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Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level.

Victoria Savalei1, Mijke Rhemtulla2

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Journal of Educational and Behavioral Statistics : a Quarterly Publication Sponsored by the American Educational Research Association and the American Statistical Association
|December 26, 2017
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Summary
This summary is machine-generated.

A new two-stage maximum likelihood (TSML) method effectively handles missing item-level data in statistical modeling. This analytic approach performs comparably to item-level multiple imputation (MI) without requiring imputations.

Keywords:
item-level missing datamultiple imputationstructural equation modelingtwo-stage estimation

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Many statistical models utilize scale scores or parcels as variables, which are linear composites of raw item data.
  • Existing methods struggle to appropriately handle missing data at the individual item level in these composite variables.
  • Item-level multiple imputation (MI) offers a solution but requires imputation, not direct analytic estimation.

Purpose of the Study:

  • To develop a novel analytic estimation method for addressing item-level missing data directly from incomplete datasets.
  • To introduce a variant of two-stage maximum likelihood (TSML) methodology as an analytic equivalent to item-level MI.
  • To compare the performance of the new TSML approach against existing methods for handling item-level missing data.

Main Methods:

  • Development of a two-stage maximum likelihood (TSML) variant specifically designed for item-level missing data.
  • Comparison of the proposed TSML approach with scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI.
  • Evaluation of parameter estimates obtained directly from incomplete data using the TSML method.

Main Results:

  • The newly developed TSML approach provides a unique set of parameter estimates directly from incomplete item-level data.
  • The TSML method demonstrates superior performance as an analytic approach compared to scale-level FIML and available-case ML.
  • The performance of the TSML approach is found to be similar to that of item-level multiple imputation (MI).

Conclusions:

  • The proposed TSML methodology is the best analytic approach for handling item-level missing data.
  • TSML offers an effective alternative to item-level MI, providing direct parameter estimation without imputation.
  • Recommendations are made for the implementation of TSML in statistical software and for further research.