MCAR-IITA: A Procedure for Identifying Structures among Items from Incomplete Data
Le résumé fourni par la source
Effective adaptive instruction depends on knowing what a student has mastered and what they are ready to learn next. Traditional scale scores, such as those obtained from item response (IRT) models, provide only limited support for this purpose. Consequently, several recent approaches conduct student assessment within hierarchical structures of test items or competencies. While these approaches are theoretically promising, practically applicable methods for identifying such structures from empirical data remain scarce, particularly in the presence of missing item responses. To address this gap, this contribution introduces MCAR-IITA— a new implementation of Inductive Item Tree Analysis (IITA) that provides three variants of handling missing re-sponses. To evaluate its performance, the following research question was addressed: At what percentage of missing re-sponses can the proposed variants still recover the underlying structure? Response data were simulated along a true structure under two conditions: 1) without noise and 2) with noise, using fixed careless errors and guessing probabilities of .20 per item. Miss-ing responses were introduced incrementally across condi-tions, and performance was assessed in terms of the proportion of missing data up to which the true structure could still be recovered. Additionally, the same procedure was applied to a subset of the PISA 2003 dataset based on a previously identi-fied structure. For each dataset, the three variants were evaluat-ed using 1,000 replications. On average, the algorithm recovered the underlying structure with a missing response percentage of 28.47\% (SD = 4.49pp) for the error-free simulation, 19.93\% (SD = 4.44pp) for the simulation with noise, and 15.41\% (SD = 4.65pp) for the PISA data. As this study examines a first implementation and is restricted to a limited set of conditions, the results provide an initial estimate of missing-data tolerance while highlighting substan-tial opportunities for further optimization.
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