🤖 AI Summary
This study addresses a critical limitation in cross-cultural research: single-item measures lack latent variable proxies and multiple indicators, rendering conventional tests for differential item functioning (DIF) or measurement invariance (MI) infeasible. To overcome this challenge, the authors propose a penalized heteroskedastic ordered probit model that integrates regularization techniques to resolve identification and estimation issues inherent in single-item data. This approach enables, for the first time, a viable framework for conducting DIF and MI analyses with single-item responses, thereby circumventing the traditional reliance on multi-item scales or multiple indicators. The method offers a novel analytical tool for cross-cultural comparisons in resource-constrained settings or contexts where only single-item measures are available, significantly expanding the scope of valid cross-cultural inference under practical constraints.
📝 Abstract
Differential item functioning (DIF) or measurement invariance (MI) testing for single-item assessments has previously been impossible. Part of the issue is that there are no conditioning variables to serve as a proxy for the latent variable--regression-based DIF methods. Another reason is that factor-analytic approaches require multiple items to estimate parameters. In this technical working paper, I propose an approach for evaluating DIF/MI in a single-item assessment of a construct. The current methods should NOT replace using multiple-indicator MG-CFA/IRT analyses of DIF/MI or regression mased methods when possible. More items generally provide significantly better construct coverage and provide more rigorous DIF/MI evaluation.