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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Simple imputation methods versus direct likelihood analysis for missing item scores in multilevel educational data.

Damazo T Kadengye1, Wilfried Cools, Eva Ceulemans

  • 1Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium. Trevor.Kadengye@kuleuven-kortrijk.be

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Summary

Direct likelihood analysis effectively handles missing item response data in multilevel educational settings, providing unbiased parameter estimates comparable to complete data. Some multiple imputation methods also showed effectiveness, unlike simpler imputation techniques.

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Area of Science:

  • Educational Research
  • Psychometrics
  • Statistics

Background:

  • Missing data is common in educational research, particularly in multilevel item response theory (IRT) datasets.
  • Ignoring missing data can bias the estimation of IRT model parameters.
  • Various imputation methods and direct likelihood analysis have been proposed to address missing data in IRT.

Purpose of the Study:

  • To compare the performance of six imputation methods against direct likelihood analysis for missing item responses in multilevel educational data.
  • To evaluate the effectiveness of these methods in producing unbiased parameter estimates.

Main Methods:

  • Simulated multilevel item response data based on two empirical datasets.
  • Data were manipulated to create missing item scores (completely at random or at random).
  • An explanatory IRT model was used to analyze complete, incomplete, and imputed datasets.

Main Results:

  • Direct likelihood analysis yielded unbiased parameter estimates, comparable to complete data analysis.
  • Multiple imputation using two-way mean and corrected item mean substitution showed varying effectiveness.
  • Simple random imputation, adjusted random imputation, item means substitution, and regression imputation were less effective.

Conclusions:

  • Direct likelihood analysis is a robust method for handling missing item responses in multilevel educational data.
  • Certain multiple imputation techniques can be effective, but simpler methods are less suitable for this context.