期刊文献+

潜变量交互效应建模:告别均值结构 被引量:39

Structural Equation Modeling of Latent Interactions Without Using the Mean Structure
下载PDF
导出
摘要 潜变量交互效应建模研究近年来有了长足的发展,但模型中被认为不可缺少的均值结构往往让实际应用工作者却步。本文首先分析了潜变量交互效应模型中均值结构产生的根源;然后讨论了指标变换与均值结构的关系;接着提出了一个均值为零的潜变量交互结构,所建立的模型不需要均值结构,却不会改变主效应和交互效应等参数;最后用模拟例子对无均值结构和有均值结构的两种模型的参数估计进行了比较,结果符合理论预期,困扰人们多年的均值结构问题从此可以终结。 Estimating the interaction between variables is a particularly important theoretical,substantive,and empirical issue in psychology,as well as in many other social and behavioral sciences. Interactions between (multiple indicator) latent variables are rarely used because of the implementation complexity especially when the mean structure is known as a necessary part of any latent interaction model. There are four types of parameters related to the mean structure,which are namely,the intercepts of the y-measurement equations,the intercepts of the x-measurement equations,the intercepts of the structural equations,and the means of the exogenous latent variables. In this article,it is shown that the mean structure in the latent interaction model comes from the non-zero mean of the latent interaction construct ξ1ξ2 (the product of the two first terms). Thus,the means of the exogenous latent variables and the intercepts of the y-measurement equations are always necessary even if all indicators are mean-centered when the traditional latent interaction construct is used. By building a new latent interaction construct so that its mean is zero,we obtain a structural equation model of latent interaction in which the mean structure is no longer necessary and the parameters of main and interaction effects are unchanged. A simulation study comparing the estimated parameters and goodness of fit indices of the two latent interaction models with and without the mean structure by using the matched-pair product indicators and the unconstrained approach is demonstrated. The simulation results are consistent with the theoretical predictions. This research unambiguously shows that the mean structure problem which has unduly deterred the applied researchers for a long time can now be solved.
出处 《心理学报》 CSSCI CSCD 北大核心 2009年第12期1252-1259,共8页 Acta Psychologica Sinica
基金 国家自然科学基金项目(30870784)资助
关键词 潜变量 交互效应 结构方程 指标 均值结构 latent variable interaction effect structural equation model indicator mean structure
  • 相关文献

参考文献15

  • 1Aiken, L. S., & West, S. G. (1991). Multiple regression. Testing and interpreting interactions. Newbury Park, CA: SAGE Publications.
  • 2Algina, J., & Moulder, B. C. (2001). A note on estimating the Joreskog-Yang model for latent variable interaction using LISREL 8.3. Structural Equation Modeling, 8, 40-52.
  • 3Bollen, K. A. (1989), Structural equations with latent variables. New York: Wiley.
  • 4Coenders, G., Batista-Foguet, J. M,, & Saris, W. E. (2008). Simple, efficient and distribution-free approach to interaction effects in complex structural equation models. Quality & Quantity, 42(3), 369-396.
  • 5Jaccard, J., & Wan, C. K. (1995). Measurement analysis of interaction effects between error in the continuous predictors using multiple regression: Multiple indicator and structural equation approaches. Psychological Bulletin, 117(2), 348-357.
  • 6Joreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny -Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advances in structural equation modeling techniques (pp. 57-88). Hillsdale, NJ: LEA.
  • 7Kenny, D., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210.
  • 8Lin, G. C,, Wen, Z., Marsh, H. W., & Lin, H. S. (in press). Structural equation models of latent interactions: clarification of orthogonalizing and double-mean-centering strategies. Structural Equation Modeling.
  • 9Little, T.D., Bovaird, J.A., & Widaman,K. F. (2006). On the merits of orthogonalizing powered and product term: Implications for modeling interactions among latent variables. Structural Equation Modeling, 13(4), 497-519.
  • 10Marsh, H. W., Wen, Z., & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9(3), 275-300.

二级参考文献51

  • 1温忠麟,张雷,侯杰泰,刘红云.中介效应检验程序及其应用[J].心理学报,2004,36(5):614-620. 被引量:7815
  • 2温忠麟,侯杰泰,张雷.调节效应与中介效应的比较和应用[J].心理学报,2005,37(2):268-274. 被引量:3190
  • 3温忠麟,张雷,侯杰泰.有中介的调节变量和有调节的中介变量[J].心理学报,2006,38(3):448-452. 被引量:752
  • 4[1]Tucker L R, Lewis C. The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 1973, 38: 1~10
  • 5[2]Steiger J H, Lind J M. Statistically-based tests for the number of common factors. Paper presented at the Psychometrika Society Meeting, IowaCity, May, 1980
  • 6[3]Bentler P M, Bonett D G. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 1980, 88: 588~ 606
  • 7[4]Bentler P M. Comparative fit indices in structural models. Psychological Bulletin,1990, 107: 238~ 246
  • 8[5]McDonald R P, Marsh H W. Choosing a multivariate model: Noncentrality and goodness-of-fit. Psychological Bulletin, 1990,107: 247~ 255
  • 9[6]Marsh H W, Balla J R, Hau K T. An evaluation of incremental fit indices: A clarification of mathematical and empirical processes. In: Marcoulides G A, Schumacker R E eds. Advanced structural equation modeling techniques. Hillsdale, NJ: Erlbaum, 1996. 315~ 353
  • 10[7]Browne M W, Cudeck R. Alternative ways of assessing model fit. In: Bollen K A, Long J S eds. Testing Structural Equation Models. Newbury Park, CA: Sage, 1993. 136~ 162

共引文献1409

同被引文献816

引证文献39

二级引证文献1047

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部