An extension of the basic local independence model to multiple observed classifications

Anselmi, Pasquale, de Chiusole, Debora, Robusto, Egidio, Bacherini, Alice, Balboni, Giulia, Brancaccio, Andrea, Epifania, Ottavia M., Mazzoni, Noemi and Stefanutti, Luca (2025) An extension of the basic local independence model to multiple observed classifications. British Journal of Mathematical and Statistical Psychology, 00. pp. 1-36. ISSN 0007-1102 (In Press)

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Abstract

The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations, this is not the case. This work introduces the multiple observed classification local independence model (MOCLIM), which extends the BLIM by allowing the above probabilities to vary across populations. In the MOCLIM, each individual is characterized by proficiency, careless and guessing classes, which are observed and determine the probabilities of knowledge states, careless errors and lucky guesses of a population. Given a particular class type (proficiency, careless, or guessing), the probabilities are the same for populations with the same class but may vary between populations with different classes. Algorithms for maximum likelihood estimation of the MOCLIM parameters are provided. The results of a simulation study suggest that the true parameter values are well recovered by the estimation algorithm and that the true model can be uncovered by comparing the goodness-of-fit of alternative models. The results of an empirical application to data from Raven-like matrices suggest that the MOCLIM effectively discriminates between situations where group differences are expected and those where they are not.

Item Type:	Article
Additional Information:	Data Availability Statement: The code for simulating and estimating the MOCLIM in the open-source language Octave and inMATLAB, the MATLAB code for replicating the simulation study results and the data from the empir-ical application are available on OSF (https://osf.io/ay4nj/? view_only=334cb219d0bd4906b536ae2cc5247281).
Uncontrolled Keywords:	basic local independence model,knowledge space theory,multiple observed classification,probabilistic model,raven's matrices,statistics and probability,arts and humanities (miscellaneous),psychology(all) ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School:	Faculty of Social Sciences > School of Psychology
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	16 Dec 2025 16:30
Last Modified:	17 Dec 2025 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101438
DOI:	10.1111/bmsp.70008

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