martes, 13 de marzo de 2007

Methodology

Objectives

This methodology has a dual objective: It responds to the challenge of the lack of literature regarding the NCCs and PKOs, and also, it takes the researcher right into the best sources available that match the areas of peacekeeping and each of the non-contributors. Data collection for the NCCs represented a real challenge, because peacekeeping is a highly political issue in their countries: Anything discussed about it, could result in negative consequences for the interviewee. The researcher had to amend the original methodology to rely on bibliographical references due to the increase of the issue of complexity in the research process.

As a result, the researcher follows the advice of Gary King, Robert O. Keohane, and Sidney Verba (2004) regarding the social sciences:

In sum, social science research should be both general and specific: it should tell us something about classes of events as well as about specific events at particular places. We want to be timeless and time bound at the same time. The emphasis on either goal may vary from research endeavor to research endeavor, but both are likely to be present. Furthermore, rather than the two goals being opposed to each other, they are mutually supportive. Indeed, the best way to understand a particular event may be by using the methods of scientific inference also to study systematic patterns in similar parallel events. Events are not really unique… the fact that the events seem unique but they are interrelated, creates the issue of complexity when the researcher tries to understand then through the use of simplification, and the way to do it is through the methods of scientific inference. (2004, 12)

Therefore, the researcher uses scientific inference to fix the lack of direct knowledge about NCCs that were not available to provide information about why they do not contribute troops to UN peacekeeping missions. The authors state:

Inference is using the facts that we know, to learn about facts that we don’t know. The facts that we do not know are the subjects of our research questions, theories and hypotheses. The facts that we do know form our qualitative and quantitative data or observations. When we deal with research, we need to avoid getting frustrated with the vast amount [or lack] of information available. The solution is the search for general knowledge. That is, the best scientific way to organize facts is observable implications of some theory or hypothesis. Scientific simplification involves the productive choice of a theory (or hypothesis) to evaluate; the theory then guide us to the selection of those facts that are implications of theory. Organizing facts in terms of observable implications of a specific theory produces several important and beneficial results in designing and conducting research. (Ibid., 13)


Diplomats from NCC Missions to the UN
The objective is to obtain data about why certain countries do not contribute troops. This was the core of the data collection and where most of the efforts took place. Nevertheless, it was the most challenging part because peacekeeping is a very political issue in their home countries, and even more, not contributing with troops. This created a new dimension for data analysis for the researcher, due to the lack of response from the diplomats of the non-contributor missions. Thus, the researcher had to rely on bibliographical sources for most of the data collection process. Due to the political nature of peacekeeping, it was not possible to obtain interviews with diplomats due to many factors, but most importantly lack of trust and fear of lack of privacy. Plus, the fact that they needed to sign the Human Subjects Review “Consent Form” decreased (and in some cases, eliminated) the chances for participating.

The data collection faced a special challenge. As no previous study of this nature has been conducted for NCCs, the researcher created a theoretical data collection process during the preliminary data analysis. This data collection process was updated with the totality of the data collected.