Literature DB >> 20030965

Estimation of a four-parameter item response theory model.

Eric Loken1, Kelly L Rulison.   

Abstract

We explore the justification and formulation of a four-parameter item response theory model (4PM) and employ a Bayesian approach to recover successfully parameter estimates for items and respondents. For data generated using a 4PM item response model, overall fit is improved when using the 4PM rather than the 3PM or the 2PM. Furthermore, although estimated trait scores under the various models correlate almost perfectly, inferences at the high and low ends of the trait continuum are compromised, with poorer coverage of the confidence intervals when the wrong model is used. We also show in an empirical example that the 4PM can yield new insights into the properties of a widely used delinquency scale. We discuss the implications for building appropriate measurement models in education and psychology to model more accurately the underlying response process.

Mesh:

Year:  2009        PMID: 20030965     DOI: 10.1348/000711009X474502

Source DB:  PubMed          Journal:  Br J Math Stat Psychol        ISSN: 0007-1102            Impact factor:   3.380


  13 in total

1.  Revisiting the 4-Parameter Item Response Model: Bayesian Estimation and Application.

Authors:  Steven Andrew Culpepper
Journal:  Psychometrika       Date:  2015-09-23       Impact factor: 2.500

2.  The Effect of Upper and Lower Asymptotes of IRT Models on Computerized Adaptive Testing.

Authors:  Ying Cheng; Cheng Liu
Journal:  Appl Psychol Meas       Date:  2015-05-21

3.  On a Generalization of Local Independence in Item Response Theory Based on Knowledge Space Theory.

Authors:  Stefano Noventa; Andrea Spoto; Jürgen Heller; Augustin Kelava
Journal:  Psychometrika       Date:  2018-11-12       Impact factor: 2.500

4.  Sources of Error in IRT Trait Estimation.

Authors:  Leah M Feuerstahler
Journal:  Appl Psychol Meas       Date:  2017-10-06

5.  A Mixed Stochastic Approximation EM (MSAEM) Algorithm for the Estimation of the Four-Parameter Normal Ogive Model.

Authors:  Xiangbin Meng; Gongjun Xu
Journal:  Psychometrika       Date:  2022-06-01       Impact factor: 2.500

6.  Bayesian Item Response Theory Models With Flexible Generalized Logit Links.

Authors:  Jiwei Zhang; Ying-Ying Zhang; Jian Tao; Ming-Hui Chen
Journal:  Appl Psychol Meas       Date:  2022-05-20

7.  On the Choice of the Item Response Model for Scaling PISA Data: Model Selection Based on Information Criteria and Quantifying Model Uncertainty.

Authors:  Alexander Robitzsch
Journal:  Entropy (Basel)       Date:  2022-05-27       Impact factor: 2.738

8.  Modular Open-Source Software for Item Factor Analysis.

Authors:  Joshua N Pritikin; Micheal D Hunter; Steven Boker
Journal:  Educ Psychol Meas       Date:  2014-10-31       Impact factor: 2.821

9.  The Impact of Non-attempted and Dually-Attempted Items on Person Abilities Using Item Response Theory.

Authors:  Georgios D Sideridis; Ioannis Tsaousis; Khaleel Al Harbi
Journal:  Front Psychol       Date:  2016-10-14

10.  Expectation-Maximization-Maximization: A Feasible MLE Algorithm for the Three-Parameter Logistic Model Based on a Mixture Modeling Reformulation.

Authors:  Chanjin Zheng; Xiangbin Meng; Shaoyang Guo; Zhengguang Liu
Journal:  Front Psychol       Date:  2018-01-05
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