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“….very little effort is made to explain why and how controls related to focal variables of interest, and control variable practices have not changed much over past decade….”
A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims, but is controlled because it could influence the outcomes.
Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests).
CV is held constant or unchanged throughout the study to clarify the relationships between multiple variables. Any changes in a CV would invalidate the relationships between IV & DV.
General notion behind control variable usage is that researchers can remove predictor-criterion contamination by including confounding variables in their analyses (Spector & Brannick, 2011)
Problems of Control Variable?
Problems of Control Variable?
Inclusion of control variables, and in particular multiple control variables, is a safer and more statistically conservative approach than not including them (Carlson & Wu, 2012; Spector & Brannick, 2011). Whereas each of these assumptions relies on rather large inferential leaps, perhaps the biggest and potentially most frequently violated assumption is that control variables hold theoretically meaningful relationships with predictors and criteria (Bono & McNamara, 2011; see results by Carlson & Wu, 2012).
The inclusion of control variables not only reduces available degrees of freedom and statistical power but also has the potential to reduce the amount of explainable variance available in outcomes of interest (Becker, 2005; Breaugh, 2008; Carlson & Wu, 2012). That is, when control variables are related to the predictor or criterion, results may give the appearance that the predictor is not related to the criterion or is related in an unexpected direction (Becker, 2005; Breaugh, 2008; Carlson & Wu, 2012; Meehl, 1971; Spector & Brannick, 2011) - Leading to an incorrect conclusion.
Inclusion of control variables in most cases implicitly assumes that the control variables are somehow either contaminating the measurement of the variables of interest or affecting the underlying constructs, thus distorting observed relationships among them.
However, exclusion of control variables can also lead to an incorrect conclusion that the predictor relates to the criterion when, in fact, there is no such relationship.
Accordingly, the inclusion or exclusion of control variables has important implications for theory and practice as such decisions can change substantive study results (e.g., Rode et al., 2007) as well as limit the ability to replicate, extend, and generalize a study’s findings (Becker, 2005; Breaugh, 2006; 2008; Carlson & Wu, 2012).
What Controls are Included?
What Controls are Included?
Gender, Age, Tenure, Level of Education, Race, Personality, Organization/group size, Work Experience, Workload/hours worked, Family-Related Concerns, Industry, Income-Related Controls, Social Desirability, etc.
Decision-Making of Selecting Control Variables
Decision-Making of Selecting Control Variables
The choices and procedures regarding the handling of statistical controls should be described in detail, regardless of whether or not the control is ultimately included, to ensure transparency and maximize the likelihood of reproducibility of results in the future (Bernerth & Aguinis, 2016).
This is essential to satisfy a skeptical scientific audience that regularly lacks clear understanding as to why a study includes a given control variable or why its absence hinders scientific interpretation and advancement (Aguinis & Vandenberg, 2014).
Also, Spector and Brannick (2011) proposed that researchers should be explicit rather than implicit regarding the role of control variables and match hypotheses precisely to both the choice of variables and the choice of analyses.
Source: Bernerth and Aguinis (2016)
What would be an issue with this model?
What would be an issue with this model?
Further Readings
Further Readings
Bernerth, J. B., & Aguinis, H. (2016). A critical review and best‐practice recommendations for control variable usage. Personnel psychology, 69(1), 229-283.
Bernerth, J. B., Cole, M. S., Taylor, E. C., & Walker, H. J. (2018). Control variables in leadership research: A qualitative and quantitative review. Journal of Management, 44(1), 131-160.
Spector, P. E., & Brannick, M. T. (2011). Methodological urban legends: The misuse of statistical control variables. Organizational research methods, 14(2), 287-305.
Yoon, S. H., Kim, H. W., & Kankanhalli, A. (2021). What makes people watch online TV clips? An empirical investigation of survey data and viewing logs. International Journal of Information Management, 59, 102329.
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