Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150566
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dc.contributor.authorJoo, Seang-Hwaneen_US
dc.contributor.authorChun, Seokjoonen_US
dc.contributor.authorStark, Stephenen_US
dc.contributor.authorChernyshenko, Olexander S.en_US
dc.date.accessioned2021-06-01T03:31:44Z-
dc.date.available2021-06-01T03:31:44Z-
dc.date.issued2019-
dc.identifier.citationJoo, S., Chun, S., Stark, S. & Chernyshenko, O. S. (2019). Item parameter estimation with the general hyperbolic cosine ideal point IRT model. Applied Psychological Measurement, 43(1), 18-33. https://dx.doi.org/10.1177/0146621618758697en_US
dc.identifier.issn0146-6216en_US
dc.identifier.urihttps://hdl.handle.net/10356/150566-
dc.description.abstractOver the last decade, researchers have come to recognize the benefits of ideal point item response theory (IRT) models for noncognitive measurement. Although most applied studies have utilized the Generalized Graded Unfolding Model (GGUM), many others have been developed. Most notably, David Andrich and colleagues published a series of papers comparing dominance and ideal point measurement perspectives, and they proposed ideal point models for dichotomous and polytomous single-stimulus responses, known as the Hyperbolic Cosine Model (HCM) and the General Hyperbolic Cosine Model (GHCM), respectively. These models have item response functions resembling the GGUM and its more constrained forms, but they are mathematically simpler. Despite the apparent impact of Andrich’s work on ensuing investigations, the HCM and GHCM have been largely overlooked by applied researchers. This may stem from questions about the compatibility of the parameter metric with other ideal point estimation and model-data fit software or seemingly unrealistic parameter estimates sometimes produced by the original joint maximum likelihood (JML) estimation software. Given the growing list of ideal point applications and variations in sample and scale characteristics, the authors believe these HCMs warrant renewed consideration. To address this need and overcome potential JML estimation difficulties, this study developed a marginal maximum likelihood (MML) estimation algorithm for the GHCM and explored parameter estimation requirements in a Monte Carlo study manipulating sample size, scale length, and data types. The authors found a sample size of 400 was adequate for parameter estimation and, in accordance with GGUM studies, estimation was superior in polytomous conditions.en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Psychological Measurementen_US
dc.rights© 2018 The Author(s). Published by SAGE Publications. All rights reserved.en_US
dc.subjectSocial sciences::Psychologyen_US
dc.titleItem parameter estimation with the general hyperbolic cosine ideal point IRT modelen_US
dc.typeJournal Articleen
dc.contributor.schoolCollege of Business (Nanyang Business School)en_US
dc.identifier.doi10.1177/0146621618758697-
dc.identifier.pmid30573932-
dc.identifier.scopus2-s2.0-85058670823-
dc.identifier.issue1en_US
dc.identifier.volume43en_US
dc.identifier.spage18en_US
dc.identifier.epage33en_US
dc.subject.keywordsGeneral Hyperbolic Cosine Modelen_US
dc.subject.keywordsIdeal Pointen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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