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Erschienen in:
Optimal Scores as an Alternative to Sum Scores Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

An Joint Maximum Likelihood Estimation Approach to Cognitive Diagnosis Models

verfasst von : Youn Seon Lim, Fritz Drasgow

Erschienen in: Quantitative Psychology

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel A Cognitive Diagnosis Method Based on Mahalanobis Distance
Nächstes Kapitel An Exploratory Discrete Factor Loading Method for Q-Matrix Specification in Cognitive Diagnostic Models
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Metadaten
Titel
An Joint Maximum Likelihood Estimation Approach to Cognitive Diagnosis Models
verfasst von
Youn Seon Lim
Fritz Drasgow
Copyright-Jahr
2018
Buch
Quantitative Psychology

Print ISBN: 978-3-319-77248-6

Electronic ISBN: 978-3-319-77249-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77249-3_28