Psychometric meta‐analysis assumes that moderators are unrelated to study artifacts (e.g., criterion reliability), and that study artifacts are independent of true validities. Meeting these assumptions is important for researchers seeking to accurately partition the variance in effect sizes due to study artifacts from the variance due to meaningful moderators. Despite the critical role of these assumptions, we know very little about their tenability. To address this basic gap in the literature, we conducted three studies to determine if there are potential violations of the independence assumptions (Study 1), the prevalence of such violations (Study 2), and the consequences of violating the independence assumptions via a series of Monte Carlo simulations (Study 3). We found that violations of the independence assumptions are not only plausible but also routinely detected across a wide array of topics. Simulation results indicate that violating the independence assumptions can result in biases under certain circumstances, which are further accentuated due to the lack of stability in the estimators. We offer suggestions for the future use of psychometric meta‐analysis and discuss the implications for research focused on refining psychometric meta‐analysis.

中文翻译：

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毕竟可能不是那么独立：违反心理计量学元分析中的独立性假设的可能性，普遍性和后果

心理计量学元分析假设主持人与研究工件无关（例如，标准可靠性），并且研究工件与真实有效性无关。满足这些假设对研究人员来说是重要的，因为他们希望准确地划分由于研究伪影而造成的效应量方差与有意义的主持人方差。尽管这些假设具有至关重要的作用，但我们对其持久性知之甚少。为了解决文献中的这一基本空白，我们进行了三项研究，以确定是否存在潜在的违反独立性假设的情况（研究1），此类违反的发生率（研究2）以及一系列违反独立性假设的后果蒙特卡罗模拟（研究3）。我们发现违反独立性假设不仅是合理的，而且在广泛的主题中也经常被发现。仿真结果表明，在某些情况下，违反独立性假设会导致偏差，由于估计量缺乏稳定性，这种偏差会进一步加重。我们为心理计量荟萃分析的未来使用提供建议，并讨论对精炼心理计量荟萃分析的研究的意义。