A concomitant variable, or covariate, is a variable which we observe during the course of our research or statistical analysis, but we cannot control it and it is not the focus of our analysis.
Although concomitant variables are not given any central recognition, they may be confounding or interacting with the variables being studied. Ignoring them can lead to skewed or biased data, and so they must often be corrected for in a final analysis.
The term “noncomitant variable” is preferred by some authors (e.g. Heiberger and Holland) as the shorthand term. This is due to how “covariate” can be confused with covariance. Similarly ANCOVA has historically been called Analysis of Covariance. However, to avoid confusion with covariance, Heiberger and Holland suggests that authors use the term “Analysis using noncomitant variable” instead. The concept hasn’t really caught on, with most authors still preferring to refer to analysis of covariance rather than noncomitant variables.
Concomitant variables are also known as incidental variables or subordinate variables.
Examples of Concomitant Variables
Let’s say you had a study which compares the salaries of male vs. female college graduates. The variables being studied are gender and salary, and the primary survey questions are related to these two main topics. But, since salaries increase the longer someone has been in the workplace, the concomitant variable ‘time out of college’ has the potential to skew our data if it is not accounted for.
If this variable is observed, recorded for and accounted for in the final results, your conclusions will be more valid. Typically this is done by noting the concomitant variable (here, age) in the initial data gathering, and then running a regression to ‘equalize’ all of the data points to the same number of years out of college.
Similarly, in a study comparing the effects of soil composition on the growth of tomatoes over 20 different locations country-wide, average temperatures and hours of sunlight available to each tomato patch would both be concomitant variables that would need to be included in a final analysis in order to get valid results.
Cox, D. R. The Use of a Concomitant Variable in Selecting an Experimental Design. Biometrika, Vol. 44, No. 1/2 (Jun., 1957), pp. 150-158 Retrieved from https://www.nuffield.ox.ac.uk/users/cox/cox43.pdf on March 23, 2018
Heiberger, R. & Holland, B. (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R.
Penn State Stat 502: Analysis of Variance and Design of Experiments. Lesson 10: Analysis of Covariance (ANCOVA)
Retrieved from https://onlinecourses.science.psu.edu/stat502/node/184 on March 25, 2018
What is a Covariate? Retrieved from http://dawg.utk.edu/glossary/whatis_covariate.htm on March 25, 2018
Agr 205 Lecture Notes: Analysis of Covariance (ANCOVA, ST&D Chapter 17)
Retrieved from http://www.plantsciences.ucdavis.edu/agr205/lectures/2011_lectures/l13_ancova.pdf on March 25, 2018
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need to post a correction? Please post a comment on our Facebook page.