Literature DB >> 26609743

A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times.

Dylan Molenaar1, Francis Tuerlinckx2, Han L J van der Maas1.   

Abstract

A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.

Mesh:

Year:  2015        PMID: 26609743     DOI: 10.1080/00273171.2014.962684

Source DB:  PubMed          Journal:  Multivariate Behav Res        ISSN: 0027-3171            Impact factor:   5.923


  17 in total

1.  Modeling Differences Between Response Times of Correct and Incorrect Responses.

Authors:  Maria Bolsinova; Jesper Tijmstra
Journal:  Psychometrika       Date:  2019-08-28       Impact factor: 2.500

2.  Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times.

Authors:  Dylan Molenaar; Paul de Boeck
Journal:  Psychometrika       Date:  2018-02-01       Impact factor: 2.500

3.  Speed and accuracy on the Hearts and Flowers task interact to predict child outcomes.

Authors:  Marie Camerota; Michael T Willoughby; Clancy B Blair
Journal:  Psychol Assess       Date:  2019-04-29

4.  Development of an itemwise efficiency scoring method: Concurrent, convergent, discriminant, and neuroimaging-based predictive validity assessed in a large community sample.

Authors:  Tyler M Moore; Steven P Reise; David R Roalf; Theodore D Satterthwaite; Christos Davatzikos; Warren B Bilker; Allison M Port; Chad T Jackson; Kosha Ruparel; Adam P Savitt; Robert B Baron; Raquel E Gur; Ruben C Gur
Journal:  Psychol Assess       Date:  2016-02-11

5.  On the Speed Sensitivity Parameter in the Lognormal Model for Response Times and Implications for High-Stakes Measurement Practice.

Authors:  Benjamin Becker; Dries Debeer; Sebastian Weirich; Frank Goldhammer
Journal:  Appl Psychol Meas       Date:  2021-06-09

6.  A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data.

Authors:  Esther Ulitzsch; Steffi Pohl; Lale Khorramdel; Ulf Kroehne; Matthias von Davier
Journal:  Psychometrika       Date:  2021-12-02       Impact factor: 2.290

7.  A heteroscedastic generalized linear model with a non-normal speed factor for responses and response times.

Authors:  Dylan Molenaar; Maria Bolsinova
Journal:  Br J Math Stat Psychol       Date:  2017-02-03       Impact factor: 3.380

8.  Conditional Dependence between Response Time and Accuracy: An Overview of its Possible Sources and Directions for Distinguishing between Them.

Authors:  Maria Bolsinova; Jesper Tijmstra; Dylan Molenaar; Paul De Boeck
Journal:  Front Psychol       Date:  2017-02-16

9.  Leveraging item accuracy and reaction time to improve measurement of child executive function ability.

Authors:  Marie Camerota; Michael T Willoughby; Brooke E Magnus; Clancy B Blair
Journal:  Psychol Assess       Date:  2020-09-07

10.  Measuring Ability, Speed, or Both? Challenges, Psychometric Solutions, and What Can Be Gained From Experimental Control.

Authors:  Frank Goldhammer
Journal:  Measurement ( Mahwah N J)       Date:  2015-12-07
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