example of conceptual framework in research proposal input process output

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

Last Updated May 6, 2023 10:37 AM

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.

Browse all articles written by Jewel Kyle Fabula

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• Knowledge Base
• Methodology
• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 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: visualize 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 visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

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.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

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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• Writing Tips

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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• Knowledge Base
• Methodology
• 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 Write the Conceptual Framework in a Research Proposal

Many of the users of Simplyeducate.me post a lot of queries in the high traffic article I wrote titled:  Conceptual Framework: A Step-by-Step Guide on How to Make One . The article intends to provide useful tips on how to write the conceptual framework in a research proposal. 

Despite the step-by-step, simplified guide on how to write the conceptual framework, the many questions posed by the readers suggest that they are unable to comprehend fully well the contents of the article. Through time, more than 400 readers commented in that single article (Note: Now, the comments are still increasing after I removed the comments up to July 2019 in my attempt to write another e-book about the Questions and my answers. Someone told me she learned a lot from the Q&A, so I compiled them into an e-book).

Interest in the topic is quite high. At this writing, more than 2,500 (update on 2/23/20: 8,000) users read the article daily. Aside from grateful comments, readers keep on asking a lot of questions about how to go about their conceptual framework despite the illustrative example.

Detailed Questions on Conceptual Framework

Some of those who commented asked too specific questions related to their research topics. Several masters degree candidates even send manuscripts for review and comments, eagerly waiting for my response.

Did you read it?

Many of those questions make sense, while others show the dilemma of a beginning researcher. Some users did not read the article at all. The material already discussed answers to their questions.

Among those common questions asked pertains to the determination of the independent and the dependent variables. Discernment of the difference between these types of variables appears to be difficult for many. 

Also, questions indicate a failure to relate one’s research topic in the article on how to write the conceptual framework in a research proposal. Nevertheless, I oblige by answering so fundamental questions giving detailed suggestions and examples.

Reviewing the Literature Takes Time

However, answering questions on specific research topics proves to be time-consuming. I have to review the literature to make sure that my answer will be backed up by science. Reviewing the literature takes a lot of time.

Although I enjoyed answering the questions, I cannot respond to all the specific queries on how to build one’s conceptual framework.

Writing in Simplyeducate.me is a hobby, a way to share my understanding of the research process. I admit that my ideas are subject to scrutiny, and I thankfully respond to readers who point out overlooked points or glaring errors.

E-book on How to Write the Conceptual Framework in a Research Proposal

To be more effective in addressing the readers’ queries, I wrote the e-book titled “ Conceptual Framework Development Handbook: A Step-by-Step Guide with Five Practical Examples .” The e-book is a compilation of all conceptual framework related articles that I previously wrote in this site and other blogging websites. 

I combined lecture materials in graduate school and personal experience in researching to enrich the discussion. Further, recognizing the effectiveness of examples to illustrate the concept, I added five concrete examples using actual  scientific papers  to the e-book. The task was tedious, but it seems the e-book has fulfilled its purpose. 

Thus, for those who find difficulty in writing the conceptual framework in a research proposal, the e-book detailing the steps on how to write the conceptual framework in a research proposal is a must-have. For those who have availed of this publication, the author will be happy to receive comments, suggestions, and healthy criticisms to further enrich this work— all for the sake of better research outputs and discovery.

If you are patient enough to browse in this site, chances are, you will find answers to your research-related questions. If not, then my e-book on How to Write a Thesis in the Information Age compiles all the research tips I wrote in this site and other websites with review questions as well as exercises.

Please message me about that specific topic you would like to know more about, and I will respond with an article related to your query.

Related Posts

How to Write the Results and Discussion: 9 Tips

4 elements of a good abstract, using a matrix to write your research proposal, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

I just ordered the guide to writing a conceptual framework. However, I have an immediate need to submit two paragraphs describing my underlying conceptual or theoretical framework. My research question is as follows “How does lack of emotional intelligence explain the dysfunctional relations that often exist between lawyers and their subordinates? This piece is part of a series of paragraphs that will make up the dissertation prospectus, which is what the assignment is asking for.

My initial thought based on research and discussions I’ve had during the course, is that I will need a conceptual framework. However, upon carefully studying both definitions, it appears I may need both. I am a little uncertain about this. Your assistance would be greatly appreciated. Dortmund paying for your time.

Hi my name mutasse. Am phd studwnt at malaysia and suppose submit my entire work by 2 weeks so actually my prof ask me to redo the framework So i want to buy your items -the book but it saying need to 20 days for delivery so can o get it as pdf and im willing to pay by visa plz

Thank you for your e-book detailing the steps on how to write the conceptual framework in a research proposal. Anyone will find answers to his/her research-related questions.

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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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 (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

19 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.

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Conceptual Framework – How to Develop it for Research

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In academic writing, a conceptual framework serves as a key component of the research methodology , providing a schematic representation of the concepts and their proposed relationships. This tool not only guides data collection and interpretation but also clarifies the research question and hypothesis. The conceptual framework aims to make research conclusions more meaningful and generalizable. It outlines the purpose and the importance of the research topic .

Inhaltsverzeichnis

• 1 Conceptual Framework – In a Nutshell
• 2 Definition: Conceptual framework
• 3 Conceptual framework: Independent vs. Dependent variables
• 4 Conceptual framework: Moderating variables
• 5 Conceptual framework: Mediating variables
• 6 Conceptual framework: Control variables

Conceptual Framework – In a Nutshell

• The conceptual framework is a model used to show the relationship between the independent vs. dependent variables in a research problem .
• Researchers consider several variables in a conceptual framework, including control variables, mediating variables, and monitoring variables.
• It is important to identify control variables in a conceptual framework to minimize their effect on the findings of a study.

Definition: Conceptual framework

A conceptual framework is a visual model that illustrates the anticipated relationship between the cause and effect variables. It highlights the research goals and creates a layout of their relationship to form meaningful conclusions. The conceptual framework is usually drawn from the literature review during the early stages of research to form appropriate research questions .

Conceptual framework: Independent vs. Dependent variables

Researchers define independent vs. dependent variables to test for cause and effect when developing a conceptual framework.

Example of dependent vs. independent variables

You want to investigate whether drivers with more experience are involved in fewer accidents.

• Hypothesis: The more experience a driver has, the fewer accidents they are likely to be involved in .
• Independent variable:  The  years of experience
• Dependent variable:  The expected cause and the number of accidents

Unlike above, causal relationships in a conceptual framework usually have more than one independent variable .

Conceptual framework: Moderating variables

Moderating variables influence the strength of the relationship between two variables in a conceptual framework. They are used to determine the external validity of the research conclusions based on their ability to strengthen, negate or otherwise affect the association between the independent vs. dependent variables.

Moderating variables are helpful in a conceptual framework because they illustrate the relationship between different variables in a research topic .

Income levels can predict general happiness, although the relationship may be stronger for younger people than for older workers. Age is the moderating variable in this conceptual framework.

Moderators can be divided into categorical variables such as religion, blood group, or race and quantitative variables like height, age, and income.

In our study of drivers’ experience and accidents, we can introduce age as the moderating variable. In this case, a driver’s age can influence the effect of years of experience on the number of accidents. The researcher expects that ‘age’ moderates the effect of experience on road safety.

Conceptual framework: Mediating variables

A conceptual framework also takes mediating variables into account. They illustrate the impact of an independent variable on a dependent variable by showing how and why the effect occurs. A variable is considered a mediator if:

• It is caused by an independent variable.
• It affects the dependent variable.
• The statistical correlation between the dependent and the independent variable is more significant when it is considered than when it’s not.

Researchers use mediation analysis to test if a variable is a mediator using ANOVA and linear regression analysis. ANOVA (Analysis of Variance) tests the presence and strength of the statistical differences between the means calculated from several independent samples.

ANOVA: Determines the effects of age, gender, and disposable income on average consumer spending per month.

Linear regression: Predicts the value of a dependent variable based on the value of the independent variable.

The main aims of linear regression in a conceptual framework are to test the effectiveness of a group of predictor values in predicting a result and identifying the significant predictors of the outcome.

An individual’s body weight has a linear relationship with their weight. The researcher expects that as the height of the person increases, their weight increases. A set of observations can be plotted on a scatter plot to illustrate the strength of the correlation between the variables.

Conceptual framework: Control variables

Control variables are also considered in the conceptual framework. They define factors controlled by the researcher as it may affect the findings of a study even though it is of no interest to the researcher when designing a conceptual framework.

Control variables are used to improve the validity of a research study by reducing the effect of other variables outside the scope of the study. They help researchers to determine the relationship between the key variables under observation.

Control variables can be managed directly by keeping them constant, for instance choosing participants within the same age group. They can also be managed indirectly by using random samples to reduce their effect.

Example of control variables in the conceptual framework

In our study of driver’s experience and accident rates, the weather may affect the rate of accidents. However, our primary focus is not on the relationship between the weather and accident rates, although it may affect the findings of our study. Therefore, The ‘weather’ is added as a control variable in our conceptual framework.

If a researcher fails to control some variables, it may be difficult to prove that they did not affect the research outcome. Control variables are used in experimental research to guarantee that the observable results are exclusively caused by the experimental design.

Variables in a conceptual framework can be controlled by:

• Random assignment – Selecting random groups ensures there are no identifiable differences, which may skew your conclusions.
• Statistical controls – You can isolate the effects of the control variable by measuring and controlling it.
• Standardized procedures – Researchers should ensure the same methods are applied in all the groups in a study. Only the independent variables should be altered across groups to observe how they affect the dependent variable.

What is the conceptual framework in research?

It is an illustration of the relationship between variables in a study. It is used to form the hypothesis that guides the methods of research.

What are control variables?

Control variables are factors that are directly or indirectly controlled by the researchers. They are extraneous variables that may affect the observations in a study.

Where are conceptual frameworks used?

Conceptual frameworks are used in multiple social sciences and humanities. They help in formulating and investigating the research problem.

What is a moderating variable in a conceptual framework?

A moderating variable influences the effect of an independent variable on a dependent variable. It is used to measure the impact of an additional variable on the dependent-independent variable relationship.

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IResearchNet

Input-Process-Output Model

Much of the work in organizations is accomplished through teams. It is therefore crucial to determine the factors that lead to effective as well as ineffective team processes and to better specify how, why, and when they contribute. Substantial research has been conducted on the variables that influence team effectiveness, yielding several models of team functioning. Although these models differ in a number of aspects, they share the commonality of being grounded in an input-process-output (IPO) framework. Inputs are the conditions that exist prior to group activity, whereas processes are the interactions among group members. Outputs are the results of group activity that are valued by the team or the organization.

The input-process-output model has historically been the dominant approach to understanding and explaining team performance and continues to exert a strong influence on group research today. The framework is based on classic systems theory, which states that the general structure of a system is as important in determining how effectively it will function as its individual components. Similarly, the IPO model has a causal structure, in that outputs are a function of various group processes, which are in turn influenced by numerous input variables. In its simplest form, the model is depicted as the following:

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Input —> Process —> Output

Inputs reflect the resources that groups have at their disposal and are generally divided into three categories: individual-level factors, group-level factors, and environmental factors. Individual-level factors are what group members bring to the group, such as motivation, personality, abilities, experiences, and demographic attributes. Examples of group-level factors are work structure, team norms, and group size. Environmental factors capture the broader context in which groups operate, such as reward structure, stress level, task characteristics, and organizational culture.

Processes are the mediating mechanisms that convert inputs to outputs. A key aspect of the definition is that processes represent interactions that take place among team members. Many different taxonomies of teamwork behaviors have been proposed, but common examples include coordination, communication, conflict management, and motivation.

In comparison with inputs and outputs, group processes are often more difficult to measure, because a thorough understanding of what groups are doing and how they complete their work may require observing members while they actually perform a task. This may lead to a more accurate reflection of the true group processes, as opposed to relying on members to self-report their processes retrospectively. In addition, group processes evolve over time, which means that they cannot be adequately represented through a single observation. These difficult methodological issues have caused many studies to ignore processes and focus only on inputs and outputs. Empirical group research has therefore been criticized as treating processes as a “black box” (loosely specified and unmeasured), despite how prominently featured they are in the IPO model. Recently, however, a number of researchers have given renewed emphasis to the importance of capturing team member interactions, emphasizing the need to measure processes longitudinally and with more sophisticated measures.

Indicators of team effectiveness have generally been clustered into two general categories: group performance and member reactions. Group performance refers to the degree to which the group achieves the standard set by the users of its output. Examples include quality, quantity, timeliness, efficiency, and costs. In contrast, member reactions involve perceptions of satisfaction with group functioning, team viability, and personal development. For example, although the group may have been able to produce a high-quality product, mutual antagonism may be so high that members would prefer not to work with one another on future projects. In addition, some groups contribute to member well-being and growth, whereas others block individual development and hinder personal needs from being met.

Both categories of outcomes are clearly important, but performance outcomes are especially valued in the teams literature. This is because they can be measured more objectively (because they do not rely on team member self-reports) and make a strong case that inputs and processes affect the bottom line of group effectiveness.

Steiner’s Formula

Consistent with the IPO framework, Ivan Steiner derived the following formula to explain why teams starting off with a great deal of promise often end up being less than successful:

Actual productivity = potential productivity – process loss

Although potential productivity is the highest level of performance attainable, a group’s actual productivity often falls short of its potential because of the existence of process loss. Process loss refers to the suboptimal ways that groups operate, resulting in time and energy spent away from task performance. Examples of process losses include group conflict, communication breakdown, coordination difficulty, and social loafing (group members shirking responsibility and failing to exert adequate individual effort). Consistent with the assumptions of the IPO model, Steiner’s formula highlights the importance of group processes and reflects the notion that it is the processes and not the inputs (analogous to group potential) that create the group’s outputs. In other words, teams are a function of the interaction of team members and not simply the sum of individuals who perform tasks independently.

Limitations of the IPO Model

The major criticism that has been levied against the IPO model is the assumption that group functioning is static and follows a linear progression from inputs through outputs. To incorporate the reality of dynamic change, feedback loops were added to the original IPO model, emanating primarily from outputs and feeding back to inputs or processes. However, the single-cycle, linear IPO path has been emphasized in most of the empirical research. Nevertheless, in both theory and measurement, current team researchers are increasingly invoking the notion of cyclical causal feedback, as well as nonlinear or conditional relationships.

Although the IPO framework is the dominant way of thinking about group performance in the teams literature, relatively few empirical studies have been devoted to the validity of the model itself. In addition, research directly testing the input-process-output links has frequently been conducted in laboratory settings, an approach that restricts the number of relevant variables that would realistically occur in an organization. However, although the IPO model assumes that process fully mediates the association between inputs and outputs, some research has suggested that a purely mediated model may be too limited. Therefore, alternative models have suggested that inputs may directly affect both processes and outputs.

Without question, the IPO model reflects the dominant way of thinking about group performance in the groups literature. As such, it has played an important role in guiding research design and encouraging researchers to sample from the input, process, and output categories in variable selection. Recent research is increasingly moving beyond a strictly linear progression and incorporating the reality of dynamic change. In addition, alternatives to the traditional IPO model have been suggested in which processes are not purely mediated.

References:

• Hackman, J. R. (1987). The design of work teams. In J. Lorsch (Ed.), Handbook of organizational behavior (pp. 315-342). New York: Prentice Hall.
• Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517-543.
• Steiner, I. D. (1972). Group process and productivity. New York: Academic Press.
• Group Dynamics
• Industrial-Organizational Psychology

A Comprehensive Guide to Input-Process-Output Models

Updated: January 31, 2024 by Ken Feldman

Are you looking for a business improvement tool that is intuitive, simple to use, and visual in nature? Do you want to explore your internal business process and make sure you understand all of the inputs, outputs, and potential error states? 

If you are answering yes to these questions, then using input-process-output could be the perfect methodology for you. Let’s find out more. 

Overview: What is input-process-output (I-P-O)? 

Input-process-output (I-P-O) is a structured methodology for capturing and visualizing all of the inputs, outputs, and process steps that are required to transform inputs into outputs. It is often referred to, interchangeably, as an I-P-O model or an I-P-O diagram, both of which make reference to the intended visual nature of the method. 

A simple example is shown below from research in healthcare.

https://www.researchgate.net/figure/The-Input-Process-Output-diagram-of-the-proposed-system_fig2_323935725

As the methodology is incredibly versatile, it is used across many industries and sectors with (inevitably) some modifications and adaptations. These can include, for example, the addition of feedback loops from output to input, in doing so creating models analogous to closed-loop control theory.

Typically, we would use I-P-O in the “define” stage of a Six Sigma DMAIC project and follow a specific method for generating the model. The steps are:

• Decide upon the process steps that will be in scope of the I-P-O model. Try to ensure the the scope is manageable with, ideally, less than 10 process steps defined.
• List all of the possible outputs, including potential error states.
• List all of the inputs to your process steps, using clear descriptive language.
• Create a visual I-P-O model.
• Check that the inputs are transformed to the outputs via the process steps as shown in the model. 

Often, it can be helpful to have the team that’s generating the I-P-O model complete a Gemba walk. Visiting the actual place of work and viewing the process in action can tease out some of the less obvious inputs and outputs and contributes to continuous improvement of the existing process steps.

2 benefits and 1 drawback of I-P-O 

Used correctly, the I-P-O model offers a simple, practical, and efficient way to analyse and document a transformation process. Let’s explore some benefits and drawbacks of I-P-O.

1. It’s visual and easy to explain

It’s often said that the best business improvement tools are simple to use, intuitive, and visual, and I-P-O ticks all three of these boxes. A sheet of paper, marker pen, and an enthusiastic team willing to contribute will get you a long way. It’s also versatile, suitable for use with the executive management group as well as the wider business improvement team.

2. It’s easy to execute

There is a clear and simple methodology to generate I-P-O models, and this helps you recognise and document all of the possible inputs, outputs, and error states. As it’s visual, it’s easy to update and change as the team explores many potential inputs and outputs.

3. It’s internally focused without regard for external customers or suppliers   

Developing I-P-O models is usually all about internal business processes, and we often hear this called micro-process-mapping. This typically means we do not consider our external suppliers and customers in the analysis. However, don’t worry, we have complimentary models such as SIPOC and COPIS that help us make sense of the bigger (macro) picture.

Why is I-P-O important to understand? 

For such a relatively simple mapping tool, it provides a really powerful insight into our internal business processes. Let’s dig a little deeper.

It helps with defining your key process input variables

Once we’ve documented and visualised our inputs and outputs, we can turn our attention to determining and controlling which inputs provide a significant impact on the output variation — these are known as our key process input variables . 

It’s aligned with Six Sigma and Lean principles 

In a classic Six Sigma and Lean project approach, we strive to reduce process variation and remove defects and waste. With I-P-O, we identify inputs, outputs, and error states from our processes so we can begin to explore and understand the Y(output) = f ((X) input) equation.

It’s the perfect springboard to create full process maps 

Once we have created I-P-O models, we have the perfect starting place for generating complete process maps . This could be moving on to value stream mapping , spaghetti maps, or one of many other types of process maps that are available.

An industry example of I-P-O 

A government agency with multiple departments was embarking upon a business transformation project to improve customer service times and efficiency. As part of the transformation project, a Six Sigma Black Belt who was assigned to the activity was requested to explore and document existing processes and prepare the teams for process improvement.

The Black Belt chose to create I-P-O models due to the ease of use and versatility of the approach. Each of the business departments designated a team to work on the I-P-O models and, alongside the Black Belt, defined the process scope, ensuring this was of manageable size. 

With the teams in place and scope defined the process outputs were brainstormed and captured visually using whiteboards. The corresponding inputs were added, and the I-P-O models checked for completeness.

Generating the I-P-O models highlighted a number of potential output error states that were subsequently investigated as part of the business transformation project and contributed to improved customer service times. As the models were captured visually on whiteboards, they were easily updated during the project and used to inform staff of their contribution towards continuous improvement.

3 best practices when thinking about I-P-O 

Like many process-driven mapping activities, there are some key things for us to consider when creating I-P-O models. Let’s look at three of these.  

1. Remember: It’s a team sport; don’t go it alone 

Even relatively simple processes have multiple inputs and outputs. Often we find that different team members have detailed knowledge of specific process inputs and outputs, and we should make good use of this collective knowledge.

2. Make sure the scope is achievable

Don’t be overly ambitious with the scope and try to include too many process steps for your I-P-O model. If you find yourself listing 10 or more process steps, it’s probably time to stop and re-evaluate.

3. Consider all of the inputs and outputs 

Be diligent, get all the team involved, and make sure there is no bias — we don’t want to just list the things we think should be inputs and outputs in an ideal world. In addition, we should consider and document all of the possible output error states.

Frequently Asked Questions (FAQ) about I-P-O

Is i-p-o related to sipoc .

It can be a logical next step to create a SIPOC model from an I-P-O model. With SIPOC, we consider both suppliers (S) and customers (C) in the analysis, the so-called wider or bigger picture. With I-P-O, we concentrate more on the internal business process.

Where do I start with an I-P-O model? 

Start by defining the processes that are in scope, making sure the scope is manageable. Then consider and document all of the possible outputs from the process steps before moving on to capture the inputs.

Do I need a software program to generate I-P-O models? 

Definitely not. You can start with paper, pen, and a pack of sticky notes. However, there are a number of free templates available for download that can help you and your team as you start to populate the I-P-O model.

A final thought on I-P-O

Ease of use and versatility are just two of the major plus points of developing I-P-O models for your internal business processes. Add in their highly visual nature, and this means you can easily engage your team on a journey to continuous improvement.

About the Author

Ken Feldman

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Conceptual frameworks and empirical approaches used to assess the impact of health research: an overview of reviews

1 Centro Cochrane Italiano, Istituto Ricerche Farmacologiche Mario Negri, Milano, Italia

Lorenzo Moja

2 Dipartimento di Sanità Pubblica-Microbiologia-Virologia, Università degli Studi, Milano, Italia

Vanna Pistotti

Andrea facchini.

3 Istituto Ortopedico Rizzoli, Bologna, Italia

4 Università degli Studi di Bologna, Bologna, Italia

Alessandro Liberati

5 Dipartimento di Oncologia, Ematologia e Malattie dell'Apparato Respiratorio, Università di Modena e Reggio Emilia, Modena, Italia

6 Agenzia Sanitaria e Sociale Regionale dell'Emilia Romagna, Bologna, Italia

Associated Data

How to assess the impact of research is of growing interest to funders, policy makers and researchers mainly to understand the value of investments and to increase accountability. Broadly speaking the term "research impact" refers to the contribution of research activities to achieve desired societal outcomes. The aim of this overview is to identify the most common approaches to research impact assessment, categories of impact and their respective indicators.

We systematically searched the relevant literature (PubMed, The Cochrane Library (1990-2009)) and funding agency websites. We included systematic reviews, theoretical and methodological papers, and empirical case-studies on how to evaluate research impact. We qualitatively summarised the included reports, as well the conceptual frameworks.

We identified twenty-two reports belonging to four systematic reviews and 14 primary studies. These publications reported several theoretical frameworks and methodological approaches (bibliometrics, econometrics, ad hoc case studies). The "payback model" emerged as the most frequently used. Five broad categories of impact were identified: a) advancing knowledge, b) capacity building, c) informing decision-making, d) health benefits, e) broad socio-economic benefits. For each proposed category of impact we summarized a set of indicators whose pros and cons are presented and briefly discussed.

Conclusions

This overview is a comprehensive, yet descriptive, contribution to summarize the conceptual framework and taxonomy of an heterogeneous and evolving area of research. A shared and comprehensive conceptual framework does not seem to be available yet and its single components (epidemiologic, economic, and social) are often valued differently in different models.

It is widely accepted that research is a crucial investment to foster innovation, knowledge advancement, and social and economic development. For example a knowledge gain is assumed to result from biomedical and basic research; if such an output is then properly translated in a short but reasonable time lag, it will lead to a better health status for populations and patients. Much of the information produced is not easily transferable to patient care and this has led to the concept of the so called "translational blocks" [ 1 ]. Evidence produced by applied types of health research, such as the "comparative effectiveness" and "health services research" elicits its potential impact in a more straightforward way. Health care systems, which are nowadays increasingly keen to directly support research, are interested in overcoming the translational blocks and to facilitate a quicker return of their investment in terms of information that would help selecting the more effective and cost effective interventions so that quality and appropriateness can be maximised [ 2 - 4 ].

By definition, research activities are risky and their returns highly unpredictable. So, any attempt to increase the research system's effectiveness, and to assure and monitor quality, is welcome by the whole scientific community and funders [ 5 ]. Competition on limited resources and different funding modalities also raise additional concerns. From the limited available evidence on the proportion of investments by research stream, funding is skewed toward biomedical and basic research which, by definition, require more time to have an impact [ 6 ]. This has raised a debate between those who ask for a priority-setting based on the ability to produce relevant, usable, and transferable outputs and those supporting the view that research should be driven only by the researchers' interest. If left only into the close boundaries of the "research communities", there is a concrete risk that the priority setting becomes self-referent and the "bidirectional dialogue" between those that generate relevant questions from observation in clinical practice and those that are responsible to generate the new knowledge remains very limited [ 7 ]. Monitoring and measuring research impact is a complex objective requiring the involvement of many actors within the research pipeline. In the past two decades, many theoretical frameworks and methodological approaches to measure research impact and returns have been developed. The payback [ 8 ], the cost-benefit [ 9 , 10 ], and the decision making impact models [ 11 ] are examples of evaluation approaches reported in scientific and health policy literature. A partial list with a qualitative description of the most common frameworks is reported in Table ​ Table1. 1 . All these models share a multidimensional approach achieved by categorizing impact and benefit in many dimensions and trying to integrate them [ 12 ]. A set of indicators and metrics are then generally associated to each category of impact. For example, bibliometric indicators (e.g. impact factor) are highly reported as a measure of the diffusion and awareness of research results. These indicators, though welcome to some extent because of their directness, are at best only surrogate measures of impact. Moving toward more robust metrics, such as those measuring the health status or the economic benefit of a population, is a complex task but in some way essential [ 13 ].

A qualitative description of the most widespread frameworks for the evaluation of research impact

The objective of this overview is to describe the conceptual and methodological approaches to evaluate the impact of biomedical and health research. Specifically, we aimed at collecting and qualitatively summarizing what is available in the biomedical literature in terms of theoretical framework and methodologies, with a specific focus on the most valid and reliable indicators of impact. Our objective was also to see whether this qualitative analysis would have allowed the identification of a preferred model to measure research impact and to identify the desirable elements (i.e. dimensions to be considered, robust and reliable indicators) that a reference model should have.

Other key elements in the "research governance debate" such as the analysis of different modes and approaches to research funding, prioritization of topics, or the analysis of barriers and facilitators to the translation of research results were beyond the scope of this overview.

In the context of this overview, the term "research impact" refers to any type of output of research activities which can be considered a "positive return" for the scientific community, health systems, patients, and the society in general. We refer to any type of health-related research, basic and biomedical, such as new drug or technology development - and applied research such as clinical trials, health service research, and health technology assessment (HTA).

The complexity and heterogeneity of the topic made the conceptualization of this overview much less straightforward than typical review on medical interventions. We therefore followed the methodology recommended by the Cochrane Collaboration for preparing "overviews of review" rather than following the steps involved in critically appraising the primary studies for a systematic review (SR) [ 14 ]. We first searched for SRs summarizing theoretical model or methodological approaches as well as empirical assessment of health research funding programs. To increase the comprehensiveness of our search we also sought primary studies (case studies) not included in the selected SRs or published in languages other than English. We included studies describing conceptual or methodological approaches to evaluate the impact of health research programs and the empirical evaluation of specific programs, funders, research teams, clinical area, etc. In both cases, to be eligible for this review the study should have mentioned specific impact categories and the indicators and metrics used to measure this impact.

Given the broad perspective of the review, the methodology to identify relevant studies comprised an iterative process. We first performed a systematic search on bibliographic databases (Medline and The Cochrane Library) using a modified version of the search strategy proposed by Martin Buxton and collaborators [ 12 ]. The search was limited to the SRs published between 1990 and 2009. We also tried to include relevant studies and reports not included in the eligible SRs (i.e. publications in French, Italian, and Spanish) published between 2007 and 2009. Besides bibliographic databases, we screened the research funding and Charity's Foundations websites cited in the eligible studies, looking for the grey literature (i.e. additional reports not indexed in bibliographic databases). In fact, a large part of the literature in this field would be made up of heterogeneous publications and critical appraisal reports published by the main funding agencies. Details on the search strategies used and the searched websites are reported in Additional file 1 .

Finally, we screened the citations reported in the included publications and assessed the literature in the field already retrieved by authors. We did not contact study or report authors.

Two reviewers independently selected relevant publications by screening titles and abstracts. After the retrieval of the selected full text publications and, if needed, of associated publications, we extracted the following details: objective, country and setting, evaluation time lag, conceptual model methodology, main results and conclusions.

From the analysis of the literature and with reference to the more widely accepted theoretical models we attempted a description of the categories of impact more frequently measured, focusing on the indicators and metrics for each category.

Given the heterogeneity of study designs, the different objectives and the lack of a standard methodology between studies, we did not perform a quality assessment of the methods used in different studies.

From the bibliographic databases and web searches we identified 1064 records. Among these 38 potentially relevant publications were retrieved as full text (Figure ​ (Figure1). 1 ). Sixteen publications were excluded, i.e. because descriptive, [ 15 ] or dealing with prioritization of research topics rather than impact [ 16 , 17 ]. We included 22 publications, referring to 18 studies: four SRs [ 12 , 18 - 21 ] and 14 primary studies [ 8 , 9 , 22 - 36 ]. For each study, we synthesized the overall objective, scientific area evaluated, country and setting, and the time lag of the evaluation.

Flow of studies through the different phases of the overview .

Hereafter we briefly presents the four SRs. For details please refer to Additional file 2 . The included SRs presented and discussed comparative analyses of theoretical models and empirical evaluations performed in several countries from 1990. They all used a public funder's viewpoint (e.g. central or regional governments, WHO). Besides the evaluation of specific research programs, each SR also reported a description of theoretical models used as a framework for the assessment and a more or less explicit description of categories of impact and indicators used in the evaluation. Both the reviews by Hanney et al and the one from the Canadian Academy of Health Science answered to the broad question "how to measure research impact". The first aimed at assessing how the impact of the UK National Health Service Health Technology Assessment program should be measured and collected the available models, their strengths and weaknesses [ 12 ]. The latter was interested in defining the impact of the Canadian health research and to answer to the broad questions "is there a best method to evaluate the impacts of health research in Canada?" and "are there best metrics that could be used to assess those impacts?" [ 20 ]. These SRs included studies aimed at describing conceptual or methodological approaches to evaluate the impact of health research programs as well as empirical applications of different assessment strategies (desk analysis, interview, peer-review evaluation, case-studies, etc.) and tools for measuring impact (indicators and metrics). The review published by Buxton et al in 2004 focused on the estimation of the economic value of research to society. The review reported an analysis of benefits in terms of direct cost savings to the healthcare system, commercial development, healthy workforce, and intrinsic value to society due to the health gain [ 18 ]. Lastly, Coryn et al. have reported a comparative analysis of 16 national models and mechanisms used to evaluate research and allocate research funding [ 21 ].

Hereafter we briefly presents the results of the included primary studies. For details please refer to Additional file 3 . The studies covered a broad range of evaluation exercises sponsored by public and private research funding agencies. All studies have been conducted in UK, Australia, Canada, and USA, with few exceptions [ 26 , 32 ]. These studies were highly heterogeneous in terms of the applied theoretical frameworks and methodology. The unit under evaluation included researchers (from one single researcher to teams and whole institutions) but also medical discipline (e.g. cardiovascular disease research) or type of grants (e.g. from public institutions, charities, foundations, etc.).

The large majority applied a bottom-up evaluation approach, where information goes from any "producers" of research to any target of research outputs [ 37 ]. Two studies applied a more strictly econometric approach used to estimate return on investments in a top down manner [ 9 , 36 ]. The method often used were the desk analysis, peer reviewer evaluation, interviews and questionnaires to principal investigators or to stakeholders with a variety of roles in the research production and utilization.

Across the majority of the SRs and primary studies, research impact was assessed alongside several dimensions, which can be grouped into five categories: "advancing knowledge", "capacity building", "informing decision-making", "health benefits", and "broad socio-economic benefits". Each category, further split into subcategories, had a set of indicators and metrics capable of providing the size of the impact (see Table ​ Table2). 2 ). The more frequently quoted dimensions of impact were advancing knowledge (using bibliometric and citational approaches), capacity building (mainly using a desk analysis approach), and informing decision-making (through the evaluation of how and to what extent research findings are included into the decisional processes, i.e. guidelines). The potential benefits of a research activity on population's health or its socio-economic status were more rarely addressed by the literature as they are, obviously, less directly linked and more complex to assess. In other words, these categories, with their respective indicators, span into a gradient going from surrogate but easy to be measured outcomes, (i.e. bibliometric and citational data) to demanding but relevant outcomes (i.e. morbidity, quality of life). Bibliometric indicators (number of publications, impact factor, citation indexes, etc.) were a case in point here. They were widely considered, reported, and to some extent, accepted due to the fact that they are easy to measure and outputs that can be straightforwardly attributed to a specific research activity. Only the studies adopting an econometric viewpoint [ 18 ] or evaluating a specific research area, such as primary care [ 27 , 28 ] or health system effectiveness [ 29 ] did not quote (or quoted with less emphasis) bibliometric indicators.

Description of possible impact categories and relative indicators ( adapted from Canadian Academy of Health Science [ 20 ])

Main findings

This overview of reviews shows that the assessment of the impact of, or benefits from, health research is an issue of growing interest, mainly in those countries (UK, Canada, Australia, USA) that invest more in research. Research in this area focuses on three broad areas: i) theoretical frameworks and models aiming at assessing research impact with respect to multidimensional and integrated categories; ii) methodological approaches to the evaluation exercise; and iii) development of valid and reliable indicators and metrics.

A common and key feature of most of the used models is the multidimensional conceptualization and categorization of research impact. Different impact aspects are connected and integrated using a variety of theoretical approaches (i.e. Logic model for the Payback framework). Assessment of research impact that consider more than one category are indeed valued for their ability to capture multifaceted processes.

Several empirical approaches have been used to practically assess research impact: desk analysis, including bibliometrics, peer reviews, interviews, ad hoc case studies. The latter seems a reliable methodology: case study implies an explicit and a priori choice to start and conduct an evaluation exercise with specific aims and features. However, they can be at risk of "conceptualization bias" and "reporting bias" especially when they are designed or carried out retrospectively. Finally, feasibility and costs of case studies are also a major barrier to their conduct and subsequent use. In general, the methodology should be as flexible and adaptable as possible to many assessment questions, viewpoints, settings, and type of research and should guarantee the quality of collected data.

The lack of standard terminology, the multifaceted nature of the evaluation, and the heterogeneity of the empirical experiences make it hard to identify a preferred model. The most cited impact dimensions are related to knowledge, public health and socio-economic advancements. The Payback model, [ 38 - 41 ] and its adaptation into the Canadian framework [ 20 ] emerged as the most frequently quoted. Both are based on explicit assumptions (positive and negative), have been applied to empirical evaluation, and produce transparent categories of impact, indicators, and limitations of the models. They can be considered comprehensive as assure a global approach to the evaluation of biomedical and health research impact. The identification of appropriate indicators is a critical step in any impact assessment exercise, and assessing research impact makes no exception to this. Indicators can be defined as factors or variables that provide simple and reliable means to measure impact, changes to an intervention, or performance [ 42 ]. Ideally, a set of a few robust, valid, shared, transferable, comparable, and feasible indicators able to synthesize research impact should be developed for any assessment. As a matter of fact, the usefulness of indicators highly depends on the evaluation purposes and the level of aggregation of the unit of analysis: for instance, citation indicators partially capture the impact on knowledge advancements as they only consider published literature, they are artificially skewed by journal's impact factor and can be misleading when applied to individuals. Moreover, an indicator itself informs only on a single aspect of research impact, thus sets of indicators are always advisable.

This overview highlights the methodological limitations of the studies carried out in this field, which are briefly summarized below.

First, the vast majority of the studies were retrospective, based on interviews to principal investigators or funders, and mainly focused to record the projects' achievements rather that their pitfalls and limitations. This could lead to several biases, such as selective recall or reporting of (positive) results. The second major limitation is linked to the "attribution", the possibility to postulate a causal link between observed (or expected to be observed) changes and a specific research activity [ 19 ]. Another limitation is linked to the possibility of understanding what would have happened without "that" research activity (counterfactual). As very rarely a "control" situation is available, the identification of baseline measures and context factors is important in understanding what any counterfactual might have looked like. Finally, it is not commonly appreciated that substantial time lags exist between research funding and measurement of outputs. An impact assessment should be planned choosing an appropriate time window, which highly depends on the considered type of research and dimension of the impact.

Limitations of this overview

The main limitations of this overview concern the study retrieval process and the definition of the eligibility criteria. We experienced several difficulties in planning the search strategy, all caused by the heterogeneity of definition and the lack of a standard terminology to describe "research impact". Bearing this in mind, we adopted an approach capable of maximizing sensitivity rather than specificity, that is the application of broad inclusion criteria and the use of several sources of information, not only bibliographic databases. As expected, only 30% of the included publications were found through the traditional biomedical databases (i.e. Medline). Beyond a high variability in the way of indexing these articles, this could be due to a limited interest in the publication of these evaluations, which were considered, at least in the last decade, an administrative duty rather than a scientific activity. Thus many relevant studies were retrieved in the "grey literature" (i.e. funding agency's reports) and not in scientific journals. Even if we were not able to apply a systematic approach to website consultation, we believe this effort had increased the comprehensiveness of our search.

Another methodological limitation of this overview is that we did not estimate the level of publication bias and selective publication in this field. Finally, as our analysis include study up to 2009, we did not capture new important emerging approaches to impact assessment, such as the Research Excellence Framework (formerly RAE) [ 43 ].

The main message of this overview is that the evaluation of the research impact is as yet a heterogeneous, and evolving discipline. Multidimensional conceptual frameworks appear to be adequate as they take into account several aspects of impact and use advanced and analytical approaches (i.e. epidemiologic, economic, and social) to their evaluation. It remains to be clarified how different impact dimensions should be valued and balanced by assessors to fit them to their specific purposes and contexts. Added values to the multidimensional approach are to pursue an explicit planning of the assessment exercise and to carried out the alongside the development of research programs, through monitoring and prospective data collection.

This overview should be seen as a preliminary step toward a shared conceptual framework and taxonomy to assess research impact rather than an indication of an ultimate model, that probably appears unrealistic.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors were involved in the conception of the study. RB, LM, VP, and AL were responsible for the study design and acquisition, analysis, and interpretation of data. VP was responsible for the search strategy design and the study retrieval. All authors contributed to the preparation of the final text of the article, they critically revised it for important intellectual content and gave final approval to the manuscript.

Source of funding

This overview was developed within the project "Research impact in Emilia Romagna: an open archive of health research outputs. Evaluation of research products and capacity building" funded by the Programma di Ricerca Regione Università 2007-2009, Regione Emilia Romagna, Italy.

The funder had no role in study design, collection, analysis, interpretation of data, writing of the manuscript, and in the decision to submit the manuscript for publication.

We acknowledge that this paper is a revised version of a first overview published in Italian in the journal "Politiche Sanitarie" ( http://www.politichesanitarie.it , Banzi, R. Pistotti, V., Moja, L., Facchini A., Liberati A. Valutazione dell'impatto della ricerca biomedica e sanitaria: revisione sistematica di letteratura Politiche Sanitarie 2010, 11(3): 175-195).

Supplementary Material

Appendix 1 . Search strategy used for Medline (up to May 2009) and website searched to retrieve relevant report not published in the scientific journals

Table s1 . Qualitative description of the included SRs

Table s2 . Qualitative description of primary studies not included in the previous mentioned reviews

Acknowledgements

We thank Christine Kieran from the University of Modena and Reggio Emilia for the final editing of the manuscript and for editorial support.

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