title: Naïve Tests of Basic Local Independence Model's Invariance.
abstract: The basic local independence model (BLIM) is a probabilistic model for knowledge structures, characterized by the property that lucky guess and careless error parameters of the items are independent of the knowledge states of the subjects. When fitting the BLIM to empirical data, a good fit can be obtained even when the invariance assumption is violated. Therefore, statistical tests are needed for detecting violations of this specific assumption. This work provides an extension to theoretical results obtained by de Chiusole, Stefanutti, Anselmi, and Robusto (2013), showing that statistical tests based on the partitioning of the empirical data set into two (or more) groups are not adequate for testing the BLIM's invariance assumption. A simulation study confirms the theoretical results.
authors.affiliation: Università di Padova (Italy).
authors.forename: Debora de
authors.initials: Dd
authors.lastname: Chiusole
authors.affiliation: Università di Padova (Italy).
authors.forename: Luca
authors.initials: L
authors.lastname: Stefanutti
authors.affiliation: Università di Padova (Italy).
authors.forename: Pasquale
authors.initials: P
authors.lastname: Anselmi
authors.affiliation: Università di Padova (Italy).
authors.forename: Egidio
authors.initials: E
authors.lastname: Robusto
mesh_terms: D006801:Humans; D015233:Models, Statistical; D011594:Psychometrics / Q000379:methods*
keywords: basic local independence model; bipartition models; knowledge space theory; parameter invariance assumption