Analyzing group differences and measurement fairness in process data: a sequential response model with covariates

This article introduces the sequential response model with covariates (SRM-C) for analyzing process data, with emphasis on three key capabilities: detecting potential measurement bias in response processes, evaluating group differences in ability distributions and improving parameter estimation precision. The SRM-C combines measurement and structural components, with the measurement component modeling response sequences conditional on abilities and covariates, and the structural component characterizing group-specific ability distributions. Sparsity assumptions implemented through horseshoe prior distributions address identification issues within the Bayesian framework. Monte Carlo simulations demonstrated robust parameter recovery and effective differential item functioning (DIF) detection. An empirical analysis of PISA problem-solving data illustrated the model’s utility in distinguishing ability differences from potential measurement bias. The SRM-C offers a comprehensive framework for understanding group differences in process data while ensuring measurement fairness.