A Comparison of Full Information Maximum Likelihood, Multiple Imputation, and Bayesian Approach in Overall Goodness of Fit Assessment of Structural Equation Modeling with Missing Data

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Korean Journal of Psychology: General

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P-ISSN1229-067X
E-ISSN2734-1127
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Vol.33 No.2

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A Comparison of Full Information Maximum Likelihood, Multiple Imputation, and Bayesian Approach in Overall Goodness of Fit Assessment of Structural Equation Modeling with Missing Data

Korean Journal of Psychology: General / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127

2014, v.33 no.2, pp.507-533

(University of Oklahoma)

(2014). A Comparison of Full Information Maximum Likelihood, Multiple Imputation, and Bayesian Approach in Overall Goodness of Fit Assessment of Structural Equation Modeling with Missing Data. Korean Journal of Psychology: General, 33(2), 507-533.

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Abstract

In practical applications of any statistical modeling, including structural equation modeling(SEM), virtually every data set contains missing values. It is a well known fact that improper handling of missing data can exert harmful impact on subsequent statistical inferences in a variety of ways to varying degrees. In the context of SEM, the full information maximum likelihood(FIML) has been arguably the most popular method for addressing missing data. Despite of being yet less widely known to majority of applied researchers as flexible alternatives to FIML, multiple imputation (MI) procedures and Bayesian approaches have recently begun to emerge as viable solutions among many applied researchers. An important objective of this article is to introduce these methods to applied researchers in an accessible manner using SEM as the context. Structural equation modeling actually involves the process of proposing, estimating, and evaluating the researcher’s hypothesis that is believed to be underlying and purported in generating the observed data. Therefore, it is essential to evaluate the overall goodness-of-fit of the posited model in any given application. FIML, MI and Bayesian approaches, respectively, yield the chi-square, , , and the posterior predictive modeling checking (PPMC) p-value as statistical tools for the assessment of data-model fit. Another important objective of this article is to study performance of these model evaluation tools in the context of SEM. Further, relative performance of these data-model fit assessment tools is to be evaluated with respect to their Type I error rates and power. The performance of these assessment tools, except the chi-square statistics, has never been evaluated nor been compared within the context of SEM. The initial results provided in the present article is believed to not only enhance the knowledge base regarding the characteristics of these assessment tools under missing data, but also provide an initial guideline for the proper use of these assessment tools in the real-world data analysis especially in the application of SEM with missing data.

keywords: 결측치, 공분산구조모형, 최대우도, 다중대체, 베이지안, missing data, structural equation modeling, full information maximum likelihood, multiple imputation, Bayesian

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Article Contents

Vol.33 No.2

A Comparison of Full Information Maximum Likelihood, Multiple Imputation, and Bayesian Approach in Overall Goodness of Fit Assessment of Structural Equation Modeling with Missing Data

Abstract

Reference

Korean Journal of Psychology: General