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The role of nonnormality and model s...

Sun, Shaojing.

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The role of nonnormality and model specification in testing latent variable interactions: A Monte Carlo study.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The role of nonnormality and model specification in testing latent variable interactions: A Monte Carlo study./
作者:	Sun, Shaojing.
面頁冊數:	115 p.
附註:	Adviser: Timothy Konold.
Contained By:	Dissertation Abstracts International67-09A.
標題:	Education, Tests and Measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3235101
ISBN:	9780542889028

The role of nonnormality and model specification in testing latent variable interactions: A Monte Carlo study.
Sun, Shaojing.

The role of nonnormality and model specification in testing latent variable interactions: A Monte Carlo study. - 115 p.

Adviser: Timothy Konold.

Thesis (Ph.D.)--University of Virginia, 2006.

This study examined the effects of data nonnormality, model misspecification, sample size, and effect size on testing latent variable interactions through an inspection of the Joreskog and Yang's (1996) model. Mattson's (1997) method was used to generate nonnormal latent variables in this Monte Carlo study. One covariance parameter was deleted for investigating the influence of misspecified models. The simulation involved a balanced experimental design, with 3 x 2 x 3 x 3 = 54 combinations. Data analysis focused on bias of estimating parameters, standard errors, model fit indexes. Variance partition was conducted to further examine the unique and combined influence of the factors (i.e., data nonnormality, model specification, sample size, effect size). Results indicated that data nonnormality and model misspecification had large effects on fit indexes (e.g., SRMR, RMSEA). Also, severe nonnormality led to a large bias of estimating the interaction effect. Implications of and recommendations for testing latent variable interactions were discussed.

ISBN: 9780542889028Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

The role of nonnormality and model specification in testing latent variable interactions: A Monte Carlo study.
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520 $a This study examined the effects of data nonnormality, model misspecification, sample size, and effect size on testing latent variable interactions through an inspection of the Joreskog and Yang's (1996) model. Mattson's (1997) method was used to generate nonnormal latent variables in this Monte Carlo study. One covariance parameter was deleted for investigating the influence of misspecified models. The simulation involved a balanced experimental design, with 3 x 2 x 3 x 3 = 54 combinations. Data analysis focused on bias of estimating parameters, standard errors, model fit indexes. Variance partition was conducted to further examine the unique and combined influence of the factors (i.e., data nonnormality, model specification, sample size, effect size). Results indicated that data nonnormality and model misspecification had large effects on fit indexes (e.g., SRMR, RMSEA). Also, severe nonnormality led to a large bias of estimating the interaction effect. Implications of and recommendations for testing latent variable interactions were discussed.
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