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第258行： − − − − The researchers next looked for the presence of a mediated moderation effect. Regression analyses revealed that the type of prime (morality vs. might) mediated the moderating relationship of participants’ social value orientation on PDG behaviour. Prosocial participants who experienced the morality prime expected their partner to cooperate with them, so they chose to cooperate themselves. Prosocial participants who experienced the might prime expected their partner to compete with them, which made them more likely to compete with their partner and cooperate with the authorities. In contrast, participants with a pro-self social value orientation always acted competitively. − − 接下来，研究人员寻找中和效应的存在。回归分析显示，初始类型(道德与可能)中介了参与者的社会价值取向对 PDG 行为的调节关系。那些经历过道德优势的亲社会参与者希望他们的伴侣与他们合作，所以他们选择了自己合作。亲社会的参与者经历了可能的主要期望他们的伴侣与他们竞争，这使他们更有可能与他们的伴侣竞争并与当局合作。相比之下，具有亲自我社会价值取向的参与者总是在竞争中行动。 − − − − − − Muller, Judd, and Yzerbyt (2005) except in the case of no omitted variables. − − Muller，Judd，and Yzerbyt (2005) ，除了没有省略变量的情况。 − − − To illustrate, assume that the error terms of M and Y − − 为了说明，假设 m 和 y 的错误项 − − − are correlated. Under such conditions, the − − 是相关的。在这种情况下, − − − structural coefficient B and A (between M and Y and between Y and X) − − 结构系数 b 和 a (介于 m 和 y 之间，y 和 x 之间) − − − can no longer be estimated by regressing Y on X and M. − − 不能再通过 x 和 m 的 y 回归来估计。 − − − − In fact, the regression slopes may both be nonzero − − 事实上，回归斜率可能都是非零的 − − even when C is zero. This has two − − 即使 c 是零。这里有两个 − − − consequences. First, new strategies must be devised for − − 后果。首先，必须制定新的战略 − − − estimating the structural coefficients A,B and C. Second, − − 估计结构系数 a，b 和 c, − − − − the basic definitions of direct and indirect effects − − 直接和间接影响的基本定义
→Full versus partial mediation
==Full versus partial mediation==
==Full versus partial mediation==
A mediator variable can either account for all or some of the observed relationship between two variables.
A mediator variable can either account for all or some of the observed relationship between two variables.
'''Full mediation'''
'''Full mediation'''
Maximum evidence for mediation, also called full mediation, would occur if inclusion of the mediation variable drops the relationship between the independent variable and dependent variable (see pathway ''c'' in diagram above) to zero.
Maximum evidence for mediation, also called full mediation, would occur if inclusion of the mediation variable drops the relationship between the independent variable and dependent variable (see pathway ''c'' in diagram above) to zero.
[[File:Full Mediation Model.png|thumb|Full Mediation Model]]
[[File:Full Mediation Model.png|thumb|Full Mediation Model]]
'''Partial mediation'''
'''Partial mediation'''
[[File:Mediation.jpg|thumb|The Partial Mediation Model Includes a Direct Effect]]
[[File:Mediation.jpg|thumb|The Partial Mediation Model Includes a Direct Effect]]
Partial mediation maintains that the mediating variable accounts for some, but not all, of the relationship between the independent variable and dependent variable. Partial mediation implies that there is not only a significant relationship between the mediator and the dependent variable, but also some direct relationship between the independent and dependent variable.
Partial mediation maintains that the mediating variable accounts for some, but not all, of the relationship between the independent variable and dependent variable. Partial mediation implies that there is not only a significant relationship between the mediator and the dependent variable, but also some direct relationship between the independent and dependent variable.
In order for either full or partial mediation to be established, the reduction in variance explained by the independent variable must be significant as determined by one of several tests, such as the [[Sobel test]].<ref name="Sobel, M. E. 1982 pp. 290">{{cite journal | last1 = Sobel | first1 = M. E. | year = 1982 | title = Asymptotic confidence intervals for indirect effects in structural equation models | journal = Sociological Methodology | volume = 13 | pages = 290–312 | doi = 10.2307/270723 | jstor = 270723 }}</ref> The effect of an independent variable on the dependent variable can become nonsignificant when the mediator is introduced simply because a trivial amount of variance is explained (i.e., not true mediation). Thus, it is imperative to show a significant reduction in variance explained by the independent variable before asserting either full or partial mediation.
In order for either full or partial mediation to be established, the reduction in variance explained by the independent variable must be significant as determined by one of several tests, such as the [[Sobel test]].<ref name="Sobel, M. E. 1982 pp. 290">{{cite journal | last1 = Sobel | first1 = M. E. | year = 1982 | title = Asymptotic confidence intervals for indirect effects in structural equation models | journal = Sociological Methodology | volume = 13 | pages = 290–312 | doi = 10.2307/270723 | jstor = 270723 }}</ref> The effect of an independent variable on the dependent variable can become nonsignificant when the mediator is introduced simply because a trivial amount of variance is explained (i.e., not true mediation). Thus, it is imperative to show a significant reduction in variance explained by the independent variable before asserting either full or partial mediation.
It is possible to have statistically significant indirect effects in the absence of a total effect.<ref name=Hayes>{{cite journal | last1 = Hayes | first1 = A. F. | year = 2009 | title = Beyond Baron and Kenny: Statistical mediation analysis in the new millennium | journal = Communication Monographs | volume = 76 | issue = 4| pages = 408–420 | doi = 10.1080/03637750903310360 }}</ref> This can be explained by the presence of several mediating paths that cancel each other out, and become noticeable when one of the cancelling mediators is controlled for. This implies that the terms 'partial' and 'full' mediation should always be interpreted relative to the set of variables that are present in the model.
It is possible to have statistically significant indirect effects in the absence of a total effect.<ref name=Hayes>{{cite journal | last1 = Hayes | first1 = A. F. | year = 2009 | title = Beyond Baron and Kenny: Statistical mediation analysis in the new millennium | journal = Communication Monographs | volume = 76 | issue = 4| pages = 408–420 | doi = 10.1080/03637750903310360 }}</ref> This can be explained by the presence of several mediating paths that cancel each other out, and become noticeable when one of the cancelling mediators is controlled for. This implies that the terms 'partial' and 'full' mediation should always be interpreted relative to the set of variables that are present in the model.
In all cases, the operation of "fixing a variable" must be distinguished from that of "controlling for a variable," which has been inappropriately used in the literature.<ref name="Robins"/><ref name="Kaufman"/> The former stands for physically fixing, while the latter stands for conditioning on, adjusting for, or adding to the regression model. The two notions coincide only when all error terms (not shown in the diagram) are statistically uncorrelated. When errors are correlated, adjustments must be made to neutralize those correlations before embarking on mediation analysis (see [[Bayesian Networks]]).
In all cases, the operation of "fixing a variable" must be distinguished from that of "controlling for a variable," which has been inappropriately used in the literature.<ref name="Robins"/><ref name="Kaufman"/> The former stands for physically fixing, while the latter stands for conditioning on, adjusting for, or adding to the regression model. The two notions coincide only when all error terms (not shown in the diagram) are statistically uncorrelated. When errors are correlated, adjustments must be made to neutralize those correlations before embarking on mediation analysis (see [[Bayesian Networks]]).
==Sobel's test==
==Sobel's test==