Joint Testlet Cognitive Diagnosis Modeling for Paired Local Item Dependence in Response Times and Response Accuracy

Zhan, Peida; Liao, Manqian; Bian, Yufang

doi:10.3389/fpsyg.2018.00607

Cited by 22 publications

(17 citation statements)

References 62 publications

(89 reference statements)

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“…The RMSE ranged from 0.391 to 0.618 across conditions, which was acceptable because the latent trait was measured by only five binary attributes. The results were similar to those found in the literature of the HO-DINA model (de la Torre and Douglas, 2004 ; Huang and Wang, 2014 ; Zhan et al, 2018a , b ). The longer the test length and the higher the item quality, the smaller the RMSE and the larger the Cor, indicating a better recovery.…”

Section: Resultssupporting

confidence: 91%

“…The longer the test length and the higher the item quality, the smaller the RMSE and the larger the Cor, indicating a better recovery. In addition, in previous studies about the HO-DINA model (e.g., de la Torre and Douglas, 2004 ; Zhan et al, 2018a , b ), the correlation coefficient of the true and estimated higher-order ability is approximately ranged from 0.6 to 0.8; However, in the HO-PINC model, the correlation coefficient is generally higher than 0.95, indicating that the higher-order ability can be better recovered in the HO-PINC model than in the HO-DINA model.…”

Section: Resultsmentioning

confidence: 87%

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Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Zhan

Wang²,

Jiao

et al. 2018

Front. Psychol.

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Existing cognitive diagnosis models conceptualize attribute mastery status discretely as either mastery or non-mastery. This study proposes a different conceptualization of attribute mastery as a probabilistic concept, i.e., the probability of mastering a specific attribute for a person, and developing a probabilistic-input, noisy conjunctive (PINC) model, in which the probability of mastering an attribute for a person is a parameter to be estimated from data. And a higher-order version of the PINC model is used to consider the associations among attributes. The results of simulation studies revealed a good parameter recovery for the new models using the Bayesian method. The Examination for the Certificate of Proficiency in English (ECPE) data set was analyzed to illustrate the implications and applications of the proposed models. The results indicated that PINC models had better model-data fit, smaller item parameter estimates, and more refined estimates of attribute mastery.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 87%

Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Zhan

Wang²,

Jiao

et al. 2018

Front. Psychol.

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“…This approach is yet limited because in many applications indicators do not necessarily represent an interrelated dimension(s) of a generic construct and thus models can suffer from estimation challenges. Other applications of modeling conditional dependence can be found in Qu et al (1996), Wang and Wilson (2005), Im (2017), Hansen et al (2016), and Zhan et al (2018).…”

Section: Current Methods For Handling Conditional Dependencementioning

confidence: 99%

Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis

2020

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A fundamental assumption underlying latent class analysis (LCA) is that class indicators are conditionally independent of each other, given latent class membership. Bayesian LCA enables researchers to detect and accommodate violations of this assumption by estimating any number of correlations among indicators with proper prior distributions. However, little is known about how the choice of prior may affect the performance of Bayesian LCA. This article presents a Monte Carlo simulation study that investigates (1) the utility of priors in a range of prior variances (i.e., strongly non-informative to strongly informative priors) in terms of Type I error and power for detecting conditional dependence and (2) the influence of imposing approximate independence on model fit of Bayesian LCA. Simulation results favored the use of a weakly informative prior with large variance-model fit (posterior predictive p-value) was always satisfactory when the class indicators were either independent or dependent. Based on the current findings and the additional literature, this article offers methodological guidelines and suggestions for applied researchers.

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“…In practice, testlets can be used in cognitive diagnosis assessment. Although it is not conceptually challenging to add a set of random effect parameters into CDMs, limited efforts have been made to the development of testlet CDMs (e.g., Hansen et al, 2016;Liao & Jiao, 2016;Zhan, Li, Wang, Bian, & Wang, 2015;Zhan, Liao, & Bian, 2018).…”

Section: A Dina Model For Testlet Designmentioning

confidence: 99%

Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

Zhan

Jiao²,

Man

et al. 2019

Journal of Educational and Behavioral Statistics

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In this article, the JAGS software program is systematically introduced to fit common Bayesian cognitive diagnosis models (CDMs), including the deterministic inputs, noisy "and" gate (DINA) model, the deterministic inputs, noisy "or" gate (DINO) model, the linear logistic model, the reduced reparameterized unified model (rRUM), and the log-linear CDM (LCDM).The unstructured latent structural model and the higher-order latent structural model are both introduced. We also show how to extend those models to consider the polytomous attributes, the testlet effect, and the longitudinal diagnosis. Finally, an empirical example is presented as a tutorial to illustrate how to use the JAGS codes in R. Keywords: cognitive diagnosis modeling, Bayesian estimation, Markov chain Monte Carlo, DINA model, DINO model, rRUM, testlet, longitudinal diagnosis, polytomous attributes 2013), their commercialization prevents unauthorized users especially students from accessing these software without purchasing. In this article, we demonstrate how to use the freeware, JAGS, to fit several popular CDMs and present the code. It is expected that the researchers can adapt the code to fit extended CDMs,which cannot be fitted in existing software or packages for their research or application purposes. In general, JAGS makes it easy to construct a Markov chain for parameters. It does not require users to derive the posterior distribution of the model parameters by hand. Movereover,, the R2jags package (Version 0.5-7; Su & Yajima, 2015) in R could be easily used to call the JAGS. Furthermore, It should be noted that the JAGS code presented in this study can be generalized easily to other BUGS software programs by minor editing including WinBUGS and OpenBUGS 2 . The following sections first illustrate JAGS codes for five CDMs: (1) the DINA model; (2) the DINO model; (3) the LLM; (4) the rRUM, and (5) the LCDM. Besides those five models, which are based on the unstructured (or saturated) latent structuralels, the higher-order latent structural model (de la Torre & Douglas, 2004) is also demonstrated. Further, the extensions to the polytomous attributes, the testlet effect, and the longitudinal diagnosis using JAGS are presentedas well. Lastly, an empirical example analysis is conductedto illustrate how to use the R2jags package to run the JAGS code.

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Joint Testlet Cognitive Diagnosis Modeling for Paired Local Item Dependence in Response Times and Response Accuracy

Cited by 22 publications

References 62 publications

Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis

Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

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