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The R package CDM for cognitive diagnosis models. / George, Ann Cathrice; Robitzsch, Alexander; Kiefer, Thomas et al.

In: Journal of Statistical Software, Vol. 74, No. 2, 20.10.2016, p. 1-24.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

George, AC, Robitzsch, A, Kiefer, T, Groß, J & Ünli, A 2016, 'The R package CDM for cognitive diagnosis models', Journal of Statistical Software, vol. 74, no. 2, pp. 1-24. https://doi.org/10.18637/jss.v074.i02

APA

George, A. C., Robitzsch, A., Kiefer, T., Groß, J., & Ünli, A. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1-24. https://doi.org/10.18637/jss.v074.i02

Vancouver

George AC, Robitzsch A, Kiefer T, Groß J, Ünli A. The R package CDM for cognitive diagnosis models. Journal of Statistical Software. 2016 Oct 20;74(2):1-24. https://doi.org/10.18637/jss.v074.i02

Author

George, Ann Cathrice ; Robitzsch, Alexander ; Kiefer, Thomas et al. / The R package CDM for cognitive diagnosis models. In: Journal of Statistical Software. 2016 ; Vol. 74, No. 2. pp. 1-24.

BibTeX

@article{bf7e2966f02944b19687aec3fd1a05d4,
title = "The R package CDM for cognitive diagnosis models",
abstract = "This paper introduces the R package CDM for cognitive diagnosis models (CDMs). The package implements parameter estimation procedures for two general CDM frameworks, the generalized-deterministic input noisy-and-gate (G-DINA) and the general diagnostic model (GDM). It contains additional functions for analyzing data under these frameworks, like tools for simulating and plotting data, or for evaluating global model and item fit. The paper describes the theoretical aspects of implemented CDM frameworks and it illustrates the usage of the package with empirical data of the common fraction subtraction test by Tatsuoka (1984). ",
keywords = "Methodological research and development, R, cognitive diagnosis models, general diagnostic model, structured latent class models, skill diagnosis",
author = "George, {Ann Cathrice} and Alexander Robitzsch and Thomas Kiefer and J{\"u}rgen Gro{\ss} and Ali {\"U}nli",
year = "2016",
month = oct,
day = "20",
doi = "10.18637/jss.v074.i02",
language = "English",
volume = "74",
pages = "1--24",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "2",

}

RIS

TY - JOUR

T1 - The R package CDM for cognitive diagnosis models

AU - George, Ann Cathrice

AU - Robitzsch, Alexander

AU - Kiefer, Thomas

AU - Groß, Jürgen

AU - Ünli, Ali

PY - 2016/10/20

Y1 - 2016/10/20

N2 - This paper introduces the R package CDM for cognitive diagnosis models (CDMs). The package implements parameter estimation procedures for two general CDM frameworks, the generalized-deterministic input noisy-and-gate (G-DINA) and the general diagnostic model (GDM). It contains additional functions for analyzing data under these frameworks, like tools for simulating and plotting data, or for evaluating global model and item fit. The paper describes the theoretical aspects of implemented CDM frameworks and it illustrates the usage of the package with empirical data of the common fraction subtraction test by Tatsuoka (1984).

AB - This paper introduces the R package CDM for cognitive diagnosis models (CDMs). The package implements parameter estimation procedures for two general CDM frameworks, the generalized-deterministic input noisy-and-gate (G-DINA) and the general diagnostic model (GDM). It contains additional functions for analyzing data under these frameworks, like tools for simulating and plotting data, or for evaluating global model and item fit. The paper describes the theoretical aspects of implemented CDM frameworks and it illustrates the usage of the package with empirical data of the common fraction subtraction test by Tatsuoka (1984).

KW - Methodological research and development

KW - R

KW - cognitive diagnosis models

KW - general diagnostic model

KW - structured latent class models

KW - skill diagnosis

U2 - 10.18637/jss.v074.i02

DO - 10.18637/jss.v074.i02

M3 - Journal article

VL - 74

SP - 1

EP - 24

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 2

ER -

ID: 624371