Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis
dc.contributor.author | González Pérez, Mariano | |
dc.contributor.author | Susi García, María Del Rosario | |
dc.contributor.author | Barrio De Santos, Ana Rosa | |
dc.contributor.author | Antona Peñalba, Beatriz | |
dc.date.accessioned | 2023-06-17T13:18:57Z | |
dc.date.available | 2023-06-17T13:18:57Z | |
dc.date.issued | 2018-08-28 | |
dc.description | Received: September 27, 2017; Accepted: July 30, 2018; Published: August 28, 2018 Editor: José M. González-Méijome, Universidade do Minho, PORTUGAL | |
dc.description.abstract | Purpose: To quantify the levels of performance (symptom severity) of the computer-vision symptom scale (CVSS17), confirm its bifactorial structure as detected in an exploratory factor analysis, and validate its factors as subscales. Methods: By partial credit model (PCM), we estimated CVSS17 measures and the standard error for every possible raw score, and used these data to determine the number of different performance levels in the CVSS17. In addition, through discriminant analysis, we checked that the scale's two main factors could classify subjects according to these determined levels of performance. Finally, a separate Rasch analysis was performed for each CVSS17 factor to assess their measurement properties when used as isolated scales. Results: We identified 5.8 different levels of performance. Discriminant functions obtained from sample data indicated that the scale's main factors correctly classified 98.4% of the cases. The main factors: Internal symptom factor (ISF) and external symptom factor (ESF) showed good measurement properties and can be considered as subscales. Conclusion: CVSS17 scores defined five different levels of performance. In addition, two main factors (ESF and ISF) were identified and these confirmed by discriminant analysis. These subscales served to assess either the visual or the ocular symptoms attributable to computer use. | |
dc.description.department | Depto. de Optometría y Visión | |
dc.description.faculty | Fac. de Óptica y Optometría | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/50768 | |
dc.identifier.doi | 10.1371/journal.pone.0202173 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.officialurl | https://doi.org/10.1371/journal.pone.0202173 | |
dc.identifier.relatedurl | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202173 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/13018 | |
dc.issue.number | 8 | |
dc.journal.title | PLoS ONE | |
dc.language.iso | eng | |
dc.page.initial | e0202173 | |
dc.publisher | Public Library Science | |
dc.rights | Atribución 3.0 España | |
dc.rights.accessRights | open access | |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject.cdu | 004.3:617.75 | |
dc.subject.cdu | 159.9.072 | |
dc.subject.keyword | Eyes | |
dc.subject.keyword | Factor analysis | |
dc.subject.keyword | Ophthalmology | |
dc.subject.keyword | Questionnaires | |
dc.subject.keyword | Eigenvalues | |
dc.subject.keyword | Research validity | |
dc.subject.keyword | Age groups | |
dc.subject.keyword | Psychometrics | |
dc.subject.ucm | Estadística aplicada | |
dc.subject.ucm | Psicometría | |
dc.subject.ucm | Optometría | |
dc.subject.ucm | Óptica fisiológica | |
dc.subject.ucm | Análisis de datos | |
dc.subject.ucm | Estadística | |
dc.subject.unesco | 6105.05 Psicometría | |
dc.subject.unesco | 2209.15 Optometría | |
dc.subject.unesco | 1209 Estadística | |
dc.subject.unesco | 1209.03 Análisis de datos | |
dc.title | Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis | |
dc.type | journal article | |
dc.volume.number | 13 | |
dspace.entity.type | Publication | |
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relation.isAuthorOfPublication | 4d93581c-fa46-4362-886f-96b13e817c13 | |
relation.isAuthorOfPublication.latestForDiscovery | ca5eda64-f98e-4d56-b225-70cac495f2ea |
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