The R Package CDM for Diagnostic Modeling

Robitzsch, Alexander; George, Ann Cathrice

doi:10.1007/978-3-030-05584-4_26

Cited by 75 publications

(111 citation statements)

References 82 publications

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“…Achievement estimates across educational systems are available in the international report (OECD, 2016c). Second, achievement distributions are estimated using TAM 2.12-18 (Robitzsch, Kiefer, & Wu, 2018). All of the simulated conditions (described next) are repeated 100 times for each country.…”

Section: Methodsmentioning

confidence: 99%

Measuring Widening Proficiency Differences in International Assessments: Are Current Approaches Enough?

Rutkowski

Liaw

2018

Educational Measurement

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Participation in international large‐scale assessments has grown over time with the largest, the Programme for International Student Assessment (PISA), including more than 70 education systems that are economically and educationally diverse. To help accommodate for large achievement differences among participants, in 2009 PISA offered low‐performing systems the option of including an easier set of items in the assessment with an aim of providing improved achievement estimates. However, there remains a lack of evidence on the performance of this design innovation. As such, we simulate a design that closely mirrors the PISA 2015 math assessment in order to empirically examine the benefits of including easy items for low‐performing countries. We extend the PISA design to include increased numbers of easy items and items that are easier than currently implemented. Findings show that the current PISA approach provides little advantage compared to a common test for all participants. Our study also demonstrates persistent bias, low coverage rates, and low correlations between generating and estimated proficiency under current designs. Through our simulation we also show that to improve achievement estimation for low performers about half of the items would need to be made significantly easier.

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Section: Methodsmentioning

confidence: 99%

Measuring Widening Proficiency Differences in International Assessments: Are Current Approaches Enough?

Rutkowski

Liaw

2018

Educational Measurement

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“…The maximization of ( 7 ) is conducted using an expectation-maximization (EM) algorithm (see Section 3.3 for general description). The estimation approach of Chen et al ( 2017 ) is implemented in the R package CDM ( George et al 2016 ; Robitzsch and George 2019 ).…”

Section: Regularized Latent Class Analysismentioning

confidence: 99%

“… Chen et al ( 2017 ) used the RLCM to derive partially ordered latent classes in cognitive diagnostic modeling. Wang and Lu ( 2020 ) also applied the RLCM for estimating hierarchies among latent classes (see also Robitzsch and George 2019 ). Using the RLCM with an analysis of hierarchies may be considered as a preceding method of confirmatory approaches to latent class modeling.…”

Section: Regularized Latent Class Analysismentioning

confidence: 99%

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

Robitzsch

2020

J. Intell.

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The last series of Raven’s standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

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“…The maximization in Equation 7 is conducted using an expectation-maximization (EM) algorithm (see Section 3.3 for general description). The estimation approach of [26] is implemented in the R package CDM [45,46].…”

Section: Regularized Latent Class Analysis For Dichotomous Item Respomentioning

confidence: 99%

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

Robitzsch¹

2020

Preprint

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The last series of Raven's standard progressive matrices (SPM-LS) test were studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCM). For dichotomous item response data, an alternative estimation approach for RLCMs is proposed. For polytomous item responses, different alternatives for performing regularized latent class analysis are proposed. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes.

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The R Package CDM for Diagnostic Modeling

Cited by 75 publications

References 82 publications

Measuring Widening Proficiency Differences in International Assessments: Are Current Approaches Enough?

Measuring Widening Proficiency Differences in International Assessments: Are Current Approaches Enough?

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

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

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