Browsing by Subject "Missing observations (Statistics)"
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Item Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data(2003) Furlow, Carolyn Florence; Beretvas, Susan NatashaThis study compared the effects of different methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under various patterns of missingness on the estimation of correlation parameters and the resulting SEM parameters and fit indices. Univariate weighting methods for synthesizing correlations are frequently used. An alternative multivariate method for pooling correlation matrices involves using generalized least squares (GLS), where the dependencies of the correlations within the same matrix are taken into consideration (Becker, 1992). Since previous research has reported poor performance with GLS versus univariate weighting procedures, a revised GLS method, W-COV GLS, was used. Both the W-COV GLS procedure and univariate weighting were compared using correlations transformed with Fisher’s z versus untransformed correlations. There is frequently a problem when synthesizing correlation matrices due to the effects of missing data. One type of missing data scenario is the file-drawer problem (Rosenthal, 1979) in which a potential selection bias may occur whereby correlations that are non-significant are not reported. The performance of the different synthesis methods were assessed under different degrees and types of missingness including an approximation of the file-drawer problem using listwise and pairwise deletion to handle missing data. Results from this study indicated comparable performance of univariate weighting with the z transformation and W-COV GLS procedures, both with and without the transformation, for estimating the correlation parameters and ensuing parameters of the structural model. However, the W-COV GLS procedure performed slightly better in estimating the standard errors of the paths in the structural model and for the chi-squared test of data-model fit. When data were MCAR then there was almost no relative bias detected but when data were MNAR there were unacceptably high levels of relative bias in estimation of the correlation and SEM model parameters as well as high model rejection rates regardless of method used to synthesize correlations. Pairwise deletion resulted in higher incorrect rejection rates and larger bias in the standard error estimates for the SEM model than did listwise deletion. Inaccurate standard error estimates were found for several of the paths and attributed to the use of a correlation matrix with SEM.Item The performance of missing data treatments for longitudinal data with a time-varying covariate(2005) Adachi, Eishi; Pituch, Keenan A.The purpose of this study was to investigate the performance of missing data treatments for longitudinal data with a time-varying covariate. For a longitudinal study, data are collected repeatedly from the same individual, and the occurrence of missing observations due to attrition is common. When longitudinal data are nested within individuals, missing data treatments may need to take the nested data structures into account. This study compared the performance of four missing data treatments: listwise deletion and three multiple imputation methods. Nested data structures were ignored in single-level multiple imputation. On the other hand, multivariate multiple imputation addressed nested data structures by data manipulation and multilevel multiple imputation addressed them in the imputation model. The performance of listwise deletion was investigated to compare the performance of multiple imputation methods with a traditional method. In Study 1, longitudinal data with missing observations were simulated. The experimental conditions were sample size, missing rate, missing patterns, and degree of systematic nonresponse. After missing data were treated by four missing data treatments, a two-level mixed-effects model was applied. Bias in estimation of the regression coefficients, standard errors, and variances were investigated. In Study 2, the four missing data treatments were applied to empirical data from a longitudinal study on persons with multiple sclerosis, to demonstrate the applicability of the four missing data treatments. Study 1 showed that multivariate multiple imputation and multilevel multiple imputation resulted in less biased estimates than the other two methods for most study conditions. Single-level multiple imputation resulted in biased variance estimates under all experimental conditions. Listwise deletion also produced biased estimates, especially for the standard error for the fixed effects of time invariant variables. With the application of empirical data, inferences of cross-level interaction fixed-effects were in disagreement among the four methods. The use of multivariate multiple imputation and multilevel multiple imputation found a significant cross-level interaction whereas the other methods did not. The results showed that nested structures should not be ignored at the imputation stage of multiple imputation.