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Estimation of Person Ability under Rapid and Effortful Responding

The goal of the present study was to extend earlier work on the estimation of person theta using maximum likelihood estimation in R by accounting for rapid guessing. This paper provides a modified R function that accommodates person thetas using the Rasch or 2PL models and implements corrections for...

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Autores principales: Sideridis, Georgios, Alahmadi, Maisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505393/
https://www.ncbi.nlm.nih.gov/pubmed/36135608
http://dx.doi.org/10.3390/jintelligence10030067
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author Sideridis, Georgios
Alahmadi, Maisa
author_facet Sideridis, Georgios
Alahmadi, Maisa
author_sort Sideridis, Georgios
collection PubMed
description The goal of the present study was to extend earlier work on the estimation of person theta using maximum likelihood estimation in R by accounting for rapid guessing. This paper provides a modified R function that accommodates person thetas using the Rasch or 2PL models and implements corrections for the presence of rapid guessing or informed guessing behaviors. Initially, a sample of 200 participants was generated using Mplus in order to demonstrate the use of the function with the full sample and a single participant in particular. Subsequently, the function was applied to data from the General Aptitude Test (GAT) and the measurement of cognitive ability. Using a sample of 8500 participants, the present R function was demonstrated. An illustrative example of a single participant, assumed to be either a rapid responder or a successful guesser, is provided using MLE and BME. It was concluded that the present function can contribute to a more valid estimation of person ability.
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spelling pubmed-95053932022-09-24 Estimation of Person Ability under Rapid and Effortful Responding Sideridis, Georgios Alahmadi, Maisa J Intell Article The goal of the present study was to extend earlier work on the estimation of person theta using maximum likelihood estimation in R by accounting for rapid guessing. This paper provides a modified R function that accommodates person thetas using the Rasch or 2PL models and implements corrections for the presence of rapid guessing or informed guessing behaviors. Initially, a sample of 200 participants was generated using Mplus in order to demonstrate the use of the function with the full sample and a single participant in particular. Subsequently, the function was applied to data from the General Aptitude Test (GAT) and the measurement of cognitive ability. Using a sample of 8500 participants, the present R function was demonstrated. An illustrative example of a single participant, assumed to be either a rapid responder or a successful guesser, is provided using MLE and BME. It was concluded that the present function can contribute to a more valid estimation of person ability. MDPI 2022-09-13 /pmc/articles/PMC9505393/ /pubmed/36135608 http://dx.doi.org/10.3390/jintelligence10030067 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sideridis, Georgios
Alahmadi, Maisa
Estimation of Person Ability under Rapid and Effortful Responding
title Estimation of Person Ability under Rapid and Effortful Responding
title_full Estimation of Person Ability under Rapid and Effortful Responding
title_fullStr Estimation of Person Ability under Rapid and Effortful Responding
title_full_unstemmed Estimation of Person Ability under Rapid and Effortful Responding
title_short Estimation of Person Ability under Rapid and Effortful Responding
title_sort estimation of person ability under rapid and effortful responding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505393/
https://www.ncbi.nlm.nih.gov/pubmed/36135608
http://dx.doi.org/10.3390/jintelligence10030067
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