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Bootstrap-Calibrated Interval Estimates for Latent Variable Scores in Item Response Theory

Published online by Cambridge University Press:  01 January 2025

Yang Liu and
Ji Seung Yang
Yang Liu*
Affiliation:
Department of Human Development and Quantitative Methodology, University of Maryland
Ji Seung Yang
Affiliation:
Department of Human Development and Quantitative Methodology, University of Maryland
*
Correspondence should be made to Yang Liu, Department of Human Development and Quantitative Methodology, University of Maryland, 1230B Benjamin Building, College Park, MD 20742 USA. Email: yliu87@umd.edu
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Abstract

In most item response theory applications, model parameters need to be first calibrated from sample data. Latent variable (LV) scores calculated using estimated parameters are thus subject to sampling error inherited from the calibration stage. In this article, we propose a resampling-based method, namely bootstrap calibration (BC), to reduce the impact of the carryover sampling error on the interval estimates of LV scores. BC modifies the quantile of the plug-in posterior, i.e., the posterior distribution of the LV evaluated at the estimated model parameters, to better match the corresponding quantile of the true posterior, i.e., the posterior distribution evaluated at the true model parameters, over repeated sampling of calibration data. Furthermore, to achieve better coverage of the fixed true LV score, we explore the use of BC in conjunction with Jeffreys’ prior. We investigate the finite-sample performance of BC via Monte Carlo simulations and apply it to two empirical data examples.

Keywords

item response theoryscoringpredictive inferencebootstrap

Information

Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 2 , June 2018, pp. 333 - 354
Copyright
Copyright © 2017 The Psychometric Society

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