Social Network Analysis and Qualitative Comparative Analysis
Qualitative Comparative Analysis
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Transcript of Qualitative Comparative Analysis
Qualitative Comparative Analysis
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
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
Qualitative Comparative Analysis (QCA)
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
When do we use it ?
QCA uses as units of analysis crisp and fuzzy sets and subsets
How?
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)
Crisp sets
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 out0.40 = more out than in0.20 = mostly out0.00 = fully out
! Are not well suited for conventional truth table analysis !
Fuzzy sets
Crisp vs. Fuzzy sets
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 “don’t 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)
Crisp-set analysis
Truth tables list the logically possible combinations of causal conditions and the outcome associated with each combination
Truth tables help us to see clearly the similarities, differences and contradictions between cases
The number of combination is a geometric function of the number of causal conditions (number of causal combinations = , where k is the number of causal conditions)
Truth tables
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
conditionsOutcome :
Obesity
1 1 0 0 0 0 12 0 1 1 0 1 13 1 1 1 0 1 14 0 0 0 1 0 05 0 1 0 0 0 06 0 0 0 1 0 07 0 1 1 1 1 18 0 1 0 1 0 09 1 0 0 0 0 1
10 1 1 1 0 1 1
Example:
The number of combinations for this example will be
Cases
Genes and family
history (G)Unhealthy food (U)
Inactive lifestyle (L) Environment (E) Health
conditions (H)Outcome : Obesity (O)
1,9 1 0 0 0 0 12 0 1 1 0 1 13, 10 1 1 1 0 1 14,6 0 0 0 1 0 05 0 0 0 0 0 07 0 1 1 1 1 18 0 1 0 1 0 0
Truth table: configuration and minimization
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
Software
For crisp-set analysis: fs/QCA TOSMANA QCA 3.0
For fuzzy-set analysis: fs/QCA
Free and user friendly softwares
Regression analysis vs. QCA
QCA offers an alternative approach, bridging the qualitative and quantitative methods and it’s 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
To summarize:
Thank you