Current Directions in Mediation Analysis

ABSTRACT— Mediating variables continue to play an important role in psychological theory and research. A mediating variable transmits the effect of an antecedent variable on to a dependent variable, thereby providing more detailed understanding of relations among variables. Methods to assess mediation have been an active area of research for the last two decades. This paper describes the current state of methods to investigate mediating variables.     

从效应量应有的性质看中介效应量的合理性

效应量的作用有两个方面, 一是弥补了统计检验的不足, 二是使得效应有可比性。结合统计显著性和效应量, 才能得出适当的统计结论。效应量应当具有一些基本性质, 包括与测量单位无关、单调性、不受样本容量的影响。国际上流行的中介效应量κ平方就是因为缺乏单调性而引发质疑和研究, 从而被彻底终结了其作为中介效应量的合法性。R平方型中介效应量同样有缺乏单调性的问题。文末讨论了如何报告中介效应量以及有待研究的问题。     

The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes,confidence intervals,and meta‐analysis

Estimation based on effect sizes, confidence intervals, and meta‐analysis usually provides a more informative analysis of empirical results than does statistical significance testing, which has long been the conventional choice in psychology. The sixth edition of the American Psychological Association Publication Manual now recommends that psychologists should, wherever possible, use estimation and base their interpretation of research results on point and interval estimates. We outline the Manual's recommendations and suggest how they can be put into practice: adopt an estimation framework, starting with the formulation of research aims as ‘How much?’ or ‘To what extent?’ questions. Calculate from your data effect size estimates and confidence intervals to answer those questions, then interpret. Wherever appropriate, use meta‐analysis to integrate evidence over studies. The Manual's recommendations can assist psychologists improve they way they do their statistics and help build a more quantitative and cumulative discipline.     

On the sampling interpretation of confidence intervals and hypothesis tests in the context of conditional maximum likelihood estimation

In the context ofconditional maximum likelihood (CML) estimation, confidence intervals can be interpreted in three different ways, depending on the sampling distribution under which these confidence intervals contain the true parameter value with a certain probability. These sampling distributions are (a) the distribution of the data given theincidental parameters, (b) the marginal distribution of the data (i.e., with the incidental parameters integrated out), and (c) the conditional distribution of the data given the sufficient statistics for the incidental parameters. Results on the asymptotic distribution of CML estimates under sampling scheme (c) can be used to construct asymptotic confidence intervals using only the CML estimates. This is not possible for the results on the asymptotic distribution under sampling schemes (a) and (b). However, it is shown that theconditional asymptotic confidence intervals are also valid under the other two sampling schemes. I am indebted to Theo Eggen, Norman Verhelst and one of Psychometrika's reviewers for their helpful comments.     

类别变量的中介效应分析

在心理学和其他社科研究领域,研究者能熟练地进行连续变量的中介效应分析,但面对自变量、中介变量或(和)因变量为类别变量的中介效应分析,研究者往往束手无策。在阐述类别自变量中介分析方法的基础上,我们建议使用整体和相对中介相结合的类别自变量中介分析方法,并给出了分析流程。以二分因变量为例,讨论了中介变量或(和)因变量为类别变量的中介分析方法的发展过程(即尺度统一的过程),建议通过检验Za×Zb的显著性来判断中介效应的显著性。用二个实际例子演示如何进行类别变量的中介效应分析。最后展望了类别变量的中介效应分析研究的拓展方向。     

An Empirical Investigation of Partial Effect Sizes in Meta-Analysis of Correlational Data

ABSTRACT. The partial correlation and the semi-partial correlation can be seen as measures of partial effect sizes for the correlational family. Thus, both indices have been used in the meta-analysis literature to represent the relationship between an outcome and a predictor of interest, controlling for the effect of other variables in the model. This article evaluates the accuracy of synthesizing these two indices under different situations. Both partial correlation and the semi-partial correlation appear to behave as expected with respect to bias and root mean squared error (RMSE). However, the partial correlation seems to outperform the semi-partial correlation regarding Type I error of the homogeneity test (Q statistic). Although further investigation is needed to fully understand the impact of meta-analyzing partial effect sizes, the current study demonstrates the accuracy of both indices.     

参数和非参数Bootstrap方法的简单中介效应分析比较

采用数据模拟技术比较了(偏差校正和未校正的)参数和非参数Bootstrap方法在简单中介效应分析中的表现。结果表明,1)偏差校正的Bootstrap法的总体表现优于未校正的Bootstrap方法,但在某些条件下会高估第Ⅰ类错误率,导致在 时的置信区间偏差较大。2)参数Bootstrap方法优于非参数Bootstrap方法,偏差校正的参数百分位残差Bootstrap法的综合表现最优,且具有适用范围广,对原始样本依赖性小的优点,最具实用性。