Using data drawn from the Cultural Capital and Social Exclusion study, we examine the relationship between social class membership and cultural participation and taste in the areas of music, reading, television and film, visual arts, leisure, and eating out. Using Geometric Data Analysis, we examine the nature of the two most important axes which distinguish `the space of lifestyles'. By superimposing socio-demographic variables on this cultural map, we show that the first, most important, axis is indeed strongly associated with class. We inductively assess which kind of class boundaries can most effectively differentiate individuals within this `space of lifestyles'.The most effective model distinguishes a relatively small professional class (24%) from an intermediate class of lower managerial workers, supervisors, the self-employed, senior technicians and white collar workers (32%) and a relatively large working class which includes lower supervisors and technicians (44%).

This paper investigates several methods available for testing comparisons between treatments in a repeated-measurement design, within the framework of the general multinormal model. Various ratios are considered, and the weakest assumptions required for the validity of each 9 ratio are presented. When several .F ratios are valid, power considerations are introduced for purposes of choice.Methods enabling the testing of validity assumptions are also presented, together with alternative multivariate statistics which can be used when no 9 ratio is valid. All procedures presented in the paper are illustrated by a numerical example.

In experimental data analysis when it conies to assessing the importance of effects of interest, 2 situations are commonly met. In Situation 1, asserting largeness is sought: "The effect is large in the population." In Situation 2, asserting smallness is sought: "The effect is small in the population." In both situations, as is well known, conventional significance testing is far from satisfactory. The claim of this article is that Bayesian inference is ideally suited to making adequate inferences. Specifically, Bayesian techniques based on "noninformative" priors provide intuitive interpretations and extensions of familiar significance tests. The use of Bayesian inference for assessing importance is discussed elementarily by comparing 2 treatments, then by addressing hypotheses in complex analysis of variance designs.