What, When and How?
Dumitrela Negură BA
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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
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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
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It is used in comparative case-oriented and in
small scale research, for studying a small-tomoderate 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
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QCA uses as units of analysis crisp and fuzzy
sets and subsets
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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)
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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
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! 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
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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)
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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 = 𝟐𝒌 , where k is
the number of causal conditions)
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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
Genes and
family
history
Inactive
lifestyle
Unhealthy food
Health
conditions
Environment
Outcome :
Obesity
Cases
1
1
0
0
0
0
1
2
0
1
1
0
1
1
3
1
1
1
0
1
1
4
0
0
0
1
0
0
5
0
1
0
0
0
0
6
0
0
0
1
0
0
7
0
1
1
1
1
1
8
0
1
0
1
0
0
9
1
0
0
0
0
1
10
1
1
1
0
1
1
The number of combinations for this example will be 25 = 32
Genes and
family
history (G)
Unhealthy food
(U)
Inactive
lifestyle (L)
Environment (E)
Health
conditions (H)
Outcome :
Obesity (O)
Cases
1,9
1
0
0
0
0
1
2
0
1
1
0
1
1
3, 10
1
1
1
0
1
1
4,6
0
0
0
1
0
0
5
0
0
0
0
0
0
7
0
1
1
1
1
1
8
0
1
0
1
0
0
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).
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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:
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fs/QCA
TOSMANA
QCA 3.0
For fuzzy-set analysis:
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fs/QCA
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QCA offers an alternative approach, bridging
the qualitative and quantitative methods and
it’s used for small scale research
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Used for assessing causation
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Uses theory-set relationships
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Not hard to use but it demands good
knowledge of theory and cases
Thank you 