Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

Chen Wei Liu*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

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.

原文英語
頁(從 - 到)159-177
頁數19
期刊Applied Psychological Measurement
45
發行號3
DOIs
出版狀態已發佈 - 2021 五月

ASJC Scopus subject areas

  • 社會科學(雜項)
  • 心理學(雜項)

指紋

深入研究「Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data」主題。共同形成了獨特的指紋。

引用此