Rank order data

Rankings and partial rankings, with or without ties, can be conveniently analyzed using ISGROUPS=0 and STKEEP=No

 

Each row is an element or object to be ranked.

 

Each column is a set of rankings by a respondent. In the item label, place any interesting demographics about the respondent doing the ranking.

 

ISGROUPS=0  - Each respondent (column) has their own "ranking scale". This is equivalent to the Partial Credit model.

 

STKEEP=No - tied ranks are OK, but if the ranking goes: 1, 2, 2, 4, 5, .... in the data then, from a Rasch perspective this should be: 1, 2, 2, 3, 4, ....  which is what STKEEP=NO does for us.

 

The elements (rows) will have measures and fit statistics indicating respondent preference. The fit statistics for the respondents (columns) will indicate the extent of agreement of each respondent with the consensus. The measures for the respondents are usually meaningless (or the same) and can be ignored.

 

Fit statistics, DIF and DPF analysis, and contrast analysis of residuals are all highly informative.

 

Elements rated by only one respondent will be correctly measured (with large standard errors), except if that element is ranked top or bottom by that respondent. When that happens, construct a dummy ranking column in which the extreme elements are not-extreme. Give that ranking a low weight with IWEIGHT=.

 

If every ranking set includes every element, and ties are not allowed, then the elements can be columns and the respondents can be rows. ISGROUPS=0 is not required.

 

Example:

In the data, as collect, the ranked objects are columns, the respondents are rows, and the rankings as numbers.

 

Set up a standard rating-scale analysis of these data.

 

Run an analysis in Winsteps. Ignore the results except to verify that the data have been input correctly.

 

Now we want the respondents as columns and the objects as columns:

 

Winsteps "output files", "transpose". TRPOFILE=

 

Edit the transposed control file with ISGROUPS=0 and STKEEP=NO

 

Now run the Winsteps analysis. The results should make sense. The objects (rows) should have measures and fit statistics indicating respondent preference. The fit statistics for the respondents (columns) will indicate the extent of agreement of each respondent with the consensus.


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Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn, 2024 George Engelhard, Jr. & Jue Wang Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
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Other Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
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