中介效应可以分析自变量对因变量的影响过程和作用机制, 已成为分析多个变量之间关系的一种重要统计方法。最近20年, 中介效应成了研究方法的一个热点。从中介效应的检验方法、效应量、类别变量的中介效应检验、纵向数据的中介效应检验和模型拓展(包括多重中介、多层中介、有调节的中介和有中介的调节模型)五个方面系统总结了国内中介效应的方法学研究的发展历程。最后对中介效应的国外方法学研究进展和中介效应的未来研究方向做了讨论和拓展。

<p id="p00005">Being able to analyze the influence mechanism of independent variables on dependent variables, the analysis of mediation effect has become an important statistical method in multivariate research. Since the first publication of Chinese paper on the mediation effect and its analytical methods in 2004, the mediation effect has become one focus of methodological research in Chinese Mainland, which is systematically reviewed in this paper.</p> <p id="p00010">Firstly, the simple mediation model is reviewed with concept identification: how to distinguish between mediation and suppression effects, partial and complete mediation effects, and mediation effect and moderation effect. Then, methodological research on mediation effects in China&#x02019;s Mainland is divided into five aspects: testing method for mediation effects, mediation effect size measure, mediation effect involving categorical variables or longitudinal data, and extended mediation model. They are summarized as follows.</p> <p id="p00015">To test <i>ab</i>&#x02260;0,the easiest way is to test <i>a</i>&#x02260;0 and <i>b</i>&#x02260;0. These sequential tests are actually not the same as the joint significance tests because the Type-I error rates are rather different. If the test result is <i>a</i>&#x02260;0 and <i>b</i>&#x02260;0, then <i>ab</i>&#x02260;0 can be inferred with the Type-I error rate less than the significance level 0.05 (the preset significance level), while the Type-I error rate of the joint significance tests is 0.0975. However, if at least one of <i>a</i>&#x02260;0 and<i> b</i>&#x02260;0 does not hold, the sequential tests should not be used, since its statistical power is less than other alternative test methods discussed in the paper. Anyway, Bootstrap methods are preferred because they provide interval estimation of the mediation effect with a higher power. Furthermore, if appropriate prior information is available, the Bayesian method is also recommended.</p> <p id="p00020">It is believed that <i>&#x003ba;</i>², <i>R</i>²-type and so on are not suitable as mediation effect size measures because of no monotonicity. Although <inline-formula>$\upsilon ={{(ab)}^{2}}$</inline-formula> is monotonic, it is not as simple and clear as the mediation effect (<i>ab</i>) itself. It is recommended that when the signs of <i>ab</i> and <i>c</i> are consistent, the standardized estimation of <i>ab</i> and <i>ab/c</i> should be reported.</p> <p id="p00025">Mediation analysis with multi-categorical independent variables and with a two-condition within-participant design are discussed when categorical variables are concerned in mediation effect models.</p> <p id="p00030">There are two types of model development in mediation analysis with longitudinal data. One is continuous time model and multilevel time-varying coefficient model that could be used to test time-varying effect of mediation effect. The other is random-effects cross-lagged panel model and multilevel autoregressive mediation model that could be adopted to examine individuals-varying effect of mediation effect. In addition, latent growth mediation model or multilevel mediation model in mediation effect analysis could be adopted only when the involved causal relationship is instant. Otherwise, cross-lagged panel model, continuous time model, or multilevel autoregressive mediation model should be adopted.</p> <p id="p00035">The extensions of the mediation model include multiple mediation model, multilevel mediation model, single-level and multilevel moderated mediation model as well as mediated moderation model. These extended models can be used for both the analysis of observed variables and latent variables.</p> <p id="p00040">Finally, the recent development of foreign methodological research on mediation effects is discussed, including potential outcome mediation analysis, confounder control in mediation analysis, robust mediation analysis, and power analysis of mediation effects. Moreover, integration of new statistical techniques has become a new feature of methodological research of mediation effects, for example, exploratory mediation analysis via regularization, bi-factor mediation analysis, latent class mediation analysis, and network mediation analysis.</p>