (Section  4.2 ).

Implications and applications

The primary advantage of our approach to objectivity is that, in contrast to traditional theories of objectivity, it can be applied in science. For instance, our notion can be used to assess and address currently salient problems in science (i.e., the replication crisis: Harris, 2017 ) and evaluate suggested solutions to problematic scientific practices. Concretely, our conceptualization of objectivity can be captured in an tool that can be tested and calibrated (for an example, see Appendix  A ).

Increasing objectivity of scientific methods is a necessary step in remedying problems, such as the replication crisis (e.g., Harris, 2017 ). This crisis is constituted by the fact that results from many scientific experiments are not reproduced in replication studies (for a discussion see: Open Science Collaboration, 2015 , Romero, 2016 ). Concretely, that experiments with similar or identical designs conducted by different scientists (or by the same researchers for the second time) delivered widely different results. The exact percentage of replicablility is unknown, though some indication might be gleaned from large scale replication projects (e.g., Open Science Collaboration, 2015 , Klein et al., 2018 ). In the case of the Open Science Collaboration ( 2015 ), hundreds of scientists collaborated to attempt replication of one-hundred experiments published in prestigious psychological journals. Less than half of the attempts were successful; 17 clearly a disappointing result.

Replicability can be compromised by many factors. One of them is the misuse of degrees of freedom (e.g. Simmons et al., 2011 , Wicherts et al., 2016 ). Specifically, biased studies are more likely to deliver results which fit the particular interest of the scientist (Section  3.1 ), or general interest in positive results or absence of negative results (Section  3.2 ), which therefore will likely disagree with the results of unbiased experiments; decreasing the overall replicability. Now, if the objectivity of scientific practice (i.e., resistance against bias and questionable research practices) is increased, then replicability on any reasonable metric will increase. In light of that, increasing objectivity seems to be a necessary steps toward solving the replication crisis and its effectiveness will be clearly observable in the published scientific literature.

In addition, our notion gives clear indications of which suggested solution to problematic scientific practices will most likely be successful. Some of these restrict scientists directly (e.g., preregistration requirement, random sampling, randomization, etc.), while others make it harder to exploit degrees of freedom (e.g. blind analysis). Because of that, they improve the objectivity to a certain extent. On the other hand, for some of the proposals it is not clear if they are capable of improving objectivity. The Reformist Package is an example of such a proposal. It requires that the first author of a paper on a scientific experiment states all potential conflicts of interest. This amounts to explicitly listing all sources of funding that supported his/her work and claiming full responsibility for the result and decision to publish it. The Reformist Package has some proponents in scientific literature (e.g., Stelfox et al., 1998 ) and some of the most important scientific journals (e.g., Lancet, Journal of the American Medical Association, etc.) adopted it in their publishing policy. However, according to our conceptualization, it is not clear at all if the proposal improves the objectivity. The Package is forcing scientists to reveal potential causes of systematic bias in the form of financial ties, but it does not safeguard the experiment against actions that can introduce this bias. Our conceptualization predicts that the Reformist Package is ineffective in dealing with the influence funding agencies have, via their researchers, on the results. This is corroborated by the dissatisfaction concerning its ineffectiveness common in current literature (e.g., Schafer, 2004 ), and is supported by the results of empirical research (e.g., Cain et al., 2005 ).

Additionally, our notion can be used to assess the objectivity of research practices reported in scientific papers (e.g., through a checklist; see Appendix  A ). As an example, we can use the previously mentioned, notorious precognition paper by (Bem, 2011 ). This article reports nine experiments that allegedly provide evidence for the hypothesis that future events affect human beliefs (precognition). These results are treated by scientists with skepticism because the existence of precognition is inconsistent with laws of nature (e.g., the second law of thermodynamics), common sense, and everyday experience. Not surprisingly, the subsequent replication attempt failed (e.g., Ritchie et al., 2012 , Galak et al., 2012 ) and evidence of the use of QRPs was found (e.g., Schimmack, 2012 , Francis, 2014 , Schimmack, 2020 ).

The procedures employed for the nine experiments would not score high on our conception of objectivity. Some aspects of the design of the experiment promote objectivity, the outcome measure and intervention are directly connected to the studied phenomenon and the allocation of subjects was random. On the flip side, participants were exclusively psychology students, the study was not preregistered, and neither the blind analysis nor multiverse analysis was used. 18 The absence of such countermeasures makes the experiment susceptible to the QRPs. An example of such a practice is looking at the initial data of many started experiments and continuing only those that look ’promising’; i.e., only continue with studies that show high initial effect sizes that are due to random chance alone (for evidence for this claim, see Schimmack, 2020 ).

This is an intuitive result given the skepticism concerning the results of precognition. Moreover, as we have seen, the subsequent replication failed to replicate the original result and evidence suggesting the the QRP were used during the experiment. The objectivity of many other older experiments will be similarly disappointing. The methodological problems central to our conceptualization of objectivity were not widely acknowledged and the countermeasures against them were rarely implemented. This may seem to be a disappointing consequence but it is consistent with low rates of replicability of classical studies (e.g., Klein et al., 2018 ) and acknowledges the recent rapid development in scientific methodology.

Finally, our conceptualization of objectivity is compatible with, and follows the spirit of many traditional theories of objectivity. Our notion is based on the intuition that objectivity is essentially about minimizing the influences that the individual traits of a scientist have on her research (results). This intuition inspired many other conceptualizations of objectivity, for instance, value-free objectivity, procedural objectivity or Koskinen’s theory (Section  2 ). Specifically, the value-free conception of objectivity claims that a scientific justification is objective as long as it is not influenced by non-epistemic values. However and in contrast to our conception, the value-free objectivity is hard to assess and therefore use in practice. This is the case because there is no reliable way to assess and test what was the motivation behind any methodological choice.

The same goes for procedural objectivity. This proposal has been previously criticized in Jukola ( 2017 ) and we identify two additional problems. First, as in case of VFI, it is prohibitively difficult to verify if a given process is objective in this sense. Secondly, the conceptualization is too restrictive. For example, when statistical methods are used to analyze data it is always the case that the result of an experiment will be different when conducted second time at some level of precision. Furthermore, there is always the possibility of false-positives and false-negatives. Therefore, it seems that no such study can be objective in the sense of the procedural objectivity. Our conceptualization does not suffers from those two difficulties.

Another feature that distinguishes our conceptualization is that it explicitly requires the scientific procedure in question to be demonstrably resilient to problematic practices rather than just free of them. This makes our conceptualization testable (as the presence of the countermeasures is evident in contrast to the presence of the problems) and distinguishes it from other proposals based on similar intuitions. For example, the conceptualization of objectivity as minimizing epistemic risks which arise from the imperfections of epistemic agents from (Koskinen, 2020 ) does no include such an external transparency requirement. Under such a conceptualization, a given scientific procedure could objective, but this would be inaccessible to anybody (e.g., a reviewer of an article) except the responsible scientist. Our theory does not suffer from this problem.

Furthermore, our notion is consistent with all descriptive theories, because we do not claim anything about how the concept is used and understood by scientists or natural language users. Besides, some of these descriptive conceptualizations seem to be based on the above mentioned intuition as well. For example, the epistemic risk account of objectivity of (Koskinen, 2020 ), seems to be similar in spirit to our proposal. It claims that objectivity consists in averting epistemic risks arising from imperfections of epistemic agents. Adhering to the recommendations of our proposal averts some of such risks, for example, the risk of delivering a biased result due to study design choices (Section  3.1 ). In other words, her description of how objectivity is understood fits to a certain extent with our recommendations. Regulatory objectivity, described in (Cambrosio et al., 2006 ), is another example of a descriptive conceptualization based on the same intuition. It is built on the historical analysis of objectivity from (Daston & Galison, 1992 , 2010 ). Regulatory objectivity consists of conventions which aim to ensure research quality, specifically:

Regulatory objectivity , that is based on the systematic recourse to the collective production of evidence. Unlike forms of objectivity that emerged in earlier eras, regulatory objectivity consistently results in the production of conventions, sometimes tacit and unintentional but most often arrived at through concerted programs of action. (Cambrosio et al., 2006 , p.1)

Recent developments are interpreted as the emergence of a new type of objectivity. Implementing and developing such conventions fit our recommendations for the prevention of methodological choices that can bias results or inflate false-positive rates. Again, there is coherence between our normative proposal and the descriptive theory which describes how scientists understand the objectivity.

Conclusions

Let us once again imagine our scientists, Dr. Jane Summers. Dr. Summers is starting a new experiment (e.g., the effects of caffeine on attention, short-term memory, and long-term memory in psychologically healthy adults) but this time she has a grasp on the notion of objectivity and will include (some of) the objectivity promoting precautions. Specifically, when she designs the study, she ensures that for all intents and purposes the participants selection is random from the population of interest (e.g., males and females, age 21 and up that do not suffer from psychological disorders) and that the non-response rate is not biased (e.g., equal non-response in age and gender), that the measurement instruments come with published validation (i.e., standardized test for attention and memory), the participants’ allocation to conditions (e.g., coffee with a high dose of caffeine or decaffeinated coffee) is random, and the experiment is double-blinded (i.e., both participant and experimenter are unaware of experiment condition and purpose). Dr. Summers preregisters the study design and the analysis (e.g., structural equation model) of the main effect of interest (e.g., caffeine positively affects long-term memory, mediated by attention and short-term memory). She will have her data blinded and processed by an independent researcher. In addition, she reserves a room for a multiverse analysis. In Dr. Summers’ case, not much is known about the complex relation between dependent and independent variables and its mediation or moderation by participant characteristics (e.g., sex, age, daily caffeine consumption, etc.). Thus, apart from the main model suggested by theory and previous research, she wishes to explore other theoretically possible options. Specifically, she performs and reports the results of the analyses of all theoretically possible models and summarizes their results in a multiverse analysis. By taking these steps, Dr. Summers restricts many ways in which her study can be biased and thereby improves the objectivity of her work.

Similar steps may be taken in order to improve the objectivity of Bem’s ( 2011 ) experiments. The main problem with the experiments is the (possible) use of QRPs. In particular, he seemed to have started many experiments and only continued collecting data on those that showed ‘promising’ results (Schimmack, 2020 ). This could be countered by ensuring preregistration of all initial studies and requiring an appropriate analysis plan if Bem intended to apply sequential analyses. If the diagnosis by Schimmack ( 2020 ) is correct, this alone would significantly increase the reliability of the experiments to the point that it would be highly improbable that they would deliver the suspicious result. In addition, one could require a multiverse analysis over control variables (e.g., gender) and experimental variation (e.g., subcategories of stimuli). As an example for such a requirement, in one of the experiments the precognition effect was observed for pornographic stimuli, but not for neutral stimuli (Bem, 2011 ). In brief, it is to be expected that implementing the safeguards proposed in this paper would have prevented Bem from getting his results that humans have the ability to feel the future.

To summarize, in this paper we have presented a practicable notion of scientific objectivity. In our opinion, popular disquisitions on objectivity are focused on what the concept means and how it is used, but they do not provide scientists with any guidance on how to improve or assess the objectivity of their work. We presented our empirically informed version of via negativa approach to objectivity and conceptualization of objectivity as methodological resilience. Finally, we showed that and how this new conceptualization can plausibly be used by scientists. In the present form, our theory is far from perfect or complete. At the same time, like science itself, it has the potential to be adjusted and developed to move ever closer to adequacy and completeness.

Declarations

The authors have contributed equally to the manuscript. There are no conflicts of interest. The research was supported by Starting Investigator Grant No. 640638 (“OBJECTIVITY—Making Scientific Inferences More Objective”) of the European Research Council (ERC).

Acknowledgements

We would like to thank Jan Sprenger, Mattia Androleti, Rafał Urbaniak, Julian Reiss, Antonio Negro, Walter Veit, and the anonymous referees for their useful comments.

Appendix A: Setup of a checklist for objectivity assessment

Given the negative nature of our notion, our checklist consists of questions assessing how susceptible a study is to suffer from the mentioned problematic practices. Concretely, a checklist consists of yes-no questions that indicate the presence or absence of features that prevent problematic practices.

Questions concerning the study being bias-resilient: 19

• Was (were) the outcome measure(s) directly related to the phenomenon of interest as stated in the research aim or research question? (e.g., ‘death rate’ to ‘death by cardiac arrest’)
• Was (were) the intervention(s) clearly related to phenomenon of interest as stated in the research aim or research question? (e.g., ‘cardiac arrest reducing medication’ to ‘death by cardiac arrest’)
• Was sampling procedure random?
• Was the sampling procedure capable of producing a sample representative of the population? (i.e., do inclusion/exclusion criteria allow all member of the population)
• When the subjects are volunteers, was the (non-)response rate similar across participant characteristics? (e.g., equal between men and women)
• Was the allocation of the subjects to the experiment conditions random?
• Were both the experimenter and the subjects blind to the experiment condition?
• Was the drop-out rate of subjects similar across the experiment conditions?
• If any answer to these questions was ’no’, were proper steps taken to ameliorate the potential bias that could have resulted from it?

Questions concerning the study being resilient against bias and false-positive rate inflation due to questionable research practices:

• Was the study preregistered?
• Management of missing and incomplete data.
• Pre-processing of data (e.g., how to clean and normalize).
• Data processing and dealing with violation of statistical assumptions.
• Management of outliers.
• Statistical analysis/model.
• Dependent variable(s) of the model.
• Predictors/covariates of the model.
• Estimation method and computation of standard errors.
• Inference criteria.
• If the final report did not completely conform to the preregistration, was the particular deviation handled by blinding data or blinded analysis?
• If the final report did not completely conform to the preregistration, was the particular deviation handled by using a multiverse analysis and reporting all (theoretically) possible ways of handling this case?
• If the study was not preregistered, was blinded data management and analysis used?
• If the study was not preregistered and the data management and analysis was not blinded, was a type of multiverse analysis performed?

It needs to be noted that such a tool needs to be further developed, tested, and calibrated. To be useful, this objectivity checklist should of course be (to a large extent) reliable and valid. In the first case, inter-rater reliability and intra-rater reliability should be assessed. I.e., have different subjects use the tool and measure the agreement between their results (Cohen, 1960 ; Fleiss, 1971 ), and have subjects use the tool on two or more different occasions on the same material and measure the similarity of results between these occasions (Gwet, 2008 ). Close similarity between scores indicate that users will in general give similar scores when using the checklist. If there are questions in the checklist that score low on inter-rater and/or intra-rater reliability, then they should be rephrased or dropped. The validity of the checklist can be assessed by empirically verifying if research that scores high on the checklist are less prone to produce problematic results (e.g., have a higher replication rate) in comparison to research that score low on the checklist (i.e., criterion validity). For now, we do not have a scoring system of the checklist. The easiest scoring system would be to use the proportion of ’yes’ answers of the total number of questions that are relevant for the report that is evaluated. However, this scoring system should be further developed and tested. Also, scientific practice could be simulated to assess to what extent bias and false-positive rate inflation could still be introduced with varying levels of objectivity according to the checklist. Results from such simulation studies could also be used to calibrate the scoring system. Finally, scientists could be enlisted to perform mock research to attempt to bias outcomes and/or produce false-positive results with varying levels of objectivity safeguards in play. These mock studies might identify weaknesses and gaps in the tool (and objectivity conceptualization), which could be used when calibrating the tool and supplementing/removing elements.

1 Dr. Summers is a fictional character, though we postulate that she is representative of current practitioners who work on the improvement of research practice and her considerations of potential problems result from a synthesis of methods textbooks that have recently fallen from grace for recommending questionable practice.

2 A similar description of objectivity can be found on Wikipedia ( 2019 ).

3 Interestingly, there are not many references on the importance of objectivity for science. Scientists we spoke to consider its relevance obvious and self-explanatory to such an extent that it does not warrant explicit explanation and justification.

4 For reasons clarified in Sections  3 and  4 , we restrict our definition to research that works with non-qualitative (quantitative or countable) data.

5 We address this limitation in more detail in Section  4 .

6 Despite this descriptive goal, there are some normative remarks in the paper. For example, Douglas refers to her earlier work (Douglas, 2000 ) in which she argued that one of the conceptualizations of objectivity, value-free objectivity, is impossible to realize in practice. However, this does not detract from the main aim.

7 One may wonder how the discussed descriptive theories would do if put in the normative role. We believe that the theories would face similar difficulties. The theories from (Douglas, 2004 ) and Koskinen ( 2020 ) seems to be too general to be useful in their present formulation. Douglas’ theory is composed of many individual notions all of which generate different predictions. It is not supplemented with a rule describing which notion to use in each individual case. Similarly, if we translate Koskinen’s theory into methodological advice, something like, a scientific procedure is objective to the degree to which it minimizes epistemic risks which arise from imperfections of scientists, it seems too general to be useful. At the same time, we believe that a concretization of Koskinen’s theory could lead to a plausible normative proposal, in a very general sense similar to our proposal. These observations do not constitute objection against the theories, they were proposed as descriptions, and criticizing their performance beyond their original purpose is not a fair criticism.

8 The World Health Organization defines health as ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.’ (World Health Organization, 1948 , p.100). Which is essentially a negative definition augmented with an well-being requirement. The definition is considered controversial and with little added benefit over the original negative definition (e.g., Jadad and O’Grady, 2008 ). Adding ‘well-being’ just kicks the can down the road.

9 In this paper, the negative definition of ‘health’ is used as an analogy to clarify our approach. Our conceptualization has no stake in this definition or its controversies.

10 Some might argue that medical science is not as successful as it purports to be and suffers from diminishing returns (Stegenga, 2018 ). However, this does not detract from the fact that medical science is successful to at least some degree and the mentioned problems do not necessarily result from a lack of a positive definition of health (see for instance Firestein, 2015 ).

11 For clarification, bias, as discussed in this paper, is used to indicate systematic error introduced by behavior of the scientist (e.g., Ioannidis, 2005 ). This is different from bias in psychology (e.g., MacCoun, 1998 ) where it is used to classify cognitive heuristics (e.g., confirmation bias, bandwagon effect, anchoring, etc.). These heuristics might indirectly influence results, but these distant causes are irrelevant to our approach.

12 No matter how precise an instrument is and no matter how stringent the evidence requirements are, there is always a non-zero probability that the result of a study does not reflect reality (e.g., positive outcome of a HIV test when the person is actually HIV negative). Statistical methods of analysis come with certain rules and assumptions, which must be followed in order for this probability to have a known maximum. In other words, if a study is performed according to its rules, none of the assumptions are violated, and it is repeated a large number of times, the proportion of false-positive results (i.e., an effect is observed while actually no effect exists) is at most equal to this probability, which is or can be known.

13 In-depth discussion of these strategies is beyond the scope of this paper. Function, benefits, and limitations of these strategies can be found in the cited papers.

14 Note that it is not the testing of whether or not certain potentially biasing actions were made during the research, but what precautions were in place to preclude such actions.

15 Similar checklists have been developed and are in wide use as tools for assessing methodological quality of studies (e.g., Downs & Black, 1998 , Sindhu et al., 1997 ) when, for instance, appraised for inclusion into a systematic review (e.g., Haidich, 2010 ). Recently, a checklist to assess scientific transparency published (Aczel et al., 2020 ) that awaits application.

16 Procedural objectivity is the claim that there is objectivity when different scientists using the same procedure get the same/similar results (e.g., Porter, 1995 ). This is one of the notions included in Douglas’ ( 2004 ) pluralist account of objectivity.

17 A clear and formal definitions of replication is still absent and several benchmarks were used in this paper. On none of them did the replication rate exceed 50%.

18 We acknowledge that these practices were not widely known at the time. However, this does not mean that we cannot assess previous practice by current standards.

19 These questions pertain only to experimental research. However, the questions can be adapted for observational research.

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Contributor Information

Noah van Dongen, Email: moc.liamg@negnodnavnnn .

Michał Sikorski, Email: moc.liamg@iksrokisplahcim .

• Aczel B, Szaszi B, Sarafoglou A, Kekecs Z, Kucharskỳ S, Benjamin D, Chambers CD, Fisher A, Gelman A, Gernsbacher MA, et al. A consensus-based transparency checklist. Nature human behaviour. 2020; 4 (1):4–6. doi: 10.1038/s41562-019-0772-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Altmejd A, Almenberg AD, Forsell E, Ho T-H, Huber J, Imai T, Johannesson M, Kirchler M, Nave G, Camerer C. Predicting the replicability of social science lab experiments. PloS One. 2019; 14 (12):e0225826. doi: 10.1371/journal.pone.0225826. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anvari F, Lakens D. The replicability crisis and public trust in psychological science. Comprehensive Results in Social Psychology. 2018; 3 (3):266–286. doi: 10.1080/23743603.2019.1684822. [ CrossRef ] [ Google Scholar ]
• Bakker M, van Dijk A, Wicherts JM. The Rules of the Game Called Psychological Science. Perspectives on Psychological Science. 2012; 7 (6):543–554. doi: 10.1177/1745691612459060. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bem DJ. Feeling the future: experimental evidence for anomalous retroactive influences on cognition and affect. Journal of personality and social psychology. 2011; 100 (3):407–425. doi: 10.1037/a0021524. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bennett, C.M., Baird, A., Miller, M., & Wolford, G. (2010). of serendipitous and unexpected results neural correlates of interspecies perspective taking in the post-mortem atlantic salmon : An argument for proper multiple comparisons correction.
• Betz G. In defence of the value free ideal. European Journal for Philosophy of Science. 2013; 3 (2):207–220. doi: 10.1007/s13194-012-0062-x. [ CrossRef ] [ Google Scholar ]
• Biddle J. Lessons from the vioxx debacle: What the privatization of science can teach us about social epistemology. Social Epistemology. 2007; 21 (1):21–39. doi: 10.1080/02691720601125472. [ CrossRef ] [ Google Scholar ]
• Brown M. The source and status of values for socially responsible science. Philosophical Studies. 2013; 163 :67–76. doi: 10.1007/s11098-012-0070-x. [ CrossRef ] [ Google Scholar ]
• Bueter A. The irreducibility of value-freedom to theory assessment. Studies in History and Philosophy of Science Part A. 2015; 49 :18–26. doi: 10.1016/j.shpsa.2014.10.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cain DM, Loewenstein G, Moore DA. The dirt on coming clean: Perverse effects of disclosing conflicts of interest. The Journal of Legal Studies. 2005; 34 (1):1–25. doi: 10.1086/426699. [ CrossRef ] [ Google Scholar ]
• Cambrosio A, Keating P, Schlich T, Weisz G. Regulatory objectivity and the generation and management of evidence in medicine. Social Science & Medicine. 2006; 63 (1):189–199. doi: 10.1016/j.socscimed.2005.12.007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carney, D. (2016). My position on “power poses”. http://faculty.haas.berkeley.edu/dana_carney/ , Accessed April 15, 2020.
• Carroll HA, Toumpakari Z, Johnson L, Betts JA. The perceived feasibility of methods to reduce publication bias. PloS One. 2017; 12 (10):1–19. doi: 10.1371/journal.pone.0186472. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chambers CD. Registered reports: a new publishing initiative at cortex. Cortex. 2013; 49 (3):609–610. doi: 10.1016/j.cortex.2012.12.016. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen J. A coefficient of agreement for nominal scales. Educational and psychological measurement. 1960; 20 (1):37–46. doi: 10.1177/001316446002000104. [ CrossRef ] [ Google Scholar ]
• Daston L, Galison P. The image of objectivity. Representations. 1992; 40 :81–128. doi: 10.2307/2928741. [ CrossRef ] [ Google Scholar ]
• Daston, L., & Galison, P. (2010). Objectivity. New York: Zone Books.
• Douglas H. Inductive risk and values in science. Philosophy of Science. 2000; 67 (4):559–579. doi: 10.1086/392855. [ CrossRef ] [ Google Scholar ]
• Douglas H. The irreducible complexity of objectivity. Synthese. 2004; 138 (3):453–473. doi: 10.1023/B:SYNT.0000016451.18182.91. [ CrossRef ] [ Google Scholar ]
• Douglas, H. (2009). Science, policy, and the value-free ideal. University of Pittsburgh Press.
• Douven, I., Elqayam, S., Singmann, H., & van Wijnbergen-Huitink, J. (2018). Conditionals and inferential connections: Toward a new semantics. Thinking and Reasoning, pp 1–41. 10.1080/13546783.2019.1619623.
• Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. Journal of epidemiology and community health. 1998; 52 :377–384. doi: 10.1136/jech.52.6.377. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Elliott KC, McKaughan DJ. How values in scientific discovery and pursuit alter theory appraisal. Philosophy of Science. 2009; 76 (5):598–611. doi: 10.1086/605807. [ CrossRef ] [ Google Scholar ]
• Fanelli D, Costas R, Ioannidis JPA. Meta-assessment of bias in science. Proceedings of the National Academy of Sciences. 2017; 114 (14):3714–3719. doi: 10.1073/pnas.1618569114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Feynman RP. Cargo cult science. Engineering and Science. 1974; 37 (7):10–13. [ Google Scholar ]
• Firestein, S. (2015). Failure: Why science is so successful. Oxford University Press.
• Fleiss JL. Measuring nominal scale agreement among many raters. Psychological bulletin. 1971; 76 (5):378–382. doi: 10.1037/h0031619. [ CrossRef ] [ Google Scholar ]
• Francis G. The frequency of excess success for articles in psychological science. Psychonomic bulletin & review. 2014; 21 (5):1180–1187. doi: 10.3758/s13423-014-0601-x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Galak J, Leboeuf R, Nelson LD, Simmons J. Correcting the past: Failures to replicate psi. Journal of personality and social psychology. 2012; 103 :933–948. doi: 10.1037/a0029709. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gervais, W. (2017). Post publication peer review. http://willgervais.com/blog/2017/3/2/post-publication-peer-review , Accessed April 15, 2020.
• Gigerenzer G. Mindless statistics. The Journal of Socio-Economics. 2004; 33 (5):587–606. doi: 10.1016/j.socec.2004.09.033. [ CrossRef ] [ Google Scholar ]
• Goldacre, B. (2014). Bad pharma: how drug companies mislead doctors and harm patients. Macmillan.
• Gwet K. Intrarater reliability. Methods and Applications of Statistics in Clinical Trials. 2008; 2 :473–485. [ Google Scholar ]
• Hacking, I. (2015). Let not talk about objectivity. In J Y Tsou, A Richardson, & F Padovani (Eds.) Objectivity in Science . Springer Verlag.
• Haidich AB. Meta-analysis in medical research. Hippokratia. 2010; 14 (Suppl 1):29–37. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Harding, S. (2015). Objectivity for sciences from below. In J Y Tsou, A Richardson, & F Padovani (Eds.) Objectivity in Science . Springer Verlag.
• Harris, R.F. (2017). Rigor mortis: how sloppy science creates worthless cures, crushes hope, and wastes billions. New York: Basic Books.
• Hawkins CB, Nosek BA. Motivated independence? implicit party identity predicts political judgments among self-proclaimed independents. Personality and Social Psychology Bulletin. 2012; 38 (11):1437–1452. doi: 10.1177/0146167212452313. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hicks DJ. A new direction for science and values. Synthese. 2014; 191 (14):3271–95. doi: 10.1007/s11229-014-0447-9. [ CrossRef ] [ Google Scholar ]
• Ioannidis JohnPA. Why most published research findings are false. PLoS medicine. 2005; 2 (8):e124. doi: 10.1371/journal.pmed.0020124. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jadad AR, O’Grady L. How should health be defined? BMJ. 2008; 337 :a2900. doi: 10.1136/bmj.a2900. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• John LK, Loewenstein G, Prelec D. Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science. 2012; 23 (5):524–532. doi: 10.1177/0956797611430953. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jones M, Sugden R. Positive confirmation bias in the acquisition of information. Theory and Decision. 2001; 50 (1):59–99. doi: 10.1023/A:1005296023424. [ CrossRef ] [ Google Scholar ]
• Jukola S. On ideals of objectivity, judgments, and bias in medical research – a comment on stegenga. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences. 2017; 62 :35–41. doi: 10.1016/j.shpsc.2017.02.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kass RE, Caffo BS, Davidian M, Meng XL, Yu B, Reid N. Ten Simple Rules for Effective Statistical Practice. PLoS Computational Biology. 2016; 12 (6):e1004961. doi: 10.1371/journal.pcbi.1004961. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kerr NL. Harking: Hypothesizing after the results are known. Personality and Social Psychology Review. 1998; 2 (3):196–217. doi: 10.1207/s15327957pspr0203_4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klein RA, Vianello M, Hasselman F, Adams BG, Reginald B, Adams J, Alper S, et al. Many labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science. 2018; 1 (4):443–490. doi: 10.1177/2515245918810225. [ CrossRef ] [ Google Scholar ]
• Koskinen I. Defending a risk account of scientific objectivity. The British Journal for the Philosophy of Science. 2020; 71 :1187–1207. doi: 10.1093/bjps/axy053. [ CrossRef ] [ Google Scholar ]
• Leuschner A. Pluralism and objectivity: Exposing and breaking a circle. Studies in History and Philosophy of Science Part A. 2012; 43 (1):191–198. doi: 10.1016/j.shpsa.2011.12.030. [ CrossRef ] [ Google Scholar ]
• Lilienfeld SO. Public skepticism of psychology: why many people perceive the study of human behavior as unscientific. American Psychologist. 2012; 67 (2):111–129. doi: 10.1037/a0023963. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lindsay, D.S. (2015). Replication in psychological science. Sage Publications Sage CA: Los Angeles, CA.
• Longino, H.E. (1990). Science as social knowledge: Values and objectivity in scientific inquiry. Princeton University Press.
• Longino, H.E. (1996). Cognitive and non-cognitive values in science: Rethinking the dichotomy’.
• Longino, H.E. (2004). How values can be good for science. In P K Machamer G Wolters (Eds.) Science, Values, and Objectivity (pp. 127–142). University of Pittsburgh Press.
• MacCoun R. Biases in the interpretation and use of research results. Annual review of psychology. 1998; 49 :259–87. doi: 10.1146/annurev.psych.49.1.259. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• MacCoun R, Perlmutter S. Blind analysis: hide results to seek the truth. Nature News. 2015; 526 (7572):187–189. doi: 10.1038/526187a. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malički M, Marušić A. Is there a solution to publication bias? researchers call for changes in dissemination of clinical research results. Journal of Clinical Epidemiology. 2014; 67 (10):1103–1110. doi: 10.1016/j.jclinepi.2014.06.002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Megill, A. (1994). Four senses of objectivity. In Rethinking Objectivity .
• Nelson JA. The power of stereotyping and confirmation bias to overwhelm accurate assessment: The case of economics, gender, and risk aversion. Journal of Economic Methodology. 2014; 21 (3):211–231. doi: 10.1080/1350178X.2014.939691. [ CrossRef ] [ Google Scholar ]
• Nelson LD, Simmons J, Simonsohn U. Psychology’s Renaissance. Annual Review of Psycholgy. 2018; 69 :1–24. doi: 10.1146/annurev-psych-122216-011542. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115 (11):2600–2606. doi: 10.1073/pnas.1708274114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Open Science Collaboration Estimating the reproducibility of psychological science. Science. 2015; 349 (6251):aac4716. doi: 10.1126/science.aac4716. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pashler H, Wagenmakers EJ. Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science. 2012; 7 :528–530. doi: 10.1177/1745691612465253. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Porter, T.M. (1995). Trust in numbers: The pursuit of objectivity in science and public life. Princeton University Press. [ PubMed ]
• Reiss, J., & Sprenger, J. (2017). Scientific objectivity. In E N Zalta (Ed.) The Stanford Encyclopedia of Philosophy . Winter 2017. Metaphysics Research Lab, Stanford University.
• Ritchie S, Wiseman R, French C. Failing the future: Three unsuccessful attempts to replicate bem’s ‘retroactive facilitation of recall’ effect. PloS One. 2012; 7 :e33423. doi: 10.1371/journal.pone.0033423. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rohrer, J.M., DeBruine, L., Heyman, T., Jones, B.C., Schmukle, S., Silberzahn, R., Uhlmann, E.L., Willén, R M, Carlsson, R., Lucas, R.E., & et al. (2018). Putting the self in self-correction. PsyArXiv 10.31234/osf.io/exmb2, accessed April 15, 2020. [ PMC free article ] [ PubMed ]
• Romero F. Can the behavioral sciences self-correct? a social epistemic study. Studies in History and Philosophy of Science Part A. 2016; 60 :55–69. doi: 10.1016/j.shpsa.2016.10.002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rothwell PM. External validity of randomised controlled trials:to whom do the results of this trial apply? The Lancet. 2005; 365 (9453):82–93. doi: 10.1016/S0140-6736(04)17670-8. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rudner R. The scientist qua scientist makes value judgments. Philosophy of Science. 1953; 20 (1):1–6. doi: 10.1086/287231. [ CrossRef ] [ Google Scholar ]
• Safer DJ. Design and reporting modifications in industry-sponsored comparative psychopharmacology trials. The Journal of nervous and mental disease. 2002; 190 (9):583–592. doi: 10.1097/00005053-200209000-00002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schafer A. Biomedical conflicts of interest: a defence of the sequestration thesis—learning from the cases of nancy olivieri and david healy. Journal of Medical Ethics. 2004; 30 (1):8–24. doi: 10.1136/jme.2003.005702. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scharp, K. (2013). Replacing truth. Oxford University Press UK.
• Schimmack U. The ironic effect of significant results on the credibility of multiple-study articles. Psychological methods. 2012; 17 :551–566. doi: 10.1037/a0029487. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schimmack, U. (2020). Why the journal of personality and social psychology should retract article doi: 10.1037/a0021524 ”feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect” by daryl j. bem. https://replicationindex.com/2018/01/05/bem-retraction/ , Accessed on 16 April 2020. [ PubMed ]
• Searle, J.R. (1975). A taxonomy of illocutionary acts. In K Gunderson (Ed.) Language, Mind and Knowledge (pp. 344–369). University of Minnesota Press.
• Simmons JP, Nelson LD, Simonsohn U. False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological science. 2011; 22 (11):1359–1366. doi: 10.1177/0956797611417632. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simons DJ. The value of direct replication. Perspectives on Psychological Science. 2014; 9 (1):76–80. doi: 10.1177/1745691613514755. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sindhu F, Carpenter L, Seers K. Dovelopment of a tool to rate the quality assessment of randomized controlled trials using a delphi technique. Journal of Advanced Nursing. 1997; 25 (6):1262–1268. doi: 10.1046/j.1365-2648.1997.19970251262.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sober, E. (2007). Evidence and value freedom. pdfs.semanticscholar.org, 1–13, accessed April 15, 2020.
• Stapel, D. (2012). Ontsporing. Prometheus Amsterdam.
• Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018; 15 (4):40–43. doi: 10.1111/j.1740-9713.2018.01174.x. [ CrossRef ] [ Google Scholar ]
• Steegen S, Tuerlinckx F, Gelman A, Vanpaemel W. Increasing transparency through a multiverse analysis. Perspectives on Psychological Science. 2016; 11 (5):702–712. doi: 10.1177/1745691616658637. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steel D. Epistemic values and the argument from inductive risk. Philosophy of Science. 2010; 77 (1):14–34. doi: 10.1086/650206. [ CrossRef ] [ Google Scholar ]
• Stegenga J. Is meta-analysis the platinum standard of evidence? Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences. 2011; 42 (4):497–507. doi: 10.1016/j.shpsc.2011.07.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stegenga, J. (2018). Medical nihilism. Oxford University Press.
• Stelfox HT, Chua G, O’Rourke K, Detsky AS. Conflict of interest in the debate over calcium-channel antagonists. The New England journal of medicine. 1998; 338 (2):101–106. doi: 10.1056/NEJM199801083380206. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Szucs D. A Tutorial on Hunting Statistical Significance by Chasing N. Frontiers in psychology. 2016; 7 :1444. doi: 10.3389/fpsyg.2016.01444. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tarski, A. (1936). The concept of truth in formalized languages. In A Tarski (Ed.) Logic, Semantics, Metamathematics (pp. 152–278). Oxford University Press.
• Travers J, Marsh S, Williams M, Weatherall M, Caldwell B, Shirtcliffe P, Aldington S, Beasley R. External validity of randomised controlled trials in asthma: to whom do the results of the trials apply? Thorax. 2007; 62 (3):219–223. doi: 10.1136/thx.2006.066837. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• US Department of Health and Human Services. (2001). National toxicology program’s report of the endocrine disruptors low-dose. http://ntp-server.niehs.nih.gov/ntp/htdocs/liason/LowDosePeer-FinalRpt.pdf .
• van Bavel JJ, Mende-Siedlecki P, Brady WJ, Reinero DA. Contextual sensitivity in scientific reproducibility. Proceedings of the National Academy of Sciences. 2016; 113 (23):6454–6459. doi: 10.1073/pnas.1521897113. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wicherts JM, Veldkamp CLS, Augusteijn HEM, Bakker M, van Aert RCM, van Assen MALM. Degrees of freedom in planning, running, analyzing, and reporting psychological studies: A checklist to avoid p-hacking. Frontiers in Psychology. 2016; 7 :1832. doi: 10.3389/fpsyg.2016.01832. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wikipedia. (2019). Objectivity (science) . https://en.wikipedia.org/wiki/Objectivity_(science) , Accessed on 18 March 2019.
• Wilholt T. Bias and values in scientific research. Studies in History and Philosophy of Science Part A. 2008; 40 (1):92–101. doi: 10.1016/j.shpsa.2008.12.005. [ CrossRef ] [ Google Scholar ]
• World Health Organization. (1948). Constitution of the world health organization. World Health Organization.
• Wright J. Rescuing objectivity: A contextualist proposal. Philosophy of the Social Sciences. 2018; 48 (4):385–406. doi: 10.1177/0048393118767089. [ CrossRef ] [ Google Scholar ]
• Ziman J. Is science losing its objectivity? Nature. 1996; 382 :751–754. doi: 10.1038/382751a0. [ CrossRef ] [ Google Scholar ]

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1 Objective and subjective research perspectives

Research in social science requires the collection of data in order to understand a phenomenon. This can be done in a number of ways, and will depend on the state of existing knowledge of the topic area. The researcher can:

• Explore a little known issue. The researcher has an idea or has observed something and seeks to understand more about it (exploratory research).
• Connect ideas to understand the relationships between the different aspects of an issue, i.e. explain what is going on (explanatory research).
• Describe what is happening in more detail and expand the initial understanding (explicatory or descriptive research).

Exploratory research is often done through observation and other methods such as interviews or surveys that allow the researcher to gather preliminary information.

Explanatory research, on the other hand, generally tests hypotheses about cause and effect relationships. Hypotheses are statements developed by the researcher that will be tested during the research. The distinction between exploratory and explanatory research is linked to the distinction between inductive and deductive research. Explanatory research tends to be deductive and exploratory research tends to be inductive. This is not always the case but, for simplicity, we shall not explore the exceptions here.

Descriptive research may support an explanatory or exploratory study. On its own, descriptive research is not sufficient for an academic project. Academic research is aimed at progressing current knowledge.

The perspective taken by the researcher also depends on whether the researcher believes that there is an objective world out there that can be objectively known; for example, profit can be viewed as an objective measure of business performance. Alternatively the researcher may believe that concepts such as ‘culture’, ‘motivation’, ‘leadership’, ‘performance’ result from human categorisation of the world and that their ‘meaning’ can change depending on the circumstances. For example, performance can mean different things to different people. For one it may refer to a hard measure such as levels of sales. For another it may include good relationships with customers. According to this latter view, a researcher can only take a subjective perspective because the nature of these concepts is the result of human processes. Subjective research generally refers to the subjective experiences of research participants and to the fact that the researcher’s perspective is embedded within the research process, rather than seen as fully detached from it.

On the other hand, objective research claims to describe a true and correct reality, which is independent of those involved in the research process. Although this is a simplified view of the way in which research can be approached, it is an important distinction to think about. Whether you think about your research topic in objective or subjective terms will determine the development of the research questions, the type of data collected, the methods of data collection and analysis you adopt and the conclusions that you draw. This is why it is important to consider your own perspective when planning your project.

Subjective research is generally referred to as phenomenological research. This is because it is concerned with the study of experiences from the perspective of an individual, and emphasises the importance of personal perspectives and interpretations. Subjective research is generally based on data derived from observations of events as they take place or from unstructured or semi-structured interviews. In unstructured interviews the questions emerged from the discussion between the interviewer and the interviewee. In semi-structured interviews the interviewer prepares an outline of the interview topics or general questions, adding more as needs emerged during the interview. Structured interviews include the full list of questions. Interviewers do not deviate from this list. Subjective research can also be based on examinations of documents. The researcher will attribute personal interpretations of the experiences and phenomena during the process of both collecting and analysing data. This approach is also referred to as interpretivist research. Interpretivists believe that in order to understand and explain specific management and HR situations, one needs to focus on the viewpoints, experiences, feelings and interpretations of the people involved in the specific situation.

Conversely, objective research tends to be modelled on the methods of the natural sciences such as experiments or large scale surveys. Objective research seeks to establish law-like generalisations which can be applied to the same phenomenon in different contexts. This perspective, which privileges objectivity, is called positivism and is based on data that can be subject to statistical analysis and generalisation. Positivist researchers use quantitative methodologies, which are based on measurement and numbers, to collect and analyse data. Interpretivists are more concerned with language and other forms of qualitative data, which are based on words or images. Having said that, researchers using objectivist and positivist assumptions sometimes use qualitative data while interpretivists sometimes use quantitative data. (Quantitative and qualitative methodologies will be discussed in more detail in the final part of this course.) The key is to understand the perspective you intend to adopt and realise the limitations and opportunities it offers. Table 1 compares and contrasts the perspectives of positivism and interpretivism.

Some textbooks include the realist perspective or discuss constructivism, but, for the purpose of your work-based project, you do not need to engage with these other perspectives. This course keeps the discussion of research perspectives to a basic level.

Search and identify two articles that are based on your research topic. Ideally you may want to identify one article based on quantitative and one based on qualitative methodologies.

Now answer the following questions:

• In what ways are the two studies different (excluding the research focus)?
• Which research perspective do the author/s in article 1 take in their study (i.e. subjective or objective or in other words, phenomenological/interpretivist or positivist)?
• What elements (e.g. specific words, sentences, research questions) in the introduction reveal the approach taken by the authors?
• Which research perspective do the author/s in article 2 take in their study (i.e. subjective or objective, phenomenological/interpretivist or positivist)?
• What elements (e.g. specific words, sentences, research questions) in the introduction and research questions sections reveal the approach taken by the authors?

This activity has helped you to distinguish between objective and subjective research by recognising the type of language and the different ways in which objectivists/positivists and subjectivists/interpretivists may formulate their research aims. It should also support the development of your personal preference on objective or subjective research.

Get science-backed answers as you write with Paperpal's Research feature

How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your research topic
• Capture reader interest
• Summarize existing research
• Position your own approach
• Define your specific research problem and problem statement
• Highlight the novelty and contributions of the study
• Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

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Table of Contents

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The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

• Scope: The topic covered in the research paper
• Context: Background of your topic
• Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

• It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
• It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
• It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
• It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

• What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
• What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
• What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

• Highlight the importance of the research field or topic
• Describe the background of the topic
• Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

• Reveal a gap in existing research or oppose an existing assumption
• Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

• State the purpose of your study
• Highlight the key characteristics of your study
• Describe important results
• Highlight the novelty of the study.
• Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

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Paperpal Copilot is a generative AI-powered academic writing assistant. It’s trained on millions of published scholarly articles and over 20 years of STM experience. Paperpal Copilot helps authors write better and faster with:

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With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

How to use Paperpal to write the Introduction section

Step 1: Sign up on Paperpal and click on the Copilot feature, under this choose Outlines > Research Article > Introduction

Step 2: Add your unstructured notes or initial draft, whether in English or another language, to Paperpal, which is to be used as the base for your content.

Step 3: Fill in the specifics, such as your field of study, brief description or details you want to include, which will help the AI generate the outline for your Introduction.

Step 4: Use this outline and sentence suggestions to develop your content, adding citations where needed and modifying it to align with your specific research focus.

Step 5: Turn to Paperpal’s granular language checks to refine your content, tailor it to reflect your personal writing style, and ensure it effectively conveys your message.

You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

• Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
• Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
• Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
• Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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How to Write the Rationale of the Study in Research (Examples)

What is the Rationale of the Study?

The rationale of the study is the justification for taking on a given study. It explains the reason the study was conducted or should be conducted. This means the study rationale should explain to the reader or examiner why the study is/was necessary. It is also sometimes called the “purpose” or “justification” of a study. While this is not difficult to grasp in itself, you might wonder how the rationale of the study is different from your research question or from the statement of the problem of your study, and how it fits into the rest of your thesis or research paper. 

The rationale of the study links the background of the study to your specific research question and justifies the need for the latter on the basis of the former. In brief, you first provide and discuss existing data on the topic, and then you tell the reader, based on the background evidence you just presented, where you identified gaps or issues and why you think it is important to address those. The problem statement, lastly, is the formulation of the specific research question you choose to investigate, following logically from your rationale, and the approach you are planning to use to do that.

Table of Contents:

How to write a rationale for a research paper , how do you justify the need for a research study.

• Study Rationale Example: Where Does It Go In Your Paper?

The basis for writing a research rationale is preliminary data or a clear description of an observation. If you are doing basic/theoretical research, then a literature review will help you identify gaps in current knowledge. In applied/practical research, you base your rationale on an existing issue with a certain process (e.g., vaccine proof registration) or practice (e.g., patient treatment) that is well documented and needs to be addressed. By presenting the reader with earlier evidence or observations, you can (and have to) convince them that you are not just repeating what other people have already done or said and that your ideas are not coming out of thin air. 

Once you have explained where you are coming from, you should justify the need for doing additional research–this is essentially the rationale of your study. Finally, when you have convinced the reader of the purpose of your work, you can end your introduction section with the statement of the problem of your research that contains clear aims and objectives and also briefly describes (and justifies) your methodological approach. 

When is the Rationale for Research Written?

The author can present the study rationale both before and after the research is conducted. 

• Before conducting research : The study rationale is a central component of the research proposal . It represents the plan of your work, constructed before the study is actually executed.
• Once research has been conducted : After the study is completed, the rationale is presented in a research article or  PhD dissertation  to explain why you focused on this specific research question. When writing the study rationale for this purpose, the author should link the rationale of the research to the aims and outcomes of the study.

What to Include in the Study Rationale

Although every study rationale is different and discusses different specific elements of a study’s method or approach, there are some elements that should be included to write a good rationale. Make sure to touch on the following:

• A summary of conclusions from your review of the relevant literature
• What is currently unknown (gaps in knowledge)
• Inconclusive or contested results  from previous studies on the same or similar topic
• The necessity to improve or build on previous research, such as to improve methodology or utilize newer techniques and/or technologies

There are different types of limitations that you can use to justify the need for your study. In applied/practical research, the justification for investigating something is always that an existing process/practice has a problem or is not satisfactory. Let’s say, for example, that people in a certain country/city/community commonly complain about hospital care on weekends (not enough staff, not enough attention, no decisions being made), but you looked into it and realized that nobody ever investigated whether these perceived problems are actually based on objective shortages/non-availabilities of care or whether the lower numbers of patients who are treated during weekends are commensurate with the provided services.

In this case, “lack of data” is your justification for digging deeper into the problem. Or, if it is obvious that there is a shortage of staff and provided services on weekends, you could decide to investigate which of the usual procedures are skipped during weekends as a result and what the negative consequences are. 

In basic/theoretical research, lack of knowledge is of course a common and accepted justification for additional research—but make sure that it is not your only motivation. “Nobody has ever done this” is only a convincing reason for a study if you explain to the reader why you think we should know more about this specific phenomenon. If there is earlier research but you think it has limitations, then those can usually be classified into “methodological”, “contextual”, and “conceptual” limitations. To identify such limitations, you can ask specific questions and let those questions guide you when you explain to the reader why your study was necessary:

Methodological limitations

• Did earlier studies try but failed to measure/identify a specific phenomenon?
• Was earlier research based on incorrect conceptualizations of variables?
• Were earlier studies based on questionable operationalizations of key concepts?
• Did earlier studies use questionable or inappropriate research designs?

Contextual limitations

• Have recent changes in the studied problem made previous studies irrelevant?
• Are you studying a new/particular context that previous findings do not apply to?

Conceptual limitations

• Do previous findings only make sense within a specific framework or ideology?

Study Rationale Examples

Let’s look at an example from one of our earlier articles on the statement of the problem to clarify how your rationale fits into your introduction section. This is a very short introduction for a practical research study on the challenges of online learning. Your introduction might be much longer (especially the context/background section), and this example does not contain any sources (which you will have to provide for all claims you make and all earlier studies you cite)—but please pay attention to how the background presentation , rationale, and problem statement blend into each other in a logical way so that the reader can follow and has no reason to question your motivation or the foundation of your research.

Background presentation

Since the beginning of the Covid pandemic, most educational institutions around the world have transitioned to a fully online study model, at least during peak times of infections and social distancing measures. This transition has not been easy and even two years into the pandemic, problems with online teaching and studying persist (reference needed) . 

While the increasing gap between those with access to technology and equipment and those without access has been determined to be one of the main challenges (reference needed) , others claim that online learning offers more opportunities for many students by breaking down barriers of location and distance (reference needed) .  

Rationale of the study

Since teachers and students cannot wait for circumstances to go back to normal, the measures that schools and universities have implemented during the last two years, their advantages and disadvantages, and the impact of those measures on students’ progress, satisfaction, and well-being need to be understood so that improvements can be made and demographics that have been left behind can receive the support they need as soon as possible.

Statement of the problem

To identify what changes in the learning environment were considered the most challenging and how those changes relate to a variety of student outcome measures, we conducted surveys and interviews among teachers and students at ten institutions of higher education in four different major cities, two in the US (New York and Chicago), one in South Korea (Seoul), and one in the UK (London). Responses were analyzed with a focus on different student demographics and how they might have been affected differently by the current situation.

How long is a study rationale?

In a research article bound for journal publication, your rationale should not be longer than a few sentences (no longer than one brief paragraph). A  dissertation or thesis  usually allows for a longer description; depending on the length and nature of your document, this could be up to a couple of paragraphs in length. A completely novel or unconventional approach might warrant a longer and more detailed justification than an approach that slightly deviates from well-established methods and approaches.

Consider Using Professional Academic Editing Services

Now that you know how to write the rationale of the study for a research proposal or paper, you should make use of our free AI grammar checker , Wordvice AI, or receive professional academic proofreading services from Wordvice, including research paper editing services and manuscript editing services to polish your submitted research documents.

You can also find many more articles, for example on writing the other parts of your research paper , on choosing a title , or on making sure you understand and adhere to the author instructions before you submit to a journal, on the Wordvice academic resources pages.

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The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points derived from the findings of your study and, if applicable, where you recommend new areas for future research. For most college-level research papers, two or three well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in describing the key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  your answer to the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [first identified in your literature review section] has been addressed by your research and why this contribution is significant.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers an opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The general function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by clearly summarizing the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. However, make sure that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your paper.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, concise language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique, new, or crucial contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem but that further investigations should take place beyond the scope of your investigation.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data [this is opposite of the introduction, which begins with general discussion of the context and ends with a detailed description of the research problem]. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way. If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge leading to positive change.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good source would be from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results from your study to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion framed around the implications and significance of your findings [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study. They should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social and behavioral sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority as a researcher by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader about the study's validity and realiability.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining that they are reaching the end of your paper. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper. This why the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and position your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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