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Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation


Wang, Ting; Strobl, Carolin; Zeileis, Achim; Merkle, Edgar C (2018). Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation. Psychometrika, 83(1):132-155.

Abstract

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests' theoretical background and implementation are detailed, and the tests' abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests' use in practice is also provided.

Abstract

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests' theoretical background and implementation are detailed, and the tests' abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests' use in practice is also provided.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Date:2018
Deposited On:30 Jan 2018 15:36
Last Modified:22 Feb 2018 02:04
Publisher:Springer
ISSN:0033-3123
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/s11336-017-9591-8
PubMed ID:29150815

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