2009
DOI: 10.1080/10705510802569918
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Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data

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Cited by 75 publications
(49 citation statements)
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References 39 publications
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“…The FIML procedure is recommended in several studies on missing data in longitudinal data analysis because of unbiased parameter estimation, less convergence failures, high parameter and model fit estimation efficiency in the case of time-specific dropouts. This even holds for missing data proportions of 25 to 50% (Enders & Bandalos, 2001;Jeličić, Phelps, & Lerner, 2009;Raykov, 2005;Shin, Davison, & Long, 2009).…”
Section: Missing Data: Estimation and Patternmentioning
confidence: 78%
“…The FIML procedure is recommended in several studies on missing data in longitudinal data analysis because of unbiased parameter estimation, less convergence failures, high parameter and model fit estimation efficiency in the case of time-specific dropouts. This even holds for missing data proportions of 25 to 50% (Enders & Bandalos, 2001;Jeličić, Phelps, & Lerner, 2009;Raykov, 2005;Shin, Davison, & Long, 2009).…”
Section: Missing Data: Estimation and Patternmentioning
confidence: 78%
“…the Time 2 variables) were very high (kurtosis values all greater than 3.0 and some as high as 11.0). Prior researchers have cautioned against imputing data when missing values are associated with non-normality (Shin, Davison, & Long, 2009). Therefore, given the large sample of respondents who completed both observations, the fact that missing data were expected given the study design, and the large percent of imputed values required based on the Time 1 survey, we highlight the results obtained with our completer-only sample.…”
Section: Journal Of Clinical Psychology December 2011mentioning
confidence: 87%
“…Results indicated that data did not contain extreme collinearity; therefore, there were no correlations large enough among ECI measurements to bias the standard errors and significance tests in planned analyses (Shin, Davidson, & Long, 2009). Univariate outliers were inspected with frequency distributions of z -scores.…”
Section: Methodsmentioning
confidence: 99%