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Related Experiment Videos

Conditional mixed models with crossed random effects.

Fabian S Tibaldi1, Geert Verbeke, Geert Molenberghs

  • 1Eli Lilly and Co., Mont-Saint-Guibert 1348, Belgium. tibaldifa@lilly.com

The British Journal of Mathematical and Statistical Psychology
|November 1, 2007
PubMed
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This study introduces new methods for analyzing complex hierarchical data using crossed random-effects models. These models address computational challenges in estimating variance components for continuous and binary outcomes in psychometric research.

Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Linear mixed-effects models are standard for hierarchical data but face computational issues with crossed random-effects.
  • Estimating variance components is challenging with limited observations per cross-classified level.
  • Existing pseudolikelihood methods for binary data also present estimation difficulties.

Purpose of the Study:

  • To propose a novel method for fitting two-level crossed random-effects models for continuous outcomes.
  • To develop a crossed random-effects model for binary data using conditional logistic regression and pseudolikelihood.
  • To evaluate the proposed methods through a psychometric case study and simulation.

Main Methods:

  • Conditional linear mixed-effects model theory for continuous outcomes.

Related Experiment Videos

  • Combination of conditional logistic regression and pseudolikelihood estimation for binary outcomes.
  • Application to psychometric data with crossed items and participants.

    • Main Results:

    • The proposed method effectively fits crossed random-effects models for continuous data.
    • The combined approach successfully models binary data in a crossed design.
    • Simulation study validates the operational characteristics of the developed methods.

    Conclusions:

    • The developed methods offer viable solutions for complex crossed random-effects models.
    • These approaches enhance the analysis of hierarchical data in fields like psychometrics.
    • The study provides robust tools for variance component estimation in challenging data structures.