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Updated: May 30, 2026
A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
Published on: February 11, 2014
Rasch analysis in the development of a simplified version of the National Eye Institute Visual-Function
Jonathan W Kowalski1, Anne M Rentz, John G Walt
1Global Health Outcomes Strategy and Research, Allergan Inc, 2525 Dupont Drive, Irvine, CA 92612, USA. kowalski_jonathan@allergan.com
Purpose:
Preference-based health measures value how people feel about the desirability of a health state. Generic measures may not effectively capture the impact of vision loss from ocular diseases. Disease-targeted measures could address this limitation. This study developed a vision-targeted health state classification system based on the National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25).
Methods:
Secondary analysis of NEI VFQ-25 data from studies of patients with central (n = 932)- and peripheral-vision loss (n = 2,451) were used to develop a health state classification system. Classical test theory and Rasch analyses were used to identify a smaller set of NEI VFQ-25 items suitable for the central- and peripheral-vision-loss groups.
Results:
Rasch analysis of the NEI VFQ-25 items using the peripheral vision-loss data indicated that 11 items fit a unidimensional model, while 14 NEI VFQ-25 items fit using the central-vision-loss data. Combining peripheral-vision-loss data and central-vision-loss data resulted in 9 items fitting a unidimensional model. Six items covering near vision, distance vision, social vision, role difficulties, vision dependency, and vision-related mental health were selected for the health-state classification.
Conclusions:
The derived health-state classification system covers relevant domains of vision-related functioning and well-being.
