Seminar in Psychometrics

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A Flexible IRT Framework For Latent DIF Detection

Date and time: September 27, 2022 (3:40 PM CET)
Place ICS CAS plenary room (room 318), Pod Vodárenskou věží 2, Prague 8, also on Zoom.

Abstract. The measurement validity of instruments like a questionnaire or a test is established by ascertain that it is measurement invariant across the items. For this purpose, it is standard procedure to assess the presence of differential item functioning (DIF), which evaluates measurement invariance on item level. When DIF detection is not based on manifest groups but on latent groups the problem is typically referred to as latent DIF detection, which will be the focus of this talk. To that end, I will present a flexible modeling framework that combines a general latent factor model with a latent class model to capture both normal response behavior under no DIF, and deviant behavior due to DIF. In the model, a sparse DIF effect parameter is introduced that is allowed to vary between the latent classes identified by the model. Each item response distribution is consequently modeled as a function of a latent variable measuring the underlying construct of the questionnaire or test, and of group membership. No prior knowledge of DIF-free items is required, instead, they are identified through an L_1 penalty on the DIF effect parameter in the marginal likelihood function. An EM algorithm for model estimation is proposed, where the maximization step is carried out using a quasi-Newton proximal algorithm. Results based on both simulated and empirical data together with theoretical results will be presented.

Gabriel Wallin
London School of Economics and Political Science

Gabriel Wallin is a Research Fellow at the Department of Statistics at London School of Economics and Political Science where he belongs to the Social Statistics research group. Previously, he was a postdoctoral researcher at the French national research institute Inria. He received his PhD in 2020 from the Department of Statistics at Umeå University, Sweden, under the supervision of Professor Marie Wiberg.

Research of Dr. Wallin is centered around fairness and interpretability of statistical models and machine learning algorithms for the social and behavioral sciences. So far, he has addressed these topics by developing new methodology for e.g. model-based clustering, multivariate outlier detection and exploratory factor analysis.