Qualitative comparative analysis

In statistics, qualitative comparative analysis (QCA) is a data analysis technique for determining which logical conclusions a data set supports. The analysis begins with listing and counting all the combinations of variables observed in the data set, followed by applying the rules of logical inference to determine which descriptive inferences or implications the data supports. The technique was originally developed by Charles Ragin in 1987. In statistics, qualitative comparative analysis (QCA) is a data analysis technique for determining which logical conclusions a data set supports. The analysis begins with listing and counting all the combinations of variables observed in the data set, followed by applying the rules of logical inference to determine which descriptive inferences or implications the data supports. The technique was originally developed by Charles Ragin in 1987. In the case of categorical variables, QCA begins by listing and counting all types of cases which occur, where each type of case is defined by its unique combination of values of its independent and dependent variables. For instance, if there were four categorical variables of interest, {A,B,C,D}, and A and B were dichotomous (could take on two values), C could take on five values, and D could take on three, then there would be 60 possible types of observations determined by the possible combinations of variables, not all of which would necessarily occur in real life. By counting the number of observations that exist for each of the 60 unique combination of variables, QCA can determine which descriptive inferences or implications are empirically supported by a data set. Thus, the input to QCA is a data set of any size, from small-N to large-N, and the output of QCA is a set of descriptive inferences or implications the data supports.