The comparative efficacy of imputation methods for missing data in structural equation model\-ing.

DOI10.1016/S0377-2217(02)00578-7zbMath1113.62361OpenAlexW2060755421MaRDI QIDQ1406954

Alan Olinsky, Lisa Harlow, Shaw K. Chen

Publication date: 7 September 2003

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(02)00578-7

zbMATH Keywords

RegressionMultivariate statisticsImputationMissing dataStructural equation modeling

Mathematics Subject Classification ID

Applications of statistics (62P99)

Related Items (8)

Missing data imputation through GTM as a mixture of \(t\)-distributions ⋮ Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework ⋮ Imputation techniques for incomplete data in quadratic discriminant analysis ⋮ The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty ⋮ Dealing with missing data based on data envelopment analysis and halo effect ⋮ A new approach for data editing and imputation ⋮ Strategies for handling missing data in longitudinal studies with questionnaires ⋮ Multiple imputation of discrete and continuous data by fully conditional specification

Uses Software

• SPSS
• LISREL
• Amos
• PRELIS
• Mx

Cites Work

• On structural equation modeling with data that are not missing completely at random
• The estimation of variance-covariance and correlation matrices from incomplete data
• Inference and missing data
• Moments and Distributions of Estimates of Population Parameters from Fragmentary Samples
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: The comparative efficacy of imputation methods for missing data in structural equation model\-ing.