types of variables

Confounding variables : Definition , importance and ways to mitigate their effect


Table of Contents

A confounding variable or a confounder is a string that is connected to both the independent and dependent variable in consideration. These are the variables which usually remain unconsidered but have a major sway over the research outcome . Understanding and factoring confounding variables is an important task that must be performed in order to ensure that the observations made are accurate.

Confounding variables

Confounding variables are external variables that have an impact on the dependant as well as independent variables. These variables must be studied to evaluate the nature of their relationship with both the aspect to maximize precision. These variables , however, cannot be controlled and so measuring the impact it has can be a difficult task .

  • Independent variables: These variables are standalone factors that can be manipulated to study their causal effect upon the dependant variable.
  • Dependant variables : Dependant variables are the ones whose functioning is affected by the change in one or more than one independant variables.
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  • For example : In a study where the type of music that people listen to is correlated with their productivity, the music genres become an independent variable and can be easily changed by the researcher to test its effect on the listening audience. On the other hand , the producivity levels of the participants becomes a dependant variable that is studied based on the changes made in the music genres.
  • Sometimes , these factors can be interchanged as well. The correlation between petrol prices and petrol demand can be studied both ways
    1. The petrol prices may rise or fall depending upon the demand that customers have. In this case , petrol prices become a dependant variable impacted by the demand levels (independent variable)
    2. The demand for petrol may fall or rise depending upon the price levels. Here, the demand of petrol becomes a dependant variable .

The importance of confounding variables

The results of the experiment or research is dependant on the confounding variables’ degree of influence over both the independent and dependent variables. Disregarding this influence can result in innaccurate results that might be skewed. For maintaining the authenticity and validity of the research, it becomes important to consider the confounding variables.

 For example : The connection between advertising expenditure by companies and their market share cannot be studied in isolation. Many confounfing variables like the amount of competition must be considered and factored into the research for increased precision.

Tips on decreasing impact of confounding variables

Distribute confounders equally

 In this method , the underlying confounders are evenly distributed in the test subjects to minimize their effect due to variation. This distribution makes it easy for the researcher to disregard confounders while studying the results of the research. This is an artificial method of sample selection that makes it easy to study certain independent variables and their effect on the dependant variables without adjusting for extraneous influence.

For example: The test subjects in the study of academic performance of a student and the causal effect of screen time on it , the researcher needs to standardize the eligibility of participants . So if the researcher decides to study college going students who are also doing an internship at the same time, the participants of the study must be first year college students who are interning for the same tenure or performing the same duties as an intern. This eliminates the variation in their individual results due to differences in criterias that they meet . It also makes it easier for the researcher to disregard the confounders ( in this case , age, year of study , internship tenure and duties) and focus only on the screen time of students.

Restrict the study 

Finding samples with participants that meet the eligibility criterias and are conveniently available for the research can be a difficult task. Instead, elimination of such identified confounders is a more feasible option to go for.

In such a methodology, researchers identify the confounding variables which can influence research results. Thereafter, they eliminate these confounding variables altogether and make the eligibility criterias of participants based explicitly on the fact that they do not meet these confounding variables. So instead of ticking boxes for meeting criterias, researchers need to ensure that they do not come under any of the confounding variable categories.

 For example: In the above study of student’s academic performance, the researcher would search for students belonging to the same sex, same year and without any internship experience at all. This eliminates the need for the researcher to look for students indulged in internships and narrow the research to students belonging to the same sex, thus, streamlining the research process.

 However, researchers don’t generally prefer this practice as limits the test subjects, can be tedious for large number of confounding variables and needs a meticulous list of restrictions to minimize the sway of confounders. In the above case for example, if the study is limited to studying male students only, then the results would only project one side of the coin without shedding light on the female subjects. Furthermore, there is a need t define the exact age group and year of study that the researcher is looking for, which makes the assessment of eligibility a difficult task.


The easiest and the most common way of minimizing confounding variables’ impact is by using a sufficiently large group of people and selecting sample participants in a randomized manner. In this way , the confounding characteristics get averaged out equally among all the test subjects without making it a headache to look for characteristics in individual participants. This reduces distortion and makes the selction process quick and easy.

 A study of confounding characteristics in the initial selected group can be done to verify the average presence of confounding variables. In case of differences , slight adjustments can be done to maintain uniformity.

 For example : The study of students and their academic performance can be easily done by including all the students with varying confounders in the target group and randomly selecting the students from this group to be included in the sample. In this way , students with all the characteristics gets a representation in the sample and their presence gets averaged out . Moreover , it also takes care of many unidentified confounders which may have been ignored by the researcher .

 This method only finds practical application for large target groups and the probability of initial success in randomization also vary.

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