The previous chapter revealed strong statistical relationships between the use of child soldiers and rebel type, funding, and duration. These results, while consistent with my theory, tell only part of the story. It is necessary to explore the mechanisms identified in Chapter 3 in a way that can better illustrate the fluid and dynamic aspects of the causal
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story. Therefore, I use case studies to further illustrate the determinants of child soldier use. With systematic small-n research, I am able to rule out some alternative explanations of recruitment decisions that cannot be adequately controlled with quantitative analyses. The quantitative tests are valuable, but qualitative research can help assess aspects of the theory that statistics cannot uncover.
One of the most important reasons for using small-n research for the study of child soldiers is to more effectively rule out alternative explanations. This project is focused on a phenomenon that remains understudied. Therefore, it is difficult to compile an exhaustive list of control variables for the large-n models. For example, one of the most common suggestions for differences in child protection is the ubiquitous "culture." This is a fuzzy concept that is notoriously difficult to measure (Zadeh 1965). It can mean one thing to a researcher (e.g., religion) and something different altogether with another (e.g., farming practices). Treatment of children is easily susceptible to cultural arguments. The cases discussed here explore local norms where rebel groups operate and compare these to international norms and those of the rebel groups themselves. The small-n research conducted here serves to rule out such competing explanations.
A second major strength of qualitative research is the depth of analysis that is possible. Quantitative research explores many cases, but ultimately researchers are limited in the depth of their knowledge. Qualitative research is the reverse of this;
"knowing more about less" rather than "less about more" (Ragin 2000:22). In this project, I aim to remedy both by engaging in an in-depth analysis of multiple cases.
This rectifies the conceptual stretching employed for the large-n study. For
me to better explore the child soldier variable of the large-n analysis. In all but the Somaliland case, I explore the volume or numbers of child soldiers used by each group, as well as the age, under 15 years. That is, exploring the child soldier variable beyond a "2." It is not the case that the other groups in the large-n data do not offer any child protection. Rather, such behavior is captured by the less-than-ideal "0" in the child soldier use measure. Of the four cases, only Somaliland and the SPLM offer substantive child protection. For them, I "unpack" the "0" on their child soldier measure. Another way to think about following the norm against child soldier use is as a continuous variable. With my 2,1,0 indicator, I am only getting part of the picture. The cases let me extend the measure above 2 (extremely young ages) and below 0 (more than just not letting them be soldiers).
Even using the more nuanced age-related variables does not solve the problem of loss of information. The extent of use and the number of children used by each group is valuable information that is, at this point, almost impossible to find for the full sample of cases. The task is more manageable for groups involved in the four civil wars explored in this section. Another benefit offered by the richness of detail is to extend the concept of non-use of child soldiers into the realm of child protection. Thus, if groups fail to use child soldiers (like Somaliland, Chapter 6) or stop using them (like the Sudan People's Liberation Movement, Chapter 8) qualitative research allows greater exploration into the policies of the group that help children. This broadens the measurement of child soldier use from positive (child soldiers present) to negative (higher levels of child protection). This would be difficult to do with large-n analysis.
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Third, I am better able to explore causal mechanisms behind my theory with qualitative research. I provide a causal story in Chapter 3. In Chapter 5, I offer empirical support for this story, what should be seen if the story is true. With qualitative case studies, the decision-making processes that lie at the heart of the theory can be explicated. Certainty will remain elusive; individuals retain private information. However, with detailed examination of records, policies, secondary source analysis, and personal interviews, these data lead to strong inferences about the private information of decision- makers. Such exploration reveal better how the goals of the rebellion and funding sources influence the behavior and characteristics of the rebellion itself (George and Bennett 2005).
Finally, small-n research will help identify other important variables whose relationship was dismissed in the statistical analysis. Statistical insignificance is not the same thing as having no relationship. Case studies have an advantage in exploratory research (Gerring 2004). Therefore, the analysis that follows will be able to identify where additional variables play a role even if those variables did not have statistical significance in the large-n analysis.