What is Qualitative Comparative Analysis? David Byrne, University of Durham

Organization of Presentation Complex causation – representing through configurations Necessary and / or Sufficient Causal complexes Not variables but set-theoretic relationships Truth Tables – what they are and an example Boolean reduction? Variants of QCA – a listing

Configurational Complexity If … we live in a world of great causal complexity, then a common pattern will be for outcomes to result from different combinations of causal conditions. (C.C.Ragin Fuzzy Set Social Science 2000 103)

Causes in complex causation We must be careful to distinguish between the idea of NECESSARY cause and SUFFICIENT cause. To be necessary a cause must be present but unless it is also sufficient the effect will not follow. In configurational analysis where multiple configurations can engender the same effect, we establish different sufficient configurations but none are necessary.

Set Theoretic Relationships Not only are set-theoretic relationships central to the analysis of social data, they are also central to almost all forms of social science theorizing. Most theoretical arguments … concern set-theoretic relationships, not linear relationships between variables. (C.C.Ragin Fuzzy Set Social Science 2000 xiv)

Truth Tables Each logical combination of values on the independent variable is represented by one row of a truth table. Once this part of the truth table is constructed, each row is assigned an output value (a score of 1 or 0 on the dependent variable) based on the scores of the cases which share that combination of scores on the independent variables. Thus, both the different combinations of input values (independent variables) and their associated output values (the dependent variable) are summarized in a truth table. ….. each row is not a single case but a summary of all the cases with a certain combination of input values. In this respect, a truth table is like a cell from a multi-way cross-classification of several categorical independent variables. (C.C. Ragin – The Comparative Method1987 87)

Table Two – Truth Table for NE England Secondary Schools comp religious High % fsm High % statements mixed sixthform Number Yconsist 1 26 0.769231 18 0.611111 17 0.588235 14 11 0.909091 9 0.888889 8 0.5 7 0.428571 3 0.333333 2

Reduction Using Boolean Methods Ragin has developed a set of methods which use Boolean algebra to reduce the number of causal combinations by the application of de Morgan’s law to produce reduced sets of combinations which can be considered to represent sufficient causal configurations.

Fuzzy Set QCA A conventional (or "crisp") set is dichotomous: An case is either "in" or "out" of a set, for example, the set of Protestants. Thus, a conventional set is comparable to a binary variable with two values, 1 ("in," i.e., Protestant) and 0 ("out," i.e., non-Protestant). A fuzzy set, by contrast, permits membership in the interval between 0 and 1 while retaining the two qualitative states of full membership and full non-membership. Thus, the fuzzy set of Protestants could include individuals who are "fully in" the set (fuzzy membership = 1.0), some who are "almost fully in" the set (membership = .90), some who are neither "more in" nor "more out" of the set (membership = .5, also known as the "crossover point"), some who are "barely more out than in" the set (membership = .45), and so on down to those who are "fully out" of the set (membership = 0). It is up to the researcher to specify procedures for assigning fuzzy membership scores to cases, and these procedures must be both open and explicit so that they can be evaluated by other scholars. http://www.u.arizona.edu/~cragin/fsQCA/ Charles Ragin

Tosmana - MVQCA MVQCA is, as the name suggests, an extension of QCA. Unlike fs/QCA, which uses fuzzyset scaled variable with probabilistic tests to, among other things, avoid dichotomous measurement (Ragin 2000), MVQCA retains the main idea of QCA performing a synthesis of a data set with the result that cases with the same value of the outcome variable are covered by a parsimonious solution. The solution contains one or several implicants which each cover a number of cases with this outcome, while no cases with a different outcome are explained by any of the implicants. While QCA only allows dichotomous variables to be processed, MVQCA also includes multi-value variables in the analysis. In fact, MVQCA is a generalisation of QCA, and each dichotomous variable is a multi-value variable (with two possible values). Cronqvist 2005

TQCA A Technique for Adding Temporality to Qualitative Comparative Analysis Neal Caren New York University Aaron Panofsky New York University As originally developed by Charles Ragin in The Comparative Method (1987), qualitative comparative analysis (QCA) has been used extensively by comparative and historical sociologists as an effective tool for analyzing data sets of medium-N populations. Like many other methods, however, QCA is atemporal and obscures the sequential nature of paths of causation. QCA ignores the order of events by treating combinations of attributes as though they occur simultaneously rather than as unfolding over time. While preserving the essential strengths of QCA, the authors present a modification that is capable of capturing the temporal nature of causal interactions. This modification involves a hybrid of Boolean algebra and sequence analysis to create a parsimonious set of solutions. This technique is referred to as temporal qualitative comparative analysis, or TQCA.

References TQCA – Caren and Panofsky in Sociological Methods & Research, Vol. 34, No. 2, 147-172 (2005) DOI: 10.1177/0049124105277197 © 2005 SAGE Publications Charles Ragin The Comparative Method University of California Press 1987 Charles Ragin Fuzzy-Set Social Science University of Chicago Press 2000 Lasse Cronqvist 2005 Introduction to Multi-Value Qualitative Comparative Analysis (MVQCA) Web: http://www.tosmana.org/resources/introduction_to_mvqca.pdf