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  • Title: Why measurement matters for measuring patient vision outcomes.
    Author: Mallinson T.
    Journal: Optom Vis Sci; 2007 Aug; 84(8):675-82. PubMed ID: 17700332.
    Abstract:
    Optometrists, by definition, care deeply about measurement. This brief review article considers the essential features of measurement that make many optometric instruments so useful and how patient-centered survey instruments such as vision-related quality of life questionnaires, can be analyzed using contemporary psychometric methods, so that they also conform to these essential features of measurement. These features include unidimensionality, hierarchical order, and equal interval scaling. Optometrists demand these features because they need to make meaningful comparisons both between patients and over time. Questionnaires about visual function or health-related quality of life, typically involve a series of rating scale type items that are added up to produce a total raw score. Yet total raw scores, which are ordinal, do not exhibit the essential properties of measurement. The Rasch Model, developed by Georg Rasch in 1956, converts ordinal-level raw score data into interval measures that demonstrate the essential features of measurement. Under the Rasch model any obtained score (response) is conceptualized as the difference between the amount of a trait reflected in an item, i.e., how "hard" the item is, and the ability of the person responding to the item. The Rasch model estimates the log odds probability (logit) for any response by any person. Logits are equal interval, representing equal amounts of the construct being measured across the entire range of the construct. Logits define the hierarchical order of items, how hard or easy items are, and the Rasch model specifies that this order of items must be invariant for all persons, that is, must be unidimensional. There are numerous software packages available for applying the Rasch model, all provide methods for evaluating how well data demonstrate unidimensionality, hierarchical order, and equal interval scaling. These can be used in the development, assessment or revision of questionnaires to optimize measurement.
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