Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory

By Brian W. Junker, Klaas Sijtsma. 2001

1,028 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b104f1bf5809f2a5246c6637e7913c2a1bf65cc3

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Citations in the corpus

Skills Diagnosis Using IRT-Based Latent Class Models (2007, methodology): “The following conjunctive models are included in our review: DINA (deterministic input noisy and) model of Haertel (1984, 1989, 1990); NIDA model of Junker and Sijtsma (2001) ; HYBRID model of Gitomer and Yamamoto (1991); unified model (UM) of DiBello, Stout, and Roussos (1995); reparameterized uni”
The Generalized DINA Model Framework. (2011, background): “Examples of specific CDMs include the deterministic inputs, noisy “and” gate (DINA; de la Torre, 2009b; Junker & Sijtsma, 2001 ), and the reduced reparametrized unified model (R-RUM; Hartz, 2002; Roussos, DiBello, Stout, Hartz, Henson, & Templin, 2007); examples of general CDMs include the log-line”
Statistical Refinement of the Q-Matrix in Cognitive Diagnosis (2013, methodology): “…response to item j between examinees who possess all the attributes required for a correct response to that item and examinees who do not, for observed item responses conforming to the Deterministic Input, Noisy Output, ‘‘And’’ gate (DINA) model (Junker & Sijtsma, 2001; Macready & Dayton, 1977).”
On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, methodology): “The DINA model has been fit within a fully Bayesian framework using Markov chain Monte Carlo (MCMC) methods (e.g., de la Torre & Douglas, 2004, 2008; Henson et al., 2009; Junker & Sijtsma, 2001).”
The Effects of Q-Matrix Misspecification on Parameter Estimates and Classification Accuracy in the DINA Model (2008, methodology): “The DINA model (e.g., de la Torre & Douglas, 2004; Junker, 1999; Junker & Sijtsma, 2001; see Macready & Dayton, 1977) is, like most multiple classification models, a noncompensatory model that uses a conjunctive condensation function (Maris, 1995, 1999).”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, methodology): “The NIDA model (Noisy Input Deterministic And) (Junker and Sijtsma 2001) is a conjunctive model: All skills involved in an item must be mastered to succeed.”
Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data (2008, methodology): “This version of the NIDA model, also given in Junker and Sijtsma (2001) and de la Torre and Douglas (2004), is a simplified version of the conjunctive model introduced in Maris (1999).”
Developing, Analyzing, and Using Distractors for Multiple-Choice Tests in Education: A Comprehensive Review (2017, methodology): “To make inferences about students’ attribute mastery, de la Torre (2009) modified the deterministic-input noisy “and” gate (DINA; Junker & Sijtsma, 2001) model to allow item responses to have more than two categories.”
A Cognitive Diagnostic Modeling of Attribute Mastery in Massachusetts, Minnesota, and the U.S. National Sample Using the TIMSS 2007 (2011, methodology): “However, this article shows that there is a greater wealth of fine-grained information that can be translated directly for classroom application at the attribute level when a cognitive diagnostic model (CDM) such as the deterministic, inputs, noisy, “and” gate (Junker & Sijtsma, 2001) model is used.”
Identifiability of restricted latent class models with binary responses (2016, methodology): “…developed in recent decades includes the DINA (Deterministic Input, Noisy ‘And’ gate) and NIDA (Noisy Inputs, Deterministic ‘And’ gate) models (Junker and Sijtsma, 2001; de la Torre and Douglas, 2004), the reparam-eterized uniﬁed/fusion model (RUM) (DiBello, Stout, and Roussos, 1995; Hartz,…”
Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models (2008, methodology): “Two commonly used cognitive diagnosis models are the DINA (short for Deterministic Inputs, Noisy And gate; Haertel, 1989; Junker & Sijtsma, 2001; Macready & Dayton, 1977) and NIDA (short for Noisy Inputs, Deterministic And gate; Junker & Sijtsma, 2001; Maris, 1999).”
Cognitively Diagnostic Assessments and the Cognitive Diagnosis Model Framework (2014, methodology): “These parameters are simply “false negative and false positive rates” (Junker & Sijtsma, 2001, p. 263).”