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Covariance Structure Model Fit Testing Under Missing Data: An Application of the Supplemented EM Algorithm.

Li Cai1, Taehun Lee2

  • 1a Graduate School of Education and Information Studies , University of California , Los Angeles.

Multivariate Behavioral Research
|January 13, 2016
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Summary
This summary is machine-generated.

The Supplemented EM algorithm resolves issues in covariance structure modeling with missing data by providing a chi-square goodness-of-fit statistic. This method is implemented in a SAS macro for practical application.

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Area of Science:

  • Statistics
  • Psychometrics
  • Computational Statistics

Background:

  • Two-stage fitting of covariance structure models often lacks an asymptotically chi-square distributed goodness-of-fit statistic.
  • Ignorable missing data presents a persistent challenge in statistical modeling.

Purpose of the Study:

  • To introduce and validate the Supplemented EM algorithm for covariance structure models with missing data.
  • To provide a computational method yielding a chi-square goodness-of-fit statistic.

Main Methods:

  • Application of the Supplemented EM algorithm (Meng & Rubin, 1991).
  • Development of a SAS macro for implementing the proposed method.
  • Validation through a simulation study and analysis of empirical datasets.

Main Results:

  • The Supplemented EM algorithm successfully provides an asymptotically chi-square distributed goodness-of-fit statistic.
  • The SAS macro implementation facilitates practical use of the method.
  • Empirical applications demonstrate the algorithm's utility in confirmatory factor analysis and latent curve modeling.

Conclusions:

  • The Supplemented EM algorithm offers a viable solution for goodness-of-fit testing in covariance structure models with missing data.
  • The developed SAS macro enhances the accessibility and application of this statistical technique.