Literature DB >> 26760724

Item Response Modeling of Paired Comparison and Ranking Data.

Alberto Maydeu-Olivares1, Anna Brown2.   

Abstract

The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally presented as scaling models, that is, stimuli-centered models, they can also be used as person-centered models. In this article, we discuss how Thurstone's model for comparative data can be formulated as item response theory models so that respondents' scores on underlying dimensions can be estimated. Item parameters and latent trait scores can be readily estimated using a widely used statistical modeling program. Simulation studies show that item characteristic curves can be accurately estimated with as few as 200 observations and that latent trait scores can be recovered to a high precision. Empirical examples are given to illustrate how the model may be applied in practice and to recommend guidelines for designing ranking and paired comparisons tasks in the future.

Entities:  

Year:  2010        PMID: 26760724     DOI: 10.1080/00273171.2010.531231

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


  9 in total

1.  Item Response Models for Forced-Choice Questionnaires: A Common Framework.

Authors:  Anna Brown
Journal:  Psychometrika       Date:  2014-12-10       Impact factor: 2.500

2.  Item Response Theory Models for Ipsative Tests With Multidimensional Pairwise Comparison Items.

Authors:  Wen-Chung Wang; Xue-Lan Qiu; Chia-Wen Chen; Sage Ro; Kuan-Yu Jin
Journal:  Appl Psychol Meas       Date:  2017-04-09

3.  Fit Indices for Measurement Invariance Tests in the Thurstonian IRT Model.

Authors:  HyeSun Lee; Weldon Z Smith
Journal:  Appl Psychol Meas       Date:  2019-12-26

4.  Remarkable properties for diagnostics and inference of ranking data modelling.

Authors:  Cristina Mollica; Luca Tardella
Journal:  Br J Math Stat Psychol       Date:  2022-02-07       Impact factor: 2.410

5.  Genome-Enabled Prediction Methods Based on Machine Learning.

Authors:  Edgar L Reinoso-Peláez; Daniel Gianola; Oscar González-Recio
Journal:  Methods Mol Biol       Date:  2022

6.  On the Statistical and Practical Limitations of Thurstonian IRT Models.

Authors:  Paul-Christian Bürkner; Niklas Schulte; Heinz Holling
Journal:  Educ Psychol Meas       Date:  2019-02-22       Impact factor: 2.821

7.  Modeling Faking in the Multidimensional Forced-Choice Format: The Faking Mixture Model.

Authors:  Susanne Frick
Journal:  Psychometrika       Date:  2021-12-20       Impact factor: 2.290

8.  Development and Validation of the Behavioral Tendencies Questionnaire.

Authors:  Nicholas T Van Dam; Anna Brown; Tom B Mole; Jake H Davis; Willoughby B Britton; Judson A Brewer
Journal:  PLoS One       Date:  2015-11-04       Impact factor: 3.240

9.  The Motivational Value Systems Questionnaire (MVSQ): Psychometric Analysis Using a Forced Choice Thurstonian IRT Model.

Authors:  Josef Merk; Wolff Schlotz; Thomas Falter
Journal:  Front Psychol       Date:  2017-09-20
  9 in total

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