example of conceptual framework in research paper

How To Make Conceptual Framework (With Examples and Templates)

We all know that a research paper has plenty of concepts involved. However, a great deal of concepts makes your study confusing.

A conceptual framework ensures that the concepts of your study are organized and presented comprehensively. Let this article guide you on how to make the conceptual framework of your study.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

At a glance: free conceptual framework templates.

Too busy to create a conceptual framework from scratch? No problem. We’ve created templates for each conceptual framework so you can start on the right foot. All you need to do is enter the details of the variables. Feel free to modify the design according to your needs. Please read the main article below to learn more about the conceptual framework.

Conceptual Framework Template #1: Independent-Dependent Variable Model

Conceptual framework template #2: input-process-output (ipo) model, conceptual framework template #3: concept map, what is a conceptual framework.

A conceptual framework shows the relationship between the variables of your study.  It includes a visual diagram or a model that summarizes the concepts of your study and a narrative explanation of the model presented.

Why Should Research Be Given a Conceptual Framework?

Imagine your study as a long journey with the research result as the destination. You don’t want to get lost in your journey because of the complicated concepts. This is why you need to have a guide. The conceptual framework keeps you on track by presenting and simplifying the relationship between the variables. This is usually done through the use of illustrations that are supported by a written interpretation.

Also, people who will read your research must have a clear guide to the variables in your study and where the research is heading. By looking at the conceptual framework, the readers can get the gist of the research concepts without reading the entire study. 

Related: How to Write Significance of the Study (with Examples)

What Is the Difference Between Conceptual Framework and Theoretical Framework?

Both of them show concepts and ideas of your study. The theoretical framework presents the theories, rules, and principles that serve as the basis of the research. Thus, the theoretical framework presents broad concepts related to your study. On the other hand, the conceptual framework shows a specific approach derived from the theoretical framework. It provides particular variables and shows how these variables are related.

Let’s say your research is about the Effects of Social Media on the Political Literacy of College Students. You may include some theories related to political literacy, such as this paper, in your theoretical framework. Based on this paper, political participation and awareness determine political literacy.

For the conceptual framework, you may state that the specific form of political participation and awareness you will use for the study is the engagement of college students on political issues on social media. Then, through a diagram and narrative explanation, you can show that using social media affects the political literacy of college students.

What Are the Different Types of Conceptual Frameworks?

The conceptual framework has different types based on how the research concepts are organized 1 .

1. Taxonomy

In this type of conceptual framework, the phenomena of your study are grouped into categories without presenting the relationship among them. The point of this conceptual framework is to distinguish the categories from one another.

2. Visual Presentation

In this conceptual framework, the relationship between the phenomena and variables of your study is presented. Using this conceptual framework implies that your research provides empirical evidence to prove the relationship between variables. This is the type of conceptual framework that is usually used in research studies.

3. Mathematical Description

In this conceptual framework, the relationship between phenomena and variables of your study is described using mathematical formulas. Also, the extent of the relationship between these variables is presented with specific quantities.

How To Make Conceptual Framework: 4 Steps

1. identify the important variables of your study.

There are two essential variables that you must identify in your study: the independent and the dependent variables.

An independent variable is a variable that you can manipulate. It can affect the dependent variable. Meanwhile, the dependent variable is the resulting variable that you are measuring.

You may refer to your research question to determine your research’s independent and dependent variables.

Suppose your research question is: “Is There a Significant Relationship Between the Quantity of Organic Fertilizer Used and the Plant’s Growth Rate?” The independent variable of this study is the quantity of organic fertilizer used, while the dependent variable is the plant’s growth rate.

2. Think About How the Variables Are Related

Usually, the variables of a study have a direct relationship. If a change in one of your variables leads to a corresponding change in another, they might have this kind of relationship.

However, note that having a direct relationship between variables does not mean they already have a cause-and-effect relationship 2 . It takes statistical analysis to prove causation between variables.

Using our example earlier, the quantity of organic fertilizer may directly relate to the plant’s growth rate. However, we are not sure that the quantity of organic fertilizer is the sole reason for the plant’s growth rate changes.

3. Analyze and Determine Other Influencing Variables

Consider analyzing if other variables can affect the relationship between your independent and dependent variables 3 .

4. Create a Visual Diagram or a Model

Now that you’ve identified the variables and their relationship, you may create a visual diagram summarizing them.

Usually, shapes such as rectangles, circles, and arrows are used for the model. You may create a visual diagram or model for your conceptual framework in different ways. The three most common models are the independent-dependent variable model, the input-process-output (IPO) model, and concept maps.

a. Using the Independent-Dependent Variable Model

You may create this model by writing the independent and dependent variables inside rectangles. Then, insert a line segment between them, connecting the rectangles. This line segment indicates the direct relationship between these variables. 

Below is a visual diagram based on our example about the relationship between organic fertilizer and a plant’s growth rate. 

b. Using the Input-Process-Output (IPO) Model

If you want to emphasize your research process, the input-process-output model is the appropriate visual diagram for your conceptual framework.

To create your visual diagram using the IPO model, follow these steps:

• Determine the inputs of your study . Inputs are the variables you will use to arrive at your research result. Usually, your independent variables are also the inputs of your research. Let’s say your research is about the Level of Satisfaction of College Students Using Google Classroom as an Online Learning Platform. You may include in your inputs the profile of your respondents and the curriculum used in the online learning platform.
• Outline your research process. Using our example above, the research process should be like this: Data collection of student profiles → Administering questionnaires → Tabulation of students’ responses → Statistical data analysis.
• State the research output . Indicate what you are expecting after you conduct the research. In our example above, the research output is the assessed level of satisfaction of college students with the use of Google Classroom as an online learning platform.
• Create the model using the research’s determined input, process, and output.

Presented below is the IPO model for our example above.

c. Using Concept Maps

If you think the two models presented previously are insufficient to summarize your study’s concepts, you may use a concept map for your visual diagram.

A concept map is a helpful visual diagram if multiple variables affect one another. Let’s say your research is about Coping with the Remote Learning System: Anxiety Levels of College Students. Presented below is the concept map for the research’s conceptual framework:

5. Explain Your Conceptual Framework in Narrative Form

Provide a brief explanation of your conceptual framework. State the essential variables, their relationship, and the research outcome.

Using the same example about the relationship between organic fertilizer and the growth rate of the plant, we can come up with the following explanation to accompany the conceptual framework:

Figure 1 shows the Conceptual Framework of the study. The quantity of the organic fertilizer used is the independent variable, while the plant’s growth is the research’s dependent variable. These two variables are directly related based on the research’s empirical evidence.

Conceptual Framework in Quantitative Research

You can create your conceptual framework by following the steps discussed in the previous section. Note, however, that quantitative research has statistical analysis. Thus, you may use arrows to indicate a cause-and-effect relationship in your model. An arrow implies that your independent variable caused the changes in your dependent variable.

Usually, for quantitative research, the Input-Process-Output model is used as a visual diagram. Here is an example of a conceptual framework in quantitative research:

Research Topic : Level of Effectiveness of Corn (Zea mays) Silk Ethanol Extract as an Antioxidant

Conceptual Framework in Qualitative Research

Again, you can follow the same step-by-step guide discussed previously to create a conceptual framework for qualitative research. However, note that you should avoid using one-way arrows as they may indicate causation . Qualitative research cannot prove causation since it uses only descriptive and narrative analysis to relate variables.

Here is an example of a conceptual framework in qualitative research:

Research Topic : Lived Experiences of Medical Health Workers During Community Quarantine

Conceptual Framework Examples

Presented below are some examples of conceptual frameworks.

Research Topic : Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract in the Blood Glucose Level of Swiss Mice (Mus musculus)

Figure 1 presents the Conceptual Framework of the study. The quantity of gabi leaf extract is the independent variable, while the Swiss mice’s blood glucose level is the study’s dependent variable. This study establishes a direct relationship between these variables through empirical evidence and statistical analysis . 

Research Topic : Level of Effectiveness of Using Social Media in the Political Literacy of College Students

Figure 1 shows the Conceptual Framework of the study. The input is the profile of the college students according to sex, year level, and the social media platform being used. The research process includes administering the questionnaires, tabulating students’ responses, and statistical data analysis and interpretation. The output is the effectiveness of using social media in the political literacy of college students.

Research Topic: Factors Affecting the Satisfaction Level of Community Inhabitants

Figure 1 presents a visual illustration of the factors that affect the satisfaction level of community inhabitants. As presented, environmental, societal, and economic factors influence the satisfaction level of community inhabitants. Each factor has its indicators which are considered in this study.

Tips and Warnings

• Please keep it simple. Avoid using fancy illustrations or designs when creating your conceptual framework. 
• Allot a lot of space for feedback. This is to show that your research variables or methodology might be revised based on the input from the research panel. Below is an example of a conceptual framework with a spot allotted for feedback.

Frequently Asked Questions

1. how can i create a conceptual framework in microsoft word.

First, click the Insert tab and select Shapes . You’ll see a wide range of shapes to choose from. Usually, rectangles, circles, and arrows are the shapes used for the conceptual framework. 

Next, draw your selected shape in the document.

Insert the name of the variable inside the shape. You can do this by pointing your cursor to the shape, right-clicking your mouse, selecting Add Text , and typing in the text.

Repeat the same process for the remaining variables of your study. If you need arrows to connect the different variables, you can insert one by going to the Insert tab, then Shape, and finally, Lines or Block Arrows, depending on your preferred arrow style.

2. How to explain my conceptual framework in defense?

If you have used the Independent-Dependent Variable Model in creating your conceptual framework, start by telling your research’s variables. Afterward, explain the relationship between these variables. Example: “Using statistical/descriptive analysis of the data we have collected, we are going to show how the <state your independent variable> exhibits a significant relationship to <state your dependent variable>.”

On the other hand, if you have used an Input-Process-Output Model, start by explaining the inputs of your research. Then, tell them about your research process. You may refer to the Research Methodology in Chapter 3 to accurately present your research process. Lastly, explain what your research outcome is.

Meanwhile, if you have used a concept map, ensure you understand the idea behind the illustration. Discuss how the concepts are related and highlight the research outcome.

3. In what stage of research is the conceptual framework written?

The research study’s conceptual framework is in Chapter 2, following the Review of Related Literature.

4. What is the difference between a Conceptual Framework and Literature Review?

The Conceptual Framework is a summary of the concepts of your study where the relationship of the variables is presented. On the other hand, Literature Review is a collection of published studies and literature related to your study. 

Suppose your research concerns the Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract on Swiss Mice (Mus musculus). In your conceptual framework, you will create a visual diagram and a narrative explanation presenting the quantity of gabi leaf extract and the mice’s blood glucose level as your research variables. On the other hand, for the literature review, you may include this study and explain how this is related to your research topic.

5. When do I use a two-way arrow for my conceptual framework?

You will use a two-way arrow in your conceptual framework if the variables of your study are interdependent. If variable A affects variable B and variable B also affects variable A, you may use a two-way arrow to show that A and B affect each other.

Suppose your research concerns the Relationship Between Students’ Satisfaction Levels and Online Learning Platforms. Since students’ satisfaction level determines the online learning platform the school uses and vice versa, these variables have a direct relationship. Thus, you may use two-way arrows to indicate that the variables directly affect each other.

• Conceptual Framework – Meaning, Importance and How to Write it. (2020). Retrieved 27 April 2021, from https://afribary.com/knowledge/conceptual-framework/
• Correlation vs Causation. Retrieved 27 April 2021, from https://www.jmp.com/en_ph/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
• Swaen, B., & George, T. (2022, August 22). What is a conceptual framework? Tips & Examples. Retrieved December 5, 2022, from https://www.scribbr.com/methodology/conceptual-framework/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

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Educational resources and simple solutions for your research journey

What is a Conceptual Framework and How to Make It (with Examples)

A strong conceptual framework underpins good research. A conceptual framework in research is used to understand a research problem and guide the development and analysis of the research. It serves as a roadmap to conceptualize and structure the work by providing an outline that connects different ideas, concepts, and theories within the field of study. A conceptual framework pictorially or verbally depicts presumed relationships among the study variables.

The purpose of a conceptual framework is to serve as a scheme for organizing and categorizing knowledge and thereby help researchers in developing theories and hypotheses and conducting empirical studies.

In this post, we explain what is a conceptual framework, and provide expert advice on how to make a conceptual framework, along with conceptual framework examples.

Table of Contents

What is a Conceptual Framework in Research

Definition of a conceptual framework.

A conceptual framework includes key concepts, variables, relationships, and assumptions that guide the academic inquiry. It establishes the theoretical underpinnings and provides a lens through which researchers can analyze and interpret data. A conceptual framework draws upon existing theories, models, or established bodies of knowledge to provide a structure for understanding the research problem. It defines the scope of research, identifying relevant variables, establishing research questions, and guiding the selection of appropriate methodologies and data analysis techniques.

Conceptual frameworks can be written or visual. Other types of conceptual framework representations might be taxonomic (verbal description categorizing phenomena into classes without showing relationships between classes) or mathematical descriptions (expression of phenomena in the form of mathematical equations).

Figure 1: Definition of a conceptual framework explained diagrammatically

Conceptual Framework Origin

The term conceptual framework appears to have originated in philosophy and systems theory, being used for the first time in the 1930s by the philosopher Alfred North Whitehead. He bridged the theological, social, and physical sciences by providing a common conceptual framework. The use of the conceptual framework began early in accountancy and can be traced back to publications by William A. Paton and John B. Canning in the first quarter of the 20 th century. Thus, in the original framework, financial issues were addressed, such as useful features, basic elements, and variables needed to prepare financial statements. Nevertheless, a conceptual framework approach should be considered when starting your research journey in any field, from finance to social sciences to applied sciences.

Purpose and Importance of a Conceptual Framework in Research

The importance of a conceptual framework in research cannot be understated, irrespective of the field of study. It is important for the following reasons:

• It clarifies the context of the study.
• It justifies the study to the reader.
• It helps you check your own understanding of the problem and the need for the study.
• It illustrates the expected relationship between the variables and defines the objectives for the research.
• It helps further refine the study objectives and choose the methods appropriate to meet them.

What to Include in a Conceptual Framework

Essential elements that a conceptual framework should include are as follows:

• Overarching research question(s)
• Study parameters
• Study variables
• Potential relationships between those variables.

The sources for these elements of a conceptual framework are literature, theory, and experience or prior knowledge.

How to Make a Conceptual Framework

Now that you know the essential elements, your next question will be how to make a conceptual framework.

For this, start by identifying the most suitable set of questions that your research aims to answer. Next, categorize the various variables. Finally, perform a rigorous analysis of the collected data and compile the final results to establish connections between the variables.

In short, the steps are as follows:

• Choose appropriate research questions.
• Define the different types of variables involved.
• Determine the cause-and-effect relationships.

Be sure to make use of arrows and lines to depict the presence or absence of correlational linkages among the variables.

Developing a Conceptual Framework

Researchers should be adept at developing a conceptual framework. Here are the steps for developing a conceptual framework:

1. Identify a research question

Your research question guides your entire study, making it imperative to invest time and effort in formulating a question that aligns with your research goals and contributes to the existing body of knowledge. This step involves the following:

• Choose a broad topic of interest
• Conduct background research
• Narrow down the focus
• Define your goals
• Make it specific and answerable
• Consider significance and novelty
• Seek feedback.

 2. Choose independent and dependent variables

The dependent variable is the main outcome you want to measure, explain, or predict in your study. It should be a variable that can be observed, measured, or assessed quantitatively or qualitatively. Independent variables are the factors or variables that may influence, explain, or predict changes in the dependent variable.

Choose independent and dependent variables for your study according to the research objectives, the nature of the phenomenon being studied, and the specific research design. The identification of variables is rooted in existing literature, theories, or your own observations.

3. Consider cause-and-effect relationships

To better understand and communicate the relationships between variables in your study, cause-and-effect relationships need to be visualized. This can be done by using path diagrams, cause-and-effect matrices, time series plots, scatter plots, bar charts, or heatmaps.

4. Identify other influencing variables

Besides the independent and dependent variables, researchers must understand and consider the following types of variables:

• Moderating variable: A variable that influences the strength or direction of the relationship between an independent variable and a dependent variable.
• Mediating variable: A variable that explains the relationship between an independent variable and a dependent variable and clarifies how the independent variable affects the dependent variable.
• Control variable: A variable that is kept constant or controlled to avoid the influence of other factors that may affect the relationship between the independent and dependent variables.
• Confounding variable: A type of unmeasured variable that is related to both the independent and dependent variables.

Example of a Conceptual Framework

Let us examine the following conceptual framework example. Let’s say your research topic is “ The Impact of Social Media Usage on Academic Performance among College Students .” Here, you want to investigate how social media usage affects academic performance in college students. Social media usage (encompassing frequency of social media use, time spent on social media platforms, and types of social media platforms used) is the independent variable, and academic performance (covering grades, exam scores, and class attendance) is the dependent variable.

This conceptual framework example also includes a mediating variable, study habits, which may explain how social media usage affects academic performance. Study habits (time spent studying, study environment, and use of study aids or resources) can act as a mechanism through which social media usage influences academic outcomes. Additionally, a moderating variable, self-discipline (level of self-control and self-regulation, ability to manage distractions, and prioritization skills), is included to examine how individual differences in self-control and discipline may influence the relationship between social media usage and academic performance.

Confounding variables are also identified (socioeconomic status, prior academic achievement), which are potential factors that may influence both social media usage and academic performance. These variables need to be considered and controlled in the study to ensure that any observed effects are specifically attributed to social media usage. A visual representation of this conceptual framework example is seen in Figure 2.

Figure 2: Visual representation of a conceptual framework for the topic “The Impact of Social Media Usage on Academic Performance among College Students”

Key Takeaways

Here is a snapshot of the basics of a conceptual framework in research:

• A conceptual framework is an idea or model representing the subject or phenomena you intend to study.
• It is primarily a researcher’s perception of the research problem. It can be used to develop hypotheses or testable research questions.
• It provides a preliminary understanding of the factors at play, their interrelationships, and the underlying reasons.
• It guides your research by aiding in the formulation of meaningful research questions, selection of appropriate methods, and identification of potential challenges to the validity of your findings.
• It provides a structure for organizing and understanding data.
• It allows you to chalk out the relationships between concepts and variables to understand them.
• Variables besides dependent and independent variables (moderating, mediating, control, and confounding variables) must be considered when developing a conceptual framework.

Frequently Asked Questions

What is the difference between a moderating variable and a mediating variable.

Moderating and mediating variables are easily confused. A moderating variable affects the direction and strength of this relationship, whereas a mediating explains how two variables relate.

What is the difference between independent variables, dependent variables, and confounding variables?

Independent variables are the variables manipulated to affect the outcome of an experiment (e.g., the dose of a fat-loss drug administered to rats). Dependent variables are variables being measured or observed in an experiment (e.g., changes in rat body weight as a result of the drug). A confounding variable distorts or masks the effects of the variables being studied because it is associated both with dependent variable and with the independent variable. For instance, in this example, pre-existing metabolic dysfunction in some rats could interact differently with the drug being studied and also affect rat body weight.

Should I have more than one dependent or independent variable in a study?

The need for more than one dependent or independent variable in a study depends on the research question, study design, and relationships being investigated. Note the following when making this decision for your research:

• If your research question involves exploring the relationships between multiple variables or factors, it may be appropriate to have more than one dependent or independent variable.
• If you have specific hypotheses about the relationships between several variables, it may be necessary to include multiple dependent or independent variables.
• Adequate resources, sample size, and data collection methods should be considered when determining the number of dependent and independent variables to include.

What is a confounding variable?

A confounding variable is not the main focus of the study but can unintentionally influence the relationship between the independent and dependent variables. Confounding variables can introduce bias and give rise to misleading conclusions. These variables must be controlled to ensure that any observed relationship is genuinely due to the independent variable.

What is a control variable?

A control variable is something not of interest to the study’s objectives but is kept constant because it could influence the outcomes. Control variables can help prevent research biases and allow for a more accurate assessment of the relationship between the independent and dependent variables. Examples are (i) testing all participants at the same time (e.g., in the morning) to minimize the potential effects of circadian rhythms, (ii) ensuring that instruments are calibrated consistently before each measurement to minimize the influence of measurement errors, and (iii) randomization of participants across study groups.

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Conceptual Framework Guide: Meaning, Structure, and Examples

Published 16 Oct 2023

Conceptual frameworks are the architects of successful research endeavors, providing the blueprint for understanding complex phenomena and guiding researchers through their investigative journey. In this article, we delve into a conceptual framework's essence, its meaning, structural components, and the systematic steps to craft one. Whether you’re a seasoned researcher or just embarking on your research journey, understanding the art and science of conceptual frameworks is key to achieving clarity, focus, and rigor in your dissertation. 

What is a conceptual framework? 

Before we delve into how to create one, let’s consider the conceptual framework’s meaning. A conceptual framework is a structure or a set of concepts and principles that guide and inform a research study. It serves as a foundation for understanding the research problem and for making decisions about how to investigate it. It has the following purposes:

• Explaining how the key concepts or variables interact to clarify the research issue.
• Defining the purpose and scope of a thesis.
• Organizing ideas and clarifying concepts.
• Introducing the investigation and showcasing its contribution to the field, encompassing relevant concepts even from neighboring domains as long as they’re critical to the issue.

It’s a valuable tool whenever you need to structure your thinking, clarify connections between variables, or provide a theoretical basis for your research or analysis. Its application can benefit a wide range of academic and practical contexts.

Components of a conceptual framework in research

There are several components this model typically includes. Let’s enumerate them and consider them in detail.

• Problem statement: Defining the specific issue the conceptual model addresses, guiding the choice of relevant constructs.
• Concepts: Fundamental principles characterizing the studied phenomenon, fostering a common vocabulary.
• Constructs: Precise, measurable variables representing broader conceptual ideas.
• Propositions or hypotheses: Ideas describing relationships within the framework.
• Assumptions: Fundamental beliefs shaping the framework, explicit or implicit.
• Boundaries: Constraints specifying the focus of inquiry.
• Context: Broader cultural, societal, and historical influences on the phenomenon.
• Interconnections: Relationships among framework elements.
• Variables: Elements subject to measurement or observation.
• Methodology: Research approaches, data collection, analysis methods, and ethics.
• Literature review: Overview of available research, identifying gaps.
• Outcomes and implications: Anticipated study results and contributions to knowledge and practice.

These components collectively provide a structured framework for conducting research and analyzing complex phenomena. Learners use them to guide their work, develop hypotheses, and facilitate a deeper understanding of the subject matter.

How to write a conceptual framework: 5 steps

Writing a conceptual framework involves several steps to develop a logical and structured foundation for your dissertation. Discover our step-by-step guide.

Step 1. Identification of the research problem.

The initial step entails pinpointing the research issue the work intends to address. It involves recognizing gaps in the existing body of knowledge and specifying the precise issue the thesis aims to explore.

Step 2. Conducting a comprehensive literature review.

The next phase involves a thorough literature review to delineate existing models, theories, and frameworks pertaining to the research question . This process assists the learner in identifying the essential concepts and variables that require consideration in the study.

Step 3. Definition of essential concepts and variables.

Subsequently, the researcher should define the crucial variables and concepts relevant to the study. It includes providing clear definitions for the terminology and recognizing the factors that will be measured or observed during the investigation.

Step 4. Development of a theoretical framework.

When the essential variables and concepts have been established, the researcher constructs a theoretical framework. This step involves delineating the connections between the variables and concepts and visually representing these links.

Step 5. Validation.

The final step includes the validation of the theoretical framework through the usage of empirical data. It involves collecting and analyzing data to assess the accuracy and validity of the relationships identified within the framework.

Writing a conceptual model is an iterative process that may undergo refinements as your investigation progresses. It serves as a roadmap for your study, guiding your research design, data collection, and analysis, ultimately contributing to developing new knowledge in your field.

Advantages and limitations of conceptual framework

These models offer distinct advantages in research, such as providing clarity and focus by elucidating ideas and concentrating on fundamental concepts. They serve as the structural underpinning for well-thought-out investigation, introducing existing theories to foster further study. These frameworks also facilitate hypothesis development, formulating testable research questions. Importantly, they enhance communication and comprehension among students working within a common framework.

However, it’s crucial to acknowledge their limitations. Conceptual models can sometimes oversimplify intricate phenomena, potentially leading to incomplete or inaccurate interpretations. Their development is subjective, and variations among researchers may yield diverse interpretations. Rigidity in a framework can constrain adaptability in the research process, potentially limiting the exploration of unexpected findings. Developing a robust framework is time-consuming and necessitates a deep understanding of the subject matter. Lastly, there’s a risk of introducing bias when selecting specific concepts or theories, potentially impacting the objectivity of the dissertation.

Conceptual framework examples

Here are some examples of frameworks used in various fields of research.

• Healthcare;

Social Determinants of Health Model: This framework explains how economic and social aspects, such as income, education, and access to healthcare, impact an individual’s health and well-being.

Bloom's Taxonomy: This model categorizes different levels of cognitive learning, from basic knowledge acquisition to higher-order thinking skills like analysis and synthesis.

Resource-Based View (RBV) of the Firm: This model explores how a company's unique resources and capabilities contribute to its competitive advantage.

• Political Science;

Institutionalism: Researchers in political science often use institutional frameworks to study the impact of political institutions (e.g., executives, legislatures, and judiciaries) on policy outcomes and governance.

• Communication;

Media Effects Theories: These models explore how media content, such as news or entertainment, can influence individuals’ attitudes, beliefs, and behaviors.

These are just a few examples of the many models used across various disciplines. Researchers often choose a conceptual framework example that best aligns with their studies' specific research questions and objectives.

What is the difference between conceptual and theoretical frameworks? 

The first means a preliminary, high-level representation of key concepts in a study. At the same time, a theoretical framework is a more detailed and specific structure that draws upon existing theories to explain and predict relationships between variables. Researchers often use both models in their studies, with the conceptual framework serving as an initial foundation and the theoretical framework providing a deeper theoretical basis for the dissertation.                     

How do mediator and moderator variables contrast?  

A mediator variable elucidates the mechanism by which two variables are interconnected, whereas a moderator variable influences the intensity and direction of that association.                  

How do independent variables and dependent variables differ? 

The key distinction between independent and dependent variables lies in their roles and relationships within academic writing. The independent variable is the factor being tested or manipulated, while the dependent variable is the outcome that is measured to assess the impact of the independent variable.

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Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

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To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

Supplementary Material

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualise your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, ‘hours of study’, is the independent variable (the predictor, or explanatory variable)
• The expected effect, ‘exam score’, is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (‘hours of study’).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualising your expected cause-and-effect relationship.

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the ‘effect’ component of the cause-and-effect relationship.

Let’s add the moderator ‘IQ’. Here, a student’s IQ level can change the effect that the variable ‘hours of study’ has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our ‘IQ’ moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the ‘IQ’ moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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How to Make a Conceptual Framework

• 6-minute read
• 2nd January 2022

What is a conceptual framework? And why is it important?

A conceptual framework illustrates the relationship between the variables of a research question. It’s an outline of what you’d expect to find in a research project.

Conceptual frameworks should be constructed before data collection and are vital because they map out the actions needed in the study. This should be the first step of an undergraduate or graduate research project.

What Is In a Conceptual Framework?

In a conceptual framework, you’ll find a visual representation of the key concepts and relationships that are central to a research study or project . This can be in form of a diagram, flow chart, or any other visual representation. Overall, a conceptual framework serves as a guide for understanding the problem being studied and the methods being used to investigate it.

Steps to Developing the Perfect Conceptual Framework

• Pick a question
• Conduct a literature review
• Identify your variables
• Create your conceptual framework

1. Pick a Question

You should already have some idea of the broad area of your research project. Try to narrow down your research field to a manageable topic in terms of time and resources. From there, you need to formulate your research question. A research question answers the researcher’s query: “What do I want to know about my topic?” Research questions should be focused, concise, arguable and, ideally, should address a topic of importance within your field of research.

An example of a simple research question is: “What is the relationship between sunny days and ice cream sales?”

2. Conduct a Literature Review

A literature review is an analysis of the scholarly publications on a chosen topic. To undertake a literature review, search for articles with the same theme as your research question. Choose updated and relevant articles to analyze and use peer-reviewed and well-respected journals whenever possible.

For the above example, the literature review would investigate publications that discuss how ice cream sales are affected by the weather. The literature review should reveal the variables involved and any current hypotheses about this relationship.

3. Identify Your Variables

There are two key variables in every experiment: independent and dependent variables.

Independent Variables

The independent variable (otherwise known as the predictor or explanatory variable) is the expected cause of the experiment: what the scientist changes or changes on its own. In our example, the independent variable would be “the number of sunny days.”

Dependent Variables

The dependent variable (otherwise known as the response or outcome variable) is the expected effect of the experiment: what is being studied or measured. In our example, the dependent variable would be “the quantity of ice cream sold.”

Next, there are control variables.

Control Variables

A control variable is a variable that may impact the dependent variable but whose effects are not going to be measured in the research project. In our example, a control variable could be “the socioeconomic status of participants.” Control variables should be kept constant to isolate the effects of the other variables in the experiment.

Finally, there are intervening and extraneous variables.

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Intervening Variables

Intervening variables link the independent and dependent variables and clarify their connection. In our example, an intervening variable could be “temperature.”

Extraneous Variables

Extraneous variables are any variables that are not being investigated but could impact the outcomes of the study. Some instances of extraneous variables for our example would be “the average price of ice cream” or “the number of varieties of ice cream available.” If you control an extraneous variable, it becomes a control variable.

4. Create Your Conceptual Framework

Having picked your research question, undertaken a literature review, and identified the relevant variables, it’s now time to construct your conceptual framework. Conceptual frameworks are clear and often visual representations of the relationships between variables.

We’ll start with the basics: the independent and dependent variables.

Our hypothesis is that the quantity of ice cream sold directly depends on the number of sunny days; hence, there is a cause-and-effect relationship between the independent variable (the number of sunny days) and the dependent and independent variable (the quantity of ice cream sold).

Next, introduce a control variable. Remember, this is anything that might directly affect the dependent variable but is not being measured in the experiment:

Finally, introduce the intervening and extraneous variables. 

The intervening variable (temperature) clarifies the relationship between the independent variable (the number of sunny days) and the dependent variable (the quantity of ice cream sold). Extraneous variables, such as the average price of ice cream, are variables that are not controlled and can potentially impact the dependent variable.

Are Conceptual Frameworks and Research Paradigms the Same?

In simple terms, the research paradigm is what informs your conceptual framework. In defining our research paradigm we ask the big questions—Is there an objective truth and how can we understand it? If we decide the answer is yes, we may be working with a positivist research paradigm and will choose to build a conceptual framework that displays the relationship between fixed variables. If not, we may be working with a constructivist research paradigm, and thus our conceptual framework will be more of a loose amalgamation of ideas, theories, and themes (a qualitative study). If this is confusing–don’t worry! We have an excellent blog post explaining research paradigms in more detail.

Where is the Conceptual Framework Located in a Thesis?

This will depend on your discipline, research type, and school’s guidelines, but most papers will include a section presenting the conceptual framework in the introduction, literature review, or opening chapter. It’s best to present your conceptual framework after presenting your research question, but before outlining your methodology.

Can a Conceptual Framework be Used in a Qualitative Study?

Yes. Despite being less clear-cut than a quantitative study, all studies should present some form of a conceptual framework. Let’s say you were doing a study on care home practices and happiness, and you came across a “happiness model” constructed by a relevant theorist in your literature review. Your conceptual framework could be an outline or a visual depiction of how you will use this model to collect and interpret qualitative data for your own study (such as interview responses). Check out this useful resource showing other examples of conceptual frameworks for qualitative studies .

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How to Use a Conceptual Framework for Better Research

A conceptual framework in research is not just a tool but a vital roadmap that guides the entire research process. It integrates various theories, assumptions, and beliefs to provide a structured approach to research. By defining a conceptual framework, researchers can focus their inquiries and clarify their hypotheses, leading to more effective and meaningful research outcomes.

What is a Conceptual Framework?

A conceptual framework is essentially an analytical tool that combines concepts and sets them within an appropriate theoretical structure. It serves as a lens through which researchers view the complexities of the real world. The importance of a conceptual framework lies in its ability to serve as a guide, helping researchers to not only visualize but also systematically approach their study.

Key Components and to be Analyzed During Research

• Theories: These are the underlying principles that guide the hypotheses and assumptions of the research.
• Assumptions: These are the accepted truths that are not tested within the scope of the research but are essential for framing the study.
• Beliefs: These often reflect the subjective viewpoints that may influence the interpretation of data.
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Together, these components help to define the conceptual framework that directs the research towards its ultimate goal. This structured approach not only improves clarity but also enhances the validity and reliability of the research outcomes. By using a conceptual framework, researchers can avoid common pitfalls and focus on essential variables and relationships.

For practical examples and to see how different frameworks can be applied in various research scenarios, you can Explore Conceptual Framework Examples .

Different Types of Conceptual Frameworks Used in Research

Understanding the various types of conceptual frameworks is crucial for researchers aiming to align their studies with the most effective structure. Conceptual frameworks in research vary primarily between theoretical and operational frameworks, each serving distinct purposes and suiting different research methodologies.

Theoretical vs Operational Frameworks

Theoretical frameworks are built upon existing theories and literature, providing a broad and abstract understanding of the research topic. They help in forming the basis of the study by linking the research to already established scholarly works. On the other hand, operational frameworks are more practical, focusing on how the study’s theories will be tested through specific procedures and variables.

• Theoretical frameworks are ideal for exploratory studies and can help in understanding complex phenomena.
• Operational frameworks suit studies requiring precise measurement and data analysis.

Choosing the Right Framework

Selecting the appropriate conceptual framework is pivotal for the success of a research project. It involves matching the research questions with the framework that best addresses the methodological needs of the study. For instance, a theoretical framework might be chosen for studies that aim to generate new theories, while an operational framework would be better suited for testing specific hypotheses.

Benefits of choosing the right framework include enhanced clarity, better alignment with research goals, and improved validity of research outcomes. Tools like Table Chart Maker can be instrumental in visually comparing the strengths and weaknesses of different frameworks, aiding in this crucial decision-making process.

Real-World Examples of Conceptual Frameworks in Research

Understanding the practical application of conceptual frameworks in research can significantly enhance the clarity and effectiveness of your studies. Here, we explore several real-world case studies that demonstrate the pivotal role of conceptual frameworks in achieving robust research conclusions.

• Healthcare Research: In a study examining the impact of lifestyle choices on chronic diseases, researchers used a conceptual framework to link dietary habits, exercise, and genetic predispositions. This framework helped in identifying key variables and their interrelations, leading to more targeted interventions.
• Educational Development: Educational theorists often employ conceptual frameworks to explore the dynamics between teaching methods and student learning outcomes. One notable study mapped out the influences of digital tools on learning engagement, providing insights that shaped educational policies.
• Environmental Policy: Conceptual frameworks have been crucial in environmental research, particularly in studies on climate change adaptation. By framing the relationships between human activity, ecological changes, and policy responses, researchers have been able to propose more effective sustainability strategies.

Adapting conceptual frameworks based on evolving research data is also critical. As new information becomes available, it’s essential to revisit and adjust the framework to maintain its relevance and accuracy, ensuring that the research remains aligned with real-world conditions.

For those looking to visualize and better comprehend their research frameworks, Graphic Organizers for Conceptual Frameworks can be an invaluable tool. These organizers help in structuring and presenting research findings clearly, enhancing both the process and the presentation of your research.

Step-by-Step Guide to Creating Your Own Conceptual Framework

Creating a conceptual framework is a critical step in structuring your research to ensure clarity and focus. This guide will walk you through the process of building a robust framework, from identifying key concepts to refining your approach as your research evolves.

Building Blocks of a Conceptual Framework

• Identify and Define Main Concepts and Variables: Start by clearly identifying the main concepts, variables, and their relationships that will form the basis of your research. This could include defining key terms and establishing the scope of your study.
• Develop a Hypothesis or Primary Research Question: Formulate a central hypothesis or question that guides the direction of your research. This will serve as the foundation upon which your conceptual framework is built.
• Link Theories and Concepts Logically: Connect your identified concepts and variables with existing theories to create a coherent structure. This logical linking helps in forming a strong theoretical base for your research.

Visualizing and Refining Your Framework

Using visual tools can significantly enhance the clarity and effectiveness of your conceptual framework. Decision Tree Templates for Conceptual Frameworks can be particularly useful in mapping out the relationships between variables and hypotheses.

Map Your Framework: Utilize tools like Creately’s visual canvas to diagram your framework. This visual representation helps in identifying gaps or overlaps in your framework and provides a clear overview of your research structure.

Analyze and Refine: As your research progresses, continuously evaluate and refine your framework. Adjustments may be necessary as new data comes to light or as initial assumptions are challenged.

By following these steps, you can ensure that your conceptual framework is not only well-defined but also adaptable to the changing dynamics of your research.

Practical Tips for Utilizing Conceptual Frameworks in Research

Effectively utilizing a conceptual framework in research not only streamlines the process but also enhances the clarity and coherence of your findings. Here are some practical tips to maximize the use of conceptual frameworks in your research endeavors.

• Setting Clear Research Goals: Begin by defining precise objectives that are aligned with your research questions. This clarity will guide your entire research process, ensuring that every step you take is purposeful and directly contributes to your overall study aims. \
• Maintaining Focus and Coherence: Throughout the research, consistently refer back to your conceptual framework to maintain focus. This will help in keeping your research aligned with the initial goals and prevent deviations that could dilute the effectiveness of your findings.
• Data Analysis and Interpretation: Use your conceptual framework as a lens through which to view and interpret data. This approach ensures that the data analysis is not only systematic but also meaningful in the context of your research objectives. For more insights, explore Research Data Analysis Methods .
• Presenting Research Findings: When it comes time to present your findings, structure your presentation around the conceptual framework . This will help your audience understand the logical flow of your research and how each part contributes to the whole.
• Avoiding Common Pitfalls: Be vigilant about common errors such as overcomplicating the framework or misaligning the research methods with the framework’s structure. Keeping it simple and aligned ensures that the framework effectively supports your research.

By adhering to these tips and utilizing tools like 7 Essential Visual Tools for Social Work Assessment , researchers can ensure that their conceptual frameworks are not only robust but also practically applicable in their studies.

How Creately Enhances the Creation and Use of Conceptual Frameworks

Creating a robust conceptual framework is pivotal for effective research, and Creately’s suite of visual tools offers unparalleled support in this endeavor. By leveraging Creately’s features, researchers can visualize, organize, and analyze their research frameworks more efficiently.

• Visual Mapping of Research Plans: Creately’s infinite visual canvas allows researchers to map out their entire research plan visually. This helps in understanding the complex relationships between different research variables and theories, enhancing the clarity and effectiveness of the research process.
• Brainstorming with Mind Maps: Using Mind Mapping Software , researchers can generate and organize ideas dynamically. Creately’s intelligent formatting helps in brainstorming sessions, making it easier to explore multiple topics or delve deeply into specific concepts.
• Centralized Data Management: Creately enables the importation of data from multiple sources, which can be integrated into the visual research framework. This centralization aids in maintaining a cohesive and comprehensive overview of all research elements, ensuring that no critical information is overlooked.
• Communication and Collaboration: The platform supports real-time collaboration, allowing teams to work together seamlessly, regardless of their physical location. This feature is crucial for research teams spread across different geographies, facilitating effective communication and iterative feedback throughout the research process.

Moreover, the ability t Explore Conceptual Framework Examples directly within Creately inspires researchers by providing practical templates and examples that can be customized to suit specific research needs. This not only saves time but also enhances the quality of the conceptual framework developed.

In conclusion, Creately’s tools for creating and managing conceptual frameworks are indispensable for researchers aiming to achieve clear, structured, and impactful research outcomes.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

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Chiraag George is a communication specialist here at Creately. He is a marketing junkie that is fascinated by how brands occupy consumer mind space. A lover of all things tech, he writes a lot about the intersection of technology, branding and culture at large.

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Theoretical Framework Example for a Thesis or Dissertation

Published on October 14, 2015 by Sarah Vinz . Revised on July 18, 2023 by Tegan George.

Your theoretical framework defines the key concepts in your research, suggests relationships between them, and discusses relevant theories based on your literature review .

A strong theoretical framework gives your research direction. It allows you to convincingly interpret, explain, and generalize from your findings and show the relevance of your thesis or dissertation topic in your field.

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Table of contents

Sample problem statement and research questions, sample theoretical framework, your theoretical framework, other interesting articles.

Your theoretical framework is based on:

• Your problem statement
• Your research questions
• Your literature review

A new boutique downtown is struggling with the fact that many of their online customers do not return to make subsequent purchases. This is a big issue for the otherwise fast-growing store.Management wants to increase customer loyalty. They believe that improved customer satisfaction will play a major role in achieving their goal of increased return customers.

To investigate this problem, you have zeroed in on the following problem statement, objective, and research questions:

• Problem : Many online customers do not return to make subsequent purchases.
• Objective : To increase the quantity of return customers.
• Research question : How can the satisfaction of the boutique’s online customers be improved in order to increase the quantity of return customers?

The concepts of “customer loyalty” and “customer satisfaction” are clearly central to this study, along with their relationship to the likelihood that a customer will return. Your theoretical framework should define these concepts and discuss theories about the relationship between these variables.

Some sub-questions could include:

• What is the relationship between customer loyalty and customer satisfaction?
• How satisfied and loyal are the boutique’s online customers currently?
• What factors affect the satisfaction and loyalty of the boutique’s online customers?

As the concepts of “loyalty” and “customer satisfaction” play a major role in the investigation and will later be measured, they are essential concepts to define within your theoretical framework .

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Below is a simplified example showing how you can describe and compare theories in your thesis or dissertation . In this example, we focus on the concept of customer satisfaction introduced above.

Customer satisfaction

Thomassen (2003, p. 69) defines customer satisfaction as “the perception of the customer as a result of consciously or unconsciously comparing their experiences with their expectations.” Kotler & Keller (2008, p. 80) build on this definition, stating that customer satisfaction is determined by “the degree to which someone is happy or disappointed with the observed performance of a product in relation to his or her expectations.”

Performance that is below expectations leads to a dissatisfied customer, while performance that satisfies expectations produces satisfied customers (Kotler & Keller, 2003, p. 80).

The definition of Zeithaml and Bitner (2003, p. 86) is slightly different from that of Thomassen. They posit that “satisfaction is the consumer fulfillment response. It is a judgement that a product or service feature, or the product of service itself, provides a pleasurable level of consumption-related fulfillment.” Zeithaml and Bitner’s emphasis is thus on obtaining a certain satisfaction in relation to purchasing.

Thomassen’s definition is the most relevant to the aims of this study, given the emphasis it places on unconscious perception. Although Zeithaml and Bitner, like Thomassen, say that customer satisfaction is a reaction to the experience gained, there is no distinction between conscious and unconscious comparisons in their definition.

The boutique claims in its mission statement that it wants to sell not only a product, but also a feeling. As a result, unconscious comparison will play an important role in the satisfaction of its customers. Thomassen’s definition is therefore more relevant.

Thomassen’s Customer Satisfaction Model

According to Thomassen, both the so-called “value proposition” and other influences have an impact on final customer satisfaction. In his satisfaction model (Fig. 1), Thomassen shows that word-of-mouth, personal needs, past experiences, and marketing and public relations determine customers’ needs and expectations.

These factors are compared to their experiences, with the interplay between expectations and experiences determining a customer’s satisfaction level. Thomassen’s model is important for this study as it allows us to determine both the extent to which the boutique’s customers are satisfied, as well as where improvements can be made.

Figure 1 Customer satisfaction creation 

Of course, you could analyze the concepts more thoroughly and compare additional definitions to each other. You could also discuss the theories and ideas of key authors in greater detail and provide several models to illustrate different concepts.

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Designing conceptual articles: four approaches

• Theory/Conceptual
• Open access
• Published: 09 March 2020
• Volume 10 , pages 18–26, ( 2020 )

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As a powerful means of theory building, conceptual articles are increasingly called for in marketing academia. However, researchers struggle to design and write non-empirical articles because of the lack of commonly accepted templates to guide their development. The aim of this paper is to highlight methodological considerations for conceptual papers: it is argued that such papers must be grounded in a clear research design, and that the choice of theories and their role in the analysis must be explicated and justified. The paper discusses four potential templates for conceptual papers – Theory Synthesis, Theory Adaptation, Typology, and Model – and their respective aims, approach for using theories, and contribution potential. Supported by illustrative examples, these templates codify some of the tacit knowledge that underpins the design of non-empirical papers and will be of use to anyone undertaking, supervising, or reviewing conceptual research.

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Introduction

The major academic journals in the field of marketing acknowledge the need for good conceptual papers that can “bridge existing theories in interesting ways, link work across disciplines, provide multi-level insights, and broaden the scope of our thinking” (Gilson and Goldberg 2015 , p. 128). Indeed, many of the most impactful marketing papers of recent decades are conceptual as this type of research enables theory building unrestricted by the demands of empirical generalization (e.g., Vargo and Lusch 2004 ). Authors crafting conceptual papers can find valuable advice on problematizing (Alvesson and Sandberg 2011 ), theorizing and theory building (Corley and Gioia 2011 ; Cornelissen 2017 ; Shepherd and Suddaby 2017 ), and the types of conceptual contribution that warrant publication (Corley and Gioia 2011 ; MacInnis 2011 ). However, a lack of commonly accepted templates or “recipes” for building the paper means that writing a conceptual piece can be a struggle (Cornelissen 2017 ). As a result, reviewers often face conceptual papers that offer little more than a descriptive literature review or interesting but disjointed ideas.

In empirical papers, the recipe typically is the research design that provides the paper structure and logic, guiding the process of developing new knowledge and offering conventions for reporting the key elements of the research (Flick 2018 , p. 102). The research design explains how the ingredients of the study were selected, acquired, and analyzed to effectively address the research problem, and reviewers can evaluate the robustness of this process by reference to established conventions in the existing literature. As conceptual papers generally do not fit the mold of empirical research, authors and reviewers lack any such recipe book, making the critical issue of analytical rigor more challenging.

This paper addresses issues of methodology and research design for conceptual papers. The discussion is built on previous “how to” guides to conceptual research, and on examples from high quality journals to identify and illustrate different options for conceptual research design. This paper discusses four templates—Theory Synthesis, Theory Adaptation, Typology, and Model—and explicates their aims, their approach to theory use, and their contribution potential. The paper does not focus on theory building itself but supports it, as analytical rigor is a prerequisite for high quality theorizing. Nor is the focus on literature reviews or meta-analyses; while these are important non-empirical forms of research, there are well articulated existing guidelines for such articles (see for example Webster and Watson 2002 ; Palmatier et al. 2018 ).

The ultimate goal of this paper is to direct scholarly attention to the importance of a systematic approach to developing a conceptual paper. Experienced editors and reviewers have noted that researchers sometimes underestimate how difficult it is to write a rigorous conceptual paper and consider this an easy route to publishing—an essay devoid of deeper scholarship (Hirschheim 2008 ). In reality, developing a cogent argument and building a supporting theoretical explanation requires tacit knowledge and skills that doctoral programs seldom teach (Yadav 2010 ; King and Lepak 2011 ). As Fulmer puts it, “in a theoretical paper the author is faced with a mixed blessing: greater freedom and page length within which to develop theory but also more editorial rope with which to hang him/herself” ( 2012 , p. 330).

The paper is organized as follows. The next section outlines key methodological requirements for conceptual studies. Four common types of research design are then identified and discussed with supporting examples. The article ends with conclusions and recommendations for marketing scholars undertaking, supervising, or reviewing conceptual research.

Conceptual papers: some methodological requirements

The term “research design” refers to decisions about how to achieve research goals, linking theories, questions, and goals to appropriate resources and methods (Flick 2018 , p. 102). In short, the research design is a plan for collecting and analyzing evidence that helps to answer the question posed (Ragin 1994 , p. 191). Like any design, the research design should improve usability ; a good research design is the optimal tool for addressing the research problem, and it communicates the logic of the study in a transparent way. In principle, any piece of research should be designed to deliver trustworthy answers to the question posed in a credible and justified manner.

An empirical research design typically involves decisions about the underlying theoretical framing of the study as well as issues of data collection and analysis (e.g. Miller and Salkind 2002 ). Imagine, for example, an empirical paper where the authors did not argue for their sampling criteria or choice of informants, or failed to define the measures used or to show how the results were derived from the data. It can be argued that conceptual papers entail similar considerations (Table 1 ), as the omission of equivalent elements would create similar confusion. In other words, a well-designed conceptual paper must explicitly justify and explicate decisions about key elements of the study. The following sections elaborate more specifically on designing and communicating these “methodological” aspects of conceptual papers.

Explicating and justifying the choice of theories and concepts

Empirical and conceptual papers ultimately share a common goal: to create new knowledge by building on carefully selected sources of information combined according to a set of norms. In the case of conceptual papers, arguments are not derived from data in the traditional sense but involve the assimilation and combination of evidence in the form of previously developed concepts and theories (Hirschheim 2008 ). In that sense, conceptual papers are not without empirical insights but rather build on theories and concepts that are developed and tested through empirical research.

In an empirical study, the researcher determines what data are needed to address the research questions and specifies sampling criteria and research instruments accordingly. In similar fashion, a conceptual paper should explain how and why the theories and concepts on which it is grounded were selected. In simple terms, there are two possible points of departure. The first option is to start from a focal phenomenon that is observable but not adequately addressed in the existing research. The authors may inductively identify differing conceptualizations of that phenomenon, and then argue that the aspect of interest is best addressed in terms of particular concepts or theories. That is, the choice of concepts is based on their fit to the focal phenomenon and their complementary value in conceptualizing it. One key issue here is how the researcher conceptualizes the empirical phenomenon; in selecting particular concepts and theories, the researcher is de facto making an argument about the conceptual ingredients of the empirical phenomenon in question.

A second and perhaps more common approach is to start from a focal theory by arguing that a particular concept, theory, or research domain is internally incoherent or incomplete in some important respect and then introducing other theories to bridge the observed gaps. In this case, the choice of theories or concepts is based on their ability to address the observed shortcoming in the existing literature, i.e. their supplementary value. This simplified account raises a critical underlying question: what is the value that each selected concept, literature stream, or theory brings to the study, and why are they selected in preference to something else?

Explicating the role of different theories and concepts in the analysis

Conceptual papers typically draw on multiple concepts, literature streams, and theories that play differing roles. It is difficult to imagine a (published) empirical paper where the reader could not distinguish empirical data from the literature review. In a conceptual paper, however, it is sometimes difficult to tell which theories provide the “data” and which are framing the analysis. In this regard, Lukka and Vinnari ( 2014 ) drew a useful distinction between domain theory and method theory. A domain theory is “a particular set of knowledge on a substantive topic area situated in a field or domain” (ibid, p. 1309)—that is, an area of study characterized by a particular set of constructs, theories, and assumptions (MacInnis 2011 ). A method theory, on the other hand, is “a meta-level conceptual system for studying the substantive issue(s) of the domain theory at hand” (Lukka and Vinnari 2014 , p. 1309). For example, Brodie et al. ( 2019 ) sought to advance engagement research (domain theory) by drawing new perspectives from service-dominant logic (method theory). The distinction is relative rather than absolute; whether a particular theory is domain or method theory depends on its role in the study in question (Lukka and Vinnari 2014 ). Indeed, a single study can accommodate multiple domain and method theories.

In a conceptual paper, one crucial function of the research design is to explicate the role of each element in the paper; failure to explain this is likely to render the logic of “generating findings” practically invisible to the reader. Defining the roles of different theories also helps to indicate the paper’s positioning, and how its contribution should be evaluated. Typically, the role of the method theory is to provide some new insight into the domain theory—for example, to expand, organize, or offer a new or alternative explanation of concepts and relationships. This means that contribution usually centers on the domain theory, not the method theory (Lukka and Vinnari 2014 ). For example, marketing scholars often use established theories such as resource-based theory, institutional theory, or practice theory as method theories, but any suitable framework (even from other disciplines) can play this role. Footnote 1

Making the chain of evidence visible and easy to grasp

Conceptual papers typically focus on proposing new relationships among constructs; the purpose is thus to develop logical and complete arguments about these associations rather than testing them empirically (Gilson and Goldberg 2015 ). The issue of how to develop logical arguments is hence pivotal. As well as arguing that concepts are linked, authors must provide a theoretical explanation for that link. As that explanation demonstrates the logic of connections between concepts, it is critical for theory building (King and Lepak 2011 ).

In attempting to analyze what constitutes a good argument, Hirschheim ( 2008 ) adopted a framework first advanced by the British philosopher Toulmin ( 1958 ), according to which an argument has three necessary components: claims, grounds, and warrants. Claims refer to the explicit statement or thesis that the reader is being asked to accept as true—the outcome of the research. Grounds are the evidence and reasoning used to support the claim and to persuade the reader. In a conceptual paper, this evidence is drawn from previous studies rather than from primary data. Finally, warrants are the underlying assumptions or presuppositions that link grounds to claims. Warrants are often beliefs implicitly accepted within the given research domain—for example the assumption that organizations strive to satisfy their customers. In a robust piece of research, claims should be substantiated by sufficient grounds, and should be of sufficient significance to make a worthwhile contribution to knowledge (Hirschheim 2008 ).

In practice, the chain of evidence in a conceptual paper is made visible to the reader by explicating the key steps in the argument. How is the studied phenomenon conceptualized? What are the study’s implicit assumptions, stemming from its theoretical underpinnings? Are the premises and axioms used to ground the arguments sufficiently explicit to enable another researcher to arrive at similar analytical conclusions? Conceptual clarity, parsimony, simplicity, and logical coherence are important qualities of any academic study but are arguably all the more critical when developing arguments without empirical data.

A paper’s structure is a strong determinant of how easy it is to follow the chain of argumentation. While there is no single best way to structure a conceptual paper, what successful papers have in common is a careful matching of form and structure to theoretical purpose of the paper (Fulmer 2012 ). The structure should therefore reflect both the aims of the research and the role of the various lenses deployed to achieve those aims—in other words, the structure highlights what the authors seek to explain. A clear structure also contributes to conceptual clarity by making the hierarchy of concepts and their elements intuitively available to the reader, eliminating any noise that might distort the underlying message. As Hirschheim ( 2008 ) noted, a clear structure ensures a place for everything—omitting nothing of importance—and puts everything in its place, avoiding redundancies.

Common types of research design in conceptual papers

In marked contrast to empirical research, there is no widely shared understanding of basic types of research design in respect to conceptual papers, with the exception of literature reviews and meta-analyses. To address this issue, the present study considers four such types: Theory Synthesis, Theory Adaptation, Typology , and Model (see Table 2 ). These types serve to clarify differences of methodological approach—that is, how the argument is structured and developed—rather than the types of conceptual contributions that are the main consideration of MacInnis ( 2011 ). The four types discussed here derive from an analysis of goal setting, structuring, and logic of argumentation in multiple articles published in high quality journals. It should be said that the list is not exhaustive, and other researchers would no doubt have formulated differing perspectives. Nevertheless, the presented scheme can inspire researchers to explore and explicate one’s approach to conceptual research, and perhaps to formulate an alternative approach. It should also be noted that the goals of a conceptual article can be as varied as in any other form of academic research. Table 2 identifies some possible or likely goals for each suggested type; these are not mutually exclusive and are often combined.

Theory synthesis

A theory synthesis paper seeks to achieve conceptual integration across multiple theories or literature streams. Such papers offer a new or enhanced view of a concept or phenomenon by linking previously unconnected or incompatible pieces in a novel way. Papers of this type contribute by summarizing and integrating extant knowledge of a concept or phenomenon. According to MacInnis ( 2011 ), summarizing helps researchers see the forest for the trees by encapsulating, digesting, and reducing what is known to a manageable whole. Integration enables researchers to see a concept or phenomenon in a new way by transforming previous findings and theory into a novel higher-order perspective that links phenomena previously considered distinct (MacInnis 2011 ). For example, a synthesis paper might chart a new or unstructured phenomenon that has previously been addressed in piecemeal fashion across diverse domains or disciplines. Such papers may also explore the conceptual underpinnings of an emerging theory or explain conflicting research findings by providing a more parsimonious explanation that pulls disparate elements into a more coherent whole.

This kind of systematization is especially helpful when research on a given topic is fragmented across different literatures, helping to identify and underscore commonalities that build coherence (Cropanzano 2009 ). For example, in their review of conceptualizations of customer experience across multiple literature fields, Becker and Jaakkola’s ( 2020 ) analysis of the compatibility of various elements and assumptions provided a new integrative view that could be generalized across settings and contexts. In more mature fields, synthesis can help to identify gaps in the extant research, which is often the goal of systematic literature reviews. However, gap spotting is seldom a sufficient source of contribution as the main aim of a conceptual paper should be to enhance existing theoretical understanding on the studied phenomenon or concept. The synthesis paper represents a form of theorizing that emphasizes narrative reasoning that seeks to unveil “big picture” patterns and connections rather than specific causal mechanisms (Delbridge and Fiss 2013 ).

Although there is sometimes a fine line between theory synthesis and literature review, there remains a clear distinction between the two. While a well-crafted literature review takes stock of the field and can provide valuable insights into its development, scope, or future prospects, it remains within the existing conceptual or theoretical boundaries, describing extant knowledge rather than looking beyond it. In the case of a conceptual paper, the literature review is a necessary tool but not the ultimate objective. Moreover, in a theory synthesis paper, the role of the literature review is to unravel the components of a concept or phenomenon and it must sometimes reduce or exclude incommensurable elements. A lack of elegance occurs when authors attempt to hammer together separate research ideas in a series of “minireviews” instead of attending to a single conceptual theme (Cropanzano 2009 ). For example, a literature review that seeks to integrate multiple research perspectives may instead merely summarize in separate chapters what each has to say about the concept. Typically, different research perspectives employ differing terms and structure, or categorize conceptual elements in distinct ways. Integration and synthesis requires that the researcher explicates and unravels the conceptual underpinnings and building blocks that different perspectives use to conceptualize a phenomenon, and the looks for common ground on which to build a new and enhanced conceptualization.

A theory synthesis paper may seek to increase understanding of a relatively narrow concept or empirical phenomenon. For example, Lemon and Verhoef ( 2016 ) summarized the conceptual background and extant conceptualizations of customer journeys to produce a new integrative view. They framed the journey phenomenon in terms of the consumer purchasing process and organized the extant research within this big picture. Similarly, arguing that the knowledge base of relationship marketing and business networks perspectives was unduly fragmented, Möller ( 2013 ) deployed a metatheoretical lens to construct an articulated theory map that accommodated various domain theories, leading to the development of two novel middle-range theories.

Ultimately, a theory synthesis paper can integrate an extensive set of theories and phenomena under a novel theoretical umbrella. One good example is Vargo and Lusch’s ( 2004 ) seminal article, which pulled together key ingredients from diverse fields such as market orientation, relationship marketing, network management, and value management into a novel integrative narrative to formulate the more parsimonious framework of service-dominant logic. In so doing, they drew on resource based theory, structuration theory, and institutional theory as method theories to organize and synthesize concepts and themes from middle-range literature fields (e.g., Vargo and Lusch 2004 , 2016 ). While extant research provided the basis for a novel framework, existing concepts were decomposed into such fine-grained ingredients that the resulting integration was a new theoretical view in its own right rather than a summary of existing concepts.

Theory adaptation

Papers that focus on theory adaptation seek to amend an existing theory by using other theories. While empirical research may gradually extend some element of theory within a given context, theory-based adaptation attempts a more immediate shift of perspective. Theory adaptation papers develop contribution by revising extant knowledge—that is, by introducing alternative frames of reference to propose a novel perspective on an extant conceptualization (MacInnis 2011 ). The point of departure for such papers, then, is the problematization of a particular theory or concept. For example, the authors might argue that certain empirical developments or insights from other streams of literature render an existing conceptualization insufficient or conflicted, and that some reconfiguration or shift of perspective or scope is needed to better align the concept or theory to its purpose or to reconcile certain inconsistencies. Typically, the researcher draws from another theory that is equipped to guide this shift. The contribution of this type of a paper is often positioned to the domain where the focal concept is situated.

The starting point for the theory adaptation paper is the theory or concept of interest (domain theory). Other theories are used as tools, or method theories (Lukka and Vinnari 2014 ) to provide an alternative frame of reference to adjust or expand its conceptual scope. One “method” of adaptation is to switch the level of analysis; for example, Alexander et al. ( 2018 ) provided new insights into the influence of institutions on customer engagement by shifting from a micro level analysis of customer relationships—the prevailing view in the field—to meso and macro level views, adapting Chandler and Vargo’s ( 2011 ) process of oscillating foci. Another option is to use an established theory to explore new aspects of the domain theory (Yadav 2010 ). As one example of this type of design, Brodie et al. ( 2019 ) argued for the practical and theoretical importance of expanding the scope of engagement research in two ways: from a focus on consumers to a broad range of actors, and from dyadic firm-customer relationships to networks. As well as justifying why a particular extension or change of focus is needed, a theory adaptation paper must also show that the selected method theory is the best available option. For example, Brodie et al. ( 2019 ) explained that they employed service-dominant logic to broaden the conceptual scope of engagement research because it offered a lens for understanding actor-to-actor interactions in networks. Similarly, Hillebrand et al. ( 2015 ) used multiplicity theory to revise existing perspectives on stakeholder marketing by viewing stakeholder networks as continuous rather than discrete. They argued that this provides a more accurate understanding of markets characterized by complex value exchange and dispersed control.

A typology paper classifies conceptual variants as distinct types. The aim is to develop a categorization that “explains the fuzzy nature of many subjects by logically and causally combining different constructs into a coherent and explanatory set of types” (Cornelissen 2017 ). A typology paper provides a more precise and nuanced understanding of a phenomenon or concept, pinpointing and justifying key dimensions that distinguish the variants.

Typology papers contribute through differentiation— distinguishing, dimensionalizing, or categorizing extant knowledge of the phenomenon, construct, or theory in question (MacInnis 2011 ). Typologies reduce complexity (Fiss 2011 ). They demonstrate how variants of an entity differ, and hence organize complex networks of concepts and relationships, and may help by recognizing their differing antecedents, manifestations, or effects (MacInnis 2011 ). Typologies also offer a multidimensional view of the target phenomenon by categorizing theoretical features or dimensions as distinct profiles that offer coordinates for empirical research (Cornelissen 2017 ). For example, the classic typologies elaborated by Mills and Margulies ( 1980 ) and Lovelock ( 1983 ) assigned services to categories reflecting different aspects of the relationship between customers and the service organization, facilitating prediction of organizational behavior and marketing action. These theory-based typologies have informed numerous empirical applications.

The starting point for a typology paper is typically recognition of an important but fragmented research domain characterized by differing manifestations of a concept or inconsistent findings regarding drivers or outcomes. The researcher accumulates knowledge of the focal topic and then organizes it to capture the variability of particular characteristics of the concept or phenomenon. For example, after exploring different approaches to service innovation, Helkkula et al. ( 2018 ) proposed a typology of four archetypes. They suggested that variance within the extant research could be explained by differences of theoretical perspective and argued that each type had distinct implications for value creation.

The dimensions of a typology can also be differentiated by applying another theory (i.e. methods theory) that provides a logical explanation of why differences exist and why they are relevant. For example, to examine the boundaries of resource integration, Dong and Sivakumar ( 2017 ) developed a typology of customer participation, using dimensions drawn from resource-based theory, to address the fundamental resource deployment questions of whether there is a choice in terms of who performs a task and what task is performed (Kozlenkova et al. 2014 ).

Snow and Ketchen Jr. ( 2014 ) argued that well-developed typologies are more than just classification systems; rather, a typology articulates relationships among constructs and facilitates testable predictions (cf. Doty and Glick 1994 ). In this way, a typology can propose multiple causal relationships in a given setting (Fiss 2011 ). While a typology paper enhances understanding of a phenomenon by delineating its key variants, it can be seen to differ from a synthesis or adaptation paper by virtue of its explanatory character. This is the typology’s raison d’etre; types always explain something, and the dimensions that distinguish types account for the different drivers, outcomes, or contingencies of particular variants of the phenomenon. By accommodating asymmetric causal relations, typologies facilitate the development of configurational arguments beyond simple correlations (Fiss 2011 ).

The model paper seeks to build a theoretical framework that predicts relationships between concepts. A conceptual model describes an entity and identifies issues that should be considered in its study: it can describe an event, an object, or a process, and explain how it works by disclosing antecedents, outcomes, and contingencies related to the focal construct (Meredith 1993 ; MacInnis 2011 ). This typically involves a form of theorizing that seeks to create a nomological network around the focal concept, employing a formal analytical approach to examine and detail the causal linkages and mechanisms at play (Delbridge and Fiss 2013 ). A model paper identifies previously unexplored connections between constructs, introduces new constructs, or explains why elements of a process lead to a particular outcome (Cornelissen 2017 ; Fulmer 2012 ).

The model paper contributes to extant knowledge by delineating an entity: its goal is “to detail, chart, describe, or depict an entity and its relationship to other entities” (MacInnis 2011 ). In a conceptual article, creative scope is unfettered by data-related limitations, allowing the researcher to explore and model emerging phenomena where few empirical data are available (Yadav 2010 ). The model paper typically contributes by providing a roadmap for understanding the entity in question by delineating the focal concept, how it changes, the processes by which it operates, or the moderating conditions that may affect it (MacInnis 2011 ).

A model paper typically begins from a focal phenomenon or construct that warrants further explanation. For example, Huang and Rust ( 2018 ) sought to explain the process and mechanism by which artificial intelligence (AI) will replace humans in service jobs. They employed literature that tackles key variables associated with the target phenomenon: service research illuminates the focal phenomenon, technology-enabled services, and research across multiple disciplines discusses the likely impact of AI on human labor. By synthesizing this literature pool, they identified four types of intelligence and then built a theory that could predict the impact of AI on human service labor. This involved a particular kind of formal reasoning, supported by research from multiple disciplines and real-world applications (Huang and Rust 2018 ). In other words, the authors use method theories and deductive reasoning to explain relationships between key variables, facilitated by theories in use (MacInnis 2011 ).

Model papers typically summarize arguments in the form of a figure that depicts the salient constructs and their relationships, or as a set of formal propositions that are logical statements derived from the conceptual framework (Meredith 1993 ). For example, Payne et al. ( 2017 ) used resource-based theory to develop a conceptual model of the antecedents and outcomes of customer value propositions. While figures and propositions of this kind help the reader by condensing the paper’s main message, Delbridge and Fiss ( 2013 ) noted that they are also a double-edged sword. At their best, propositions distill the essence of an argument into a parsimonious and precise form, but by virtue of this very ability, they also put a spotlight on the weaknesses in the argument chain. According to Cornelissen ( 2017 ), the researcher should therefore be clear about the “causal agent” in any proposed relationship between constructs when developing propositions—in other words, the trigger or force that drives a particular outcome or effect. Careful consideration and justification of the choice of theories and the manner in which they are integrated to produce the arguments is hence pivotal in sharpening and clarifying the argumentation to convince reviewers and readers.

Conclusions

This paper highlights the role of methodological considerations in conceptual papers by discussing alternative types of research design, in the hope of encouraging researchers to critically assess and develop conceptual papers accordingly. Authors of conceptual papers should readily answer the following questions: What is the logic of creating new knowledge? Why are particular information sources selected, and how are they analyzed? What role does each theory play? For reviewers, assessing conceptual papers can be difficult not least because the generally accepted and readily available guidelines for evaluating empirical research seldom apply directly to non-empirical work. By asking these questions, reviewers and supervisors can evaluate whether the research design of a paper or thesis is carefully crafted and clearly communicated to the reader.

The paper identifies four types of conceptual papers—Theory Synthesis, Theory Adaptation, Typology, and Model—and discusses their aims, methods of theory use, and potential contributions. Although this list is not exhaustive, these types offer basic templates for designing conceptual research and determining its intended contribution (cf. MacInnis 2011 ). Careful consideration of these alternative types can facilitate more conscious selection of approach and structure for a conceptual paper. Researchers can also consider opportunities for combining types. In many cases, mixing two types can be an attractive option. For example, after distinguishing types of service innovation in terms of their conceptual underpinnings, Helkkula et al. ( 2018 ) synthesized a novel conceptualization of service innovation that exploited the strengths of each type and mitigated their limitations. Typologies can also provide the basis for models, and synthesis can lead to theory adaptation.

This paper highlights the many alternative routes along which conceptual papers can advance extant knowledge. We should consider conceptual papers not just as a means to take stock, but to break new ground. Empirical research takes time to accumulate, and the scope for generalization is relatively narrow. In contrast, conceptual papers can strive to advance understanding of a concept or phenomenon in big leaps rather than incremental steps. To be taken seriously, any such leap must be grounded in thorough consideration and justification of an appropriate research design.

A discussion of how different theoretical lenses can be integrated is beyond the scope of this paper, but see for example Okhuysen and Bonardi ( 2011 ) and Gioia and Pitre ( 1990 ).

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

• Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

Psst... there’s more!

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20 Comments

Thank you for giving a valuable lesson

good thanks!

VERY INSIGHTFUL

thanks for given very interested understand about both theoritical and conceptual framework

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

good, fantastic

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Thanks for shedding light on these two t opics. Much clearer in my head now.

Simple and clear!

The differences between the two topics was well explained, thank you very much!

Thank you great insight

Superb. Thank you so much.

Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

I am really grateful I found this website. This is very helpful for an MPA student like myself.

I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

I’m well inform about these two concepts in research. Thanks

I found this really helpful. It is well explained. Thank you.

very clear and useful. information important at start of research!!

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What is a Conceptual Framework?

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format.

Updated on August 28, 2023

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research.  Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results. 

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them. 

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc. 

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same. 

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research. 

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

• Selecting the appropriate research questions
• Defining the process variables (dependent, independent, and others)
• Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables. 

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research. 

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

• Provides the data for molecular docking studies to identify suitable target proteins
• Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration. 

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research. 

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm

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An Example of a Conceptual Framework with Statement of the Problem

This article shows an example of a conceptual framework. It demonstrates how a conceptual framework and the corresponding statement of the problem are organized and written in a dissertation. Take a look at how it is done, and try to make one for your paper. You may also use this in your thesis.

You may be thinking about too many theories to base your study on. However, a conceptual framework is inbuilt on a theory or model that serves as the basis for your research. Once you have decided which theory to adopt, try to figure out if that theory can best explain the phenomenon with all the associated variables in your study. The example below illustrates how this works.

Example of a Conceptual Framework

For example, students may attribute their academic performance to their teachers ( external factor ). In contrast, the teachers may attribute their teaching performance to in-service training ( external facto r) and perhaps their teaching efficacy, job satisfaction, and attitude towards the teaching profession ( internal factors ). These relationships are illustrated in Figure 1.

Statement of the Problem

Specifically, this study sought answers to the following questions:

Organized Flow of Ideas Characterize a Conceptual Framework

Now, you have learned how a theory is used and how the questions in the problem statement are formulated. Take note that the problem statement questions are arranged according to the flow of the conceptual framework.

First, it has questions on an inventory of in-service training activities , followed by the feedback . The next question is about teacher factors , then the results of student performance . The last question relates to the development of the enhanced professional development model .

Notice that all of the factors identified in the study serve as input to the final outcome of the study which is the enhanced professional development model. It is easy to conceptualize what the researcher is trying to incorporate in the training design for teachers’ professional development. It is a systematic representation of the intention, direction, and outcome of the study.

Can you make it? Yes, you can!

© 2015 January 19 M. G. Alvior Updated: 15 December 2020; 14 October 2023

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plss help me for my research its all about the effect of hand held devices and social media to the behavior of the stem student. plss help me ,i do not know how to make an SOP , conceptual frame work ,etc.

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Defining The Conceptual Framework

Making a conceptual framework, conceptual framework for dmft students, conceptual framework guide, example frameworks, additional framework resources.

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What is it?

• The researcher’s understanding/hypothesis/exploration of either an existing framework/model or how existing concepts come together to inform a particular problem. Shows the reader how different elements come together to facilitate research and a clear understanding of results.
• Informs the research questions/methodology (problem statement drives framework drives RQs drives methodology)
• A tool (linked concepts) to help facilitate the understanding of the relationship among concepts or variables in relation to the real-world. Each concept is linked to frame the project in question.
• Falls inside of a larger theoretical framework (theoretical framework = explains the why and how of a particular phenomenon within a particular body of literature).
• Can be a graphic or a narrative – but should always be explained and cited
• Can be made up of theories and concepts

What does it do?

• Explains or predicts the way key concepts/variables will come together to inform the problem/phenomenon
• Gives the study direction/parameters
• Helps the researcher organize ideas and clarify concepts
• Introduces your research and how it will advance your field of practice. A conceptual framework should include concepts applicable to the field of study. These can be in the field or neighboring fields – as long as important details are captured and the framework is relevant to the problem. (alignment)

What should be in it?

• Variables, concepts, theories, and/or parts of other existing frameworks

How to make a conceptual framework

• With a topic in mind, go to the body of literature and start identifying the key concepts used by other studies. Figure out what’s been done by other researchers, and what needs to be done (either find a specific call to action outlined in the literature or make sure your proposed problem has yet to be studied in your specific setting). Use what you find needs to be done to either support a pre-identified problem or craft a general problem for study. Only rely on scholarly sources for this part of your research.
• Begin to pull out variables, concepts, theories, and existing frameworks explained in the relevant literature.
• If you’re building a framework, start thinking about how some of those variables, concepts, theories, and facets of existing frameworks come together to shape your problem. The problem could be a situational condition that requires a scholar-practitioner approach, the result of a practical need, or an opportunity to further an applicational study, project, or research. Remember, if the answer to your specific problem exists, you don’t need to conduct the study.
• The actionable research you’d like to conduct will help shape what you include in your framework. Sketch the flow of your Applied Doctoral Project from start to finish and decide which variables are truly the best fit for your research.
• Create a graphic representation of your framework (this part is optional, but often helps readers understand the flow of your research) Even if you do a graphic, first write out how the variables could influence your Applied Doctoral Project and introduce your methodology. Remember to use APA formatting in separating the sections of your framework to create a clear understanding of the framework for your reader.
• As you move through your study, you may need to revise your framework.
• Note for qualitative/quantitative research: If doing qualitative, make sure your framework doesn’t include arrow lines, which could imply causal or correlational linkages.
• Conceptural and Theoretical Framework for DMFT Students This document is specific to DMFT students working on a conceptual or theoretical framework for their applied project.
• Conceptual Framework Guide Use this guide to determine the guiding framework for your applied dissertation research.

Let’s say I’ve just taken a job as manager of a failing restaurant. Throughout the first week, I notice the few customers they have are leaving unsatisfied. I need to figure out why and turn the establishment into a thriving restaurant. I get permission from the owner to do a study to figure out exactly what we need to do to raise levels of customer satisfaction. Since I have a specific problem and want to make sure my research produces valid results, I go to the literature to find out what others are finding about customer satisfaction in the food service industry. This particular restaurant is vegan focused – and my search of the literature doesn’t say anything specific about how to increase customer service in a vegan atmosphere, so I know this research needs to be done.

I find out there are different types of satisfaction across other genres of the food service industry, and the one I’m interested in is cumulative customer satisfaction. I then decide based on what I’m seeing in the literature that my definition of customer satisfaction is the way perception, evaluation, and psychological reaction to perception and evaluation of both tangible and intangible elements of the dining experience come together to inform customer expectations. Essentially, customer expectations inform customer satisfaction.

I then find across the literature many variables could be significant in determining customer satisfaction. Because the following keep appearing, they are the ones I choose to include in my framework: price, service, branding (branched out to include physical environment and promotion), and taste. I also learn by reading the literature, satisfaction can vary between genders – so I want to make sure to also collect demographic information in my survey. Gender, age, profession, and number of children are a few demographic variables I understand would be helpful to include based on my extensive literature review.

Note: this is a quantitative study. I’m including all variables in this study, and the variables I am testing are my independent variables. Here I’m working to see how each of the independent variables influences (or not) my dependent variable, customer satisfaction. If you are interested in qualitative study, read on for an example of how to make the same framework qualitative in nature.

Also note: when you create your framework, you’ll need to cite each facet of your framework. Tell the reader where you got everything you’re including. Not only is it in compliance with APA formatting, but also it raises your credibility as a researcher. Once you’ve built the narrative around your framework, you may also want to create a visual for your reader.

See below for one example of how to illustrate your framework:

If you’re interested in a qualitative study, be sure to omit arrows and other notations inferring statistical analysis. The only time it would be inappropriate to include a framework in qualitative study is in a grounded theory study, which is not something you’ll do in an applied doctoral study.

A visual example of a qualitative framework is below:

Some additional helpful resources in constructing a conceptual framework for study:

• Problem Statement, Conceptual Framework, and Research Question. McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg
• Building a Conceptual Framework: Philosophy, Definitions, and Procedure
• https://www.scribbr.com/dissertation/conceptual-framework/
• https://www.projectguru.in/developing-conceptual-framework-in-a-research-paper/

Conceptual Framework Research

A conceptual framework is a synthetization of interrelated components and variables which help in solving a real-world problem. It is the final lens used for viewing the deductive resolution of an identified issue (Imenda, 2014). The development of a conceptual framework begins with a deductive assumption that a problem exists, and the application of processes, procedures, functional approach, models, or theory may be used for problem resolution (Zackoff et al., 2019). The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013). In applied research the application of theory in problem solving focuses on how theory in conjunction with practice (applied action) and procedures (functional approach) frames vision, thinking, and action towards problem resolution. The inclusion of theory in a conceptual framework is not focused on validation or devaluation of applied theories. A concise way of viewing the conceptual framework is a list of understood fact-based conditions that presents the researcher’s prescribed thinking for solving the identified problem. These conditions provide a methodological rationale of interrelated ideas and approaches for beginning, executing, and defining the outcome of problem resolution efforts (Leshem & Trafford, 2007).

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). Just as with traditional research, a theory does not or cannot be expected to explain all phenomenal conditions, a conceptual framework is not a random identification of disparate ideas meant to incase a problem. Instead it is a means of identifying and constructing for the researcher and reader alike an epistemological mindset and a functional worldview approach to the identified problem.

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Imenda, S. (2014). Is There a Conceptual Difference between Theoretical and Conceptual Frameworks? Sosyal Bilimler Dergisi/Journal of Social Sciences, 38(2), 185.

Leshem, S., & Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education & Teaching International, 44(1), 93–105. https://doi-org.proxy1.ncu.edu/10.1080/14703290601081407

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

Zackoff, M. W., Real, F. J., Klein, M. D., Abramson, E. L., Li, S.-T. T., & Gusic, M. E. (2019). Enhancing Educational Scholarship Through Conceptual Frameworks: A Challenge and Roadmap for Medical Educators . Academic Pediatrics, 19(2), 135–141. https://doi-org.proxy1.ncu.edu/10.1016/j.acap.2018.08.003

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Estimating the causal effects of income on health: how researchers’ definitions of “income” matter

• Erik Igelström 1 ,
• Daniel Kopasker 1 ,
• Peter Craig 1 ,
• Jim Lewsey 2 &
• Srinivasa Vittal Katikireddi 1  

BMC Public Health volume  24 , Article number:  1572 ( 2024 ) Cite this article

Metrics details

There is a well-established cross-sectional association between income and health, but estimates of the causal effects of income vary substantially. Different definitions of income may lead to substantially different empirical results, yet research is often framed as investigating “the effect of income” as if it were a single, easily definable construct.

Methods/Results

The aim of this paper is to introduce a taxonomy for definitional and conceptual issues in studying individual- or household-level income for health research. We focus on (1) the definition of the income measure (earned and unearned; net, gross, and disposable; real and nominal; individual and household; relative and absolute income) and (2) the definition of the causal contrast (amount, functional form assumptions/transformations, direction, duration of change, and timing of exposure and follow-up). We illustrate the application of the taxonomy to four examples from the published literature.

Conclusions

Quantified estimates of causal effects of income on health and wellbeing have crucial relevance for policymakers to anticipate the consequences of policies targeting the social determinants of health. However, much prior evidence has been limited by lack of clarity in distinguishing between different causal questions. The present framework can help researchers explicitly and precisely articulate income-related exposures and causal questions.

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Introduction

Socioeconomic conditions have a large influence on health and wellbeing, and the social determinants of health are a major focus for both public health research and policymaking [ 1 , 2 ]. In particular, there is a well-established association between income and many health outcomes, and evidence that changes in income can change health [ 3 , 4 , 5 , 6 ]. However, reported estimates of the causal effects of income on health vary substantially.

Different definitions of income (for example, individual versus household income) may lead to substantially different empirical results [ 7 ]. Despite this, many studies have been framed as investigating “the effect of income” as if it were a single, easily definable construct, without recognising these nuances and their implications for the generalisability and transferability of results. This makes it difficult to understand whether heterogeneity between studies reflects genuine differences between populations or contexts, or merely different methodological and definitional choices.

To address this difficulty, we present a taxonomy for definitional and conceptual issues to consider when studying income as an epidemiological exposure, and discuss their implications in terms of psychosocial and material pathways from income to health [ 8 ]. Our discussion of these issues is structured around (1) how income is measured, and (2) how the causal contrast is defined (Fig.  1 ). We illustrate the use of this taxonomy by applying it to four published studies. We limit the scope to individual- or household-level income; hence, we are not considering the effects of area-level income characteristics on both individual and area-level health.

Visual overview of key definitional and conceptual issues in studying individual- or household-level income as an epidemiological exposure

Defining the income measure

The first issue to consider is how income is being defined. Differently defined measures of income are interrelated but not interchangeable, and different types of income are likely to affect health in different ways. It is in this respect similar to many other epidemiological variables, where related variables may be used for similar purposes, but cannot be treated interchangeably. In practice, choices about what measure to use are often dictated by the nature and limitations of the data used, rather than deliberately made by the researcher. This may limit the kinds of questions that can be asked, and it is essential that researchers adopt an appropriate interpretation of results given the available income measures.

Income source

Income data are often disaggregated by income source. It is common to distinguish between earned and unearned income . Earned income encompasses income obtained through the supply of labour, e.g., salaries or wages from employment, or income from business activities or self-employment. Unearned income in principle encompasses all other sources, including government benefits, income from investments or property (such as interest, dividends, rent, and capital gains), retirement income, inheritances, lottery winnings, and gifts. The nature of the data source will also affect what income sources are captured: for example, tax register data may only encompass reported taxable income, and thus omit informal, illegal, non-monetary or otherwise unrecorded receipts. When income is self-reported, the context and nature of the question asked will also affect the income sources a respondent considers and the accuracy of their response.

Neoclassical economic theory generally assumed that money is a fungible resource, and hence that a rational person would make the same decisions in response to a given amount of income regardless of the source. However, this is often not the case in reality: the “mental accounting” processes that underlie economic decisions are now appreciated to be more complex [ 9 ]. For example, if a cash transfer is labelled as being for a specific purpose, it may be more likely to be used for that purpose [ 10 ], and windfall income may be spent differently from regular or expected income [ 9 ]. The practical upshot of this is that the causal effects of two different interventions on income might differ depending on the type of income targeted, even if the amounts are identical.

Costs deducted: gross, net, and disposable income

A person’s gross income is their total income from all sources, prior to taxes being deducted. For many material pathways to health, such as a person’s ability to buy nutritious food or engage in leisure activities, what matters is not necessarily gross income, but how much of it is available to spend.

Net income refers to income after direct taxes have been subtracted (Fig.  2 ). Disposable income is what remains of the disposable income after subtracting non-discretionary costs , which may be defined differently in different contexts and data sources. Non-discretionary costs generally include housing costs (rent or mortgage repayments), and may also include other costs that can be considered necessary or unavoidable: for example, repayments of non-housing debt (including student debt), utilities, food, transport, healthcare, and clothing (sometimes including for dependents). All other costs are considered discretionary. Although these terms seem to imply a normative judgement, the distinction is typically drawn in a coarse and arbitrary way, and does not necessarily reflect what individuals in a given context genuinely consider unavoidable or dispensable [ 11 ]. They are thus often best viewed as purely technical terms, whose precise meaning needs to be specified.

Illustration of how income measures can be constructed by combining or subtracting other measures

Adjustment for inflation: real and nominal income

When comparing amounts of money over time, it is generally necessary to consider inflation – that is, the rate at which the prices of goods and services change over time. Measures of real income have been adjusted for inflation, such that a change reflects a genuine change in “purchasing power” (i.e., the amount of goods and services that can be obtained with a given amount). Real amounts are generally expressed in terms of the equivalent monetary amount in some given baseline year (e.g. “2010 US dollars”). The unadjusted amount is called nominal income . When comparing incomes across multiple time points, it is typically more appropriate to use a measure of real rather than nominal income.

Real or inflation-adjusted amounts are calculated with reference to a price index, which may be based on macroeconomic measures such as gross domestic product or the price of a fixed set of goods and services [ 12 ]. While these measures describe general trends in price changes, the extent of inflation is often different across different goods and services, and the impact can differ across population groups: for example, increases in food and energy prices may affect low-income households more than high-income households [ 13 ].

Unit of analysis: individual or household

An individual’s own personal income is not always the best way to capture the financial resources they have access to: for example, people may rely on the income of their spouses, parents, children, or others in addition to their own. For this reason, it is often useful to aggregate income by household or some other group. A household is typically defined as a group of people who live in the same dwelling, but more complex definitions may be appropriate depending on the context [ 14 ].

The concept of a household is necessarily simplistic, and does not account for more complex family relationships, such as shared care for children between multiple households. Because of the gendered distribution of both labour market participation and wages, the discrepancy between individual and household income measures is often greater for women [ 7 ]. It is important to consider how the choice of income measure in a study may affect different populations, particularly when stratifying by gender.

Adjustment for household composition

When comparing incomes across households, household size and composition need to be taken into account, since higher income in a larger household may be offset by higher costs. Since these costs do not increase uniformly for each additional person, simply calculating the per capita income is likely to be misleading; instead, equivalisation scales are often used to calculate an equivalised household income based on the number and ages of household members. Multiple such scales are in use [ 15 ]; Table  1 illustrates how equivalised household income is calculated using the modified OECD scale [ 16 ]. Although such standard equivalence scales are widely used, their validity and accuracy in a specific time and place are rarely tested or justified; they may not necessarily reflect true cost differences experienced by households, which are of course highly context-dependent.

Comparison: absolute and relative income

Income can either be measured in absolute terms (i.e., in units of currency) or in relative terms compared to the income distribution in some reference group (for example, income rank or percentile). It has been suggested that relative income, i.e., one’s actual or perceived position in the income distribution, may have an effect on some outcomes (particularly wellbeing) independently of absolute amount [ 17 , 18 ]. Relative income can be expressed in terms of income quantiles (e.g., quintiles, deciles, or percentiles), or by employing a threshold defined in terms of the income distribution: for example, poverty is often defined as an income below some proportion of the median income. The categories defined by quantiles or poverty thresholds are necessarily somewhat arbitrary, and individuals just above or just below a threshold are likely to share a lot of characteristics. Hence it is worth noting that a change in poverty status or income quintile may sometimes represent only a small change in actual circumstances.

Relative and absolute measures of income capture substantially different things, and the difference is particularly relevant when the income distribution itself changes over time. For example, even a large increase in a household’s absolute income would not change that household’s position in the relative income distribution if household income increased by a similar percentage across the population. Whether this property is desirable or not depends entirely on the research question at hand.

Defining the causal contrast

Following dominant practice in epidemiology and quantitative social science, we will assume that a causal effect has to be defined in terms of a causal contrast – intuitively, we must be able to answer the question “the effect of what, compared to what?” [ 19 ]. For a single intervention at a single point in time, the causal contrast is typically between the two potential outcomes where an individual received the intervention and where they did not. Since income varies continuously over a person’s life, the possibilities for defining different causal contrasts are much wider. For time-varying exposures, causal contrasts are typically conceived as comparing two different exposure regimens; i.e., well-defined sequences of exposures [ 20 ]. As with the choice of income measure, we will see that the choice of causal contrast can fundamentally affect which causal mechanisms are involved. In other words, the different causal contrasts implied by different study designs are not merely different strategies for estimating the same “true” causal effect of income, but instead often estimate substantially different effects.

For simplicity, we will mostly describe causal contrasts in this section in terms of income changes ; i.e., increases or decreases that last for a certain length of time. Some prior literature has drawn a distinction between income change and income level, where differences in level represent persistent and often structural inequalities that may affect health in a distinct way [ 21 ]. By using this terminology, we do not mean to suggest that persistent differences in income level are unimportant or should not be regarded as causes. Rather, we propose that for the purpose of defining causal effects, thinking in terms of change promotes clarity. Comparing two individuals whose income level has differed throughout their lives is conceptually different from comparing two individuals whose income levels only recently diverged. Defining causal contrasts in terms of change emphasises the importance of specifying when the counterfactual scenarios diverge. We suggest that the distinction between “level” and “change” is not rigid, and is primarily a question of timescales: while the term “change” implies a short-term exposure and “level” a longer-term difference, both can be understood as referring to different exposure regimens.

Amount, functional form, and transformations

The first feature of an income change that needs to be described is its size. In some settings, such as trials and policy evaluations, the intervention may be an income change of a specific amount. In others, individuals may be exposed to differently sized income changes, and we may want to infer a single effect estimate. We may also need to generalise from the observed changes what effect a differently sized change would have. In all these cases, we need to make assumptions about the functional form of the relationship.

The simplest functional form is a linear relationship, where a £1 change in income would always have the same average effect on health, and, say, a £10 change an effect 10 times as great. This would imply, for example, that a £500 increase in monthly income from £500 to £1,000 would have the same effect on health as an increase from £2,000 to £2,500, and that a £1,000 increase in either situation would have twice that effect. It is clear from both cross-sectional and longitudinal evidence that such a linear relationship is unlikely: additional income appears to make a greater difference to health at the lower end of the income scale [ 22 ]. Hence, it is usually necessary to apply some transformation to an income variable before using it as a predictor in a statistical model.

Perhaps the most commonly used transformation is the logarithm. A change in the logarithm of income (“log income”) represents a percentage change rather than a unit change: for example, an increase of 0.693 in log income corresponds to a doubling in income, regardless of whether this means a change from £100 to £200 or from £5,000 to £10,000. A 0.01 change in log income corresponds to approximately a 1% change in income, 0.02 to approximately 2%, and so forth; however, this rule of thumb becomes increasingly inaccurate at higher percentages.

The interpretations above are applicable when income is first log-transformed, and then the change in log income is calculated. Occasionally, changes in income are calculated first and then log-transformed. Importantly, this “log of change” is mathematically very different from the “change in log”, and cannot be interpreted as straightforwardly.

A well-known limitation of the log transformation is that it cannot be applied to zero or negative values. The inverse hyperbolic sine transformation (arsinh) is sometimes used as an alternative that does not have this limitation. Except for values very close to zero, a change in arsinh-transformed income is nearly identical to the equivalent change in log income, and can be interpreted in the same way.

In practice, visualising the relationship between the transformed or untransformed income variable and the outcome variable (for example, using a binned scatter plot) can be a useful way to assess whether a given transformation is reasonable. If a log or arsinh transformation is insufficient, more complex approaches can be used, including splines, fractional polynomials, or interactions with position in the income distribution. These may allow for more fine-grained or assumption-free analyses, but may also make the interpretation of numerical results more difficult. Reporting predicted probabilities or marginal effects may be more practically useful than regression coefficients.

Regardless of how the functional form of the income–health relationship is represented, we must be careful about generalising beyond the specific, observed circumstances of a study. For example, if a study sample only contained examples of income changes of 1–5%, the study is likely to be informative only about changes of a similar scale, unless we are prepared to make strong assumptions about the functional form beyond the observed values. Although we could, mathematically, report the results in terms of “the effect of a 10% change” (or even greater), we would not necessarily be justified in interpreting them as such.

Direction of change

It may be important to distinguish between income gains and losses. Many analytical approaches rely on the assumption that the positive effect of an increase would be the same size as the negative effect of an equivalent decrease. However, this is unlikely to be true. The asymmetry of gains and losses is a key feature of prospect theory, which focuses on the behavioural responses to anticipated changes [ 23 ]. There is also evidence that income losses have a greater negative impact on health and wellbeing outcomes than the positive impact of income gains [ 6 , 24 , 25 ].

Duration of change

It is also important to consider how long-lasting an income change is. A cash transfer scheme, for example, may have a limited duration (e.g., a single one-off payment, a 12-month period, etc.), or may last indefinitely. Clearly, this distinction becomes increasingly important when the outcome is measured some time after the onset of the exposure, since a longer-lasting payment would add up to a greater total amount. However, the anticipated duration may also be relevant for short-term outcomes. First, an income change that is expected to be temporary may be less beneficial for some mental health outcomes than one that is expected to be permanent. Second, expectations about future income can play a role in decision-making, potentially affecting health-related as well as economic behaviour. Models for explaining such decision-making include the literature on “time discounting” [ 26 ], which focuses on how future expectations affect trade-offs between immediate and delayed gains, and the “permanent income hypothesis”, which holds that consumption behaviour is primarily influenced by one’s expected long-term income rather than actual income in the short term [ 27 ].

Variation and insecurity of income over time may in itself be an important determinant of health [ 28 ]. Various measures of economic insecurity and precarity have been proposed, ranging from subjective measures (such as perceived job security or perceived ability to raise emergency funds when needed) to objective (such as recent experience of a substantial income drop) [ 29 ]. The concept of economic insecurity is inherently related not just to income, but also to other economic variables such as wealth and debt.

Timing of exposure and follow-up

A related but distinct consideration is at what time the outcome is measured relative to the exposure. The true effect of an income change on an outcome measured after ten years may be different from the effect on the same outcome after one year. This may be because the relevant causal mechanisms take time to act, resulting in a delay, or it may be that effects are cumulative, and grow in size after prolonged exposure. For example, cancer mortality rates may take years or decades to respond to a change in income, if the main mechanisms involve changes in other environmental or behavioural exposures, which in turn affect incidence, and only subsequently mortality. On the other hand, mortality due to suicide has been observed to respond rapidly to changes in economic circumstances [ 30 ]. Even outcomes that respond quickly may also be partially mediated by slower mechanisms, in which case longer follow-up times would still be needed to capture the total long-term effects.

The timing of the income change itself during the life course is also important. Changes in household income impact child health outcomes differently and through different mechanisms than adult health [ 5 ]. Most of the available evidence on these impacts relates to health outcomes measured in children or adolescents, for obvious practical reasons: investigating the effect of income in childhood on health in adulthood requires a very long follow-up time. This may often not be practically feasible, and crucially increases the difficulty of drawing causal conclusions from non-experimental study designs.

Further considerations

Wealth and debt.

Separately from income, a person’s financial resources can be measured in terms of wealth. Whereas income denotes the flow of resources received during some time period, wealth denotes the stock of resources owned at a point in time. Wealth can consist of monetary savings, investments such as stocks or bonds, or non-monetary assets such as land and property. Wealth can itself generate income, such as interest, dividends, and rents [ 31 ]; conversely, a surplus of income over time can contribute to one’s wealth. The distribution of wealth may be as important as the distribution of income in explaining health inequalities [ 32 ], but wealth has been less frequently studied, and is less commonly available in administrative or research datasets. Similarly, personal debt appears to be associated with health outcomes [ 33 ], but rigorous causal evidence is lacking and data rarely available.

Public goods

The availability and cost of public goods such as healthcare or social care likely also affects the extent to which income influences health outcomes. A loss of income could more severely limit access to these services in a system that requires payments or insurance cover, compared to one where they are free. Indeed, socioeconomic inequalities in self-reported health and mortality appear to be weaker in countries or regions with well-developed welfare regimes or high expenditure on public goods [ 34 , 35 ].

Co-interventions

Income changes are frequently the result of events that also affect health directly, as well as via their effects on income: individuals may experience job loss, promotion, childbirth, death of a relative, and so on, while societies may undergo policy changes or natural disasters. For virtually any study, it is crucial to consider how much of any effect is attributable to the income change itself, and how much to the event that caused it. For example, when individuals lose income because of a job loss, a substantial proportion of the effect on mental health appears to be attributable to the job loss itself rather than the income loss [ 36 ] (Fig.  3 ).

Illustration of how the effect of income on health can be confounded due to causes of income change that also affect health directly

Interventions in experimental trials or quasi-experimental evaluations may occasionally consist of a cash transfer and little else, but are frequently delivered together with co-interventions (such as training or non-monetary support) or with conditionality requirements (such as job search requirements or compliance with preventive health measures), which may have a substantial independent effect on many outcomes. Natural experiment studies sometimes rely on specific events that caused income changes (such as a recession or natural disaster), and this makes it very difficult to exclude the possibility that any observed effects are due to the event itself rather than the accompanying changes in income [ 37 ].

Reverse causality

A further challenge is reverse causality: health is an important determinant of income, particularly by affecting one’s ability to work, and hence an income change can be both caused by and the cause of changes in health outcomes [ 38 ]. This is a major issue in virtually all observational study designs where the variation in income does not have a random, or at least exogenous, source: although it is most widely acknowledged in the context of cross-sectional studies, it can also be problematic in longitudinal study designs. If both income and health change between two time points, we cannot definitively know which caused which; even if we observe the income change first, we often cannot exclude the possibility that an earlier, unmeasured health factor or event was in fact the cause of both. Natural experiments and instrument-based study designs are particularly important for overcoming this problem, but are relatively underused in public health research [ 39 ].

Applied examples

To illustrate the practical application of our taxonomy, we will consider four studies investigating the effect of income on mental health using contrasting approaches (Table  2 ): a randomised trial of a conditional cash transfer scheme in New York City (study A) [ 40 ]; a natural experiment study based on the introduction of an unconditional cash transfer from casino revenue in North Carolina (study B) [ 41 ]; a fixed-effects panel study using Finnish administrative data on earned and unearned income (study C) [ 42 ]; and a study exploiting random lottery wins in a Swedish sample as a natural experiment (study D) [ 43 ]. We selected these studies to represent some of the most important causal identification strategies in this literature: randomised trial, longitudinal fixed-effects, and a policy-based and a non-policy-based natural experiment. In each of these categories, we selected a study identified in recent systematic reviews as well conducted [ 5 , 6 ]. We prioritised diversity of study design over homogeneity in outcome measure, but all studies use outcomes that can be seen as proxies for general mental health. We will see that each study has made substantively different choices about the measurement and causal contrast, some at the researchers’ discretion, and some enforced by the choice of study design. These choices crucially affect the meaning of the resulting estimates, and the extent to which the studies can be meaningfully compared or generalised.

Considering the definitions of the income measures, perhaps the most salient difference is that each study concerns a substantially different income source. Although both studies A and B concerned transfers of unearned income, the former was conditional and targeted low-income families, while the latter was unconditionally given to all households in a community. Study C instead looked at total taxable income, a large proportion of which would have been earned, while study D used lottery wins, a very specific and unusual type of unearned income. The use of individual income in study C is a potentially consequential decision, and might underestimate the financial resources of individuals who rely on the income of other household members. Accordingly, the authors conducted additional analyses using household income instead of individual income.

The causal contrast is most clearly defined in the studies with a well-defined intervention (A, B, and D), which looked at the effect of receiving versus not receiving a specified amount of additional income. In contrast, study C used all year-on-year changes in taxable income, and therefore both the amount and reason for change are unknown: or, to put it differently, the exposure is a mixture of many different amounts of and reasons for income change. Only the analyses in studies C and D require explicit functional form assumptions, since studies A and B dichotomised the exposure as either receiving or not receiving the intervention. In study D, the functional form assumptions become particularly important, since the lottery wins studied included very large amounts, in many cases orders of magnitude greater than the median income, and considerably greater than the size of the cash transfers in the other studies. It is perhaps questionable whether these quantitative effect estimates can be meaningfully compared when the exposures are so different. However, insofar as we might try to compare them, the comparison hinges on whether the effect of a massive income gain has a straightforward (say, log-linear) relationship with the effect of a more modest one.

The duration of the income changes varies widely: the cash transfer scheme in study A was a time-limited pilot, and the lottery wins in study D largely one-off windfalls; only study B represents a change that participants may plausibly have seen as stable in the long term. Study C, again, illustrates the challenge in clearly stating the causal contrast when the exposure is within-person changes broadly defined: these may be a mixture of temporary and permanent, expected and unexpected changes, but the analysis cannot distinguish these.

Follow-up time also varies widely: the exceptionally long follow-up of study D is a consequence of its unusual identification strategy (exploiting the intrinsic randomisation of a lottery) and its use of administrative data. Studies A and C are more representative of a large proportion of the existing literature, where follow-up length is limited by practical or methodological considerations: in the former, attrition and the costs of running a formal trial, and in the latter, the limitations of the identification strategy (since confounding and other biases would gradually drown out the true effect if the follow-up time were increased).

With these differences made explicit, it becomes clear that we should not expect the results of such disparate studies to converge on any single answer – even when we only consider the issues of income definition and causal contrast, and no other contextual factors that we would expect to cause additional heterogeneity. This exercise thus underscores the importance for studies to report the exposure clearly so that relevant distinctions are clear to readers [ 44 ]. We can also note that among these examples, study B was the only instance of a stable, permanent income increase, and the only study where exposure occurred during childhood. As previously discussed, there are sound theoretical reasons to expect effects on health in those circumstances to be larger. In contrast, much of the existing literature concerns relatively short-term effects of temporary income changes in adult populations [ 6 ].

Concluding remarks

Income as an epidemiological exposure is not a single, well-defined construct. The definitions of “income” and “income change” that any given study uses are not just pragmatic methodological choices, but fundamentally affect what causal pathways we can expect to be involved. Thus, we should expect to find different “causal effects of income” depending on the definitions adopted, even within unbiased studies of the same population. This source of heterogeneity has often been ignored, but has been increasingly highlighted in recent research [ 5 , 7 ].

Causal inference literature generally holds that causal effects can only be estimated for exposures that are consistent , i.e., that do not occur in multiple variations with different causal effects [ 45 ]. It can be argued that income as an exposure violates this consistency criterion in many practical applications [ 46 , 47 ]. However, income is not unique in this respect. A strong argument can be made that no epidemiological exposure satisfies the consistency assumption in the strictest sense, and rather that variations in some aspect or other can always be identified [ 47 ]. It is up to the researcher to determine which kinds of variation are problematic. Rather than simply trying to minimise consistency violations, it is perhaps more important to be clear and explicit about what different kinds of exposures and causal contrasts an estimate incorporates, how they are likely to differ, and how these differences are likely to affect the result.

The taxonomy presented here can be used to assess how far effect estimates from a given study are applicable in a given context, to clarify systematically why they might not apply, and hence to identify evidence gaps that need to be addressed. The examples discussed here illustrate that some of the more common types of evidence may be inadequate to understand the effect of persistent long-term income changes, slow-acting pathways, and long-term effects of childhood exposures.

Recent systematic reviews have made progress in explaining heterogeneity in existing evidence using subgroup analysis and meta-regression [ 5 , 6 ]. We hope this framework will inform and inspire further efforts at evidence synthesis and triangulation, where methodological variety can be harnessed as a source of information rather than seen only as a source of uncertainty [ 48 , 49 ]. Above all, we encourage researchers to aid in these efforts by being as precise as possible when defining income measures and causal contrasts in future empirical studies.

Availability of data and materials

No datasets were generated or analysed during the current study.

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EI, DK, PC, and SVK receive funding from the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). DK and SVK receive funding from the European Research Council (949582).

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Igelström, E., Kopasker, D., Craig, P. et al. Estimating the causal effects of income on health: how researchers’ definitions of “income” matter. BMC Public Health 24 , 1572 (2024). https://doi.org/10.1186/s12889-024-19049-w

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