On making causal claims: A review and recommendations

Détails

Ressource 1Télécharger: BIB_12A79F6E956F.P001.pdf (3157.88 [Ko])
Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_12A79F6E956F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
On making causal claims: A review and recommendations
Périodique
The Leadership Quarterly
Auteur(s)
Antonakis J., Bendahan S., Jacquart P., Lalive R.
ISSN
1048-9843
Statut éditorial
Publié
Date de publication
12/2010
Peer-reviewed
Oui
Volume
21
Numéro
6
Pages
1086-1120
Langue
anglais
Notes
BIB_9A79AB398C4F
Résumé
Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.
Mots-clé
Experiments, Natural Experiments, Causality, Quasi-Experimentation, Regression Discontinuity, Instrumental Variables, Difference-in-Differences, Common-Methods Variance, Two-Stage Models, Simultaneous Equations.
Web of science
Open Access
Oui
Création de la notice
16/06/2011 8:46
Dernière modification de la notice
08/05/2019 14:43
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