Missing Data Techniques for Structural Equation Modeling.

2003/11/01 by Paul D. Allison · 1,345 citations
Decision Sciences · Engineering · Mathematics · #Computer science #Data mining #Econometrics #Engineering #Estimation #Imputation (statistics) #Mathematics #Maximum likelihood #Meta-analysis and systematic reviews #Missing data #Pairwise comparison #Psychometric Methodologies and Testing #Statistical Methods and Bayesian Inference #Statistical analysis #Statistical model #Statistics #Structural equation modeling

paper · doi:10.1037/0021-843x.112.4.545

published in Journal of Abnormal Psychology 112(4), 545-557 (American Psychological Association)

openalex publication_date 2003/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.

Citations

• Inference and missing data

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