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Assessing Growth in a Diagnostic Classification Model Framework
Psychometrika, 2018A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively.
Matthew J Madison
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Identifiability of Diagnostic Classification Models
Psychometrika, 2016Diagnostic classification models (DCMs) are important statistical tools in cognitive diagnosis. In this paper, we consider the issue of their identifiability. In particular, we focus on one basic and popular model, the DINA model. We propose sufficient and necessary conditions under which the model parameters are identifiable from the data.
Xu, Gongjun, Zhang, Stephanie
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Psychometrika, 2014
This commentary addresses the modeling and final analytical path taken, as well as the terminology used, in the paper “Hierarchical diagnostic classification models: a family of models for estimating and testing attribute hierarchies” by Templin and Bradshaw (Psychometrika, doi:10.1007/s11336-013-9362-0, 2013).
von Davier, Matthias +1 more
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This commentary addresses the modeling and final analytical path taken, as well as the terminology used, in the paper “Hierarchical diagnostic classification models: a family of models for estimating and testing attribute hierarchies” by Templin and Bradshaw (Psychometrika, doi:10.1007/s11336-013-9362-0, 2013).
von Davier, Matthias +1 more
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Nested diagnostic classification models for multiple‐choice items
British Journal of Mathematical and Statistical Psychology, 2020This study proposes and evaluates a diagnostic classification model framework for multiple‐choice items. Models in the proposed framework have a two‐level nested structure which allows for binary scoring (for correctness) and polytomous scoring (for distractors) at the same time.
Ren Liu, Haiyan Liu
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Measuring the Reliability of Diagnostic Classification Model Examinee Estimates
Journal of Classification, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jonathan Templin, Laine Bradshaw
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Joint Maximum Likelihood Estimation for Diagnostic Classification Models
Psychometrika, 2016Joint maximum likelihood estimation (JMLE) is developed for diagnostic classification models (DCMs). JMLE has been barely used in Psychometrics because JMLE parameter estimators typically lack statistical consistency. The JMLE procedure presented here resolves the consistency issue by incorporating an external, statistically consistent estimator of ...
Chiu, Chia-Yi +3 more
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Mastery Classification of Diagnostic Classification Models
2015The purpose of diagnostic classification models (DCMs) is to determine mastery or non-mastery of a set of attributes or skills. There are two statistics directly obtained from DCMs that can be used for mastery classification—the posterior marginal probabilities for attributes and the posterior probability for attribute profile.
Yuehmei Chien +2 more
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