Sample selection bias and Heckman models in strategic management research
探讨战略管理研究中Heckman模型的应用，指出关键自变量必须在第一阶段显著、lambda显著不必然代表样本选择偏差，且Heckman模型仅能处理样本导致的内生性。
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.