control variable
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WHAT IS A CONTROL VARIABLE | CONTROL VARIABLE MEANING?

Imagine you are conducting a study to observe the effects of various diets on weight loss. The primary variables of your study are the types of diets and the amount of weight loss. On the other hand, factors such as exercise, sleep, and stress levels can also impact the results you will obtain from your study. 

Even if these factors are not the main focus of your study, they just alter the outcomes if they are not kept consistent. Besides, these factors have the potential to regulate the results of the study. Even when they are not the primary variables. Hence, they are control variables.

Identifying and managing these control variables is crucial to ensure the validity of your study or experiment. By keeping these variables constant, you can be more confident that the changes you observe are due to the diets rather than external factors. This way, your study will be more accurate and give reliable results. 

It is understandable, but what is contained clarification only if you are still confused? In this guide, we discuss many important things, including what a control variable is and provide examples of control variables. By the end, you’ll have no more doubts about this variable..

WHAT IS CONTROL VARIABLE?

A control variable meaning is that it will remain constant in your study. It’s a variable that is not interested in the study’s objectives—but controlled because it could influence the outcomes of your work. Moreover, Control variables help prevent research biases like omitted variable bias from affecting results.

CONTROL VARIABLE IN DIFFERENT RESEARCH STUDIES

After the short introduction, you have a good idea of a controlled variable. Now, we will put an eye on its usage in different research studies. 

CONTROL VARIABLES IN EXPERIMENTS

In an experiment, researchers have the interest in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your experimental manipulation solely causes your results.

CONTROL VARIABLES IN NON-EXPERIMENTS 

In certain types of research, like observational studies, the researcher can’t change the primary factor they’re studying. This could be due to practical reasons or ethical issues. So, they measure other factors, known as control variables, and consider them when trying to understand the relationship between the main factors. This helps them make more accurate conclusions.

WHY ARE CONTROL VARIABLES IMPORTANT?

Control Variables will reduce the impact of confusing and other extraneous variables. You are improving the internal validity of your study. By doing this, you can easily prevent research bias and find a correlational or causal relationship between these variables of interest.

For instance, if we refer back to the weight loss example, exercise, sleep, and stress are control variables. It’s well known that these factors significantly influence weight loss. Now, when examining the effects of different diets on weight loss, don’t keep these variables constant and under control. It becomes nearly impossible to obtain accurate observations on the effectiveness of the diets.

Determining whether the weight loss is solely due to the diet would be challenging. Or if favourable conditions of the control variables influence it. Therefore, it’s crucial to manage these control variables to ensure the validity of your study. 

Note: If you want to understand a control variable, you need to understand these two variables.

INDEPENDENT AND DEPENDENT VARIABLES

INDEPENDENT VARIABLES

In an experimental study, the independent variable is that you change to examine its effects. It is known as “independent” because it will not be affected by any other study variables.

Below, you can see other names for independent variables

    • Regressors
  • Controlled Variable
  • Manipulate Variable 
  • Explanatory Variable
  • Exposure Variable 
  • And Input Variable

DEPENDENT VARIABLES

Dependent variable is altered due to the modification of an independent variable. Your independent variable “depends” on the outcome of your measuring.

Dependent variables are also referred to as:

  • Response variables 
  • Outcome variables 
  • Variables on the left-hand side

 

DIFFERENCE BETWEEN CONTROL VARIABLES AND CONTROL GROUPS

The very first difference is consistency. Such variables remain constant during the whole experiment. You don’t try to manipulate as a researcher because internal validity depends on them. Nevertheless, control variables govern the control groups. They may change depending on the variables.

DIFFERENCE IN THE CONSISTENCY

Next difference between this variable and the control group is consistency. These variables remain constant during the whole study. However, be a researcher, you do not try to manipulate because the internal validity depends on them. 

DIFFERENCE IN THE CREATION

The second difference is the method of creation of such variables and groups. Control variables derived from the research questions at the start of the study. They never depend on the sample population or respondents. 

DIFFERENCE IN THE PURPOSE

Lastly, this difference lies between control or constant variables, and groups related to the purpose. Both will serve different purposes in a research field. The control variables set base values for the measurement of the relationship between independent and dependent variables. Meanwhile, the control groups developed a frame of reference for other samples. 

WAY TO CONTROL A VARIABLE IN A SCIENTIFIC STUDY 

There are different methods to control a variable when studying a particular topic. However, there are three prevalent methods. You don’t have any any idea about these methods. Thus, a short description of those three methods is given below: 

RANDOM ASSIGNMENT

The first method is known as random assignment. This method is frequently used in experimental studies to control a variable. Moreover, this method of controlling a variable is beneficial where there are multiple groups. Random assignment helps balance the properties of groups and control variables well.

STATISTICAL CONTROLS

Occasionally, controlling all the variables and removing their effect on the results is impossible. Most often, it occurs in non-experimental research in which you have no control over a control variable. So, in this situation, you can apply the technique of statistical control. This method works by using modelling, weighting, and averaging techniques. 

 

STANDARDISED PROCEDURES

The following technique to control a variable is standardised procedures. It will work on the same principle of a daily routine. Moreover, this technique asks you to use this procedure for all the groups involved in the study. As the researcher, you don’t have to use one method to study one group and a different method to check the second group. 

WRAPPING UP

To sum up, experimentation study is only a cup of tea for some. It is not about changing one factor and getting the results. There are many things you have to consider in a research study. An example of such a variable is a control variable. This is about a variable that doesn’t change during a research study. We’ve talked about all aspects of these control variables. So, reading everything we’ve written and using the mentioned techniques is essential.

FAQ

Q: What Is A Control Variable?

Ans: A variable in a research study will remain constant. It does not have relation in research aims and objectives. Also, this variable can potentially harm the outcomes of the research study.

Q: Why control variables is essential?

Ans: Control variables are crucial because they provide a benchmark or point of comparison for assessing the results of other tests. Typically, controls are used in corporate research, cosmetic and drug testing, as well as scientific trials.

Q: Some control variable examples?

Prevalent control variables are

  • Duration of the experiment.
  • Size and composition of containers.
  • Sample volume.
  • Experimental technique

Q: What Is Internal Validity?

Ans: Internal validity is how sure you are that the link between the variables isn’t due to other factors. It’s closely tied to control variables. The more you keep these variables constant in your study, the higher the internal validity.