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# 02-CA.R · Correspondence Analysis of the 2022 municipal cross-section
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# =============================================================================
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#
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# Bourdieusian framework:
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# - Individuals (rows): 290 Swedish municipalities
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# - Active columns: population-composition counts — variables that all
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# measure "number of residents with attribute X",
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# sharing a common unit (persons). The active set is
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# deliberately restricted to age structure,
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# educational capital (Swedish-born, by gender),
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# educational attainment (Swedish-born, by gender),
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# employment sector, and national origin. These
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# variables together define the compositional profile
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# variables together define the profile
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# of the municipality's resident population.
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# - col.sup (a): educational provision counts — the research object
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# - col.sup (b): political vote and council counts — outcomes
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# - col.sup (c): infrastructure & mobility counts — dwellings,
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# - col.sup (a): educational provision counts
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# - col.sup (b): political vote and council counts
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# - col.sup (c): infrastructure & mobility counts,
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# vehicles, commuter flows, agricultural enterprises,
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# livestock; genuine counts but measuring different
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# entities (not persons), so they cannot share a
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# contingency table with the active columns
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# - Outside CA: rates, proportions, continuous measures, and
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# count variables measuring event flows (births,
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# deaths, migration) → correlated with CA row
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# deaths, migration) correlated with CA row
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# scores post-hoc
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#
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# Why restrict active variables to person-counts?
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