Assessing Parameter Invariance in the BLIM: Bipartition Models

作者：de Chiusole Debora^*; Stefanutti Luca; Anselmi Pasquale; Robusto Egidio

来源：Psychometrika, 2013, 78(4): 710-724.

DOI：10.1007/s11336-013-9325-5

• basic local independence model
• knowledge space theory
• parameter invariance

In knowledge space theory, the knowledge state of a student is the set of all problems he is capable of solving in a specific knowledge domain and a knowledge structure is the collection of knowledge states. The basic local independence model (BLIM) is a probabilistic model for knowledge structures. The BLIM assumes a probability distribution on the knowledge states and a lucky guess and a careless error probability for each problem. A key assumption of the BLIM is that the lucky guess and careless error probabilities do not depend on knowledge states (invariance assumption). This article proposes a method for testing the violations of this specific assumption. The proposed method was assessed in a simulation study and in an empirical application. The results show that (1) the invariance assumption might be violated by the empirical data even when the model%26apos;s fit is very good, and (2) the proposed method may prove to be a promising tool to detect invariance violations of the BLIM.

• 出版日期2013-10