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Xitao Fan - One of the best experts on this subject based on the ideXlab platform.
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effects of latent variable Nonnormality and model misspecification on testing structural equation modeling interactions
Journal of Experimental Education, 2011Co-Authors: Shaojing Sun, Timothy R Konold, Xitao FanAbstract:Interest in testing interaction terms within the latent variable modeling framework has been on the rise in recent years. However, little is known about the influence of Nonnormality and model misspecification on such models that involve latent variable interactions. The authors used Mattson's data generation method to control for latent variable distributional properties, and they examined how data Nonnormality and model misspecification affected latent variable interaction models in relation to varying sample sizes and different magnitudes of incorrectly constrained model parameters. The authors conducted 600 replications for each of the 54 configurations of the 4-factor completely crossed balanced deign. In general, results were suggestive of less bias under conditions of latent variable normality, large sample sizes, correctly specified models, and smaller parameters that were incorrectly constrained (i.e., misspecified). Similarly, these conditions were also found to produce better fitting models as ...
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effects of data Nonnormality and other factors on fit indices and parameter estimates for true and misspecified sem models
1997Co-Authors: Xitao FanAbstract:A Monte Carlo study was conducted to assess the effects of some potential confounding factors on structural equation modeling (SEM) fit indices and parameter estimates for both true and misspecified models. The factors investigated were data Nonnormality, SEM estimation method, and sample size. Based on the fully crossed and balanced 3x3x4x2 experimental design with 200 replications in each cell division, a total of 14,400 samples were generated and fitted to SEM models with different degrees of model misspecification. The major findings are: (1) mild to moderate data Nonnormality has little effect on SEM fit indices and parameter estimates; (2) estimation method has considerable influence on some SEM fit indices when the model was misspecified, primarily on those comparative model fit indices; and (3) some fit indices are susceptible to the influence of sample size, and show moderate downward bias under smaller sample size conditions. Previous studies in this area have simulated a correctly-specified true model, and fit indices were found to behave consistently under different estimation methods. That finding may need to be assessed again, because considerable discrepancy of some fit indices between the two estimation methods was observed for misspecified models. It is critical that simulation studies be conducted in the presence of model misspecification. (Contains 1 figure, 8 tables, and 54 references.) (Author/SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** sem norm.wp1 3/6/96' EFFECTS OF DATA Nonnormality AND OTHER FACTORS ON FIT INDICES AND PARAMETER ESTIMATES FOR TRUE AND MISSPECIFIED SEM MODELS U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Xitao Fan Utah State University Lin Wang American College Testing Bruce Thompson Texas A&M University and Baylor College of Medicine Running Head: SEM Estimates PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY . 1, uCe. Moxi/Qs'enit) TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Note: Please address correspondence concerning this paper to: Xitao Fan, Ph.D., Department of Psychology, Utah State University, Logan, Utah 84322-2810.
Weiming Luh - One of the best experts on this subject based on the ideXlab platform.
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an invertible transformation two sample trimmed t statistic under heterogeneity and Nonnormality
Statistics & Probability Letters, 2000Co-Authors: Jiin Huarng Guo, Weiming LuhAbstract:As various transformation methods are unsatisfactory to control Type-I error rate under heterogeneity and Nonnormality for the two-sample case, an invertible transformation trimmed t-statistic is proposed, and the confidence interval is derived. Simulation results and experimental data are also presented.
Robert A Johnston - One of the best experts on this subject based on the ideXlab platform.
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Nonnormality of data in structural equation models
Transportation Research Record, 2008Co-Authors: Shengyi Gao, Patricia L Mokhtarian, Robert A JohnstonAbstract:With the use of census block group data on sociodemographics, land use, and travel behavior, the cutoffs suggested in the literature for trustworthy estimates and hypothesis-testing statistics were tested, and the efficacy of deleting observations as an approach to improving multivariate normality in structural equation modeling was evaluated. It was found that the deletion of enough cases to achieve multivariate normality yielded results that were substantively different from those for the full sample and required that 17% of the sample be discarded. Alternatively, after only a few true outliers were deleted (0.8% of the sample), the measures of univariate and multivariate nonnormalities fell into the acceptable range for maximum likelihood estimation to be appropriate. The pursuit of a multivariate normal distribution by the deletion of observations should be consciously weighed against the loss of model power and generalizability in the interpretation of the results. That is, the analyst should proactively find the balance between the two extremes of (a) a model on the full sample that is unreliable because of extreme Nonnormality and (b) a model on a sample that has discarded so many cases to achieve multivariate normality that it is no longer fully representative of the desired population. It is further argued that the process of finding that balance should be exposed to the audience rather than ignored or suppressed.
Kehai Yuan - One of the best experts on this subject based on the ideXlab platform.
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univariate and multivariate skewness and kurtosis for measuring Nonnormality prevalence influence and estimation
Behavior Research Methods, 2017Co-Authors: Meghan K Cain, Zhiyong Zhang, Kehai YuanAbstract:Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78–84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential Nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of Nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.
Shu Fai Cheung - One of the best experts on this subject based on the ideXlab platform.
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the influence of Nonnormality from primary studies on the standardized mean difference in meta analysis
Behavior Research Methods, 2020Co-Authors: Rong Wei Sun, Shu Fai CheungAbstract:In this study we investigated the influence of data Nonnormality in the primary studies on meta-analysis of the standardized mean difference (SMD) for a two-independent-group design. The bias, mean squared error, and confidence interval coverage probability of the mean effect sizes under different types of population distributions were compared. Also, the performance of the Q test was examined. The results showed that oppositely skewed distributions (i.e., distributions skewed in different directions) showed poor performance for point and interval estimates of mean effect sizes in meta-analysis, especially when the tails were pointing toward each other. The previously found adverse impacts due to Nonnormality in primary studies do not disappear when primary studies with nonnormal data are meta-analyzed, even when the average sample size and number of studies are large. The results also showed that, when the tails were pointing toward each other, the Type I error rates of the Q test were inflated. We suggest that the impact of violating the assumption of normality should not be ignored in meta-analysis.
