R ESEARCH M ETHODS
N AT I O N A L R E S E A RC H U N I V E RS I T Y, H I G H E R S C H O O L
ECONOMICS
OF
P H . D . P ROGRA M M E
D R C S L EONARD
J UNE 2 0 1 1
Lecture 3
Case studies: Research Methods
O UTLINE
2

What is a case study?

Context and N

Causal reasoning

Case Selection

Building a theory: getting help from a
statistically derived case study
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W HAT IS A C ASE S TUDY ?
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C ONTEXT

The examination of a single unit within
its real life context.

The aim is to elucidate the features of a
broader set of similar cases (Gerring).
I S A C ASE S TUDY S MALL
N?
5
Common criticism: ‘how can you generalise
from one case?’
 Confusion over language and the meaning
of a ‘case’ – a case study is not one
observation (Shleifer)
 Some case studies use a survey and
therefore have multiple observations, e.g.
Middletown (cited in Gerring 2007 and Yin
2003)
 Some case studies contain nested sub-cases
or within case units, e.g. before and after.
 Even if the case under study is one individual,
there will be multiple observations of different
kinds.

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N OT N, C ONTEXT
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 The
examination of a single
unit within its real life context.
 The aim is to elucidate the
features of a broader set of
similar cases (Gerring).
 A unit is a relatively bounded
phenomenon – e.g. a nation, a
firm, a department, an
industry, a strategy, or person.
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C ONTEXT AND
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CAUSAL MECHANISMS

A study which investigates a
contemporary phenomenon
within its real life context (Yin,
2003)
 - The strength of case studies is
that they can identify causal
mechanisms, and tracing causal
mechanisms entails sensitivity to
local context (Bennett and
George, 2005)
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W HY
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
CASE STUDIES ?
Research can contain two
conflicting requirements

Data Integrity

Generalizability (high
currency)

Depends on research topic and
type of problem
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G ENERALIZABILITY
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
There is much versatility – it depends on
the nature of your case and on your
research question.

Some case studies use only qualitative
data

Some use both qualitative and
quantitative data

The use of different kinds of evidence is
very common in case study research
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T HE “S O W HAT ” Q UESTION
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
Both theory and case studies focus
on the particular and on detail.

Both theory and case studies must
deal with the ‘So What?’ question.

For both theory and case studies
in-depth understanding and
context provide a way of dealing
with the ‘so what’ question.
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E MBEDDEDNESS
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
Case study thus defined is
especially suitable for
studying phenomenon that
are high complex and/or
embedded in their cultural
context (Verschuren, 2003)
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R ATIONALITY
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•
Daniel Little illustrates this in
relation to rational choice theory
– ‘context bound rationality’ (the
authors of Analytic Narratives
refer to Daniel Little when
advocating their approach to
case study research)
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E XAMPLES OF C ASE S TUDIES

Benedict Anderson: ‘imagined
communities’ (earnings repatriation and
economic growth)

Chalmers Johnson: the ‘developmental
state’ (Ministry of Trade and Industry in
Japan)

“Smart” Growth Clusters
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R ELEVANCE OF CASE STUDIES FOR
E CONOMICS
•
Much of the case literature on how
to define a research question,
identify suitable data, link data to
theory and make appropriate
generalisations have relevance for
research design more generally.
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C ASE S ELECTION
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C ASE S TUDY OF W HAT ?
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 What
your case study is a case of will
affect your population (or unit
selection), your materials, the theoretical
propositions that inform your research
 Failure
to clarify early on what your
case study is a study of could leave you
with a list of details and facts – an
information download.
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H ELPS D ECISION -M AKING
It helps you to identify and define
potential variables of interest.
It helps you to formulate theories.
It helps you decide to which wider
conversation or ‘scholarly
mosaic’ you will contribute your
piece.
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C AVEATS
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
The objective must be causal inference (with
reference to the larger population)

Larger reference, not within group distinctions

The research must begin with understanding of
the inferences being made (not searching for
cases)

All have large and small-N implications, or
possibilities

Most important: statistical means to identify
the cases you wish to study further
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C ASE S ELECTION
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P RINCIPLES OF SELECTION
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
Typicality

Probably somewhat illusory

Most often the basis for choice

Useful in finding causal
mechanism in general cross case
relationship
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M OST
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
SIMILAR
A case that is like another case in
all respects except for either a
key independent variable or the
dependent variable of interest (2
villages similar for all major
socio-economic indicators except
one has a female high suicide
rate)
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M OST
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DIVERSE
 Achievement of maximum variance along relevant
dimensions, the diverse case method
 Minimum of 2 cases to represent the full range of
values characterizing X, Y or X/Y relationship
 Exploratory, hypothesis seeking when X or Y, and
hypothesis confirming, when X/Y
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D IVERSE ,
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
CONT
Either categorical (one from each
group), continuous (from each
extreme plus the mean or median),
break points, where causal factor is
vector of variables that can be
measured, leads to cross tabs
(sometimes you need to redefine a
variable for categorical responses)
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T HE E XTREME
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
Because of its extreme value on X or Y
(outlier), in large N, defined in terms
of the sample mean and standard
deviation

Seems to violate maxim: don’t select
on dependent variable, but not. Treat
it as not representative.

Its objective is purely exploratory

May morph into something else later
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D EVIANT
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CASE

Surprising value, anomalous

Relative to the mean of a single
distribution

By reference to cross case relationships
and are poorly explained

Relative to general model (ie, it may
change the general model)

Probe for new unspecified explanations
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M OST
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INFLUENTIAL

Need to check assumptions behind the
model

Checking cases that have influence to make
sure they fit the sample

To explore cases that may be influential in a
larger cross case study

Leverage of a case (large n)

Hat matrix, tells of potential influence

Cook’s distance (the extent to which the
coefficient would change if a case were
omitted)
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M OST SIMILAR AND MOST
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DIFFERENT

Like diverse case method

Chosen pair similar on all measured
independent variables except the one of
interest

That is, you expect that the pattern of covariation depends upon the absence of a
variable

Statistical tool: matching techniques (major
topic in econometrics) comes from experimental
logic: (difference of means test?) using a
treatment group and matching cases in the
control group
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S UMMARY
1.
There is the need to show wider relevance –
this entails offering explanations that can be
applied at a higher level of abstraction, and so
some of the detail of the case study must be
lost
2.
In most case study research, there is ongoing
interaction between theoretical propositions
and revisions to them (deduction), and
collection and analysis of empirical data
(induction).
3.
Regardless of approach to the case study rigor
and clarity are necessary (case study protocols
ala Yin help, or the structured approach of
Bennett and George part 2 do the same).
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E XAMPLE OF C ASE S ELECTION
( ADAPTED FROM G ERRING, 2 0 0 7 )

Most different: Cases that are
different in all respects except for
x and y variables, so both IBM
and GAZPROM have large scale
operations and a conservative
culture but differ in all other
respects
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T HE S TATISTICAL
S AMPLING A PPROACH
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•
Places emphasis on replication
(interview replication/within case unit
replication/case replication) in order to
saturate theoretical categories.
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U SE OF THEORY IN CASE STUDY
DESIGN AND ANALYSIS
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T HEORY BUILDING FROM
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CASES

Iterative process

Multiple investigators

Results in greater originality

Hypotheses can be proven false

Theories generated apart from evidence have
testability problems

Bottom up approach, however, may have problems of
idiosyncracy
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TO
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
TEST RELIABILITY
Reliability: Inconsistency in data
collection affects findings
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TO
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TEST VALIDITY

Validity:
-
Construct validity: are you looking at
what you think you are looking at?
Congruence between key concepts in
the research question and the material
that you gather (context is important).
-
Internal validity: causal mechanisms.
-
External validity: the scope conditions,
the domain to which generalisations
can be applied.
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Q UANTITATIVE
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•
Quantitative approach
(DSI/Geddes): Theory is used in
formulating a hypothesis against
which the findings of a
representative case study
contained many observations is
tested
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S TRUCTURED
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•
Structured/Theory Driven Approach
(Gerring.Bennett and George):
Literature informs a theory which is
modified based on limited
understanding of an empirical case.
Then through the process of data
gathering and analysis the theory is
modified and revised and informs
further data gathering and analysis:
ongoing process of deduction and
induction
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P ROBLEM D RIVEN /A NALYTIC
N ARRATIVE
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
The aim is to explain the problem of
why within a particular case one
outcome resulted rather than another

Rational actors making rational choices
are assumed

The case focuses on how institutions
shape causal linkages, particular
pathways and the choices actors make

Theory is developed along with the
empirical data
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T HE E XTENDED C ASE
M ETHOD (B URAWOY ):
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
The theoretically informed researcher extracts
what is general from the unique.
 Theory helps to explain the macro-processes
and structures within which the locally based
case is situated.
 Wider relevance is claimed not by saying that
the explanation of the study applies to many
other cases but by explaining:
(1) the wider structures in which the particular
is embedded
(2) by showing how the particular is the
incarnation of a more abstract macro-level
process.
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I NTERPRETIVE M ETHOD :
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
Tries to privilege inductively derived
insider categories and understandings.

The researcher reads a wider theoretically
literature but then brackets this out.

The researcher then immerses him/herself
in the empirical data and codes inductively
to identify indigenous ways of explaining,
before returning to the wider literature to
position, revise, contextualise etc.
(see Tavory and Timmermans, 2009 and their bibliography)
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G ENERALISING
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
Always make delimited and contingent
claims.
 The goal is rarely to refute theory, but to
identify whether and how the scope
conditions of competing theories should be
expanded or narrowed (Gerring).
 Rather than try to claim a causal effect
across a number of similar cases it is
better to specify the ways in which causal
mechanisms converge and interact in your
case.
 Rather than ‘generalise’ you may want to
extrapolate from your case to other cases,
and do so at the theoretical level
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I NTERACTION
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
In most case study research,
there is ongoing interaction
between theoretical
propositions and revisions to
them (deduction), and
collection and analysis of
empirical data (induction).
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S ELECTION ON DEPENDENT
VARIABLE : N O C ONTROL
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
Barbara Geddes – Elaborates
the concern of many
quantitative scholars that case
studies are selected on the
dependent variable
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B UILDING A THEORY FROM
CASE STUDY RESEARCH
(E ISENHARDT 1989)
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

Getting started

Can get overwhelmed by information, volume of
data

Go in with special aims: collect specific kinds of
data systematically
A priori specification of constructs

Important in the case study (ie, conflict, power,
competition, transition economy)

Of course no construct is guaranteed a place in the
resultant theory and question may shift
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U SED FOR BUILDING NOT
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TESTING THEORY

Theory building research, as
opposed to theory testing
research

Should specify ex ante the
research problem and
potentially important
variables but be open to the
theory and their relationship
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E X : STRATEGIC CHANGE AND
COMPETITIVENESS
 Choosing cases
For theoretical reasons (from
previous research), to fill
theoretical categories and provide
examples of polar types
Likely to replicate or extend the
emerging theory
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C OMBINING
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
SOURCES
Qualitative and quantitative

Theory building seems to require rich
description

But the quantitative information is the
foundation

Multiple investigators usually help

Make visits to case study sites in teams

Want divergent views
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B UILDING
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THE THEORY

Analyzing cases from within

Least codified part of project

Pure descriptions may help being flooded
with too much information

Revise construct, search for measure

Note differences between cases

Establish construct validity (important in
hypothesis testing and theory building)
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R EPLICATION
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
Cases that confirm the theory or construct are
supplemented by those that don’t, allowing for
theory extension


Example of stable coalitions (one case was not
stable, leading to reexamination of data, and it
turned out that stability occurred over time,
evolving in coalitions)
Qualitative work at this points helps understand
why something is happening (internal validity)

You must judge the strength and consistency
of a relationship across cases without an F
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G O BACK TO THE
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LITERATURE

Find confirming and conflicting studies

Overcoming the reasoning in the latter,
will help build confidence in your case

Finding differences forces you into framebraking mode, more creative, deeply
insightful

Finding similarities in other literature, also
confirms your work and enhances its
internal validity (which may rest on too
few cases)
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S ATURATION
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
Stop when you have proved your
case

Incremental learning is minimal

Stop iterating when saturated
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T HE END