Literature DB >> 29795878

A Mixture Proportional Hazards Model With Random Effects for Response Times in Tests.

Jochen Ranger1, Jörg-Tobias Kuhn2.   

Abstract

In this article, a new model for test response times is proposed that combines latent class analysis and the proportional hazards model with random effects in a similar vein as the mixture factor model. The model assumes the existence of different latent classes. In each latent class, the response times are distributed according to a class-specific proportional hazards model. The class-specific proportional hazards models relate the response times of each subject to his or her work pace, which is considered as a random effect. The latent class extension of the proportional hazards model allows for differences in response strategies between subjects. The differences can be captured in the hazard functions, which trace the progress individuals make over time when working on an item. The model can be calibrated with marginal maximum likelihood estimation. The fit of the model can either be assessed with information criteria or with a test of model fit. In a simulation study, the performance of the proposed approaches to model calibration and model evaluation is investigated. Finally, the model is used for a real data set.

Keywords:  latent class analysis; proportional hazards model; response time

Year:  2015        PMID: 29795878      PMCID: PMC5965566          DOI: 10.1177/0013164415598347

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  13 in total

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Authors:  Michael J Wenger; Bradley S Gibson
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2.  A latent trait model for response times on tests employing the proportional hazards model.

Authors:  Jochen Ranger; Tuulia Ortner
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3.  Response Time Modeling Based on the Proportional Hazards Model.

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6.  Latent class and finite mixture models for multilevel data sets.

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Journal:  Stat Methods Med Res       Date:  2007-09-13       Impact factor: 3.021

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Authors:  Rinke H Klein Entink; Jörg-Tobias Kuhn; Lutz F Hornke; Jean-Paul Fox
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8.  Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences.

Authors:  Han L J van der Maas; Dylan Molenaar; Gunter Maris; Rogier A Kievit; Denny Borsboom
Journal:  Psychol Rev       Date:  2011-04       Impact factor: 8.934

9.  CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers.

Authors:  Kirsten Bulteel; Tom F Wilderjans; Francis Tuerlinckx; Eva Ceulemans
Journal:  Behav Res Methods       Date:  2013-09

10.  Combining item response theory and diagnostic classification models: a psychometric model for scaling ability and diagnosing misconceptions.

Authors:  Laine Bradshaw; Jonathan Templin
Journal:  Psychometrika       Date:  2013-08-02       Impact factor: 2.500

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