TY - JOUR
T1 - The choice of control variables in empirical management research
T2 - How causal diagrams can inform the decision
AU - Hünermund, Paul
AU - Louw, Beyers
AU - Rönkkö, Mikko
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/12/2
Y1 - 2024/12/2
N2 - The Leadership Quarterly and the management community more broadly prioritize identifying causal relationships to inform effective leadership practices. Despite the availability of more refined causal identification strategies, such as instrumental variables or natural experiments, control variables remain a common strategy in leadership research. The current literature generally agrees that control variables should be chosen based on theory and that these choices should be reported transparently. However, the literature provides little guidance on how specifically potential controls can be identified, how many control variables should be used, and whether a potential control variable should be included. Consequently, the current empirical literature is not fully transparent on how controls are selected and may be contaminated with bad controls that compromise causal inference. Causal diagrams provide a transparent framework to address these issues. This article introduces causal diagrams for leadership and management researchers and presents a workflow for finding an appropriate set of control variables.
AB - The Leadership Quarterly and the management community more broadly prioritize identifying causal relationships to inform effective leadership practices. Despite the availability of more refined causal identification strategies, such as instrumental variables or natural experiments, control variables remain a common strategy in leadership research. The current literature generally agrees that control variables should be chosen based on theory and that these choices should be reported transparently. However, the literature provides little guidance on how specifically potential controls can be identified, how many control variables should be used, and whether a potential control variable should be included. Consequently, the current empirical literature is not fully transparent on how controls are selected and may be contaminated with bad controls that compromise causal inference. Causal diagrams provide a transparent framework to address these issues. This article introduces causal diagrams for leadership and management researchers and presents a workflow for finding an appropriate set of control variables.
UR - http://www.scopus.com/inward/record.url?scp=85210968246&partnerID=8YFLogxK
U2 - 10.1016/j.leaqua.2024.101845
DO - 10.1016/j.leaqua.2024.101845
M3 - Article
AN - SCOPUS:85210968246
SN - 1048-9843
JO - Leadership Quarterly
JF - Leadership Quarterly
M1 - 101845
ER -