Presentation on theme: "What, When and How? Dumitrela Negură BA. Introduced by Charles Ragin in 1987, when stumbling upon the causal inference problems generated by a small sample."— Presentation transcript:
Introduced by Charles Ragin in 1987, when stumbling upon the causal inference problems generated by a small sample Represents a method that bridges qualitative and quantitative analysis Why? Because it is difficult to do in-depth qualitative work with sets larger than 15 (although not impossible) and is not very meaningful to do traditional statistical approaches on sets this small
Most aspects of QCA require familiarity with cases and in-depth knowledge of the theory With QCA, it is possible to assess causation that is very complex, involving different combinations of causal conditions capable of generating the same outcome
It is used in comparative case-oriented and in small scale research, for studying a small-to- moderate number of cases in which a specific outcome has occurred, compared with those where it has not It is very useful when you have small samples (N=8 to N=200 or N=5 to N=50) Used in : sociology, psychology, political science and history but can be applied to health related research
QCA uses as units of analysis crisp and fuzzy sets and subsets
QCA was developed originally for the analysis of configurations of crisp set memberships (conventional Boolean sets) With crisp sets, each case is assigned one of two possible membership scores in each set included in a study: 1 (yes/ presence) or 0 (no/ absence)
Fuzzy sets( fs/QCA) solve the problem of trying to force-fit cases into one of two categories Fuzzy sets can have three or more categories (any value between 0 and 1): 1.00 = fully in 0.80 = mostly in 0.60 = more in than out 0.40 = more out than in 0.20 = mostly out 0.00 = fully out ! Are not well suited for conventional truth table analysis !
The simple way is to construct truth tables ( used only for crisp sets) and use Boolean algebra, considering all the logical combination of the causal conditions The three basic Boolean operators are: o logical OR (+) o logical AND (*) o logical NOT (replacing the upper case letter with a lower case letter) A dash symbol [-] represents the dont care value for a given binary variable, meaning it can be either present (1) or absent (0) The arrow  is used to express the link between a set of conditions For example: A+B *C-> Y or a+B*c->y ( where Y is the outcome)
Causal relations are interpreted in terms of necessary and sufficient conditions With necessity, the outcome is a subset of the causal condition With sufficiency, the causal condition is a subset of the outcome Boolean logic is used to reduce the table to a few statements indicating necessary and sufficient conditions and their combinations
Cases Genes and family history Unhealthy food Inactive lifestyle Environment Health conditions Outcome : Obesity 1100001 2011011 3111011 4000100 5010000 6000100 7011111 8010100 9100001 10111011
Cases Genes and family history (G) Unhealthy food (U) Inactive lifestyle (L) Environment (E) Health conditions (H) Outcome : Obesity (O) 1,9100001 2011011 3, 10111011 4,6000100 5000000 7011111 8010100 This means that we have these possible combinations: G*u*l*e*h + g*U*L*e*H + G*U*L*e*H + g*U*L*E*H -> O g*u*l*E*h + g*u*l*e*h +g*U*l*E*h -> o For example G is a sufficient condition and U is necessary but not sufficient for the outcome(O).
Because the truth tables can be very complex because of their size, a specialized software can be used The software can generate the truth table and also analyzes fuzzy sets
For crisp-set analysis: fs/QCA TOSMANA QCA 3.0 For fuzzy-set analysis: fs/QCA
QCA offers an alternative approach, bridging the qualitative and quantitative methods and its used for small scale research Used for assessing causation Uses theory-set relationships Not hard to use but it demands good knowledge of theory and cases