Abstract
The growing popularity of nonparametric statistics (Lehmann, 1975) is attested to by the proliferation of new techniques in the last couple of decades. Whereas early formulations of nonparametric statistics concentrated on uni- and bivariate techniques—a shortcoming that was identified by Siegel (1956)—in the last 20 years there has been a clear accent on their development for multivariate application. Although the multivariate implementation of nonparametric statistics is of central concern in this chapter, in order to provide a basis for an evaluation of these procedures it is necessary to deal first with some basic problems. Specifically, in this section two fundamental issues will be addressed: (1) the meaning of the term nonparametric and (2) its relation to the term distribution free, which is often used synonymously. Before beginning the discussion of nonparametric techniques, a brief overview of such critical terms as multivariate and nonparametric will be given.
This chapter was written while the author was Senior Research Scientist at the Max Planck Institute for Human Development and Education, Berlin, Federal Republic of Germany.
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von Eye, A. (1988). Some Multivariate Developments in Nonparametric Statistics. In: Nesselroade, J.R., Cattell, R.B. (eds) Handbook of Multivariate Experimental Psychology. Perspectives on Individual Differences. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0893-5_11
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