Literature DB >> 25280442

A Mixture Cure-Rate Model for Responses and Response Times in Time-Limit Tests.

Yi-Hsuan Lee1, Zhiliang Ying.   

Abstract

Many large-scale standardized tests are intended to measure skills related to ability rather than the rate at which examinees can work. Time limits imposed on these tests make it difficult to distinguish between the effect of low proficiency and the effect of lack of time. This paper proposes a mixture cure-rate model approach to address this issue. Maximum likelihood estimation is proposed for parameter and variance estimation for three cases: when examinee parameters are to be estimated given precalibrated item parameters, when item parameters are to be calibrated given known examinee parameters, and when item parameters are to be estimated without assuming known examinee parameters. Large-sample properties are established for the cases under suitable regularity conditions. Simulation studies suggest that the proposed approach is appropriate for inferences concerning model parameters. In addition, not distinguishing between the effect of low proficiency and the effect of lack of time is shown to have considerable consequences for parameter estimation. A real data example is presented to demonstrate the new model. Choice of survival models for the latent power times is also discussed.

Mesh:

Year:  2014        PMID: 25280442     DOI: 10.1007/s11336-014-9419-8

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  8 in total

1.  A nonparametric mixture model for cure rate estimation.

Authors:  Y Peng; K B Dear
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  Estimation in a Cox proportional hazards cure model.

Authors:  J P Sy; J M Taylor
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

3.  A speeded item response model: leave the harder till later.

Authors:  Yu-Wei Chang; Rung-Ching Tsai; Nan-Jung Hsu
Journal:  Psychometrika       Date:  2013-04-11       Impact factor: 2.500

4.  A Box-Cox normal model for response times.

Authors:  R H Klein Entink; W J van der Linden; J-P Fox
Journal:  Br J Math Stat Psychol       Date:  2009-01-30       Impact factor: 3.380

5.  How much power and speed is measured in this test?

Authors:  Ivailo Partchev; Paul De Boeck; Rolf Steyer
Journal:  Assessment       Date:  2011-06-10

6.  A generalized F mixture model for cure rate estimation.

Authors:  Y Peng; K B Dear; J W Denham
Journal:  Stat Med       Date:  1998-04-30       Impact factor: 2.373

7.  Analysis of survival data by the proportional odds model.

Authors:  S Bennett
Journal:  Stat Med       Date:  1983 Apr-Jun       Impact factor: 2.373

8.  The use of mixture models for the analysis of survival data with long-term survivors.

Authors:  V T Farewell
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

  8 in total
  1 in total

1.  How Do Test Takers Interact With Simulation-Based Tasks? A Response-Time Perspective.

Authors:  Yi-Hsuan Lee; Jiangang Hao; Kaiwen Man; Lu Ou
Journal:  Front Psychol       Date:  2019-04-24
  1 in total

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