Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

Robitzsch, Alexander

doi:10.3390/jintelligence8030030

Cited by 8 publications

(10 citation statements)

References 61 publications

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“…By focusing on these approaches, the computational burden of regularized SEM is noticeably reduced. Future research might investigate whether the findings obtained for SEMs transfer to other models involving latent variables such as item response models [47][48][49][50][51][52], latent class models [28,[53][54][55], or mixture models [56,57].…”

Section: Discussionmentioning

confidence: 99%

Implementation Aspects in Regularized Structural Equation Models

Robitzsch

2023

Algorithms

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This article reviews several implementation aspects in estimating regularized single-group and multiple-group structural equation models (SEM). It is demonstrated that approximate estimation approaches that rely on a differentiable approximation of non-differentiable penalty functions perform similarly to the coordinate descent optimization approach of regularized SEMs. Furthermore, using a fixed regularization parameter can sometimes be superior to an optimal regularization parameter selected by the Bayesian information criterion when it comes to the estimation of structural parameters. Moreover, the widespread penalty functions of regularized SEM implemented in several R packages were compared with the estimation based on a recently proposed penalty function in the Mplus software. Finally, we also investigate the performance of a clever replacement of the optimization function in regularized SEM with a smoothed differentiable approximation of the Bayesian information criterion proposed by O’Neill and Burke in 2023. The findings were derived through two simulation studies and are intended to guide the practical implementation of regularized SEM in future software pieces.

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Section: Discussionmentioning

confidence: 99%

Implementation Aspects in Regularized Structural Equation Models

Robitzsch

2023

Algorithms

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“…Regularization methods have also been extended to exploratory latent class with a focus on polytomous item responses (RLCM) [38]. Five different ways of performing RLCMs for polytomous data were compared through a simulation study: (1) regularizing differences in item parameters among classes, (2) among categories, or (3) both, and (4) applying fused group regularization among classes, or (5) among categories.…”

Section: Group-based Semmentioning

confidence: 99%

Tutorial on the Use of the regsem Package in R

Jacobucci

Ammerman

2021

Psych

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Sparse estimation through regularization is gaining popularity in psychological research. Such techniques penalize the complexity of the model and could perform variable/path selection in an automatic way, and thus are particularly useful in models that have small parameter-to-sample-size ratios. This paper gives a detailed tutorial of the R package regsem, which implements regularization for structural equation models. Example R code is also provided to highlight the key arguments of implementing regularized structural equation models in this package. The tutorial ends by discussing remedies of some known drawbacks of a popular type of regularization, computational methods supported by the package that can improve the selection result, and some other practical issues such as dealing with missing data and categorical variables.

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“…For example, the Wald test and likelihood ratio test have been used to select the measurement model for each item [16,[33][34][35]. Regularized CDMs have also been used to determine each item's most appropriate measurement model [36,37]. It should be noted that monotonicity constraints may need to be imposed because they are often theoretically reasonable and can stabilize the parameter estimation, especially when the sample size is small [38].…”

Section: Overview Of the Cdm Analysesmentioning

confidence: 99%

“…In addition, the CDM package allows researchers to fit the regularized G-DINA model using a variety of penalty terms. This is a flexible approach to simplifying the G-DINA model and interested readers may refer to Robitzsch [37] and Robitzsch and George [36] for more information. A caveat to the Wald and LR tests for model comparisons is that trivial discrepancy between two models may be detected when sample size is large and one should be aware that the logit link must be used when the regularized G-DINA model is specified in the CDM package.…”

Section: Cdm Calibrationmentioning

confidence: 99%

Cognitively Diagnostic Analysis Using the G-DINA Model in R

Shi

Robitzsch

et al. 2021

Psych

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Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed.

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Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

Cited by 8 publications

References 61 publications

Implementation Aspects in Regularized Structural Equation Models

Implementation Aspects in Regularized Structural Equation Models

Tutorial on the Use of the regsem Package in R

Cognitively Diagnostic Analysis Using the G-DINA Model in R

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