Qualitative Comparative Analysis (QCA) offers a new, systematic way of studying configurations of cases. QCA is used in comparative research and when using case-study research methods. The QCA analysts interprets the data qualitatively whilst also looking at causality between the variables. Thus the two-stage approach to studying causality has a qualitative first stage and a systematic second stage using QCA. QCA is truly a mixed-methods approach to research. The basic data-handling mechanism is a simple qualitative table of data. This matrix is made up of rows and columns. Its column elements can be binary (yes/no), ordinal, or scaled index variates. QCA is best suited to small- to medium-N case-study projects with between 3 and 250 cases. Crisp-set QCA uses only binary variates for its truth table. Fuzzy-set QCA also uses ordinal variates. A variate is a column of numbers representing real, not hypothetical, cases. In implementing QCA, one can code up the case-study data using NVIVO 7 software to create substantive case attributes. Multiple-level nested or non-nested cases can be handled. Fuzzy-set analysis is an optional extra stage, which also uses Boolean logic, but which is not necessary for QCA and tends not to be as qualitative as crisp-set QCA (csQCA) itself.