How to Develop a Conceptual Framework | Proofed’s Writing Tips

How to Develop a Conceptual Framework

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.

Here are the steps to developing the perfect conceptual framework:

  1. Pick a question
  2. Conduct a literature review
  3. Identify your variables
  4. 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.

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.

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