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Two-stage maximum likelihood approach for item-level missing data in regression.

Lihan Chen1, Victoria Savalei2, Mijke Rhemtulla3

  • 1University of British Columbia, Vancouver, Canada. bill.lihan@psych.ubc.ca.

Behavior Research Methods
|April 26, 2020
PubMed
Summary
This summary is machine-generated.

Available-case maximum likelihood (ACML) and scale-level full information maximum likelihood (SL-FIML) can bias psychological research. Two-stage maximum likelihood (TSML) offers a fast, accurate alternative for handling item-level missing data in regression, performing comparably to multiple imputation (MI).

Keywords:
Item levelMissing dataRegressionTwo stage

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Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • Psychological research frequently uses multi-item scales to measure constructs, but item-level missing data pose analytical challenges.
  • Traditional methods like available-case maximum likelihood (ACML) and scale-level full information maximum likelihood (SL-FIML) have limitations, including potential bias and inefficiency when dealing with missing item data.

Purpose of the Study:

  • To evaluate the performance of two-stage maximum likelihood (TSML) against existing methods for handling item-level missing data in univariate regression.
  • To compare TSML with ACML, SL-FIML, and multiple imputation (MI) under various missing data conditions (MCAR and MAR).

Main Methods:

  • A simulation study was conducted to assess the performance of ACML, SL-FIML, MI, and TSML.
  • The study focused on estimating regression coefficients in the presence of item-level missing data.

Main Results:

  • ACML and SL-FIML demonstrated performance issues, including biased parameter estimates and convergence problems, particularly with small sample sizes and high missingness.
  • TSML exhibited negligible bias, high efficiency, and good coverage, performing comparably to MI across all simulated conditions.
  • TSML's primary limitation was occasional convergence failure in challenging scenarios.

Conclusions:

  • TSML is a viable and recommended analytic approach for addressing item-level missing data in regression analyses when convergence is achieved.
  • TSML offers a computationally efficient alternative to MI, providing accurate parameter estimates and reliable statistical inference.
  • The study provides R code and a Shiny app to facilitate the implementation of TSML.