STKEEP= keep unobserved intermediate categories in structure = Yes
Unobserved categories can be dropped from rating (or partial credit) scales and the remaining category recounted during estimation. For intermediate categories only, recounting can be prevented and unobserved categories retained in the analysis. This is useful when the unobserved categories are important to the rating (or partial credit) scale logic or are usually observed, even though they happen to have been unused this time. Category Rasch-Andrich thresholds for which anchor calibrations are supplied are always maintained wherever computationally possible, even when there are no observations of a category in the current data set.
Use STKEEP=YES when there may be intermediate categories in your rating (or partial credit) scale that aren't observed in this data set, i.e., incidental or sampling zeroes.
Use STKEEP=NO when your category numbering deliberately skips over intermediate categories, i.e., structural zeroes.
Use IVALUE= to rescore each item independently to keep or eliminate unobserved categories.
STKEEP=N Eliminate unused categories and close up the observed categories.
STKEEP=Y Retain unused non-extreme categories in the ordinal categorization.
When STKEEP=Y, missing categories are retained in the rating (or partial credit) scale, so maintaining the raw score ordering. But missing categories require locally indefinite structure calibrations. If these are to be used for anchoring later runs, compare these calibrations with the calibrations obtained by an unanchored analysis of the new data. This will assist you in determining what adjustments need to be made to the original calibrations in order to establish a set of anchor calibrations that maintain the same rating (or partial credit) scale structure.
To remind yourself, STKEEP=YES can be written as STRUCTUREKEEP=YES, STRKEEP=YES or STEPKEEP=YES and other similar abbreviations starting STK, STR and STEPK.
Example 1: Incidental unobserved categories. Keep the developmentally important rating (or partial credit) scale categories, observed or not. Your small Piaget rating scale goes from 1 to 6. But some levels may not have been observed in this data set.
Example 2: Structural unobserved categories. Responses have been coded as "10", "20", "30", "40", but they really mean 1,2,3,4
CODES = "10203040"
XWIDE = 2
; if STKEEP=YES, then data are analyzed as though categories 11, 12, 13, 14, etc. could exist, which would distort the measures.
; for reporting purposes, multiply Winsteps SCORE by 10 to return to the original 10, 20, 30 categorization.
Example 3: Some unobserved categories are structural and some incidental. Rescore the data and use STKEEP=YES. Possible categories are 2,4,6,8 but only 2,6,8 are observed this time.
(a) Rescore 2,4,6,8 to 1,2,3,4 using IVALUE= or NEWSCORE=
(b) Set STKEEP=YES, so that the observed 1,3,4 and unobserved 2 are treated as 1,2,3,4
(c) For reporting purposes, multiply the Winsteps SCORE by 2 using Excel or similar software.
Example 4: There are both dichotomous items(0/1) and polytomous items(0/1/2) in the same test. Items 1-5 are dichotomies, and 6-10 are polytomies with the same rating scale:
ISGROUPS = DDDDDPPPPP ; items 1-5, the dichotomies, are in one item-group, items 6-10, the polytomies, in another.
CODES = 012 ; has all valid codes in the data file.
; For dichotomies, STKEEP= has no effect.
; For polytomies: If the data contain 0/1/2 then STKEEP= has no effect.
; If the data contain 0/2 (but no 1's), what is to be done?
STKEEP = No ; 0/2 is to be automatically recoded 0/1
STKEEP = Yes ; 0/2 is to be analyzed as 0/1/2
Incidental and Structural Zeroes: Extreme and Intermediate
For missing intermediate categories, there are two options.
If the categories are missing because they cannot be observed, then they are "structural zeroes". Specify "STKEEP=NO".
This effectively recounts the observed categories starting from the bottom category, so that 1,3,5,7 becomes 1,2,3,4.
If they are missing because they just do not happen to have been observed this time, then they are "incidental or sampling zeros". Specify "STKEEP=YES". Then 1,3,5,7 is treated as 1,2,3,4,5,6,7.
Categories outside the observed range are always treated as structural zeroes.
When STKEEP=Y, unobserved intermediate categories are imputed using a mathematical device noticed by Mark Wilson. This device can be extended to runs of unobserved categories.
Idiosyncratic rating scales: the Barthel Index
Winsteps has two ways of analyzing rating scales such as 0,2,5,8,10 in the Barthel Index.
1. STKEEP=No. Unobserved rating-scale categories are structural zeroes. This converts 0,2,5,8,10 into 0,1,2,3,4
2. STKEEP=Yes (the default). Unobserved rating-scale categories are sampling zeroes. This converts 0,2,5,8,10 into 0,1,2,3,4,5,6,7,8,9,10
It appears that the Barthel Index is using weighted categories, so its scoring implies:
0,2,5,8,10 = 0, 2x1, 2.5x2, 2.66x3, 2.5x4
Winsteps does not support different weighting of different categories. This can be done in Facets, but Facets does not output KeyForms.
An approximation in Winsteps could be:
0,2,5,8,10 ≈ 0, 2.5x1, 2.5x2, 2.5x3, 2.5x4 = (STKEEP=No) 0,1,2,3,4 with an item weight of IWEIGHT= 2.5
then edit the KeyForm from 0,1,2,3,4 to become 0,2,5,8,10
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|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 with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind||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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