Visual Analog Scales

The Visual Analog or Analogue Scale (VAS) is designed to present to the respondent a rating scale with minimum constraints. Respondents mark the location on the 10-centimeter line corresponding to the amount of pain they experienced. This gives them the greatest freedom to choose their pain's exact intensity. It also gives the maximum opportunity for each respondent to express a personal response style. VAS data of this type is recorded as the number of millimeters from the left of the line with the range 0-100.

Visual Analogue Scales (VAS):
Aitken, R. C. B. (1969). Measurement of feelings using visual analogue scales. Proceedings of the Royal Society of Medicine. 62, 989 - 993
Freyd, M. (1923). The graphic rating scale. Journal of Educational Psychology, 43, 83 - 102
Hayes, M. H. S. & D. G. Patterson (1921). Experimental development of the graphic rating method. Psychological Bulletin, 18, 98-99

Do less constraints result in better information? In a study of knee pain (Thomeé et al., 1995) a VAS scale was presented to patients. Conventional analysis treats this 101 category rating scale as already linear. Rasch analysis, however, paints a different picture. It is impossible for humans to discriminate 101 levels of pain intensity accurately. Miller (1956) suggests that 9 levels is the best we can do in any situation. Further, the transformation of pain intensity into a location on a line must make its way through a sequence of mental processes. The effect is that the use of the line by different respondents varies greatly. In the pain study, Rasch analysis indicated that the intended 101 category rating scale could be considered, at best, to contain 10 replicable category groupings - one per centimeter. Accordingly, the observed categories were collapsed into a 1-10 rating scale. Even on this scale, use of extreme categories 1, 9, 10 appeared to be influenced by idiosyncratic reactions to pain.

In many situations, VAS scales can be collapsed down to 3 or 4 replicable categories with advantage. Munshi (1990) conducted a study in which 210 air travellers each responded to 8 prompts by marking their opinions from absolute disagreement to complete agreement on 76 mm. lines. The locations of the marks were measured to the nearest 0.5 mm. This gave 153 categories! Essentially every possible distance was observed in the 1615 responses, but 23% of the markings were at the extremes. A cluster analysis was performed of the remaining points. It revealed that, when within-cluster variation is treated as random error and between-cluster variation as intentional, more than 75% of the total variance in the non-extreme data can be explained by dichotomization! Thus more than 80% of the variance in the data was explained with just 4 categories. In his analysis, 7 categories explained 98%.

It is clear in Munshi's study, that reducing from 153 to 7 categories has not lost any replicable information, but conventional statistics, such as reliabilities, would mislead the analyst into believing the opposite. Reliabilities, standard errors and separations are computed on the basis that the data are what they purport to be. Thus in the uncollapsed data, observations appear to be on a highly discriminating 153 category rating scale. Consequently standard errors are very low and separations and reliabilities very high. In fact, insightful analysis has revealed that observations are on a much less discriminating 7 category scale, with necessarily bigger standard errors and lower separations and reliabilities. To report results based on the original 153 categories would misinform ourselves and our readers.

The moral of the VAS story: be Procrustean with your Visual Analog Scales!

John Michael Linacre

Miller G.A. (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81-97.

Munshi J. (1990) A Method for Constructing Likert Scales. Research Report. Sonoma State University, CA. www.munshi.4t.com/papers/likert.html

Thomeé R., Grimby G., Wright B.D., Linacre J.M. (1995) Rasch analysis of Visual Analog Scale measurements before and after treatment of patellofemoral pain syndrome in women. Scandinavian Journal of Rehabilitation Medicine 27, 145-151.

Visual Analog Scales.Linacre J.M. … Rasch Measurement Transactions, 1998, 12:2 p. 639.

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
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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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