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Missing data techniques for structural equation modeling.

Paul D Allison1

  • 1Sociology Department, University of Pennsylvania, Philadelphia, PA 19104-6299, USA. allison@ssc.upenn.edu

Journal of Abnormal Psychology
|December 17, 2003
PubMed
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Missing data hinder structural equation model (SEM) estimation. Maximum likelihood and multiple imputation offer optimal solutions for handling missing data, preserving valuable information in SEM analyses.

Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Missing data present significant challenges in statistical analyses, particularly for complex models.
  • Traditional methods like listwise and pairwise deletion are inefficient, discarding valuable available data.

Purpose of the Study:

  • To review and discuss advanced methods for handling missing data in structural equation modeling (SEM).
  • To highlight the advantages of maximum likelihood and multiple imputation over conventional techniques.

Main Methods:

  • Discussion of maximum likelihood estimation for SEM with missing data.
  • Explanation of the multiple imputation technique and its application.
  • Comparison of the efficacy and applicability of different missing data handling methods.

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Main Results:

  • Maximum likelihood methods provide an optimal approach for SEM with missing data.
  • Multiple imputation offers statistically sound alternatives with broader applicability across models and estimation techniques.

Conclusions:

  • Researchers should utilize advanced methods like maximum likelihood or multiple imputation for SEM to ensure robust and valid results.
  • These methods improve the utilization of available data, leading to more accurate model estimations.