Journal article
Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data
Structural equation modeling, Vol.16(1), pp.70-98
01/01/2009
DOI: 10.1080/10705510802569918
Abstract
The purpose of this study is to investigate the effects of missing data techniques in longitudinal studies under diverse conditions. A Monte Carlo simulation examined the performance of 3 missing data methods in latent growth modeling: listwise deletion (LD), maximum likelihood estimation using the expectation and maximization algorithm with a nonnormality correction (robust ML), and the pairwise asymptotically distribution-free method (pairwise ADF). The effects of 3 independent variables (sample size, missing data mechanism, and distribution shape) were investigated on convergence rate, parameter and standard error estimation, and model fit. The results favored robust ML over LD and pairwise ADF in almost all respects. The exceptions included convergence rates under the most severe nonnormality in the missing not at random (MNAR) condition and recovery of standard error estimates across sample sizes. The results also indicate that nonnormality, small sample size, MNAR, and multicollinearity might adversely affect convergence rate and the validity of statistical inferences concerning parameter estimates and model fit statistics.
Details
- Title: Subtitle
- Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data
- Creators
- Tacksoo Shin - Korea Institute for Curriculum and EvaluationMark L. Davison - University of MinnesotaJeffrey D. Long - University of Minnesota
- Resource Type
- Journal article
- Publication Details
- Structural equation modeling, Vol.16(1), pp.70-98
- Publisher
- Taylor & Francis
- DOI
- 10.1080/10705510802569918
- ISSN
- 1070-5511
- eISSN
- 1532-8007
- Number of pages
- 29
- Language
- English
- Date published
- 01/01/2009
- Academic Unit
- Psychiatry; Biostatistics
- Record Identifier
- 9984280840302771
Metrics
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