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Physical Sciences and Mathematics Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
The Performance Of Multiple Imputation For Likert-Type Items With Missing Data, Walter Leite, S. Natasha Beretvas
The Performance Of Multiple Imputation For Likert-Type Items With Missing Data, Walter Leite, S. Natasha Beretvas
Journal of Modern Applied Statistical Methods
The performance of multiple imputation (MI) for missing data in Likert-type items assuming multivariate normality was assessed using simulation methods. MI was robust to violations of continuity and normality. With 30% of missing data, MAR conditions resulted in negatively biased correlations. With 50% missingness, all results were negatively biased.
An Evaluation Of Multiple Imputation For Meta-Analytic Structural Equation Modeling, Carolyn F. Furlow, S. Natasha Beretvas
An Evaluation Of Multiple Imputation For Meta-Analytic Structural Equation Modeling, Carolyn F. Furlow, S. Natasha Beretvas
Journal of Modern Applied Statistical Methods
A simulation study was used to evaluate multiple imputation (MI) to handle MCAR correlations in the first step of meta-analytic structural equation modeling: the synthesis of the correlation matrix and the test of homogeneity. No substantial parameter bias resulted from using MI. Although some SE bias was found for meta-analyses involving smaller numbers of studies, the homogeneity test was never rejected when using MI.
Applying Multiple Imputation With Geostatistical Models To Account For Item Nonresponse In Environmental Data, Breda Munoz, Virginia M. Lesser, Ruben A. Smith
Applying Multiple Imputation With Geostatistical Models To Account For Item Nonresponse In Environmental Data, Breda Munoz, Virginia M. Lesser, Ruben A. Smith
Journal of Modern Applied Statistical Methods
Methods proposed to solve the missing data problem in estimation procedures should consider the type of missing data, the missing data mechanism, the sampling design and the availability of auxiliary variables correlated with the process of interest. This article explores the use of geostatistical models with multiple imputation to deal with missing data in environmental surveys. The method is applied to the analysis of data generated from a probability survey to estimate Coho salmon abundance in streams located in western Oregon watersheds.