Case Selection Strategy

To address these validity concerns of statistical research, I complement my anal- ysis of Chapter 4 with three qualitative and comparative case studies that are se- lected according to a most-similar system design (cf. George and Bennett, 2005; Lijphart, 1975; Przeworski and Teune, 1970; Seawright and Gerring, 2008). Following Gerring (2004, p. 342), I understand a case as “a spatially bounded phenomenon” that is “observed at a single point in time or over some delimited period of time”, and a case study as an intensive study of this phenomenon with the goal of understanding a larger class of comparable phenomena. Given these definitions – and inherently linked to Mill (2002)’s “method of differ- ence”, Lijphart (1975)’s “comparative method”, as well as George and Bennett (2005)’s “controlled comparison” – selecting cases according to a most similar

system design means that I select cases that are similar on a large number of

important explanatory variables except the explanatory variables of interest. Consequently, any set of variables that are similar in the cases are irrelevant in determining the outcome, because different outcomes are observed among cases that share these variables. Any set of variables differentiating these cases can be considered as explaining distinct outcomes, such as post-interim peace and the absence thereof (Przeworski and Teune, 1970). I use such a most similar

system design for my between-case analysis not least because this approach to case selection has been identified as one of the most useful strategies for theory- and model-testing in mixed-method research designs (Gerring, 2004).

To implement a most similar system design and arrive at a selected group of cases, I use statistical matching techniques (cf. Tarrow, 2010).45 _Match- ing techniques have become increasingly popular in statistical observational or quasi-experimental research designs (notable examples include Arceneaux et al., 2006; Ho et al., 2007). For qualitative research, matching has recently been iden- tified as “an approach to purposeful case selection in large-n studies with the goal of finding comparable unites within a data set” (Nielsen, 2014, p. 7). It has in this regard been recognized as the “most useful statistical tool for identify- ing cases for in-depth analysis in a most similar setting,” also because matching means that case selection is more transparent and replicable than selecting cases by hand (Gerring, 2006, p. 134). Having said that, matching does come with similar practical research issues as if one would select cases by hand. Firstly, exact matching of control variables is often impossible, especially if the variable that is to be similar is not binary but continuous. In such instances, cases are selected in order “to maximize the variance of the independent variables and to minimize the variance of the control variables” (Lijphart, 1975, p. 164), so that cases from the control group approximate or are “close enough” to those of the treatment group (Seawright and Gerring, 2008, p. 305). Secondly, it is wise to restrict one’s analysis to “the key variables and omitting those of only marginal importance” (Lijphart, 1975, p. 159), because it can have negative consequences to condition on control variables that are not actual confounders, since matching on these controls reduces the similarity on actual confounding variables (Nielsen, 2014).

Bearing in mind these issues and heeding Lijphart’s advice to focus on the most important confounders for case selection, I started by matching cases to be similar according to the three control variables from my Cox PH models of Chapter 4 that have robust statistically significant effects on the stability of peace. These are (1) ethnic issue, or whether warring parties broke down along ethnic lines; (2) conflict intensity, or whether a foregoing armed conflict reached the level of a civil war with over 1000 battle-related deaths; and (3)

incompatibility, or whether conflict was fought over controlling a government or

a territory (cf. the coding rules listed in section 4.1). But because all three control variables are coded following binary schemes, matching produced a high number of most similar case groups, and I thus also introduced the statistically non-significant controls to the matching equation, while weighting those three control variables with statistically significant effects.46

45_{Practically, this means I employ the software environment R’s caseMatch package to}

match cases (Appendix A, Nielsen, 2014). By default, caseMatch matches cases according to an approximate matching technique that minimizes their pairwise Mahalanobis distance – a scale-invariant distance metric – in order to ensure that a selected pair of cases is as similar as the data allow (cf. Gerring, 2006).

In addition to minimizing the variance on control variables while maximizing the variance on independent variables, I require three practical research issues from the selected pair of cases, in order to address issues similar to those that have been identified for random case selection (Seawright and Gerring, 2008). Firstly, I demand that interim governments selected for case studies ruled for at

least six months. This choice is implemented to ensure that a sufficient amount

of case-specific literature exists, and that interview respondents during empirical fieldwork recall the course of a respective interim government (cf. section 5.3 and Eck, 2011). The decision should not result in a bias in that I select particularly successful or unsuccessful cases of interim government, because my statistical analysis in Chapter 4 has demonstrated that the duration of an interim period has no statistically significant effect on the stability of peace, and the substantive effect is also comparatively small. Secondly, I select cases from distinct world

regions – which “still constitutes the exception to the rule” when analyzing

polities in the Global South – because I expect this to further validate my findings by ensuring that no cultural factors underlie the stability of post-interim peace, thus permitting me to test “the universal character” of my theory and concepts (Basedau and Köllner, 2007, p. 112). Thirdly, I demand that at least one case is suitable for fieldwork in 2015 in terms of security and language issues. This case selection strategy is common in the sub-discipline of peace and conflict research, where data gathering is often risky for both the researcher and her informants (Brounéus, 2011; Höglund, 2011). For instance, I opted against matched groups of cases such as Afghanistan, Libya, and Liberia, due to travel warnings for ongoing violence or the prevalence of the Ebola virus at the time I implemented my case selection in December 2014 (but see my discussion on Nepal in section 5.3).