Parties, Candidates and Citizens On-Line - Åbo Akademi

Because the study is case-oriented with a low number of cases, the choice of
suitable research methods is limited. Nonetheless, the design of the study is variable
oriented and would require a research method which facilitates some form of variable
testing. Given these restrictions, the method of Qualitative Comparative Analysis
(henceforth abbreviated as QCA) is an appropriate solution. The method has the
advantage of being able to retain the “wealth of case-oriented material and case-derived
‘data’ in political science and convert them into a form suitable for variable based
research” (Peters 1998: 163; Ragin 1994: 304). Peters (1998: 160-2) merits the technique
as one of the tools for including qualitatively-derived data in meta-analyses.
The QCA employs the technique of Boolean algebra which is basically a form of
logical reasoning (for a detailed description of the technique, see Appendix A). The
procedure follows a logic of ‘differences can’t explain similarities’ in order to find
multiple, logical combinations of causal variables in which the dependent variable occurs
(Coverdill & Finlay 1995: 458; Ragin 1994: 312). Each case is considered holistically; the
effect of one independent variable is seen as different in each case depending on the
values on the other independent variables (Miethe & Drass 1999: 9-10).
The Boolean technique is not without its problems. Firstly, the method is
dependent on theoretical reasoning and argumentation in determining which causal
variables to include in the analysis (Peters 1998: 168). Secondly, the requirement of binary
data is problematic as societal phenomena are often quite complex and difficult to
simplify into a yes-no dichotomy (Jungerstam-Mulders 2003: 70-1; Peters 1998: 169-70).
The third problem with the Boolean technique is the limited availability of data in existing
cases. The outcomes of all possible combinations of variables are usually never tested
(Jungerstam-Mulders 2003: 71). Finally, it is not uncommon to encounter contradicting
cases where the same combination of values on the independent variables produces
different outcomes. In part, this could be due to the selected variables not being able to
discriminate between the cases. This problem is hard to overcome with QCA as the
inclusion of too many variables in the analysis is difficult (ibid). Still, Lijphart (1971: 686)
argues that deviant cases do weaken a hypothesis but are not sufficient to fully invalidate
it unless they appear in frequent numbers. Therefore, a QCA may still be conducted
including the settings which produce contradicting outcomes if one is aware of this
throughout the process (Jungerstam-Mulders 2003: 82).
Operationalizations
The dichotomizations of the variables, both independent and dependent, are discussed in
this section. As a rule of thumb, the conditions likelier to stimulate findings of
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