Guttman Parameterization of a Rating Scale

"A reparameterised form of thresholds into their principal components is the method of estimation operationalised in RUMM2020. This notion of principal components is used in the sense of Guttman (1950), who rearranged ordered categories into successive principal components, beginning with the usual linear one. They are analogous to the use of orthogonal polynomials in regression where the independent variable is ordered. The term does not refer to the common principal components analysis in which a matrix of correlation coefficients is decomposed."
Excerpted from www.rummlab.com.au

See also: Guttman Parameterization of a Rating Scale - Revisited, RMT 24:4, 2011, p. 1303.

A convenient logit-linear expression of a typical form of the Rasch polytomous model is
log_e(P_nix/P_ni(x-1)) = B_n - D_i - F_x where F_x is the centralized (Andrich, Rasch) threshold (also called step calibration) corresponding to the point on the latent variable where categories x-1 and x are predicted to be equally likely to be observed. Categories are numbered from 0 to m.

Pedler's (1987, amended) coefficients for an orthogonal-polynomial version of the rating-scale thresholds are:
T₁(x) = 1 which requires at least two categories in the rating scale
T₂(x) = 2( x - (m+1)/2 ) which requires at least three categories in the rating scale
T₃(x) = 3( x - (m+1)/2 )² - (m² - 1)/4 which requires at least four categories in the rating scale
T₄(x) = 4( x - (m+1)/2 )³ - ( x - (m+1)/2 )(3m² - 7)/5 which requires at least five categories in the rating scale

Higher-order coefficients can be obtained from:
T_k+1(x) = [(k+1)/k] ( x - (m+1)/2 )T_k(x) - ([(m² - (k-1)²)(k² - 1)]/[4(2k - 1)(2k-3)])T_k-1(x)

In accordance with Andrich and Luo (2003), these modify the Guttman parameters, θ, η, ζ:
D_i = the item difficulty
F_x =
T₁(x)*0 where 0 is the rating scale central location relative to the item difficulty
+ T₂(x)*θ where θ is the rating scale dispersion or unit
+ T₃(x)*2*η where η is the skewness
+ T₄(x)*5*ζ where ζ is the kurtosis
+ higher-order terms

This enables the Rasch threshold parameters, {F_x}, to be computed directly from the Guttman parameters, θ, η, ζ, when they are known. The numerical values of the multipliers for m = 2, 10 are shown in the Table.

Direct computation of θ, η, ζ from the {F_x} can usually be performed by means of linear regression, solving the m equations of the form above, with the {F_x} as the dependent variables, the values in the Table as the independent variables, and θ, η, ζ as the coefficients to be estimated.

Example 1: Item 14 in the RUMM2020 runAll example is a 4-category item, so m = 3. On www.rummlab.com.au, the reported estimates are θ = 2.445 and ζ = -0.160. Thus, by computation,
F₁ = -2 * 2.445 + 2 * -0.160 = -5.210
F₂ = 0 * 2.445 + -4 * -0.160 = 0.640
F₃ = 2 * 2.445 + 2* -0.160 = 4.570

The estimates reported for the {F_x} on www.rummlab.com.au are: -5.231, .641, 4.590, indicating a close match between theoretical and empirical results.

Example 2: An m=6 rating scale has category frequencies: 96, 88, 101, 168, 210, 146, 101, The {F_x} are estimated by Winsteps at -2.30, -1.75, -1.34, 0.08, 2.08, 3.23. Excel regression analysis reports θ = 0.5794, η = 0.02786, ζ = -0.002241. According to Andrich and Luo (2003, p. 209) these values have greater stability than the {F_x}. The consequent smoothed values of {F_x} are -2.21, -2.04, -1.13, 0.24, 1.82, 3.32.

John Michael Linacre

Andrich, D. & Luo, G. (2003). Conditional Pairwise Estimation in the Rasch Model for Ordered Response Categories using Principal Components. Journal of Applied Measurement, 4(3), 205-221.

Guttman, L. (1950). The principal components of scale analysis. In S.A. Stouffer, L. Guttman, E.A. Suchman, P.F. Lazarsfeld, S.A. Star and J.A. Clausen (Eds.), Measurement and Prediction, pp. 312-361. New York: Wiley

Pedler, P.J. (1987) Accounting for psychometric dependence with a class of latent trait models. Ph.D. dissertation. University of Western Australia.

Guttman Parameterization of a Rating Scale, Linacre J.M., Andrich D.A., Luo G. … Rasch Measurement Transactions, 2003, 17:3 p.944

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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