Sample selection bias and Heckman models in strategic management research

Bibliographic Data

ID23322012
AuthorsS Trevis Certo (0000-0003-3193-3316, Department of Management, W. P. Carey School of Business, Arizona State University Tempe Arizona U.S.A.), John R Busenbark (0000-0003-2290-8223, Department of Management, W. P. Carey School of Business, Arizona State University Tempe Arizona U.S.A.), Hyun‐soo Woo (0000-0003-3587-0340, Department of Management, W. P. Carey School of Business, Arizona State University Tempe Arizona U.S.A.), Matthew Semadeni (0000-0002-4240-7007, Department of Management, W. P. Carey School of Business, Arizona State University Tempe Arizona U.S.A., corresponding author)
Year2016
Volume37
Issue13
Pages2639-2657
Publication date2016-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueStrategic Management Journal (JOURNAL)
Journal identifiersISSN: 0143-2095 • E-ISSN: 1097-0266
PublisherWiley (PUBLISHER • GB)
DOI10.1002/smj.2475
OpenAlexW2151134010
LanguageEN
Citations received89
References cited31

Research summary: The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample‐induced endogeneity, but are not effective when other sources of endogeneity are present . Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision‐making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable ( OV ) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models . Copyright © 2015 John Wiley & Sons, Ltd.

Econometrics · Economics · Endogeneity · Omitted-variable bias · Sample (material) · Sample size determination · Sampling bias · Selection (genetic algorithm) · Selection bias · Statistics · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials

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