NUTS for Mixture IRT Models

Hakmani, Rehab Al; Sheng, Yunlong

doi:10.1007/978-3-030-01310-3_3

Cited by 2 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the classification accuracy rates appeared to increase as the number of items and sample sizes increased. These findings would appear to be consistent with previous research (Al Hakmani, 2018; Cho et al, 2013; Finch & French, 2012; Li et al, 2009). Results also suggest that correct classification could be likely for MixIRT models when the number of items is >15 or sample sizes are >2,500.…”

Section: Discussionsupporting

confidence: 93%

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models

Şen

Cohen

2023

Educational and Psychological Measurement

View full text Add to dashboard Cite

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike’s information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper’s information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo–Mendell–Rubin (LMR), and Vuong-Lo–Mendell–Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

show abstract

Section: Discussionsupporting

confidence: 93%

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models

Şen

Cohen

2023

Educational and Psychological Measurement

View full text Add to dashboard Cite

show abstract

Empirical evaluation of fully Bayesian information criteria for mixture IRT models using NUTS

AlHakmani

Sheng

2022

Behaviormetrika

View full text Add to dashboard Cite

This study is to evaluate the performance of fully Bayesian information criteria, namely, LOO, WAIC and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with conventional information criteria including AIC, BIC, CAIC, SABIC, and DIC. Monte Carlo simulations were carried out to evaluate these criteria under different situations. The results indicate that AIC, BIC, and their related CAIC and SABIC tend to select the simpler model and are not recommended when the actual data involve multiple latent classes. For the three fully Bayesian measures, WBIC can be used for detecting the number of latent classes for tests with at least 30 items, while WAIC and LOO are suggested to be used together with their effective number of parameters in choosing the correct number of latent classes.

show abstract

NUTS for Mixture IRT Models

Cited by 2 publications

References 34 publications

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models

Empirical evaluation of fully Bayesian information criteria for mixture IRT models using NUTS

Contact Info

Product

Resources

About