Abstract
This article shows that the recently proposed latent D-scoring model of Dimitrov is statistically equivalent to the two-parameter logistic item response model. An analytical derivation and a numerical illustration are employed for demonstrating this finding. Hence, estimation techniques for the two-parameter logistic model can be used for estimating the latent D-scoring model. In an empirical example using PISA data, differences of country ranks are investigated when using different metrics for the latent trait. In the example, the choice of the latent trait metric matters for the ranking of countries. Finally, it is argued that an item response model with bounded latent trait values like the latent D-scoring model might have advantages for reporting results in terms of interpretation.
Highlights
Equivalence of the Latent D-ScoringItem response theory (IRT; [1]) is the statistical analysis of test items in education, psychology, and other fields of social sciences
In order to illustrate the consequences of the choice of different metrics of the latent trait in multiple-group comparisons, we analyzed the data from the Programme for International Student Assessment (PISA) conducted in 2006 (PISA 2006; [30])
This article shows that the newly proposed latent D-scoring (LDS) model of Dimitrov can be interpreted as a reparametrization of the well-studied 2PL model
Summary
Item response theory (IRT; [1]) is the statistical analysis of test items in education, psychology, and other fields of social sciences. A number of test items are administered to test takers, and the interest is to infer the ability (performance or trait) of them. IRT models relate observed item responses to unobserved latent traits. Because the latent trait is unobserved, there are many plausible choices for modeling these relationships. The most popular class of IRT models comprises logistic IRT models [2]. In a series of papers, Dimitrov proposed an alternative IRT model, the so-called latent D-scoring model [3]. The main goal of this paper is to demonstrate that the newly proposed IRT model is statistically equivalent to the well-established two-parameter logistic IRT model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
