A flexible latent trait model for response times in tests

Ranger, Jochen; Kuhn, Jörg‐Tobias

doi:10.1007/s11336-011-9231-7

Cited by 39 publications

(68 citation statements)

References 54 publications

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“…The hierarchical latent variable modeling of responses and RTs (Fox, Klein Entink, & van der Linden, 2007;van der Linden, 2007;van der Linden & Glas, 2010) and the generalized linear IRT approach (Molenaar, Tuerlinckx, & van der Maas, 2015a) both assume a constant latent working speed parameter for each individual. The constant speed parameter is also assumed in the IRT approach of categorical RTs (e.g., Ranger & Kuhn, 2012) and the nonlinear regressions of IRT parameters on RTs (Ferrando & Lorenzo-Seva, 2007a, 2007b.…”

Section: Introductionmentioning

confidence: 99%

Joint Modeling of Ability and Differential Speed Using Responses and Response Times

Fox

Marianti

2016

Multivariate Behavioral Research

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With computerized testing, it is possible to record both the responses of test takers to test questions (i.e., items) and the amount of time spent by a test taker in responding to each question. Various models have been proposed that take into account both test-taker ability and working speed, with the many models assuming a constant working speed throughout the test. The constant working speed assumption may be inappropriate for various reasons. For example, a test taker may need to adjust the pace due to time mismanagement, or a test taker who started out working too fast may reduce the working speed to improve accuracy. A model is proposed here that allows for variable working speed. An illustration of the model using the Amsterdam Chess Test data is provided.

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Section: Introductionmentioning

confidence: 99%

Joint Modeling of Ability and Differential Speed Using Responses and Response Times

Fox

Marianti

2016

Multivariate Behavioral Research

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“…Therefore, in the present context we also advise the usage of information criteria for inferences about the number of the latent classes. The overall fit of the model can be assessed with a test proposed by Ranger and Kuhn (2014). A test of model fit is usually superior to graphical checks in highly parameterized models.…”

Section: Analysis Of Model Fitmentioning

confidence: 99%

“…As can be shown the sum of the squared residuals is approximately chi-square distributed. The degrees of freedom have to be corrected though in order to account for the complication that the same data set is used for model calibration and model evaluation; see Ranger and Kuhn (2014) for more details.…”

Section: Analysis Of Model Fitmentioning

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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“…In addition to the proposed test, a second test was calculated. This test is due to Ranger and Kuhn (2011). It is based on the fact that the proportional hazards model for discrete time is a standard generalized linear model; see the discussion concerning the estimation approach of Douglas et al (1999) in the previous section.…”

Section: Simulation Studymentioning

confidence: 99%

Response Time Modeling Based on the Proportional Hazards Model

Ranger

Ortner

2013

Multivariate Behavioral Research

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Response time data are regularly analyzed in psychology. When several response times are assessed per participant, it is common practice to use latent trait models in order to account for the dependency of the response times from the same participant. One such model is the proportional hazards model with random effects. Despite its popularity in survival analysis, this model is rarely used in psychology because of the difficulty of model estimation when latent variables are present. In this article, a new estimation method is proposed. This method is based on the rank correlation matrix containing Kendall's Tau coefficients and unweighted least squares estimation ( Kendall, 1938 ). Compared with marginal maximum likelihood estimation, the new estimation approach is simple, not computationally intensive, and almost as efficient. Additionally, the approach allows the implementation of a test for model fit. Feasibility of the estimation method and validity of the fit test is demonstrated with a simulation study. An application of the model to a real data set is provided.

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A flexible latent trait model for response times in tests

Abstract: response time, proportional hazard model, accelerated failure time model,

Cited by 39 publications

References 54 publications

Joint Modeling of Ability and Differential Speed Using Responses and Response Times

Joint Modeling of Ability and Differential Speed Using Responses and Response Times

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

Response Time Modeling Based on the Proportional Hazards Model

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