Investigations of many of the constructs studied in counseling psychology rely on the development of scales. Typically, scale development involves summing Likert-type items to yield a score that represents the degree to which the construct being measured is present in the respondent (Dawis, 1987). Validation may involve examination of factor structure and associations with related constructs. There are, however, exciting developments in psychometrics that may drastically improve our assessment of individuals. Fox & Jones (1998) have described and illustrated the Rasch modeling approach to scaling, which is based on item-response theory. Rasch modeling involves assessing the probability of endorsing any option on each item as a function of the item's endorsability and the respondent's agreeability, whereas classical test theory does not disentangle endorsability from agreeability. As discussed by Fox & Jones, Rasch modeling allows for generalizability across samples and items, takes into account that response options may not be psychologically equally spaced, allows for testing of unidimensionality, produces an ordered set of items, and identifies poorly functioning items as well as unexpected responses. Each of these characteristics becomes a potential advantage to be exploited.
Rasch modeling is new to the field of counseling psychology, and only time will determine whether the advantages are sufficient to warrant use. However, several of the advantages appear promising. For example, the ability to identify unexpected results has research and clinical applications. In classical test models, outliers are identified by extreme scores, but we take scores in the middle ranges to be acceptable, as long as the instrument has generally been shown to be reliable. Rasch modeling would identify a research participant who had responded randomly to the instrument (therefore scoring near the mean) or idiosyncratically to a few items. Similarly, clinicians using scales designed on the basis of Rasch modeling could identify clients who responded unexpectedly (and without resort to separate scales, such as is the case with the Minnesota Multiphasic Personality Inventory).
Bruce E. Wampold in "Necessary (but not sufficient) innovation: comment on Fox & Jones", Journal of Counseling Psychology, 1998 45:1 46-49.
Dawis RV. 1987. Scale construction. ibid. 34, 481-489.
Fox CM, Jones JA. 1998. Uses of Rasch modeling in counseling psychology research. ibid. 45:1, 30-45.
Wampold BE The Promising Advantages of Rasch Rasch Measurement Transactions, 1999, 13:2 p. 695
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
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