Communication Validity and Rating Scales: Collapsing Categories

Test validity, the extent to which a test measures what it is intended to measure, is critical. Although many researchers review content, construct, and statistical aspects of validity, even conscientious researchers usually take for granted that respondents understood the tasks they were asked to perform and then performed them in a coherent way.

Despite the fact that rating scales and response formats are the media of communication with respondents, researchers ignore "communication validity". Did the rating scale categories perform as intended? Did respondents converse with the test developer in a common language free of idiosyncratic category usage, response sets, and ambiguous terminology? Were respondents able to distinguish the response levels of each rating scale? How did they order the levels? It is pointless to examine any other form of validity until we have established that we have listened carefully to what test respondents have told us about our variable.

We want our respondents to manifest a clear definition of the variable. We also want to locate them at separate locations along the variable. Their use of the rating categories is crucial. We need respondents to provide an unambiguous hierarchical ordering of our categories. Their response behavior may not concur with our original presentation of our response categories.

Rasch analysis provides a statistical method for ascertaining and verifying respondents' perceptions of the ordering of category meanings (RMT 9:3 450-451, 9:4 464-465). Categories labeled "Don't know", "No opinion", and "Does not apply" are prime candidates for misplacement in the category hierarchy. Such category labels provoke irrelevant and evasive responses. Usually they do not belong in the hierarchy at all. It is often better not to use them or, when used, to treat their selection as missing data.

Each category is intended to increase the discrimination of the rating scale and so to increase the information in all responses. But confrontation by too many response alternatives muddles respondents. Respondents rarely make stable discriminations among more than 6 levels. Sometimes 2 or 4 levels are all they can negotiate. Excess categories introduce more noise than information by forcing respondents to make their fine choices idiosyncratically, such as by preference for even or odd numbering.

Responses to excess categories can be combined with those of adjacent categories in a "collapsing" process. When we collapse adjacent categories, we construct new categorizations. Rasch analysis provides the opportunity to study how well these new categories function. The optimal categorization is that which
a) provides the best construct definition,
b) best separates respondents along the variable,
c) produces the best fit of data to model.
These criteria usually cooperate to identify an optimal scoring solution.

Item fit for collapsed categories

person separation for collapsed categories

person fit for collapsed categories

The Figures summarize different categorizations of the responses of teachers to 19 items about reading instruction. The printed rating scale was:

No Emphasis . . . . . Major Emphasis
1 . . . . 2 . . . . 3 . . . . 4

This scale suffered from the common flaw of unlabelled (and hence not clearly defined) categories.

The Figures show the statistical implications of different collapsings. "1234" means the categories are assigned their printed ordering. "1222" means that original category "1" is retained as "1", but original categories "2", "3", and "4" are collapsed into one category "2". The statistics are almost unanimous in declaring that collapsing categories "1" and "2" provides the most informative categorization. Thus, our respondents tell us that they can only discriminate three levels of emphasis in this context. The most valid communication with our respondents is then not our printed scale of 4 theoretical categories, but their experiential scale of three empirical categories. It is on the basis of their scale that investigation of the other forms of validity is best pursued.

Communication validity and rating scales. Lopez WA. … Rasch Measurement Transactions, 1996, 10:1 p.482

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
Rasch Books and Publications: Winsteps and Facets
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 Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Rasch Models for Solving Measurement Problems (Facets), George Engelhard, Jr. & Jue Wang 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
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
Facets Tutorials - free
Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan

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