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Cui, M. (2012). Effect-Size Index for Evaluation of Model-Data Fit in Structural Equation Modeling. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4789
This study focused on developing and examining an effect-size index for evaluation of model-data fit in structural equation modeling. Based on MacCallum and his colleagues' work (1996, 2006), the discrepancy function under maximum likelihood estimation was defined as an effect-size index. The formulas for computing the expected value and variance of this index were derived for both correctly specified and mis-specified models. A simulation study was conducted to examine the performance and distributional characteristics of the index under various degrees of freedom, sample sizes, and degrees of model misspecification. The results showed that the fit function in the sample is a function of sample sizes, degrees of freedom, and degree of model misspecification. The observed sample means tended to be biased when the sample size was small. The distribution of the fit function was very similar to the chi-square distribution. It was also demonstrated that power of detecting mis-specified models was a function of the sample sizes, degrees of freedom, and degree of model misspecification. In addition, it was suggested that meta-analysis across studies can be conducted using the fit function as an effect-size index for certain models.
A Thesis submitted to the Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Yanyun Yang, Professor Directing Thesis; Betsy Becker, Committee Member; Russell Almond, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-4789
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Cui, M. (2012). Effect-Size Index for Evaluation of Model-Data Fit in Structural Equation Modeling. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4789