Meta‐Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection*
研究元回归方法在存在发表选择的研究文献中检测和估计真实实证效应的小样本表现，发现这些方法对发表选择具有稳健性，且结合两种有偏估计可大幅减少发表偏倚。
Abstract This study investigates the small‐sample performance of meta‐regression methods for detecting and estimating genuine empirical effects in research literatures tainted by publication selection. Publication selection exists when editors, reviewers or researchers have a preference for statistically significant results. Meta‐regression methods are found to be robust against publication selection. Even if a literature is dominated by large and unknown misspecification biases, precision‐effect testing and joint precision‐effect and meta‐significance testing can provide viable strategies for detecting genuine empirical effects. Publication biases are greatly reduced by combining two biased estimates, the estimated meta‐regression coefficient on precision (1/ Se ) and the unadjusted‐average effect.