Confirming Test Structure and Measurement Characteristics

Does an instrument maintain its measurement characteristics in a new situation? A particular medical condition can interact with test items at three intensity levels:
(i) to introduce minor perturbations in the observations, with no practical import;
(ii) to provide diagnostically useful misfit, but without degrading the validity of the overall measures,
(iii) to fracture the instrument into two or more subsets of items producing incompatible measures.
When an instrument is designed to produce measures of more than one attribute, interactions with medical conditions may obscure the distinctions between the measures.

The Chicago Multiscale Depression Inventory (CMDI; Nyenhuis et al., in press) is designed to produce three distinct measures for three aspects of depression: Mood (9 items), Evaluative symptoms (6 items) and Vegetative symptoms (9 items). Items are one word or a brief phrase that describe a symptom of depression. Data were collected from 433 multiple sclerosis patients. Patients rated how they felt during the last week on a 5-point scale, from 1 ("Not at all") to 5 ("Extremely"). All items were combined into one Rasch analysis, followed by a principal component analysis of the standardized residuals. Items identified as loading on the first factor (or component) in the residuals were excluded from subsequent factors. The utility of the instrument for MS patients would be supported if the factor structure reflected the instrument's intended functioning.

Figure 1 shows that the first factor in the residuals contrasts the 9 Vegetative items against the others. After removal of the Vegetative items from the analysis, the first factor contrasts, in Figure 2, the Evaluative against the Mood items. The item difficulty hierarchies can be examined to confirm the stability of the construct definitions.

The practical implications of these three factors can be investigated by constructing 3 measures for each patient, one for each subset of items. The Figures opposite show the relationships between the three sets of person measures. They are correlated across patients, confirming that they converge to measure "depression" when it is useful to regard all items as measuring one variable. The relationship between Evaluative and Mood symptoms (bottom Figure) is strongest, reflecting their clear distinction from Vegetative symptoms in Figure 1. Overall, the structure of the CMDI has been confirmed for these patients.

Chih-Hung Chang
Evanston Northwestern Healthcare

Nyenhuis, D. L., Luchetta, T., Yamamoto, C., Terrien, A., Bernardin, L., Rao, S. M., & Garron (in press). The development, standardization and initial validation of the Chicago Multiscale Depression Inventory. Journal of Personality Assessment.

Chung C.-H. (1998) Confirming Test Structure and Measurement Characteristics. Rasch Measurement Transactions 12:1 p. 622-3.

Confirming Test Structure and Measurement Characteristics. Chung C.-H. … Rasch Measurement Transactions, 1998, 12:1 p. 622-3.

Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn. 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 Facets Tutorials - free	Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre	Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
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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