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Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data.

Authors
  • Liu, Chen-Wei1
  • 1 National Taiwan Normal University, Taipei, Taiwan. , (Taiwan)
Type
Published Article
Journal
Applied psychological measurement
Publication Date
May 01, 2021
Volume
45
Issue
3
Pages
159–177
Identifiers
DOI: 10.1177/0146621621990753
PMID: 33958834
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for "complete" item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of "don't know" item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates. © The Author(s) 2021.

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