| Literature DB >> 32803390 |
Justin L Kern1, Steven Andrew Culpepper2.
Abstract
Recently, there has been a renewed interest in the four-parameter item response theory model as a way to capture guessing and slipping behaviors in responses. Research has shown, however, that the nested three-parameter model suffers from issues of unidentifiability (San Martín et al. in Psychometrika 80:450-467, 2015), which places concern on the identifiability of the four-parameter model. Borrowing from recent advances in the identification of cognitive diagnostic models, in particular, the DINA model (Gu and Xu in Stat Sin https://doi.org/10.5705/ss.202018.0420 , 2019), a new model is proposed with restrictions inspired by this new literature to help with the identification issue. Specifically, we show conditions under which the four-parameter model is strictly and generically identified. These conditions inform the presentation of a new exploratory model, which we call the dyad four-parameter normal ogive (Dyad-4PNO) model. This model is developed by placing a hierarchical structure on the DINA model and imposing equality constraints on a priori unknown dyads of items. We present a Bayesian formulation of this model, and show that model parameters can be accurately recovered. Finally, we apply the model to a real dataset.Entities:
Keywords: Bayesian statistics; four-parameter model; hierarchical DINA model; identification; slipping
Year: 2020 PMID: 32803390 DOI: 10.1007/s11336-020-09716-3
Source DB: PubMed Journal: Psychometrika ISSN: 0033-3123 Impact factor: 2.500