Original articleVisual Disability Variables. II: The Difficulty of Tasks for a Sample of Low-Vision Patients
Section snippets
Methods
The subjects and data collection procedures are identical to those described in part I,6 so we present here only a brief summary.
Results
When applied to difficulty ratings, the Rasch model provides estimates of the probability that person n will use rating category x to respond to item i. Person n has some level of visual ability, αn, and item i requires some level of visual ability, ρi, to perform the described activity with a criterion level of ease. If αn is much greater than ρi, then person n most likely will report that activity i is “not difficult.” If αn is much less than ρi, then person n most likely will report that the
Discussion
The AI is designed to elicit patient ratings of the difficulty of achieving goals that have some importance to the patient and the difficulty of performing tasks that the patient perceives are necessary to achieve those goals. Thus, each patient is given an individualized questionnaire. Although we offered patients 6 categories for rating difficulty (ranging from “not difficult” to “impossible to do without assistance”), for both goals and tasks, patients could discriminate only 4 difficulty
Conclusions
The empirical conclusions drawn from this study confirm the structure of our hierarchical ABS model: (1) visual ability measured at the task level is the same as visual ability measured at the goal level, and (2) the required visual ability for each goal corresponds to the weighted average of the required visual abilities of the tasks that must be performed to achieve the goal. An unexpected finding was that the visual ability construct (ie, that aspect of functional ability that is modified by
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2020, Clinical NeurophysiologyCitation Excerpt :When Rasch analysis is applied to difficulty ratings of a single set of items by a large group of respondents, difficulty estimates – known as item measures – are obtained for the items and ability estimates – known as person measures – for the respondents, with confidence intervals for the estimates. In addition, Rasch analysis tests the validity of the unidimensionality assumption, and can provide estimates for items and respondents along several dimensions (e.g., central resolution and peripheral visual field extent) simultaneously (Massof et al., 2005). Items in a PRO typically only cover a certain range of visual difficulty, e.g., from normal visual acuity (VA = 20/20) to profound visual impairment (VA = 20/500), and items are chosen so they cover that range in roughly equal intervals: A sparser item distribution reduces the precision and accuracy with which the person measure can be estimated.
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Supported by the National Eye Institute, National Institutes of Health (grant no. EY-012045).
No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the author(s) or on any organization with which the author(s) is/are associated.