Abstract
The traditional statistical procedure is typically based on the notion that data are a random sample from the normal population. Knowing that the bivariate normal distribution has only three parameters, mean, variance and linear correlation, the use of the normal distribution as an analytical framework leads to what we call linear analysis. This paper starts with discarding the normal distribution assumption, and then advocates total reliance on data in hand. In the social sciences, we face the majority of data to be categorical. To explain the data exhaustively, it is almost necessary to employ an empirical approach without any prior assumptions such as the distribution, levels of measurement or models for data. This framework opens up the possibility of capturing nonlinear information and multidimensionality in data, as well as of cautionary wisdom for the popular data reduction approach to data analysis.
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References
CRAMÉR, H. (1946): Mathematical Methods of Statistics. Princeton University Press, Princeton.
GREENACRE, M.J. (2004): Personal communication. Dortmund, Germany.
LIKERT, R. (1932): A technique for the measurement of attitudes. Archives of Psychology, 140, 44–53.
NISHISATO, S. (1984): Forced classification: A simple application of a quantification method. Psychometrika, 49, 25–36.
NISHISATO, S. (2000): Data types and information: Beyond the current practice of data analysis. In R. Decker and W. Gaul (Eds.) Classification and Information Processing at the Turn of the Millennium. Springer, Heidelberg, 40–51.
NISHISATO, S. (2004): A unified framework for multidimensional data analysis from dual scaling perspectives: Another look and some suggestions. Japanese Journal of Sensory Evaluation, 8, 4–10 (in Japanese).
NISHISATO, S. (2005): Correlational structure of multiple-choice data as viewed from dual scaling. A revised paper submitted for a book by J. Blasius and M.J. Greenacre (Eds.): Proceedings of a conference held in Barcelona, 2003 (the title of the book is not decided yet).
NISHISATO, S. and BABA, Y. (1999); On contingency, projection and forced classification of dual scaling. Behaviormetrika, 26, 207–219.
NISHISATO, S. and GAUL, W. (1990). An approach to marketing data analysis: Forced classification procedure of dual scaling. Journal of Marketing Research, 27, 354–360.
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Nishisato, S. (2005). Empirical Approach as a Scientific Framework for Data Analysis. In: Baier, D., Decker, R., Schmidt-Thieme, L. (eds) Data Analysis and Decision Support. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28397-8_11
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DOI: https://doi.org/10.1007/3-540-28397-8_11
Publisher Name: Springer, Berlin, Heidelberg
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