An joint maximum likelihood estimation approach to cognitive diagnosis models

Youn Seon Lim; Fritz Drasgow

doi:10.1007/978-3-319-77249-3_28

An joint maximum likelihood estimation approach to cognitive diagnosis models

Youn Seon Lim, Fritz Drasgow

School of Labor and Employment Relations

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, a simulation-based method for computing joint maximum likelihood estimates of cognitive diagnosis model parameters is proposed. The central theme of the approach is to reduce the complexity of models to focus on their most critical elements. In particular, an approach analogous to joint maximum likelihood estimation is taken, and the latent attribute vectors are regarded as structural parameters, not parameters to be removed by integration with this approach, the joint distribution of the latent attributes does not have to be specified, which reduces the number of parameters in the model. The Markov Chain Monte Carlo algorithm is used to simultaneously evaluate and optimize the likelihood function. This streamlined approach performed as well as more traditional methods for models such as the DINA, and affords the opportunity to fit more complicated models in which other methods may not be feasible.

Original language	English (US)
Title of host publication	Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017
Editors	Jorge Gonzalez, Rianne Janssen, Marie Wiberg, Dylan Molenaar, Steven Culpepper
Publisher	Springer
Pages	335-350
Number of pages	16
ISBN (Print)	9783319772486
DOIs	https://doi.org/10.1007/978-3-319-77249-3_28
State	Published - 2018
Event	82nd Annual meeting of the Psychometric Society, 2017 - Zurich, Switzerland Duration: Jul 17 2017 → Jul 21 2017

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	233
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Other

Other	82nd Annual meeting of the Psychometric Society, 2017
Country/Territory	Switzerland
City	Zurich
Period	7/17/17 → 7/21/17

Keywords

Cognitive diagnosis model
Joint maximum likelihood estimation
Simulated annealing

ASJC Scopus subject areas

General Mathematics

Online availability

10.1007/978-3-319-77249-3_28

Library availability

Discover UIUC Full Text

Cite this

Lim, Y. S., & Drasgow, F. (2018). An joint maximum likelihood estimation approach to cognitive diagnosis models. In J. Gonzalez, R. Janssen, M. Wiberg, D. Molenaar, & S. Culpepper (Eds.), Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017 (pp. 335-350). (Springer Proceedings in Mathematics and Statistics; Vol. 233). Springer. https://doi.org/10.1007/978-3-319-77249-3_28

An joint maximum likelihood estimation approach to cognitive diagnosis models. / Lim, Youn Seon; Drasgow, Fritz.
Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017. ed. / Jorge Gonzalez; Rianne Janssen; Marie Wiberg; Dylan Molenaar; Steven Culpepper. Springer, 2018. p. 335-350 (Springer Proceedings in Mathematics and Statistics; Vol. 233).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lim, YS & Drasgow, F 2018, An joint maximum likelihood estimation approach to cognitive diagnosis models. in J Gonzalez, R Janssen, M Wiberg, D Molenaar & S Culpepper (eds), Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017. Springer Proceedings in Mathematics and Statistics, vol. 233, Springer, pp. 335-350, 82nd Annual meeting of the Psychometric Society, 2017, Zurich, Switzerland, 7/17/17. https://doi.org/10.1007/978-3-319-77249-3_28

Lim YS, Drasgow F. An joint maximum likelihood estimation approach to cognitive diagnosis models. In Gonzalez J, Janssen R, Wiberg M, Molenaar D, Culpepper S, editors, Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017. Springer. 2018. p. 335-350. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-3-319-77249-3_28

Lim, Youn Seon ; Drasgow, Fritz. / An joint maximum likelihood estimation approach to cognitive diagnosis models. Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017. editor / Jorge Gonzalez ; Rianne Janssen ; Marie Wiberg ; Dylan Molenaar ; Steven Culpepper. Springer, 2018. pp. 335-350 (Springer Proceedings in Mathematics and Statistics).

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An joint maximum likelihood estimation approach to cognitive diagnosis models

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Online availability

Library availability

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Quantitative Psychology: The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017

Cite this