A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties

التفاصيل البيبلوغرافية
العنوان: A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties
المؤلفون: Madison, Matthew J., Wind, Stefanie A, Maas, Lientje, Yamaguchi, Kazuhiro, Haab, Sergio
سنة النشر: 2023
المجموعة: Statistics
مصطلحات موضوعية: Statistics - Applications, Statistics - Other Statistics
الوصف: Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent characteristics. These models are well-suited for providing diagnostic and actionable feedback to support formative assessment efforts. Several DCMs have been developed and applied in different settings. This study proposes a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate that the proposed DCM has measurement properties akin to the Rasch and 1-parameter logistic item response theory models, including test score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We discuss the implications and limitations of these developments, as well as directions for future research.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2307.16744
رقم الأكسشن: edsarx.2307.16744
قاعدة البيانات: arXiv