Missing data in principal component analysis of questionnaire data: a comparison of methods

DOI10.1080/00949655.2013.788654zbMath1453.62754OpenAlexW2113647589MaRDI QIDQ5219493

Joost R. van Ginkel, Pieter M. Kroonenberg, Henk A. L. Kiers

Publication date: 12 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.788654

zbMATH Keywords

principal component analysismissing datamultiple imputationleast-squares fittingexpectation-maximization algorithmmissing data passiveregularized principal component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to psychology (62P15) Missing data (62D10)

Related Items (3)

Chunk-wise regularised PCA-based imputation of missing data ⋮ Evaluation of multiple-imputation procedures for three-mode component models ⋮ Comparisons among several methods for handling missing data in principal component analysis (PCA)

Uses Software

• SPSS

Cites Work

• Using generalized Procrustes analysis for multiple imputation in principal component analysis
• Generalized Procrustes analysis
• Orthogonal Procrustes rotation for two or more matrices
• Weighted least squares fitting using ordinary least squares algorithms
• The orthogonal approximation of an oblique structure in factor analysis
• Inference and missing data
• Constructing bootstrap confidence intervals for principal component loadings in the presence of missing data: A multiple‐imputation approach
• Relationships Between two Methods for Dealing with Missing Data in Principal Component Analysis
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