Migrants, Minorities, Belongings and Citizenship. Glocalization and ...

2.2.4. Multidimensional belongings in glocal spaces
Respondents’ belonging patterns have been mapped along the eleven dimensions, which
are shown in the first column of Table 6. Classification of the respondents (or value
assignment to the cases) has been done in two stages. In the first stage, the
respondents were classified with respect to their responses to the questionnairequestions
asking them about their belongings. In the second stage, the in-depth
interview transcriptions, the evaluations prepared by the field-researchers and the
information given in the respective country reports were used to refine the first-stage
classification. In connection with this refinement in the second stage, some valuable
information came up which enabled us to do a more correct classification. Moreover,
some questionnaire items which were left unanswered by the respondents were indeed
answered during the in-depth interviews. Thus, the data from the in-depth interviews
enabled us to create a data set with minimum missing data.
It is important to note that the results presented in the following sections are based on
raw-data which the research partners delivered to the coordinator. It is also important to
stress that the data presented in this section as well as those in the following sections
are not from a statistically representative sample. The tables and figures are quantified
presentation of the qualitative data collected. 5 Therefore, they should not be interpreted
as a portrayal of different groups’ or countries’ representative profiles. They basically
represent the state of affairs in what we call “glocal spaces” in six European cities. The
aim at this very stage is merely to illustrate systematically the structure and features of
the data collected and to construct the variables to be used in further causality analyses
in the subsequent sections.
One aspect that should be emphasized is that most respondents have multidimensional
belongings. Table 6 illustrates the results from a categorical principal components
analysis (CATPCA) with ordinal variables. The results show five main types of
multidimensional belonging patterns in the project’s data set.
Before interpreting the results, for those readers who are not familiar with principal
components analysis with categorical variables (CATPCA), some introductory notes may
be useful. Like the conventional principal components analysis with continuous variables
(PCA), the CATPCA-procedure also uncovers the “hidden” dimensions in the relationship
between a single set of categorical variables in a given data set. In other words, it is a
5 As we shall see in the subsequent subsections, this was done by using quantification methods such as optimal
scaling (e.g. categorical principal components and categorical regression analyses).
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