Repositori institucional URV
| URV's Author/s: | FERRANDO PIERA, PERE JOAN; LORENZO SEVA, URBANO |
| Author, as appears in the article.: | Pere J. Ferrando; Urbano Lorenzo-Seva |
| Author identifier: | 0000-0002-3133-5466; 0000-0001-5369-3099 |
| Journal publication year: | 2015 |
| Publication Type: | Article |
| ISSN: | 0013-1644 |
| Abstract: | This article proposes a general parametric item response theory approach for identifying sources of misfit in response patterns that have been classified as potentially inconsistent by a global person-fit index. The approach, which is based on the weighted least squared regression of the observed responses on the model-expected responses, can be used with a variety of unidimensional and multidimensional models intended for binary, graded, and continuous responses and consists of procedures for identifying (a) general deviation trends, (b) local inconsistencies, and (c) single response inconsistencies. A free program called REG-PERFIT that implements most of the proposed techniques has been developed, described, and made available for interested researchers. Finally, the functioning and usefulness of the proposed procedures is illustrated with an empirical study based on a statistics-anxiety scale. |
| Article's DOI: | 10.1177/0013164415594659 |
| Link to the original source: | https://journals.sagepub.com/doi/abs/10.1177/0013164415594659 |
| Papper version: | info:eu-repo/semantics/acceptedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Psicologia |
| Research group: | Escalament de Variables Psicològiques i Desenvolupament de Qüestionaris |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Psychology |
| Keywords: | kernel smoothing person-fit weighted least squares regression |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2016-05-18 |
| First page: | 470 |
| Last page: | 486 |
| Journal volume: | 76 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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