Journal
JOURNAL OF BUSINESS RESEARCH
Volume 126, Issue -, Pages 472-488Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jbusres.2020.12.037
Keywords
Statistical control variables; Omitted variable bias; Confounding bias; Overcontrol bias; Endogenous selection bias; Directed acyclic graphs; Research methods
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Control variables play a role in purifying and avoiding bias in research, but adding control variables can also introduce overcontrol and endogenous selection biases, requiring careful consideration.
Although an increasing number of articles examining statistical control variables exist, the use of control variables receives less scrutiny than other methodological topics, and research has yet to show how adding or dropping a control variable can damage causal inferences. By showing the unexpected consequences of including and excluding a control variable, this article challenges the view that additional control variables always strengthens causal conclusions. This study first illustrates the purification role of statistical control variables, and then quantifies the omitted variable bias. Using the Directed Acyclic Graphs (DAG), this essay differentiates confounding bias from overcontrol and endogenous selection bias. While using control variables may at times rule out alternative explanations, it is equally possible that adding control variables introduces overcontrol and endogenous selection biases, creating alternative interpretations rather than ruling them out. This paper concludes by discussing how and whether scholars should include certain control variables.
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