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Estimating interaction effects with incomplete predictor variables.

Craig K Enders1, Amanda N Baraldi1, Heining Cham1

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This study provides clear methods for estimating interaction effects with missing data using maximum likelihood and multiple imputation. These techniques are crucial for accurate analysis when continuous variables have incomplete data.

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Estimating interaction effects with missing data, especially with continuous variables, lacks clear guidance in existing literature.
  • Common statistical analyses often face challenges with incomplete datasets, potentially biasing results.

Purpose of the Study:

  • To present and evaluate maximum likelihood (ML) and multiple imputation (MI) procedures for estimating interaction effects in the presence of missing data.
  • To outline latent variable model specifications applicable to single-indicator constructs and interaction analyses.
  • To detail methods for probing interaction effects and provide practical guidance for implementation.

Main Methods:

  • Described 3 latent variable model specifications for interaction analyses with missing data.
  • Detailed multiple imputation for interaction effects using raw score predictors.
  • Outlined centering and transformation strategies for ML and MI in popular software.

Main Results:

  • Demonstrated the application of proposed methods through real data analyses.
  • Evaluated the performance of the techniques using computer simulations.
  • Provided a thorough description of probing interaction effects for both ML and MI.

Conclusions:

  • Maximum likelihood and multiple imputation offer viable solutions for estimating interaction effects with missing continuous variables.
  • The proposed latent variable models and imputation strategies enhance the accuracy of interaction analyses.
  • Practical guidance and simulation results support the use of these methods in research.