Cognitive Diagnostic Method: Rule Space, Q-Matrix Theory and Applications

Tatsuoka, Kikumi K.; Tatsuoka, Curtis; Kelly, Anthony E.; Dean, Michael

doi:10.4324/9780203142189.ch28

Cited by 28 publications

(44 citation statements)

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“…Various diagnostic classification models (DCM) have been developed in the literature. A short list includes the conjunctive DINA and NIDA models (Junker and Sijtsma, 2001; Tatsuoka, 2002; de la Torre and Douglas, 2004; de la Torre, 2011), the reparameterized unified/fusion model (RUM) (DiBello et al, 1995), the compensatory DINO and NIDO models (Templin and Henson, 2006), the rule space method (Tatsuoka, 1985, 2009), the attribute hierarchy method (Leighton et al, 2004), and Generalized DINA models (de la Torre, 2011); see also Henson et al (2009); Rupp et al (2010) for more developments and approaches to cognitive diagnosis. The general diagnostic model (von Davier, 2005, 2008; von Davier and Yamamoto, 2004) provides a framework for the development of diagnostic models.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

Chen

Liu

et al. 2015

Journal of the American Statistical Association

162

160

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Diagnostic classification models have recently gained prominence in educational assessment, psychiatric evaluation, and many other disciplines. Central to the model specification is the so-called Q-matrix that provides a qualitative specification of the item-attribute relationship. In this paper, we develop theories on the identifiability for the Q-matrix under the DINA and the DINO models. We further propose an estimation procedure for the Q-matrix through the regularized maximum likelihood. The applicability of this procedure is not limited to the DINA or the DINO model and it can be applied to essentially all Q-matrix based diagnostic classification models. Simulation studies are conducted to illustrate its performance. Furthermore, two case studies are presented. The first case is a data set on fraction subtraction (educational application) and the second case is a subsample of the National Epidemiological Survey on Alcohol and Related Conditions concerning the social anxiety disorder (psychiatric application).

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

Chen

Liu

et al. 2015

Journal of the American Statistical Association

162

160

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“…In existing CDM studies of the TIMSS, the quantitative analysis was typically pre-determined by the researchers. For instance, several former studies [ 26 – 29 ] employed the rule space method (RSM) [ 30 ], a statistical pattern recognition method based on item response data. However, the RSM is not a probabilistic model, and does not employ likelihood-based estimation.…”

Section: Introductionmentioning

confidence: 99%

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment

Yamaguchi

Okada

2018

PLoS ONE

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A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students’ achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed.

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“…Various diagnostic classification models have been developed. A short and incomplete list of works includes Junker and Sijtsma (2001), Tatsuoka (2002), de la Torre and Douglas (2004), DiBello, Stout, and Roussos (1995), Templin and Henson (2006), Tatsuoka (1985, 2009), Leighton, Gierl, and Hunka (2004), de la Torre (2011), Henson, Templin, and Willse (2009), von Davier (2005, 2008), von Davier and Yamamoto (2004), Rupp et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Regularized Latent Class Analysis with Application in Cognitive Diagnosis

Chen

Liu

et al. 2016

Psychometrika

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Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However; parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation–maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.

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Cognitive Diagnostic Method: Rule Space, Q-Matrix Theory and Applications

Cited by 28 publications

References 0 publications

Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment

Regularized Latent Class Analysis with Application in Cognitive Diagnosis

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