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Generalized Fiducial Inference for Binary Logistic Item Response Models

Published online by Cambridge University Press:  01 January 2025

Yang Liu  and
Jan Hannig
Yang Liu*
Affiliation:
University of California, Merced
Jan Hannig
Affiliation:
The University of North Carolina at Chapel Hill
*
Correspondence should be made to Yang Liu, School of Social Sciences, Humanities and Arts, University of California, Merced, 5200 North Lake Rd, Merced, CA 95343, USA. Email: yliu85@ucmerced.edu
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Abstract

Generalized fiducial inference (GFI) has been proposed as an alternative to likelihood-based and Bayesian inference in mainstream statistics. Confidence intervals (CIs) can be constructed from a fiducial distribution on the parameter space in a fashion similar to those used with a Bayesian posterior distribution. However, no prior distribution needs to be specified, which renders GFI more suitable when no a priori information about model parameters is available. In the current paper, we apply GFI to a family of binary logistic item response theory models, which includes the two-parameter logistic (2PL), bifactor and exploratory item factor models as special cases. Asymptotic properties of the resulting fiducial distribution are discussed. Random draws from the fiducial distribution can be obtained by the proposed Markov chain Monte Carlo sampling algorithm. We investigate the finite-sample performance of our fiducial percentile CI and two commonly used Wald-type CIs associated with maximum likelihood (ML) estimation via Monte Carlo simulation. The use of GFI in high-dimensional exploratory item factor analysis was illustrated by the analysis of a set of the Eysenck Personality Questionnaire data.

Keywords

generalized fiducial inferenceconfidence intervalMarkov chain Monte Carloitem response theorytwo-parameter logistic modelexploratory item factor analysis
Type
Article
Information
Psychometrika , Volume 81 , Issue 2 , June 2016 , pp. 290 - 324
Copyright
Copyright © 2016 The Psychometric Society

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