Exploring joint maximum likelihood estimation for cognitive diagnosis models

Chia Yi Chiu, Hans Friedrich Koehn, Yi Zheng, Robert Henson

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Current methods for fitting cognitive diagnosis models (CDMs) to educational data typically rely on expectation maximization (EM) or Markov chain Monte Carlo (MCMC) for estimating the item parameters and examinees' proficiency class memberships. However, for advanced, more complex CDMs like the reduced reparameterized unified model (Reduced RUM) and the (saturated) loglinear cognitive diagnosis model (LCDM), EM and Markov chain Monte Carlo (MCMC) have the reputation of often consuming excessive CPU times. Joint maximum likelihood estimation (JMLE) is proposed as an alternative to EM and MCMC. The maximization of the joint likelihood is typically accomplished in a few iterations, thereby drastically reducing the CPU times usually needed for fitting advanced CDMs like the Reduced RUM or the (saturated) LCDM. As another attractive feature, the JMLE algorithm presented here resolves the traditional issue of JMLE estimators-their lack of statistical consistency-by using an external, statistically consistent estimator to obtain initial estimates of examinees' class memberships as starting values. It can be proven that under this condition the JMLE item parameter estimators are also statistically consistent. The computational performance of the proposed JMLE algorithm is evaluated in two comprehensive simulation studies.

Original languageEnglish (US)
Title of host publicationQuantitative Psychology Research
Subtitle of host publicationThe 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014
PublisherSpringer International Publishing
Pages263-277
Number of pages15
Volume140
ISBN (Electronic)9783319199771
ISBN (Print)9783319199764
DOIs
StatePublished - Aug 8 2015

Fingerprint

Joints
Markov Chains
class membership
Educational Models
reputation
simulation
lack
performance
Values
time

Keywords

  • Cognitive diagnosis
  • Consistency
  • Joint maximum likelihood estimation
  • Nonparametric classification

ASJC Scopus subject areas

  • Social Sciences(all)
  • Psychology(all)

Cite this

Chiu, C. Y., Koehn, H. F., Zheng, Y., & Henson, R. (2015). Exploring joint maximum likelihood estimation for cognitive diagnosis models. In Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014 (Vol. 140, pp. 263-277). Springer International Publishing. https://doi.org/10.1007/978-3-319-19977-1_19

Exploring joint maximum likelihood estimation for cognitive diagnosis models. / Chiu, Chia Yi; Koehn, Hans Friedrich; Zheng, Yi; Henson, Robert.

Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. Vol. 140 Springer International Publishing, 2015. p. 263-277.

Research output: Chapter in Book/Report/Conference proceedingChapter

Chiu, CY, Koehn, HF, Zheng, Y & Henson, R 2015, Exploring joint maximum likelihood estimation for cognitive diagnosis models. in Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. vol. 140, Springer International Publishing, pp. 263-277. https://doi.org/10.1007/978-3-319-19977-1_19
Chiu CY, Koehn HF, Zheng Y, Henson R. Exploring joint maximum likelihood estimation for cognitive diagnosis models. In Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. Vol. 140. Springer International Publishing. 2015. p. 263-277 https://doi.org/10.1007/978-3-319-19977-1_19
Chiu, Chia Yi ; Koehn, Hans Friedrich ; Zheng, Yi ; Henson, Robert. / Exploring joint maximum likelihood estimation for cognitive diagnosis models. Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. Vol. 140 Springer International Publishing, 2015. pp. 263-277
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