Purpose: The Activity Inventory (AI) is an adaptive visual function questionnaire that consists of 459 Tasks nested under 50 Goals that in turn are nested under three Objectives. Visually impaired patients are asked to rate the importance of each Goal, the difficulty of Goals that have at least some importance, and the difficulty of Tasks that serve Goals that have both some importance and some difficulty. Consequently, each patient responds to an individually tailored set of questions that provides both a functional history and the data needed to estimate the patient's visual ability. The purpose of the present article is to test the hypothesis that all combinations of items in the AI, and by extension all visual function questionnaires, measure the same visual ability variable.
Methods: The AI was administered to 1880 consecutively-recruited low vision patients before their first visit to the low vision rehabilitation service. Of this group, 407 were also administered two other visual function questionnaires randomly chosen from among the Activities of Daily Living Scale (ADVS), National Eye Institute Visual Functioning Questionnaire (NEI VFQ), 14-item Visual Functioning Index (VF-14), and Visual Activities Questionnaire (VAQ). Rasch analyses were performed on the responses to each VFQ, on all responses to the AI, and on responses to various subsets of items from the AI.
Results: The pattern of fit statistics for AI item and person measures suggested that the estimated visual ability variable is not unidimensional. Reading-related and other items requiring high visual resolution had smaller residual errors than expected and mobility-related items had larger residual errors than expected. The pattern of person measure residual errors could not be explained by the disorder diagnosis. When items were grouped into subsets representing four visual function domains (reading, mobility, visual motor, visual information), and separate person measures were estimated for each domain as well as for all items combined, visual ability was observed to be equivalent to the first principal component and accounted for 79% of the variance. However, confirmatory factor analysis showed that visual ability is a composite variable with at least two factors: one upon which mobility loads most heavily and the other upon which reading loads most heavily. These two factors can account for the pattern of residual errors. High product moment and intraclass correlations were observed when comparing different subsets of items within the AI and when comparing different VFQs.
Conclusions: Visual ability is a composite variable with two factors; one most heavily influences reading function and the other most heavily influences mobility function. Subsets of items within the AI and different VFQs all measure the same visual ability variable.