In this paper, I adopt Pierre Bourdieu's theoretical and methodological framework to investigate patterns of inequality in volunteering fields and practices in Norway. Multiple correspondence analyses of national survey data indicate a hierarchically structured social space in Norway according to total volume of capital, while the positioning of different volunteer fields and practices seems to be more egalitarian with regard to capital possession, with some exceptions. This resonates well with established notions of the Norwegian civil society model as social democratic and egalitarian. Based on the discussions and findings, and considering growing social, cultural, and economic differences in many societies, I argue for a new volunteering research agenda better tailored for investigating social inequalities and differentiation in volunteering in different societal contexts, providing a new vantage point for understanding and explaining such inequalities.

The internationalisation of political science is an uneven process. Existing studies have shown that it is characterised by centre-periphery relationships, with dominant Western countries setting the international standards of the discipline and scholars from the East and the Global South struggling to adapt to these standards. Fewer studies, however, have been devoted to the tensions that internationalisation creates within the Western world of political science. Through a study of the case of France, this article argues that some of the countries that enjoy a rather dominant position globally may still be internally divided by the changes brought about by internationalisation. The article portrays French political science as divided between institutions and scholars that are strongly connected to international venues, and others that are not. It concludes that the internationalisation of political science is best portrayed as a conflicted process and a point of contention, rather than a smooth process of gradual convergence.

Joint correspondence analysis is a technique for constructing reduced-dimensional representations of pairwise relationships among categorical variables. The technique was proposed by Greenacre as an alternative to multiple correspondence analysis. Joint correspondence analysis differs from multiple correspondence analysis in that it focuses solely on between-variable relationships. Greenacre described one alternating least-squares algorithm for conducting joint correspondence analysis. Another alternating least-squares algorithm is described in this article. The algorithm is guaranteed to converge, and does so in fewer iterations than does the algorithm proposed by Greenacre. A modification of the algorithm for handling Heywood cases is described. The algorithm is illustrated on two data sets.

We discuss a variety of methods for quantifying categorical multivariate data. These methods have been proposed in many different countries, by many different authors, under many different names. In the first major section of the paper we analyze the many different methods and show that they all lead to the same equations for analyzing the same data. In the second major section of the paper we introduce the notion of a duality diagram, and use this diagram to synthesize the many superficially different methods into a single method.

This study formulates a property of a quantification method, the principle of equivalent partitioning (PEP). When the PEP is used together with Guttman's principle of internal consistency (PIC) in a simple way, the combination offers an interesting way of analyzing categorical data in terms of the variate(s) chosen by the investigator, a type of canonical analysis. The study discusses applications of the technique to multiple-choice, rank-order, and paired comparison data.

A comprehensive approach for imposing both row and column constraints on multivariate discrete data is proposed that may be called generalized constrained multiple correspondence analysis (GCMCA). In this method each set of discrete data is first decomposed into several submatrices according to its row and column constraints, and then multiple correspondence analysis (MCA) is applied to the decomposed submatrices to explore relationships among them. This method subsumes existing constrained and unconstrained MCA methods as special cases and also generalizes various kinds of linearly constrained correspondence analysis methods. An example is given to illustrate the proposed method.

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents’ preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables while the latter is used for identifying relatively homogeneous clusters of respondents. The proposed method offers an integrated graphical display that provides information on cluster-based structures inherent in multivariate categorical data as well as the interdependencies among the data. An empirical application is presented which demonstrates the usefulness of the proposed method and how it compares to several extant approaches.

Several methods have been developed for the analysis of a mixture of qualitative and quantitative variables, and one, called PCAMIX, includes ordinary principal component analysis (PCA) and multiple correspondence analysis (MCA) as special cases. The present paper proposes several techniques for simple structure rotation of a PCAMIX solution based on the rotation of component scores and indicates how these can be viewed as generalizations of the simple structure methods for PCA. In addition, a recently developed technique for the analysis of mixtures of qualitative and quantitative variables, called INDOMIX, is shown to construct component scores (without rotational freedom) maximizing the quartimax criterion over all possible sets of component scores. A numerical example is used to illustrate the implication that when used for qualitative variables, INDOMIX provides axes that discriminate between the observation units better than do those generated from MCA.

In quantifying categorical data, constraints play an important role in characterizing the outcome. In the Guttman-type quantification of contingency tables and multiple-choice data (incidence data), the trivial solution due to the marginal constraints is typically removed before quantification; this removal, however, has the effect of distorting the shape of the total space. Awareness of this is important for the interpretation of the quantified outcome. The present study provides some relevant formulas for those cases that are affected by the trivial solution and those cases that are not. The characterization of the total space used by the Guttman-type quantification and pertinent discussion are presented.

The transformation of the Belgian party system has unfolded asymmetrically: while conservative and far-right parties have thrived in Flanders, the populist right vote has not (yet) been mobilized in Francophone Belgium. Focusing on the demand side of electoral politics, this article explores whether these disparities on the supply side mirror distinct political dimensions and cleavages in both regions. By using an innovative methodology of (class-specific) multiple correspondence analysis, we find that the Flemish and the Francophone political space are largely alike – both in terms of the composition of their political dimensions as in their cleavage structure. However, we also find that Francophones adhere to a distinctive welfare consensus, which prevents working-class embourgeoisement and likely moderates the electoral fortunes of the far right. Overall, our results challenge the common stereotypes of a ‘conservative Flanders’ and a ‘progressive Wallonia’.

In this study, we investigate how eco-social attitudes are interlinked with various modes of political action aimed at preventing environmental change and promoting social welfare. Using multiple correspondence analysis and cluster analysis, we explore the links between attitudes and political action, and associated socio-political characteristics, in the case of Sweden. Our results show a three-node pattern forming a political action triangle: individuals expressing joint support for social welfare and environmental concerns are most actively engaged in political action, while those supporting environmental concerns are sympathetic to take part in political action without actually participating, and those supporting social welfare or expressing low support for either set of concerns seem overly reluctant towards all types of political action. This pattern, which is also tied to distinctive socio-political characteristics, has wider implications for understanding the agency and the mobilization of support for tackling the multiple ecological and social crises contemporary societies are facing.

Polyphenols have been suggested as protective factors for a range of chronic diseases. However, studying the impact of individual polyphenols on health is hindered by the intrinsic inter-correlations among polyphenols. Alternatively, studying foods rich in specific polyphenols fails to grasp the ubiquity of these components. Studying overall dietary patterns would allow for a more comprehensive description of polyphenol intakes in the population. Our objective was to identify clusters of dietary polyphenol intakes in a French middle-aged population (35–64 years old). Participants from the primary prevention trial SUpplementation en VItamines et Minéraux AntioXydants (SU.VI.MAX) study were included in the present cross-sectional study (n 6092; 57·8 % females; mean age 48·7 (sd 6·4) years). The fifty most consumed individual dietary polyphenols were divided into energy-adjusted tertiles and introduced in a multiple correspondence analysis (MCA), leading to comprehensive factors of dietary polyphenol intakes. The identified factors discriminating polyphenol intakes were used in a hierarchical clustering procedure. Four clusters were identified, corresponding broadly to clustered preferences for their respective food sources. Cluster 1 was characterised by high intakes of tea polyphenols. Cluster 2 was characterised by high intakes of wine polyphenols. Cluster 3 was characterised by high intakes of flavanones and flavones, corresponding to high consumption of fruit and vegetables, and more broadly to a healthier diet. Cluster 4 was characterised by high intakes of hydroxycinnamic acids, but was also associated with alcohol consumption and smoking. Profiles of polyphenol intakes allowed for the identification of meaningful combinations of polyphenol intakes in the diet.

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

Studies of xenophobia have focused either on socio-economic context that accentuates xenophobic attitudes or on perceptions of immigrants, namely symbolic and realistic threats as well as on social distance from immigrants. This study examines closely the relationship among various components of xenophobia and their contribution in the formation of particular xenophobic groups. The analysis identified four different xenophobic groups, i.e. a) The distant xenophobic group, b) The core xenophobic group, c) The subtle xenophobic group and d) The ambivalent xenophobic group. The groups’ profiles are synthesized through negative, neutral and positive properties of overall attitudes towards immigrants, perceived threats, political xenophobia, social distance, authoritarian attitudes and individual social characteristics. The survey results demonstrate that a multidimensional conceptualization of xenophobia is needed both at the level of objective social condition and of individual and collective perceptions.