In article author defines different measurement latent models and describes specify of measurement latent constructs. In literature some examples of these models are: the “true – score” model of classical test theory, the “domain score” model, item response model, factor analysis and latent class models. This work also presents method of estimation that should be undertaken in the identification process of latent constructs. Some aspects related with adjustments in the measurement model depending on distance between respondent and their responses, are also discussed. Author describes them from the prospect of 1) distance between respondent and response on the construct (variable) map; 2) distance between different responses on the construct map and 3) difference between different respondents. Next going on to further description, author considers two types of models based on metrical items characteristics: EFA and CFA. In the exploratory factor analysis as a key latent variable model in constructs detection and their formulation is defined where 4 latent constructs are extracted. These four detected constructs (based on earlier set of 22 value items) were given the following names: “Conservatism”, “Freedom-Independence”, “Hedonistic Consumerism”, and “Life Sensitiveness”. Secondly there is implemented CFA model which reduces number of value items from 22 to 14, containing only two latent constructs called “Conservatism” and “HedonisticConsumerism”. Additionally those constructs were in the end, described with selected AIOD variables where MDS was applied. And at last constructs were defined in context of their utility for marketing activity.

latent models, constructs, customers’ values

[1] Balicki A., [2009], Statystyczna analiza wielowymiarowa i jej zastosowania społeczno-ekonomiczne, UG, Gdańsk.

[2] Bartholomew D.J., Steele F., Moustaki I., Galbraith J.I., [2008], Analysis of multivariate social science data, 2nd ed. Chapman and Hall Books, New York.

[3] Bentler P.M., Weeks D.G., [1980], Linear structural equations with latent, [in:] Long, J.S. (ed.), Testing Structural Equation Models, Sage Publications, New York.

[4] Bąk A., [2007], Application of latent variable models to consumers preferences analysis, „Acta Universitatis Lodziensis: Folia Oeconomica”, Uniwersytet Łódzki, pp. 321-329.

[5] Guttman L., [1945], A basis for analyzing test-retest reliability, „Psychometrika”, 10, pp. 255-282.

[6] Guttman L., [1954], Some necessary conditions for common-factor analysis, „Psychometrika”, 19, pp. 149-161.

[7] Kaiser H.F., [1960], The varimax criterion for analytic rotation in factor analysis, „Psychometrika” 23, pp. 187-200.

[8] Kruskal J.B., [1964], Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis, „Psychometrika”, 29, pp. 1-28.

[9] Loehlin J.C., [2004], Latent variable models: an introduction to factor, path, and structural equation, Lawrence Erlbaum Associates, New Jersey.

[10] Marcoulides G.A., Moustaki I., [2002], Latent variable and latent structure models, Lawrence Erlbaum Associates, New York.

[11] Sagan A., [2004], Badania marketingowe – podstawowe kierunki, UE Kraków.

[12] Schiffman S.S., Reynolds M.L., Young F.W., [1981], Introduction to multidimensional scaling: the theory, methods and applications, Academic Press, New York.

[13] Skrondal A., Rabe-Hesketh S., [2004], Generalized latent variable modeling, Chapman and Hall, New York.

[14] Spearman C., [1904a], The proof and measurement of association between two things, „American Journal of Psychology”, 15, pp. 72-101.

[15] Spearman C., [1904b], General intelligence objectively determined and measured, „American Journal of Psychology”, 15, pp. 201-293.

[16] Spearman C., [1910], Correlation calculated from faulty data, „British Journal of Psychology”, 3, pp. 271-295.

[17] Stevens S.S., [1946], On the theory of scales of measurement, „Science”, 103, pp. 667-680.

[18] Sztemberg-Lewandowska M., [2008], Analiza czynnikowa w badaniach marketingowych, UE Wrocław.

[19] Takane Y., Young F.W., de Leeuw J., [1978], Nonmetric individual differences in multidimensional scaling: an alternating least squares method with optimal scaling features, „Psychometrika”, 42, pp. 7-67.

[20] Tarka P., [2008], From ranking (Rokeach – RVS) to rating scales evaluation – some empirical observations on multidimensional scaling Polish and Dutch youth’s values, „Innovative Management Journal”, 1, 2, pp. 24-42.

[21] Tarkkonen L., [1987], On reliability of composite scales, Statistical studies 7 ed. Finnish Statistical Society.

[22] Thurstone L.L., [1947], Multiple factor analysis, The University Press, Chicago.

[23] Zaborski A., [2001], Skalowanie wielowymiarowe w badaniach marketingowych, AE Wrocław.

[24] Wilson M., [2005], Constructing Measures: An Item Response Modeling Approach, Lawrence Erlbaum Associates, New York.

Copyright © 2019 Statistics Poland