Sample selection bias and Heckman models in strategic management research
Sample selection bias and Heckman models in strategic management research
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DOI：
10.1002/smj.2475
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发表时间：
2016-12-01
影响因子：
8.3
通讯作者：
Semadeni, Matthew
中科院分区：
文献类型：
--
作者：
Certo, S. Trevis;Busenbark, John R.;Semadeni, Matthew
研究总结：在过去的十年中，策略学者使用赫克曼模型来解决样本选择偏差的数量增加了700%以上，但在他们如何应用和解释这些模型方面存在显著的不一致。鉴于这些差异，我们探讨了样本选择偏差的驱动因素，并回顾了Heckman模型如何减轻它。我们展示了三个重要的发现，寻求使用Heckman模型的学者：第一，在样本选择偏差存在的模型的第一阶段，感兴趣的自变量必须是一个显着的预测。其次，λ的显著性本身并不表明样本选择偏差。最后，Heckman模型占样本引起的endoorbital，但不是有效的endoorbital的其他来源时presented.Managementsummary：当非随机样本被用来测试统计关系，样本选择偏差可能会导致研究人员有缺陷的结论，可以反过来，产生负面影响的管理决策。我们研究使用赫克曼模型，这是为了解决样本选择偏差，在战略管理研究和突出的条件下，样本选择偏差时，以及当它不是。我们还区分样本选择偏差，一种形式的省略变量（OV）偏差，从更传统的OV偏差，强调它是可能的模型有样本选择偏差，传统的OV偏差，或两者兼而有之。准确识别OV偏差的类型对于有效地纠正它是至关重要的。我们最后提出了几条建议，以改善围绕Heckman模型使用的实践。版权所有（c）2015约翰威利父子有限公司
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 (c) 2015 John Wiley & Sons, Ltd.