The purpose of this study is to show how Monte Carlo analysis of meta-analytic estimators can be used to select estimators for specific research situations. Our analysis conducts 1,620 individual experiments, where each experiment is defined by a unique combination of sample size, effect heterogeneity, effect size, publication selection mechanism, and other research characteristics. We compare eleven estimators commonly used in medicine, psychology, and the social sciences. These are evaluated on the basis of bias, mean squared error (MSE), and coverage rates. For our experimental design, we reproduce simulation environments from four recent studies: Stanley, Doucouliagos, & Ioannidis (2017), Alinaghi & Reed (2018), Bom & Rachinger (2019), and Carter et al. (2019a). We demonstrate that relative estimator performance differs across performance measures. An estimator that may be especially good with respect to MSE may perform relatively poorly with respect to coverage rates. We also show that sample size and effect heterogeneity are important determinants of relative estimator performance. We use these results to demonstrate how the observable characteristics of sample size and effect heterogeneity can guide the meta-analyst in choosing the estimators most appropriate for their research circumstances. All of the programming code and output files associated with this project are available at https://osf.io/pr4mb/.
This study uses meta-analysis to analyze 557 estimates from 35 studies that estimate the effect of inward FDI on entrepreneurial activity. We address two questions: (i) Does FDI lead to greater entrepreneurial activity in host countries? (ii) What factors are responsible for the different estimates across studies? In addressing these questions, we make two methodological contributions. We extend the new Andrews-Kasy meta-analysis estimators (Andrews & Kasy, 2019) to allow for explanatory variables, and we develop a nested framework of multiple meta-analysis models that allows for testing between models and model selection. We estimate that, across all studies, the average estimated effect of FDI on entrepreneurship is positive but small in size, and statistically insignificant. In contrast, the average effect from studies that control for endogeneity is negative and statistically significant.
This study uses a meta-analysis to synthesize the effects of agricultural cooperative membership on the yield of crops and livestock. It collects 158 estimated yield effects from 42 studies, covering 19 developing countries.Our analysis finds evidence that there exists positive publication bias in the empirical literature, confirming that researchers and journals have a preference to publish articles that report positive and significant results. After correcting for publication bias, we find that cooperative membership has a small-sized and insignificant effect on the yield. The meta-regression analysis reveals that variation in the reported yield effects can be largely explained by the study attributes such as the sample type (full sample vs. subsample), membership ratio, econometric approaches (instrumental-variable based parametric approach, non-parametric approach or ordinary least square regression), effect size types (average treatment effects on the treated, average treatment effects, or coefficient), agro-product type (grain or others), and climate zones (tropical or non-tropical).
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