Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis

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.

Fig 1. Plot of the estimated measure for any CVSS17 raw score.

Plot of the estimated measure (x-axis) for any raw CVSS17 score (y-axis). Different symbols represent distinct levels of performance as indicated in the figure inset.

Fig 2. Factor loadings for CVSS17 principal components.

Plot of the factor is loading for Factor 1 (external symptom factor, horizontal axis) against Factor 2 (internal symptom factor, vertical axis) for each of the CVSS17 items. Item descriptors are shown in Table 4.

Fig 3. Discriminant analysis scatter plot of the two factors model.

Discriminant analysis scatter plot of the two factors model. Recoded external factor scores (x-axis) are plotted against recoded internal factor scores (y-axis). Different grey intensities represent distinct subject levels of performance as indicated in the figure inset.

Fig 4. Plot of the estimated measure for any ESF raw score.

Plot of the estimated measure (x-axis) for any ESF raw score (y-axis). Different symbols represent distinct levels of performance as indicated in the figure inset.

Fig 5. Plot of the estimated measure for any ISF raw score.

Plot of the estimated measure (x-axis) for any ISF raw score (y-axis). Different symbols represent distinct levels of performance as indicated in the figure inset.