潛在調節徑路模型的模型設定
Model Specification of Latent Moderated Path Model
陳淑萍;鄭中平
Shu-Ping Chen;Chung-Ping Cheng
Shu-Ping Chen;Chung-Ping Cheng
所屬期刊: |
第7卷第4期 「測驗與評量」 主編:國立成功大學教育研究所特聘教授 陸偉明 |
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系統編號: | vol027_01 |
主題: | 測驗與評量 |
出版年份: | 2011 |
作者: | 陳淑萍;鄭中平 |
作者(英文): | Shu-Ping Chen;Chung-Ping Cheng |
論文名稱: | 潛在調節徑路模型的模型設定 |
論文名稱(英文): | Model Specification of Latent Moderated Path Model |
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校院名稱: | |
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論文頁數: | 24 |
中文關鍵字: | 潛在交互作用;調節徑路分析;LISREL;調節中介;中介調節 |
英文關鍵字: | latent interaction;moderated path analysis;LISREL;mediated moderation;moderated mediation |
服務單位: | 國立政治大學心理學系博士班研究生;國立成功大學心理學系副教授 |
稿件字數: | 11648 |
作者專長: | 心理計量、結構方程模型、測量理論 |
投稿日期: | 2011/6/4 |
論文下載: | |
摘要(中文): | 中介與調節效果是社會與行為科學中,用以設想現象的分析架構,Edwards與 Lambert(2007)提出了調節路徑分析的一般性架構,同時考量中介與調節效果,而提出八種可能的模型。由於該架構基於迴歸分析,在測量誤差較大時可能影響係數估計,本研究建議將此架構推展至潛在變項的版本,而能利用結構方程模型處理測量誤差。八種模型中,基礎中介模型設定相當簡易,本研究則推導其中五種模型之模型設定與所需限制式,並提供包括基礎中介模型之六種模型之LISREL程式供使用,LISREL程式正確性則以模擬資料加以確認。 |
摘要(英文): | Mediation and moderation are two popular analytic frameworks for social and behavioral sciences. Edwards and Lambert (2007) proposed the moderated path analysis framework, which involves eight models, to incorporate mediation and moderation into a comprehensive framework. However, when there are considerable measurement errors, the above approach may lead to biased estimates since it is based on regression analysis. This study aims to extend the moderated path analysis framework into its latent variable version which is reformulated with structural equation models. By doing so, measurement errors can be taken into account. Besides the basic mediated model, model specification and associated constraints of five models are derived from the study. LISREL codes for the six models are also provided and their accuracy is demonstrated by simulated datasets. |
參考文獻: | Algina, 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. Arminger, G., & Muthen, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs-Sampler and the Metropolis-Hastings algorithm. Psychometrika, 63, 271-300. Bandalos, D. L, Finney, S. J. (2001). Item parceling issues in structural equation modeling. In G. A. Marcoulides & E. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 269-296). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. 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. Bauer, D. J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12, 513-535. Bohrusterdt, G..W. & Goldberger, A. S. (1969). On the exact covariance of products of random variables. Journal of the American Statistical Association, 64, 1439-1442. Edwards, J. R. (2008). Seven deadly myths of testing moderation in organizational research. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Received doctrine, verity, and fable in the organizational and social sciences (pp. 145-166). New York, NY: Routledge. Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22. Fredrickson, B. L., Tugade, M. M., Waugh, C. E., & Larkin, G. R. (2003). What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. Journal of Personality and Social Psychology, 84, 365-376. Holmbeck, G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 65, 599-610. Jaccard, J., & Wan, C. K. (1995). Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches. Psychological Bulletin, 117, 348-357. James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology, 69, 307-321. Joreskog, K. G., & Sorbom, D. (1996). LISREL 8: User’s reference guide. Chicago, IL: Scientific Software International. Joreskog, K. G., & Yang, F. (1996). Nonlinear structural equation Models: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 57-88). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of. latent variables. Psychological Bulletin, 96, 201-210. Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457-474. Klein, A. G., & Muthen, B. O. (2007). Quasi maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects. Multivariate Behavioral Research, 42, 647-674. Lee, S. Y., & Zhu, H. T. (2000). Statistical analysis of nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 53, 209-232. Lee, S. Y., & Zhu, H. T. (2002). Maximum likelihood estimation of nonlinear structural equation models. Psychometrika, 67, 189-210. Lin, G.-C., Wen, Z., Marsh, H. W., & Lin, H.-S. (2010). Structural equation models of latent interactions: clarification of orthogonalizing and double-mean centering strategies. Structural Equation Modeling, 17, 374–391. Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling, 13 ,497-519. Little, T. D., Cunningham, W. A., Shahar, G. & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104. Marsh, 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, 275-300. Marsh, H. W., Wen, Z., & Hau, K. T. (2006). Structural equation models of latent interaction and quadratic Effects. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 225-265). Greenwich, CT: Information Age Publishing, Inc. Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89, 852-863. Muthen, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132. Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. Saris, W. E., Batista-Foguet, J. M. & Coenders, G.. (2007). Selection of indicators for the interaction term in structural equation models with Interaction. Quality & Quantity, 41, 55-72. Schermelleh-Engel, K., Klein, A., & Moosbrugger, H. (1998). Estimating nonlinear effects using a latent moderated structural equations approach. In R. E. Schumacker & G. A. Marcoulides (Eds.), Interaction and nonlinear effects in structural equation modeling (pp. 203-238). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445. Wall, M. M., & Amemiya, Y. (2000). Estimation for polynomial structural equation models. Journal of the American Statistical Association, 95, 929-940. Wall, M. M., & Amemiya, Y. (2001). Generalized appended product indicator procedure for nonlinear structural equation analysis. Journal of Educational and Behavioral Statistics, 26, 1-29. Williams, L. J., Edwards, J. R., & Vandenberg R. J. (2003). Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29, 903–936. |
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