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In the method of triads for a set of n objects all three element sets of objects are presented to the respondents. A respondent is asked to pick out the most similar and the least similar pair. The method of triads, despite its numerous advantages, is rarely used in practice. The number of triads is a cubic function of the number of objects and increases very rapidly with the number of objects. The aim of the study is to indicate the possibility of scaling preferences based on the reduced number of triads. It has also been examined whether the change of reduced set of triads influences the results of the scaling. The results of the analysis are illustrated by an empirical example in which pref­erence scaling for different sets of triads was performed with the use of TRISOSCAL program.

Dynamic scaling is a set of methods in which the geometrical representation of the similarity data for T different time periods is made. This article presents the use of two-dynamic scaling methods for studying changes in the preferences. In the first method the location of points on the perceptual map is made on the basis of the super-dissimilarity matrix. In the second method multidimensional scaling for the respective periods is carried out and the obtained configurations are matched by transformations preserving the proportions of distances between points. The presentation of the methods is illustrated by an empirical example in which calculations were performed with use of SPSS and New MDSX packages.

Research background: So far, many methods of direct measurement of similarity in multidimensional scaling have been developed (e.g. ranking, sorting, pairwise comparison and others). The method selection affects the subjective feelings of the respondents, i.e. fatigue, weariness resulting from making numerous assessments, or difficulties in expressing similarity assessments.Purpose: In the proposed method, for all four-element sets (tetrads) of objects a respondent is asked to pick out the most similar and the least similar pair. Because the number of tetrads increases very rapidly with the number of objects, the aim of the study is to indicate the possibility of measuring similarities based on the reduced number of tetrads.Research methodology: In order to make scaling results independent from respondents’ subjective effects the analysis was made on the basis of the given distance matrix. To construct perceptual maps based on tetrads, multidimensional scaling with the use of the MINISSA program was performed. The quality of matching the resulting points configuration to the configuration determined based on the distance matrix was tested by a Procrustes statistic.Results: It was demonstrated that the choice of the incomplete set of tetrads has no significant effect on the results of multidimensional scaling, even when all pairs of objects in tetrads cannot be presented equally frequently.Novelty: An original method for calculating similarities in nonmetric multidimensional scaling.

Streszczenie: Metoda triad zaliczana jest do podstawowych metod porządkowania preferencji konsumenckich. Jest ona jednak bardzo rzadko stosowana w praktyce. Przyczyny tego należy upatrywać przede wszystkim w pracochłonności metody. Wyrażenie przez respondentów ocen podobieństwa dla k n C zestawów trzech par (n -liczba obiektów) jest uciążliwe, zwłaszcza gdy jednocześnie analizowanych jest wiele obiektów. Celem pracy jest wskazanie możliwości skalowania preferencji w oparciu o zredukowaną liczbę triad. Przeprowadzono analizę sprawdzającą, czy (a jeżeli tak to w jakim stopniu) redukcja liczby triad wpływa na ostateczne wyniki badań. Wykorzystanie metody triad zilustrowano przykładem empirycznym, w którym obliczenia i prezentację wyników przeprowadzono z wykorzystaniem programu TRISOSCAL dostępnym w pakiecie NewMDSX.