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Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies.

Eun-Young Mun1, Yan Huo2, Helene R White3

  • 1University of North Texas Health Science Center, Fort Worth, TX, United States.

Frontiers in Psychology
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian higher-order item response theory (HO-IRT) model for complex data synthesis. The developed Markov chain Monte Carlo algorithm effectively models hierarchical traits across multiple studies.

Keywords:
Bayesian estimationhigher-order IRTindividual participant datameta-analysismulti-group IRTmultivariate IRT

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Research Synthesis

Background:

  • Many psychological and clinical constructs involve multivariate higher-order constructs influencing multidimensional lower-order traits.
  • Existing measurement models struggle with complex data structures in research synthesis, like meta-analysis of individual participant data (IPD).
  • There's a need for advanced methods to model hierarchical relationships in IPD from multiple studies.

Purpose of the Study:

  • To develop a measurement model for complex item responses arising from hierarchically related lower-order and higher-order traits in multi-study IPD.
  • To formulate a multi-group, multivariate higher-order item response theory (HO-IRT) model using a Bayesian approach.
  • To create a novel Markov chain Monte Carlo (MCMC) algorithm for simultaneous estimation of structural and item parameters.

Main Methods:

  • Formulation of a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective.
  • Development of a new Markov chain Monte Carlo (MCMC) algorithm for simultaneous estimation of latent trait structures and item parameters.
  • Validation through a simulation study and analysis of real-world data from multiple studies.

Main Results:

  • The developed MCMC algorithm demonstrated feasibility in simulation studies.
  • A bivariate HO-IRT model allowing different correlation/covariance structures across studies provided the best fit for real data.
  • This outperformed univariate HO-IRT models and models with restrictive assumptions of uniform means and covariances.

Conclusions:

  • The multi-group multivariate HO-IRT model is a promising tool for research synthesis involving IPD from multiple studies.
  • It enables the derivation of a common metric for robust inference and discovery.
  • Further development and dissemination are needed to fully realize its potential.