A latent variable model for discrete multivariate psychometric waiting times

Douglas, Jeffrey A.; Kosorok, Michael R.; Chewning, Betty

doi:10.1007/bf02294320

Cited by 16 publications

(21 citation statements)

References 27 publications

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“…For example, it does not include the Poisson model for reading speed by Rasch (1980), which has been studied extensively by Jansen (e.g., 1986Jansen (e.g., , 1997aJansen (e.g., , 1997bJansen & Duin, 1992); the additive and multiplicative gamma models by Maris (1993); and the model for multivariate survival times with latent covariates by Douglas, Kosorok, and Chewning (1999). The models above have only been chosen to prepare our discussion in the next section.…”

Section: Current Modelsmentioning

confidence: 99%

A Hierarchical Framework for Modeling Speed and Accuracy on Test Items

2007

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Section: Current Modelsmentioning

confidence: 99%

A Hierarchical Framework for Modeling Speed and Accuracy on Test Items

2007

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“…Such a model was first sketched in the 1950s by Furneaux (1952). Since then, further models have been proposed (Scheiblechner, 1979;Maris, 1993;van Breukelen, 1997;Douglas, Kosorok, & Chewing, 1999;van der Linden, 2006). However, there does not seem to exist a standard model that can routinely be used for response times in tests.…”

Section: A Flexible Latent Trait Model For Response Times In Testsmentioning

confidence: 96%

A flexible latent trait model for response times in tests

Ranger

Kuhn

2011

Psychometrika

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“…It has been introduced in psychology by Douglas, Kosorok, and Chewing (1999). Ranger and Ortner (2012) adapted the proportional hazards model to response times in tests by including a random effect representing the work pace of an individual.…”

Section: The Proportional Hazards Model and Its Interpretationmentioning

confidence: 99%

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

Ranger

Kuhn

2015

Educational and Psychological Measurement

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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.

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A latent variable model for discrete multivariate psychometric waiting times

Abstract: latent variable, frailty, Metropolis-Hastings algorithm, survival analysis,

Cited by 16 publications

References 27 publications

A Hierarchical Framework for Modeling Speed and Accuracy on Test Items

A Hierarchical Framework for Modeling Speed and Accuracy on Test Items

A flexible latent trait model for response times in tests

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

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