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.

中文翻译：

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解决荟萃分析中的内生性：基于工具变量的荟萃分析结构方程模型


在管理研究中，荟萃分析通常用于汇总观察性研究的结果，这些研究缺乏对预测变量（例如调查）的随机分配，由于效应大小的相关性，这可能会给准确推断带来挑战。为了提高推断准确性，我们展示了如何将工具变量（IV）方法集成到荟萃分析中，以帮助研究人员获得无偏估计。我们基于 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 可以合并到 SEM 中，并且相关研究中的元分析效应大小可以用于 MASEM。方便的是，IV-MASEM 不需要每项主要研究都测量所有相关变量，并且它可以解决典型类型的内生性，例如遗漏变量偏差。我们阐明了如何将 IV-SEM 的原理应用于 MASEM，然后进行三个模拟来研究 IV-MASEM 与单变量荟萃分析 (UMA) 以及在仪器合适、不合适和缺失时排除 IV 的 MASEM 的有效性来自初级研究的子集。我们还提供了一项说明性研究来演示如何应用 IV-MASEM 来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。我们总结了 IV-MASEM 的局限性和未来方向。