of nationalism that would be measured by a subset (or subsets) of the items. An added advantage of
this approach is that using multiple items to measure the same basic concept should improve the
reliability of measurement. Factor analysis provides a statistical means of identifying the
hypothesized dimension or dimensions.13 A combination of apriori assessment of the individual
items and exploratory factor analysis suggested a strategy of focusing on the following seven items
(versions implemented in Ireland, other country∕nationality labels substituted as appropriate):
• “Generally speaking, Ireland is a better country than most other countries”
• “The world would be a better place if people from other countries were more like the Irish”
• “I would rather be a citizen of Ireland than of any other country in the world”
• “It is impossible for people who do not share Irish customs and traditions to become fully
Irish”
• “People should support their country even if the country is in the wrong”
• “Ireland should follow its own interests, even if this leads to conflicts with other nations”
• “How important do you think each of the following is for being truly Irish?”.........“to have
been born in Ireland”
In each case, respondents were asked to rank their responses along a scale, in the case of the
first six items, from 1 (strongly disagree) to 5 (strongly agree) and, in the case of the seventh item,
from 1 (very important) to 4 (not at all important). The seventh item was reordered to make it
consistent with the other six. Principal components analysis of these responses yielded two factors or
underlying dimensions of nationalist attitudes. As can be seen from the rotated factor loadings in
13 Factor analysis is a generic term often used to cover both principal components analysis and factor
analysis strictly speaking. Both are techniques that can be applied to a set of variables ‘.. .when the
researcher is interested in discovering which variables in the set form coherent subsets that are
relatively independent of one another. Variables that are correlated with one another but largely
independent of other subsets of variables are combined into factors. Factors are thought to reflect
underlying processes that have created the correlations among variables’ (Tabachnick and Fidell
2001,p. 582).
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