|Strongly||Somewhat||A Little||Don't Know||A Little||Somewhat||Strongly|
|Positive Item Frequencies||18,424||17,769||5,440||962||1,661||1,343||760|
|Negative Item Frequencies||2,182||5,987||7,599||1,028||4,356||9,971||15690|
|Data recoded and pivoted for construct clarity and measurement efficiency|
|Positive Item Recoding||3||2
|Positive Item Frequencies||18,354||17,769||8,967|
|Negative Item Recoding||1||2||2
|Negative Item Frequencies||2,182||13,568||14,327||15,678|
In a study of personal efficacy, a seven category rating scale was employed from "1", "Strongly Agree", to "7", "Strongly Disagree". The data are summarized in the Table. Two types of items were used, positively worded, "I like most parts of my personality", and negatively worded, "The demands of everyday life often get me down." Since I wanted to measure respondents on one variable of "personal efficacy" on which a higher score was to indicate a higher measure, I reversed the scoring of the positive items. Now a rating of "7" always indicates greatest efficacy.
Even with all rating scales oriented in the same direction, the construct definition (item ordering along the variable) was vague. Category fit statistics (RMT 9:3 450-451) signaled that respondents had been asked to discriminate an excessive number of levels of agreement.
Consideration of the categories, together with inspection of category counts and fit statistics, suggested that treating the central "Don't Know" category as missing data would remove substantial irrelevant off-variable behavior. So I omitted this category. Combining other categories in an intelligible way to maximize data-to-model fit and measurement efficiency produced the recodings in table 2. For instance, responding "Strongly Disagree" to the negative items is making a more forceful claim for personal efficacy than responding "Strongly Agree" to the socially-conforming positive items.
The item hierarchy was still irregular. Further thought solved the problem. The positive items were so socially desirable that any lack of agreement signaled a lack of efficacy. I set the dichotomization pivot between "Agree Strongly" and "Agree Somewhat" by anchoring the step in the recoded positive rating scale from category 2 to category 3 at 0.0 relative to item difficulty. The negative items were more even-handed, so I set the pivot point in the center of their recoded 4-category rating scale, by also anchoring the step scale from category 2 to category 3 in the same way. This yielded improved measurement and a clear construct.
Recoding and Pivoting: An Example. Mosenkis J. Rasch Measurement Transactions, 1997, 11:3 p. 578.
Please help with Standard Dataset 4: Andrich Rating Scale Model
|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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