Exploration and analysis of a generalized one-parameter item response model with flexible link functions
This paper primarily analyzes the one-parameter generalized logistic (1PGlogit) model, which is a generalized model containing other one-parameter item response theory (IRT) models. The essence of the 1PGlogit model is the introduction of a generalized link function that includes the probit, logit,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1248454/full |
| _version_ | 1797730998702571520 |
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| author | Xue Wang Jiwei Zhang Jing Lu Guanghui Cheng Ningzhong Shi |
| author_facet | Xue Wang Jiwei Zhang Jing Lu Guanghui Cheng Ningzhong Shi |
| author_sort | Xue Wang |
| collection | DOAJ |
| description | This paper primarily analyzes the one-parameter generalized logistic (1PGlogit) model, which is a generalized model containing other one-parameter item response theory (IRT) models. The essence of the 1PGlogit model is the introduction of a generalized link function that includes the probit, logit, and complementary log-log functions. By transforming different parameters, the 1PGlogit model can flexibly adjust the speed at which the item characteristic curve (ICC) approaches the upper and lower asymptote, breaking the previous constraints in one-parameter IRT models where the ICC curves were either all symmetric or all asymmetric. This allows for a more flexible way to fit data and achieve better fitting performance. We present three simulation studies, specifically designed to validate the accuracy of parameter estimation for a variety of one-parameter IRT models using the Stan program, illustrate the advantages of the 1PGlogit model over other one-parameter IRT models from a model fitting perspective, and demonstrate the effective fit of the 1PGlogit model with the three-parameter logistic (3PL) and four-parameter logistic (4PL) models. Finally, we demonstrate the good fitting performance of the 1PGlogit model through an analysis of real data. |
| first_indexed | 2024-03-12T11:52:12Z |
| format | Article |
| id | doaj.art-174a8437de7347a4834eab55357e1365 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-03-12T11:52:12Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-174a8437de7347a4834eab55357e13652023-08-31T05:34:10ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-08-011410.3389/fpsyg.2023.12484541248454Exploration and analysis of a generalized one-parameter item response model with flexible link functionsXue Wang0Jiwei Zhang1Jing Lu2Guanghui Cheng3Ningzhong Shi4Key Laboratory of Applied Statistics of Ministry of Education (MOE), School of Mathematics and Statistics, Northeast Normal University, Changchun, ChinaFaculty of Education, Northeast Normal University, Changchun, ChinaKey Laboratory of Applied Statistics of Ministry of Education (MOE), School of Mathematics and Statistics, Northeast Normal University, Changchun, ChinaGuangzhou Institute of International Finance, Guangzhou University, Guangzhou, ChinaKey Laboratory of Applied Statistics of Ministry of Education (MOE), School of Mathematics and Statistics, Northeast Normal University, Changchun, ChinaThis paper primarily analyzes the one-parameter generalized logistic (1PGlogit) model, which is a generalized model containing other one-parameter item response theory (IRT) models. The essence of the 1PGlogit model is the introduction of a generalized link function that includes the probit, logit, and complementary log-log functions. By transforming different parameters, the 1PGlogit model can flexibly adjust the speed at which the item characteristic curve (ICC) approaches the upper and lower asymptote, breaking the previous constraints in one-parameter IRT models where the ICC curves were either all symmetric or all asymmetric. This allows for a more flexible way to fit data and achieve better fitting performance. We present three simulation studies, specifically designed to validate the accuracy of parameter estimation for a variety of one-parameter IRT models using the Stan program, illustrate the advantages of the 1PGlogit model over other one-parameter IRT models from a model fitting perspective, and demonstrate the effective fit of the 1PGlogit model with the three-parameter logistic (3PL) and four-parameter logistic (4PL) models. Finally, we demonstrate the good fitting performance of the 1PGlogit model through an analysis of real data.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1248454/fullBayesian model evaluation criteriaitem response theoryitem characteristic curveone-parameter generalized logistic modelsSTAN software |
| spellingShingle | Xue Wang Jiwei Zhang Jing Lu Guanghui Cheng Ningzhong Shi Exploration and analysis of a generalized one-parameter item response model with flexible link functions Frontiers in Psychology Bayesian model evaluation criteria item response theory item characteristic curve one-parameter generalized logistic models STAN software |
| title | Exploration and analysis of a generalized one-parameter item response model with flexible link functions |
| title_full | Exploration and analysis of a generalized one-parameter item response model with flexible link functions |
| title_fullStr | Exploration and analysis of a generalized one-parameter item response model with flexible link functions |
| title_full_unstemmed | Exploration and analysis of a generalized one-parameter item response model with flexible link functions |
| title_short | Exploration and analysis of a generalized one-parameter item response model with flexible link functions |
| title_sort | exploration and analysis of a generalized one parameter item response model with flexible link functions |
| topic | Bayesian model evaluation criteria item response theory item characteristic curve one-parameter generalized logistic models STAN software |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1248454/full |
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