Person:
Susi García, María Del Rosario

Profile Picture
First Name
María Del Rosario
Last Name
Susi García
URI
https://hdl.handle.net/20.500.14352/77434
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
Identifiers
UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

Filters

2018 - 201912020 - 20221
Reset filters

Settings

Author: Barrio De Santos, Ana Rosa × Item Type: Publication ×

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    From linear questionnaires to computer-adaptive tests: Content development and calibration of the Digital Eye Strain Computer Adaptive Test (DESCAT)
    (2022) Susi García, María Del Rosario; González Pérez, Mariano; Barrio De Santos, Ana Rosa; Antona Peñalba, Beatriz
    We published in 2015 a linear Rasch-based scale (Computer Vision Symptom Scale, aka CVSS17) for measuring the computer-related visual and ocular symptoms in workers using video-display terminals. Because Computer adaptive testing (CAT) is currently considered a more efficient and less time-consuming (for test-takers) method than traditional linear questionnaires, we decided to create a new CAT for assessing these symptoms in general population. Therefore, the aim of our study is to identify content for this new CAT and to calibrate the items included in it.
  • Thumbnail Image
    Item
    Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis
    (PLoS ONE, 2018) González Pérez, Mariano; Susi García, María Del Rosario; Barrio De Santos, Ana Rosa; Antona Peñalba, Beatriz
    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.