In addition to independent and dependent variables, there are two other important concepts in research: concomitant variables and extraneous variables. Here's a description of each:
Concomitant Variable:
• A concomitant variable is a variable that is associated with both the independent and dependent variables, but it is not considered the cause or effect.
• It is also known as a "covariate" or a "third variable."
• Concomitant variables are often measured or controlled to account for their potential influence on the relationship between the independent and dependent variables.
• These variables can provide additional information or explain some of the variation in the dependent variable.
Example: In a study examining the relationship between exercise (independent variable) and weight loss (dependent variable), the participant's age may be a concomitant variable. Age might be associated with both exercise habits and weight loss, but it is not the cause or effect in this study. Therefore, the researcher may measure and consider age as a concomitant variable when analyzing the relationship between exercise and weight loss.
Extraneous Variable:
• An extraneous variable is an additional variable that is not intentionally studied but can potentially influence the relationship between the independent and dependent variables.
• Unlike concomitant variables, extraneous variables are considered unwanted sources of variability.
• If not properly controlled or accounted for, extraneous variables can confound the results and lead to inaccurate conclusions.
Example: In a study evaluating the effectiveness of a new medication (independent variable) on reducing anxiety levels (dependent variable), the time of day when the medication is administered may be an extraneous variable. The time of day can affect anxiety levels independently of the medication, so it needs to be controlled or accounted for to ensure that any observed changes in anxiety levels are due to the medication and not the time of day.
Controlling extraneous variables is essential to establish a clear causal relationship between the independent and dependent variables. Researchers may use various strategies such as randomization, matching, statistical techniques, or experimental design to minimize the impact of extraneous variables on the study outcomes.
Understanding concomitant and extraneous variables helps researchers identify and address potential sources of bias or confounding in their studies. By carefully considering and accounting for these variables, researchers can enhance the validity and reliability of their findings.
