Bayesian Estimation of Latent Space Item Response Models with JAGS, Stan, and NIMBLE in R

Luo, Jinwen; Carolis, Ludovica De; Zeng, Biao; Jeon, Minjeong

doi:10.3390/psych5020027

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…We specify  = 1, which serves as a placeholder in the NIMBLE and does not affect the sampling. Successful utilization of NIMBLE has been observed in various psychometric models (Liu, 2023;Luo et al, 2023;Ma & Chen, 2020;Paganin et al, 2023). We assess the parameter estimation recovery of the proposed models in the following simulation study.…”

Section: Bayesian Parameter Estimationmentioning

confidence: 99%

Inferring Individual Attributes Using Testlet-Based Visual Analogue Scaling and Beta Copula Diagnostic Classification Models

Liu

2024

Preprint

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This paper explores the inference of the latent attributes of respondents using testlet-based visual analogue scaling (VAS), which comprises multiple items ranging from 0% to 100%. Beta copula diagnostic classification models (BCDCMs) are proposed to infer and classify respondents’ latent attributes. The paper also discusses model properties, parameter estimation, goodness of fit, model comparison, and the visual presentation of results. The stability of parameter estimation is assessed through a simulation study, followed by an empirical analysis conducted on a Symptom Checklist-90 dataset to estimate respondents’ latent psychological and psychiatric symptoms. The findings indicate that BCDCMs effectively infer latent attributes and derive precise item parameters, as evidenced by the simulation study. This yields valuable diagnostic insights for symptom classification in empirical analyses, suggesting that BCDCMs are viable alternatives to traditional cutoff-score methods for VAS data.

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Section: Bayesian Parameter Estimationmentioning

confidence: 99%

Inferring Individual Attributes Using Testlet-Based Visual Analogue Scaling and Beta Copula Diagnostic Classification Models

Liu

2024

Preprint

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“…A Stan program to fit the LSIRM is provided in Section S1 in our Supplementary online Materials. Users can also find some alternative methods from the previous literature (Ho and Jeon 2023;Jeon et al 2021;Luo et al 2023). For Bayesian inference, we recommend the following prior specifications.…”

Section: Inferencementioning

confidence: 99%

A Recent Development of a Network Approach to Assessment Data: Latent Space Item Response Modeling for Intelligence Studies

Kang,

Jeon

2024

J. Intell.

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This article aims to provide an overview of the potential advantages and utilities of the recently proposed Latent Space Item Response Model (LSIRM) in the context of intelligence studies. The LSIRM integrates the traditional Rasch IRT model for psychometric data with the latent space model for network data. The model has person-wise latent abilities and item difficulty parameters, capturing the main person and item effects, akin to the Rasch model. However, it additionally assumes that persons and items can be mapped onto the same metric space called a latent space and distances between persons and items represent further decreases in response accuracy uncaptured by the main model parameters. In this way, the model can account for conditional dependence or interactions between persons and items unexplained by the Rasch model. With two empirical datasets, we illustrate that (1) the latent space can provide information on respondents and items that cannot be captured by the Rasch model, (2) the LSIRM can quantify and visualize potential between-person variations in item difficulty, (3) latent dimensions/clusters of persons and items can be detected or extracted based on their latent positions on the map, and (4) personalized feedback can be generated from person-item distances. We conclude with discussions related to the latent space modeling integrated with other psychometric models and potential future directions.

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“…The verbal aggression data has widely been used in IRT model applications [36,45,46] that employ mixture IRT models [5,17,18]. A total of 316 individuals responded to 24 items nested within three crossed factors: situation types ('Self-to-lame' and 'Other-to-blame') in four situations ('Bus', 'Train', 'Store', and 'Operator'); behavior types ('Curse', 'Scold', and 'Shout'); and behavior modes ('Want' and 'Do').…”

Section: Polytomous Responses: Verbal Aggression Datamentioning

confidence: 99%

Item Response Analysis of a Structured Mixture Item Response Model with mirt Package in R

Lee,

Suh,

Jeon

2024

Psych

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Structured mixture item response models (StrMixIRMs) are a special type of constrained confirmatory mixture item response theory (IRT) model for detecting latent performance differences in a measurement instrument by characteristic item groups, and classifying respondents according to these differences. In light of limited software options for estimating StrMixIRMs under existing frameworks, this paper proposes reparameterizing it as a confirmatory mixture IRT model using interaction effects between latent classes and item groups. The reparameterization allows for easier implementation of StrMixIRMs with multiple software programs that have mixture modeling capabilities, including open-source ones. This widens the accessibility to these models to a broad range of users and thus can facilitate research and applications of StrMixIRMs. This paper serves two main goals: First, we introduce StrMixIRMs, focusing on the proposed reparameterization based on interaction effects and its various extensions. Second, we illustrate use cases of this novel reparameterization within the mirt 1.41 package in R by employing two empirical datasets. Detailed R code with notes are provided for the applications along with an interpretation of the outputs.

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Bayesian Estimation of Latent Space Item Response Models with JAGS, Stan, and NIMBLE in R

Cited by 5 publications

References 46 publications

Inferring Individual Attributes Using Testlet-Based Visual Analogue Scaling and Beta Copula Diagnostic Classification Models

Inferring Individual Attributes Using Testlet-Based Visual Analogue Scaling and Beta Copula Diagnostic Classification Models

A Recent Development of a Network Approach to Assessment Data: Latent Space Item Response Modeling for Intelligence Studies

Item Response Analysis of a Structured Mixture Item Response Model with mirt Package in R

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