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自从上次整理了一篇《中介效应和调节效应分析方法论文献(中文)》后,收到了一些反馈。一位潜心研究中介效应和调节效应分析方法论的国内学者给我留言说我少列举了两篇,现在补上,分别是:第一,《中介效应的检验方法和效果量测验:回顾与展望》(载于《心理发展与教育》2012年第28卷第1期);第二,《基于不对称区间估计的有调节的中介效应模型检验》(载于《心理科学进展》,2014年第22卷第10期)。相较于国外而言,国内关于中介效应和调节效应分析的研究可能相对有些晚,国外在该领域的发展很久并且有很多经典的文献。我初步收集了有关中介效应和调节效应分析的英文方法论文献,共计51篇。我按照国内期刊参考文献的列法顺序依次列举出来,最早的和最近的文献横跨了几十年。
其次,我觉得我们在做中介效应和调节效应分析时一定要明确我们为什么要这样做,明确之后才应该是怎样做,怎样做的时候要分析做的是否科学。自从温忠麟教授团队于2004年开始大量发表中介效应和调节效应分析的文献后,很多来自各个领域和学科的学者都在引用这些方法论文献然后开始做自己的研究。这些研究在国内基本上所有自然科学和社会科学杂志上都能看见,但是这些研究中也存在一些用法失当甚至是错误的个例。温忠麟教授曾说过,在做完中介效应和调节效应分析后要向读者列出表示中介效应的量,具体可参见相关文献。究竟是部分中介效应呢还是完全中介效应呢?经常能够看到一些学者直接就是检验了三个方程的系数是否显著然后列了三线表就结束了,其实我个人觉得这不全面。在这里,我推荐大家看一篇文章《自尊在人格特质、社会支持和主观幸福感间的中介效应》(载于《应用心理学》,2009年第15期第2卷)。之所以推荐这篇文献,是因为作者在分析完中介效应之后很详细地列出了中介效应量的大小、计算方式等等,业内人士可以参照一下。还有一点需要指出的是,我们在分析中介效应和调节效应时要知道这需要一些前提条件。这些条件由国外学者提出,国内学者完善了。具体可以看Kashdam和Breen的《Materialismand diminished well-being:Experimentialavoidance as a mediating mechanism》(载于Journal of Social and Clinical Psychology,2007年第26卷第5期)和西南大学心理学部郑涌教授指导的硕士学位论文《控制感在物质主义和幸福感关系中的中介效应研究》(作者是王凌飞,2012年)。在这两篇文章中,学者们一致认为中介效应和调节效应分析的前提条件分别是:(1)预测变量必须与因变量相关;(2)预测变量要与潜在的中介变量相关;(3)潜在的中介变量要与因变量相关;(4)当潜在的中介变量被控制后,预测变量和因变量之间的相关消失或显著减弱。这四个前提条件很详细,业内人士在做中介效应和调节效应分析时可以自我对照进行检查。
另外,在书籍方面,国外有关中介效应和调节效应分析的书籍很多,我列举了两本比较经典的。国内有关中介效应和调节效应分析专门的书籍目前好像只有温忠麟,刘红云和侯杰泰于2012年出版的那本《调节效应和中介效应分析(Analyses of Moderatingand Mediating Effects)》。这本书是社会科学研究方法丛书中的一本,这套丛书特别好,每本都有一个侧重点。当然,想要完全掌握《调节效应和中介效应分析》这本书的内涵,必须还要先阅读由侯杰泰,温忠麟和成子娟于2004年著的那本《结构方程模型及其应用(Structural Equation Modeland Its Applications)》一书,因为两本书里面都涉及到了用LISREL软件编写程序语句的内容。
最后,我列举的文献肯定是不全的,希望业内人士能够批评指正。
1.Baron R. M.,& Kenny D. A. (1986). The moderator-mediator variable distinction in social psychological research:Conceptual,strategic,and statistical considerations. Journal of Personality and Social Psychology,51,1173–1182.
The Moderator-Mediator Variable Distinction in Social Psychological Research:Co.pdf
2.Bauer D. J.,Preacher K. J.,& Gil K. M. (2006).Conceptualizing and testing random indirect effectsand moderated mediation in multilevel models:New procedures and recommendations. Psychological Methods,11,142–163.
Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in M.pdf
3.Edwards J. R.,& Lambert L. S. (2007). Methods for integrating moderation andmediation:A general analytical framework using moderated path analysis. Psychological Methods,12,1–22.
Methods for Integrating Moderation and Mediation: A General Analytical Framewor.pdf
4.Fairchild A. J.,MacKinnon D. P.,Taborga, M. P.,& Taylor A. B. (2009). R2 effect-size measures for mediation analysis. Behavior Research Methods,41,486–498.
R2 effect-size measures for mediation analysis.pdf
5.Fairchild A. J.,&McQuillin S. D. (2010). Evaluating mediation and moderation effects in school psychology:A presentation of methods and review of current practice.Journal of School Psychology,48(1),53–84.
Evaluating mediation and moderation effects in school psychology:A presentation.pdf
6.Feinberg F.M.(2012).Mediation analysis and categorical variables:some further frontiers.Journal of Consumer Psychology,22,595-598.
Mediation analysis and categorical variables:Some further frontiers.pdf
7.Fiedler K.,Schott M.,& Meiser T.(2011).What mediation analysis can(not) do.Journal of Experimental Social Psychology,47,1231-1236.
What mediation analysis can (not) do.pdf
8.Fritz M. S.,& MacKinnon D. P. (2007).Required sample size to detect the mediated effect. Psychological Science,18,233–239.
Required sample size to detect the mediated effect.pdf
9.Fritz M. S.,Taylor A. B.,& MacKinnon D. P. (2012).Explanation of two anomalous results instatistical mediation analysis. MultivariateBehavioral Research,47,61–87.
Explanation of two anomalous results in statistical.pdf
10.Gelfand L.A.,Mensinger J.L.,& Tenhave T.(2009).Meidiation analysis:A restrospective snapshot of practice andmore recent directions.The Journal ofGeneral Psychology,136(2),153-176.
Meidiation analysis:A restrospective snapshot of practice and more recent directions.pdf
11.Hayes A. F. (2009). Beyond Baron andKenny:Statistical mediation analysis in the new millennium. CommunicationMonographs,76,408–420.
Beyond Baron and Kenny:Statistical Mediation Analysis in the New Millennium.pdf
12.Iacobucci D. (2012). Mediation analysis and categorical variables:The final frontier. Journal ofConsumer Psychology,22,582–594.
Mediation analysis and categorical variables:The ?nal frontier.pdf
13.Imai K.,Keele L.,& Tingley D. (2010). A general approach to causal mediation analysis. Psychological Methods,15,309–334.
A general approach to causal mediation analysis.pdf
14.Imai K.,Keele L.,& Yamamoto T.(2010).Identification,inference and sensitivity analysis for casual mediation effects.Statistical Science,25(1),51-71.
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects.pdf
15.James L. R.,&Brett J. M. (1984). Mediators,moderators,and tests for mediation. Journal of Applied Psychology,69,307–321.
Mediators, moderators, and tests for mediation.pdf
16.Kenny D. A.,Korchmaros J. D.,& Bolger N. (2003).Lower level mediation in multilevel models.Psychological Methods,8,115–128.
Lower level mediation in multilevel models.pdf
17.Krull J. L.,& MacKinnon D. P. (2001). Multilevel modeling of individual and group level mediated effects.MultivariateBehavioral Research,36(2),249–277.
Multilevel modeling of individual and group level mediated effects..pdf
18.Li Y.,Bienias J.L.,& Bennett D.A.(2007).Confounding in the estimation of mediation effects.Computational Statistics & Data Analysis,51,3173-3186.
Confounding in the estimation of mediation effects.pdf
19.Lockwood C. M.,& MacKinnon D. P. (1998).Bootstrapping the standard error of the mediated effect.Proceedingsof the 23rd annual meeting of SAS UsersGroup International (pp. 997−1002). Cary,NC:SAS Institute.
Bootstrapping the standard error of the mediated effect.pdf
20.MacKinnon D. P.,Warsi G.,& Dwyer J. H. (1995). A simulation study of mediated effect measures.Multivariate Behavioral Research,30,41–62.
A simulation study of mediated effect measures.pdf
21.MacKinnon D. P.,Krull J. L.,& Lockwood C. M. (2000).Equivalence of the mediation,confounding,and suppression effect. PreventionScience,1,173–181.
Equivalence of the mediation,confounding,and suppression effect.pdf
22.MacKinnon D. P.,Lockwood C. M.,Hoffman J. M.,West S. G.,& Sheets V. (2002). A comparison of methods to test mediation andother intervening variable effects.PsychologicalMethods,7,83–104.
A comparison of methods to test mediation and other intervening variable effects.pdf
23.MacKinnon D. P.,Lockwood C. M.,& Williams J. (2004). Confidence limitsfor the indirect effect:Distribution of the product and resampling methods. MultivariateBehavioral Research,39,99–128.
Confidence limits for the indirect effect:Distribution of the product and resam.pdf
24.MacKinnon D. P.,Lockwood C. M.,Brown C. H.,Wang W.,& Hoffman J. M. (2007). The intermediate endpoint effect in logisticand probit regression. ClinicalTrials,4,499–513.
The intermediate endpoint effect in logistic and probit regression.pdf
25.Mackinnon D. P.,Fairchild A. J.,& Fritz M. S. (2007).Mediation analysis. Annual Review ofPsychology,58,593–614.
26.MacKinnon D. P.,Fritz M, S.,Williams J.,& Lockwood C.M. (2007). Distribution of the product confidence limits forthe indirect effect:Program PRODCLIN. BehaviorResearch Methods,39,384–389.
Distribution of the product confidence limits for the indirect effect:Program P.pdf
27.MacKinnon D. P.,& Fairchild A. J. (2009). Current directionsin mediation analysis. CurrentDirections inPsychological Science,18,16-20.
Current directions in mediation analysis.pdf
28.MacKinnonD. P.,Coxe S.,& Baraldi A. N. (2012). Guidelines for the investigation of mediating variables in business research. Journal of Business and Psychology,27(1),1–14.
Guidelines for the investigation of mediating variables in business research.pdf
29.Mathieu J. E.,& Taylor S. R. (2006). Clarifying conditions and decision points for mediational type inferences in Organizational Behavior. Journal of OrganizationalBehavior, 27,1031–1056.
Clarifying conditions and decision points for mediational type inferences in Org.pdf
30.Muller D.,Judd C. M.,& Yzerbyt V. Y. (2005). When moderation is mediated and mediation is moderated.Journal of Personality and Social Psychology,89(6),852–863.
When moderation is meditated and when mediation is moderated.pdf
31.Preacher K. J.,& Hayes A. F. (2004). SPSS and SAS proceduresfor estimating indirect effects in simple mediation models. Behavior Research Methods,Instruments, & Computers,36,717–731.
SPSS and SAS procedures for estimating indirect effects in simple mediation models.pdf
32.Preacher K.J,& Hayes A.F.(2007).Contemporary approaches to assessing mediation in communication research.Advanced Data Analysis Methods for Communication Research,10,1-42.
Contemporary approaches to assessing mediation in communication research.pdf
33.Preacher K. J.,Rucker D. D.,& Hayes A. F. (2007).Addressing moderated mediation hypotheses: Theory,methods,and prescriptions. Multivariate Behavioral Research,42,185–227.
Addressing moderated mediation hypotheses:Theory,methods,and prescriptions.pdf
34.Preacher K. J.,& Hayes A. F. (2008a). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediatormodels. Behavior ResearchMethods,40,879–891.
Asymptotic and resampling strategies for assessing and comparing indirect effect.pdf
35.Preacher K. J.,& Hayes A. F.(2008b). Contemporary approaches to assessing mediation in communication research.In A. F. Hayes M. D. Slater and L. B. Snyder(Eds),The Sagesourcebook of advanced data analysismethods for communication research (pp. 13–54).Thousand Oaks,CA:Sage Publications.
Contemporary approaches to assessing mediation in communication research.pdf
36.Preacher K. J.,Zyphur M. J.,& Zhang Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods,15,209–233.
A general multilevel SEM framework for assessing multilevel mediation.pdf
37.Preacher K. J.,&Kelley K. (2011).Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods,16,93–115.
Effect size measures for mediation models:Quantitative strategies for communica.pdf
38.Preacher K. J.,Zhang Z.,& Zyphur M. J. (2011).Alternative methods for assessing mediation inmultilevel data:The advantages of multilevel SEM. StructuralEquation Modeling,18,161–182.
Alternative methods for assessing mediation in multilevel data:The advantages o.pdf
39.Preacher K.J.,& Selig J.P.(2012).Advantages of monte carloconfidence intervals for indirect effects. Communication Methods and Measures,6,77-98.
Advantages of Monte Carlo Con?dence Intervals for Indirect Effects.pdf
40.Rosapa P.J.,& Stone-Romero E.F.(2008).Problems with detecting assumed mediation using the hierarchical multiple regressionstragety.Human Resource Management Review,18,194-310.
Problems with detecting assumed mediation using the hierarchical multiple.pdf
41.Rucker D. D.,Preacher K. J.,Tormala Z. L.,& Petty R. E.(2011). Mediation analysis in social psychology:Current practices and new recommendations. Social andPersonality Psychology Compass,5,359–371.
Mediation analysis in social psychology:Current practices and new recommendations.pdf
42.Shrout P. E.,& Bolger N. (2002). Mediation in experimental and nonexperimental studies:New procedures and recommendations. Psychological Methods,7,422–445.
Mediation in experimental and nonexperimental studies:New.pdf
43.Sobel M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. InS.Leinhardt (Ed.),Sociological methodology (pp. 290–312).Washington,DC:American Sociological Association.
Asymptotic confidence interval for indirect effects in structural equation models.pdf
44.Spencer S. J.,Zanna M. P.,& Fong G. T. (2005).Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychologicalprocesses. Journal ofPersonality andSocial Psychology,89,845–851.
Establishing a causal chain:Why experiments are often more effective than media.pdf
45.Tofighi D.,& MacKinnon D. P. (2011). Rmediation:An R package for mediation analysis confidence intervals.Behavior Research Methods,43,692–700.
Rmediation:An R package for mediation analysis confidence intervals.pdf
46.Valeri L.,&VanderWeele T. J. (2013). Mediation analysis allowing for exposure-mediatori nteractions and causal interpretation:Theoretical assumptions and implementation withSAS and SPSS macros. PsychologicalMethods,18,137–150.
Mediation analysis allowing for exposure-mediator interactions and causal interp.pdf
47.Williams J.,&MacKinnon D. P. (2008). Resampling and distribution of the product methods for testingindirect effects in complex models. Structural Equation Modeling,15,23–51.
Resampling and distribution of the product methods for testing indirect effects .pdf
48.Wood R. E.,Goodman J. S.,Beckmann N.,& Cook A.(2008). Mediation Testing in Management Research:A Review and Proposals. OrganizationalResearch Methods,11(2),270–295.
Mediation Testing in Management Research:A Review and Proposals.pdf
49.Yuan Y.,& MacKinnon D. P. (2009). Bayesian mediation analysis. Psychological Methods,14,301–322.
Bayesian mediation Analysis.pdf
50.Zhang Z.,Zyphur M. J.,& Preacher K. J. (2009). Testing multilevel mediation usinghierarchical linear models:Problems and solutions. OrganizationalResearch Methods,12,695–719.
Testing multilevel mediation using hierarchical linear models:Problems and solutions.pdf
51.Zhao X.,LynchJr.J. G.,& Chen Q. (2010). Reconsidering Baron and Kenny:Myths andtruths about mediation analysis. Journalof Consumer Research,37,197–206.
Reconsidering Baron and Kenny:Myths and truths about mediation analysis.pdf
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