What Is a Conceptual Framework? | Tips & Examples

Conceptual-Framework-example

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.

Tip
You should construct your conceptual framework before you begin collecting your data. Conceptual frameworks are often represented in a visual format and illustrate cause-and-effect relationships.

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

Developing a conceptual framework in research

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.

Step 1: Choose your research question

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

Example: Research question
Let’s say you want to study whether students who study more hours get higher exam scores. To investigate this question, you can use methods such as an experiment or a survey to test the relationship between variables.

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.

Step 2: Select your independent and dependent variables

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.

Example: Variables
Following our example:

  • 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).

In other words, you suspect that “exam score” depends on “hours of study.” Thus, your hypothesis will be that the more hours a student studies, the better they will do on the exam.

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”).

Step 3: Visualize your cause-and-effect relationship

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).

Sample-conceptual-framework-using-an-independent-variable-and-a-dependent-variable

Step 4: Identify other influencing variables

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.

Example: Moderator
We expect that the number of hours a student studies is related to their exam score—i.e., the more you prepare, the higher your score will be.

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.

We expect that the “IQ” moderator moderates the effect that the number of study hours has on the exam score.

Sample-conceptual-framework-with-a-moderator-variable

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.

Figure-effect-without-moderator

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.

Figure-effect-with-moderator-iq-120

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.

Figure-effect-with-moderator-iq-150

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.

Example: Mediator
The mediating variable of “number of practice problems completed” comes between the independent and dependent variables.

Hours of study impacts the number of practice problems, which in turn impacts the exam score.

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

Conceptual-framework-mediator-variable

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.

Note
Keep in mind that mediating variables can be difficult to interpret. Take care when drawing conclusions from them.

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.

Example: Control variable
It is very possible that if a student feels ill, they will get a lower score on the exam. However, we are not interested in measuring health outcomes a part of our research.

This makes “health” a good candidate for a control variable. It still impacts our results, but we aren’t interested in studying it.

Now, we add “health” to our conceptual framework, but decide to keep it constant. This means we’ll only include participants who are in good health on the day of the exam.
Conceptual-framework-control-variable

Frequently asked questions about conceptual models

What’s the difference between a mediator and a moderator?

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

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

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.

Can I include more than one independent or dependent variable in a study?

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.

What is a control variable?

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.

What is a confounding variable?

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.

Is this article helpful?
Bas Swaen

Bas is co-founder of Scribbr. Bas is an experienced academic writer and loves to teach. He helps students by writing clear, simple articles about difficult topics.