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Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators.

Heining Cham1, Evgeniya Reshetnyak1, Barry Rosenfeld1

  • 1a Fordham University.

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|November 12, 2016
PubMed
Summary
This summary is machine-generated.

Researchers recommend Full Information Maximum Likelihood (FIML) for latent moderated structural equations (LMS) to accurately estimate interaction effects with missing data. This method provides unbiased estimates and robust statistical power for complex analyses.

Keywords:
Latent interactionmaximum likelihoodmissing dataproduct indicator

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Estimating interaction effects in regression models with missing data is challenging.
  • Existing methods for handling missing data in interaction effects need extension to latent variable contexts.

Purpose of the Study:

  • To investigate the performance of Full Information Maximum Likelihood (FIML) estimation for latent variable interactions using product indicator (PI) and latent moderated structural equations (LMS) methods.
  • To compare FIML for PI and LMS methods in handling incompletely observed indicators.

Main Methods:

  • Analytical comparison of FIML for PI and LMS methods.
  • A simulation study was conducted to evaluate the performance of FIML for PI and LMS.
  • Application of the methods to analyze interaction effects in advanced cancer patients.

Main Results:

  • FIML for LMS demonstrated unbiased parameter estimates with small variances.
  • The method achieved correct Type I error rates and high statistical power for interaction effects.
  • FIML for LMS is recommended for data missing completely at random (MCAR) or missing at random (MAR) under normality.

Conclusions:

  • Full Information Maximum Likelihood (FIML) is a suitable method for estimating interaction effects in latent moderated structural equations (LMS) with missing data.
  • The recommended approach enhances the accuracy and power of statistical analyses in psychological research.
  • The study provides practical guidance for researchers dealing with missing data in complex interaction models.