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Model of latent profile factor analysis for ordered categorical data

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  • Piotr Tarka

Abstract

In the literature factor analysis is admittedly a well-known and effective multivariate method in the reduction of extensive and broad data, e.g., in the analysis of too many variables. It is also known for the process of unidimensional or multidimensional scale/s construction. Typically, in many studies (especially those pertaining to market research area) a common factor analysis solution is used (based on continuous data). However, there are rarely ever undertaken studies pertaining to latent variable models where other type of data is used based on discrete variables. One of these models might be called Latent Profile Factor Analysis - LPFA. In this article author’s main objective is to propose and discuss its (LPFA) main assumptions. In order to prove the model’s functionality in practice of market research, a brief example of LPFA model for ordered categorical data (based on one-factorial solution) in reference to hedonic consumption data is given at the end of the paper.

Suggested Citation

  • Piotr Tarka, 2013. "Model of latent profile factor analysis for ordered categorical data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 171-182, March.
    • Handle: RePEc:csb:stintr:v:14:y:2013:i:1:p:171-182
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    References listed on IDEAS

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    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
    3. Irini Moustaki & Martin Knott, 2000. "Generalized latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 391-411, September.
    4. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
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