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Research Report

Qualitative Comparative Analysis (QCA): An application to compare national REDD+ policy processes

Jenniver Sehring
Kaisa Korhonen-Kurki
Maria Brockhaus
Copyright Date: Jan. 1, 2013
Pages: 34
OPEN ACCESS
https://www.jstor.org/stable/resrep02339
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Table of Contents

  1. (pp. 1-1)

    In 1987, the American social scientist Charles Ragin built the foundation for Qualitative Comparative Analysis (QCA) with his seminal book The Comparative Method. QCA is designed for the comparison of a small to intermediate number of cases. It enables systematic cross-case comparison without neglecting case complexity, allowing modest, medium-range generalization and theorizing. The aim of this working paper is to introduce QCA as a method to study policy processes. In particular, we discuss its application to the Global Comparative Study on REDD+ (GCS-REDD).¹

    The objective of GCS-REDD is to provide policy makers and practitioners with relevant knowledge to ensure effective,...

  2. (pp. 1-4)

    During the last decades, QCA has gained popularity among social scientists interested in alternative ways to analyze and compare a small or medium number of cases. It has thus far primarily been applied to political science and sociology.² QCA is a research strategy as much as a set of concrete techniques (Rihoux 2007, 365). It challenges several typical approaches of statistical methods, but also goes beyond the classical case-centered focus of traditional qualitative research. Thus, although called Qualitative Comparative Analysis, QCA is not a qualitative method in the sense of empirical qualitative research. Rather, it should be seen as a...

  3. (pp. 4-14)

    During the past 30 years, QCA has been considerably refined and developed, partly in response to criticism of the original version (Ragin 1987). Today, four main methodological variations exist within QCA. These are crisp-set QCA, fuzzy-set QCA, multi-value QCA, and two-step fuzzy-set QCA. In order to illustrate the method and its application, we used the data from Module 1 of GCS-REDD, which analyses national REDD+ processes in 12 countries (Table 1). All are forest-rich tropical developing or emerging countries with a political commitment to implement REDD+ but also with powerful drivers of deforestation, weak multilevel governance, low cross-sectoral horizontal coordination...

  4. (pp. 14-17)

    As shown above, QCA makes it possible to translate complexity in in-depth case studies into reduced and comparable formulas and to formulate inferences on enabling factors. This process can be effectively applied to REDD+, but it requires engagement by country experts and coordinators. This study used QCA both to organize data and to draw inferences from it.

    One use of QCA occurs before the analysis begins: the summarization and coherence check of data. Factors affecting successful implementation of REDD+ were explored thoroughly; the list was then narrowed to a manageable number of the most important factors, and these were operationalized...

  5. (pp. 17-20)

    This section discusses some drawbacks to QCA and the challenges in applying it to GCS-REDD and the REDD+ process.

    The selection of cases, conditions and indicators has a strong impact on the research results, including conclusions on causal relations, and therefore must be based on careful consideration and strong arguments in order to avoid subjectivity. For this study, the cases were preselected by their inclusion in GCS-REDD, which hindered theory-based case selection, and were low in number. QCA faces the same challenge as all studies with a small number of cases: only a limited number of factors or conditions can...

  6. (pp. 20-20)

    As mentioned earlier, QCA is an approach as well as a methodological tool. As an approach, it serves the cognitive interests of social science. Its central principles – multiple and conjunctural causation, identification of necessary and sufficient conditions and their combination – better reflect social reality and complex social science thinking than do statistical methods (Blatter et al. 2007, 204). Especially the notion of equifinality – that there are different but equally effective ways to reach an outcome depending on the specific context – is a much observed phenomenon. Yet conventional social science methods have not been able to capture...

  7. (pp. 21-21)

    QCA enables systematic cross-case comparison of an intermediate number of case studies. Applying QCA to CIFOR’s Global Comparative Study on REDD+, we showed how it could help to derive parsimonious and stringent research results from a multitude of in-depth case studies and participating researchers. QCA is a time-consuming process, in particular when many researchers are involved. It requires commitment and readiness to question and reflect repeatedly on the assessments made. This occurs first in exchange with other country experts who may have conflicting assessments based on other sources of knowledge, which then have to be discussed in order to achieve...