No class this
Thursday.
Homework #4 is due
Friday by 5pm.
Homework #5 is due Friday,
March 14th, by 5pm.
Political Science 15
Lecture 16:
Small-N Methods
Qualitative vs. Quantitative
Research
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“Large-N” studies look for patterns in a large
number of cases. Random selection of cases.
“Small-N” studies examine a small number of
cases in depth. Deliberate selection of cases.
Usually large-N approaches have better external
validity, while small-N approaches have better
internal validity and measurement validity.
Probabilistic versus deterministic causation.
Types of Small-N Methods
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The comparative method. Based on J.S. Mill’s
System of Logic (1843).
Most-similar method (“method of difference”).
 Most-different method (“method of agreement”).
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Qualitative comparative analysis (QCA).
Case study methods (when N = 1).
The Comparative Method:
Most-Similar Design
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The most-similar method is the most common
approach to small-N research problems in political
science.
Examine a handful of cases that are as similar as
possible, except on the outcome of interest (the
dependent variable).
Similarity of cases means we control for many
alternative explanations.
If one factor is different between cases, and outcome is
different, this is our probable cause for the outcome.
Example: Causes of Revolution
Potential Causes
England
France
Repressive Monarchy
Yes
Yes
Nonpropertied Agrarian
Proletariat
Yes
Yes
Expensive Foreign Wars
Yes
Yes
Stagnant Standard of
Living
No
Yes
Outcome
Stability
Revolution
Example: Dreze and Sen
Potential Causes
India
China
Disadvantaged Beginning
Yes
Yes
Moderate Economic
Growth
Yes
Yes
Adequate Calories Per
Person
Yes
Yes
System to Distribute
Public Resources
No
Yes
Life Expectancy
57
69
Problems with the
Most-Similar Method
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We generally treat the independent variables as
something simple (yes/no, for instance). The
more complicated the operationalization, the
harder this method is to do.
Deterministic causality.
Multiple causal factors and causal complexity are
hard or impossible to determine.
External validity is low.
The Comparative Method:
Most-Different Design
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This is the opposite of the most-similar method.
Examine a handful of cases that are as different
as possible, except on the outcome of interest
(the dependent variable), which is the same.
Difference of cases means we control for many
alternative explanations.
If one factor is the same between cases, and
outcome is the same, this is our probable cause
for the outcome.
Example: Causes of Revolution
Potential Causes
China (1927)
France (1789)
Repressive Monarchy
No
Yes
Nonpropertied Agrarian
Proletariat
Yes
Yes
Expensive Foreign Wars
No
Yes
Stagnant Standard of
Living
No
Yes
Outcome
Revolution
Revolution
Problems with the
Most-Different Method
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As with the most-similar method, we can’t use
complicated variable codings, multiple causal
factors are hard or impossible to determine, and
external validity is low.
Deterministic causality.
Case selection on the dependent variable –
without variation on the dependent variable
determining causality is extremely difficult.
This method is more useful for ruling out
“necessary” causes than determining causality.
Qualitative Comparative Analysis
(QCA)
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A medium-N method (N between a few and 50
or so). A middle ground.
Independent variables are coded as binary
(yes/no).
Sequences of these variables are entered into a
“truth table.”
Reach conclusion through Boolean logic –
which combinations of factors produce which
outcomes?
Example: Military Coups
Cases
Internal
Military
Conflict
Death of
Dictator
CIA Involvement
Coup?
9
2
3
1
2
1
1
3
0
1
0
0
1
1
0
1
0
0
1
0
1
0
1
1
0
0
0
1
0
1
1
1
0
1
1
0
1
1
1
1
Problems with QCA
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As with small-N methods, we can’t use
complicated variable codings.
Deterministic causality.
However, multiple causal factors/causal
complexity can possibly be distinguished, and
external validity is higher.
A tradeoff between large-N and small-N
positive traits.
Case Studies
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These are methods of examining a single case.
In a sense we have an N of 1, but we must make
some kind of comparison in order to make a
causal inference, either within or across cases.
This comparison can be implied.
Internal and measurement validity very high,
external validity very low – we know a lot about
one case, but very little about how our
observations will generalize.
Case Study Methods
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Extreme Case: A clear example of a hard-to-measure
concept: Nazi Germany for Fascism, North Korea for
isolated state.
Typical Case: Examine a representative or average
case in depth.
Crucial Case: Classic examples of a concept, or
most/least likely cases.
Counterfactual Case: Consider causality if an
independent variable had a different value.
Can combine case studies to examine a range of cases
(e.g., two extreme cases, one from each end of the
spectrum).
Small- versus Large-N Methods
All methods are a tradeoff between
internal, external, and measurement
validity.
 Small-N lets us go more in depth into our
cases, but generalizing is harder.
 No matter the method, our goal is the
same. We want to make a causal inference
and learn how the world works.
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