Zijun Ke, Yucheng Zhang, Zhongwei Hou, Michael J. Zyphur
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引用次数: 0
摘要
在管理研究中,荟萃分析通常用于汇总缺乏预测因素随机分配的观察性研究(如调查)的结果,由于效应大小的相关性,这可能会给准确推断带来挑战。为了提高推论的准确性,我们展示了如何将工具变量(IV)方法整合到元分析中,以帮助研究人员获得无偏估计值。我们的基于 IV 的元分析结构方程建模(IV-MASEM)方法依赖于这样一个事实:IV 可以被纳入 SEM,而来自相关研究的元分析效应大小可以用于 MASEM。方便的是,IV-MASEM 不要求每项主要研究都测量所有相关变量,而且可以解决典型类型的内生性问题,如遗漏变量偏差。我们阐明了如何将 IV-SEM 的原理应用于 MASEM,然后进行了三次模拟,研究 IV-MASEM 与单变量 Meta-Analyses (UMA) 和 MASEM 的有效性对比,后者在工具适当、不适当以及主要研究子集缺失的情况下排除了 IV。我们还提供了一个示例研究,演示如何应用 IV-MASEM 解决荟萃分析中的内生性问题,其中包括一个新的 R 函数来测试 IV 的合格条件。最后,我们总结了 IV-MASEM 的局限性和未来发展方向。
Addressing Endogeneity in Meta-Analysis: Instrumental Variable Based Meta-Analytic Structural Equation Modeling
In management research, meta-analysis is often used to aggregate findings from observational studies that lack random assignment to predictors (e.g., surveys), which may pose challenges in making accurate inferences due to the correlational nature of effect sizes. To improve inferential accuracy, we show how instrumental variable (IV) methods can be integrated into meta-analysis to help researchers obtain unbiased estimates. Our IV-based meta-analytic structural equation modeling (IV-MASEM) method relies on the fact that IVs can be incorporated into SEM, and meta-analytic effect sizes from correlational research can be used for MASEM. Conveniently, IV-MASEM does not require that each primary study measures all relevant variables, and it can address typical types of endogeneity, such as omitted variable bias. We clarify how the principles of IV-SEM can be applied to MASEM and then conduct three simulations to study the validity of IV-MASEM versus Univariate Meta-Analyses (UMA) and MASEMs that exclude IVs when the instruments were appropriate, inappropriate, and missing from a subset of primary studies. We also offer an illustrative study to demonstrate how to apply IV-MASEM to address endogeneity concerns in meta-analysis, which includes a new R function to test the qualifying conditions for IVs. We conclude with limitations and future directions for IV-MASEM.
期刊介绍:
The Journal of Management (JOM) aims to publish rigorous empirical and theoretical research articles that significantly contribute to the field of management. It is particularly interested in papers that have a strong impact on the overall management discipline. JOM also encourages the submission of novel ideas and fresh perspectives on existing research.
The journal covers a wide range of areas, including business strategy and policy, organizational behavior, human resource management, organizational theory, entrepreneurship, and research methods. It provides a platform for scholars to present their work on these topics and fosters intellectual discussion and exchange in these areas.