Conceptual-Framework Published on August 2, 2022 by Bas Swaen and Tegan George. 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.. >>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 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 causeand-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). 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-andeffect 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. 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. 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: 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. A control variable in scientific experimentation is an experimental element which is constant and unchanged throughout the course of the investigation.
