ArticlePsychometrika2020
Extending the Basic Local Independence Model to Polytomous Data.
Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-method validation of the new computerized test of fluid intelligence MatriKS.Behavior research methods · 2026Article
- Two Markov Solution Process Models for the Assessment of Planning in Problem Solving.Psychometrika · 2025Article
- PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training.Brain sciences · 2024Article
- Commentary on "Extending the Basic Local Independence Model to Polytomous Data" by Stefanutti, de Chiusole, Anselmi, and Spoto.Psychometrika · 2023Article
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Authors and funding
4 authors.
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Abstract
A probabilistic framework for the polytomous extension of knowledge space theory (KST) is proposed. It consists in a probabilistic model, called polytomous local independence model, that is developed as a generalization of the basic local independence model. The algorithms for computing "maximum likelihood" (ML) and "minimum discrepancy" (MD) estimates of the model parameters have been derived and tested in a simulation study. Results show that the algorithms differ in their capability of recovering the true parameter values. The ML algorithm correctly recovers the true values, regardless of the manipulated variables. This is not totally true for the MD algorithm. Finally, the model has been applied to a real polytomous data set collected in the area of psychological assessment. Results show that it can be successfully applied in practice, paving the way to a number of applications of KST outside the area of knowledge and learning assessment.
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