Case Study: Logic

Case Study: Logic
Konvitz J W 1987 Cartography in France 1660–1848: Science,
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from Amerindian maps on Euro-American maps of North
America. Imago Mundi 38: 9–34
Lewis G M 1993 Metrics, geometries, signs, and language:
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Case Study: Logic
1. The Case Study Defined
G. M. Lewis
The case study is a research strategy with special
implications for theoretical analysis and data collection.
A single case of a particular phenomenon is
examined intensively for the light that it can shed on a
specific problem or question. Often several methods of
data collection will be used to assemble a wide range of
information about the case. One of the main benefits
of a wide-range of data is that it permits a triangulation
of research methods, thus, providing substantial verification
of the particular phenomenon in question.
Researchers vary in why they choose to study a
single case, but most often they do so in order to
examine a proposed theory or to provide grounded
and detailed information for a new theory (Glaser and
Strauss 1967). Although many quantitative researchers
consider the case study as a limited form of
analysis, especially by comparison with methods that
collect information on a large sample of instances,
such a conclusion misconstrues fundamentally the
main purpose of the case study. The analyst who
selects and studies a single case, or even a handful of
cases, is not primarily interested in statistical inference
to a larger population of units, but rather in theoretical
analysis and inference. Nevertheless, the findings from
the study of a single case, like those from the study of
a large sample of instances, always are to be regarded
as tentative, awaiting further confirmation from additional
studies of parallel theoretical cases.
2. Why Study the Single Case?
The intensive analysis of the case study is premised on
the special advantages that it furnishes. First, the
study of single cases enables the researcher to probe a
particular question, or phenomenon, in great detail.
Such in-depth examinations ultimately permit the
researcher to acquire a degree of knowledge about the
case that is typically impossible through the examination
of a large number of cases. Moreover, such indepth
work also enables the researcher to pursue the
examination of alternative theoretical ideas, thus
ultimately arriving not merely at a thorough understanding
of the empirical facts, but ideally a careful
and correct appreciation of the most germane and
effective theoretical argument to fit these facts (Campbell
1975).
Second, by studying a single case the researcher is
able to take full account of the social, or historical,
context of the phenomenon in question. Students of
case studies regard context as essential to understanding
the nature of the phenomenon. Take, for
example, the study of children who are unruly in the
classroom. The investigator may believe that such
unruliness is the product of how the child relates both
to his peers and teacher in the context of classroom
activity. In order to appreciate and to fully understand
the nature of the child’s reactions, the investigator is
compelled to study the child in the classroom situation
(Stake 1995).
Third, the study of the single case permits the
researchertoprobecomprehensivelyintotheempirical
data at hand. In the study of the workings of a single
community, for example, the case study researcher can
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Case Study: Logic
explore a variety of dimensions of the community and
can thereby create a multidimensional, or holistic,
sense of the community rather than, let us say, a
unidimensional one based upon its size or territorial
breadth. In so doing the researcher can also emerge
with a fuller understanding of the case in question by
fashioning an integrated portrait of the case into
which the various pieces, or dimensions, fit. Fourth,
the case study provides boundaries to the nature of the
phenomenon under investigation. The case is chosen
because it represents a self-contained unit that will
permit the researcher to investigate the phenomenon
in isolation from other forces. Thus, educational
researchers will often investigate a single classroom or
school because such a case represents some unusual
qualities in which they are interested (Stake 1995).
Finally, the single case is sometimes chosen because
it represents a special illustration of the phenomenon
under investigation. Sometimes it is portrayed as the
exception to the rule, or deviant case, thereby permitting
the observer to understand some more general
phenomenon in greater depth.
3. How to Choose Cases
Great care must be exercised in the choice of the single
case. Sometimes social scientists believe that any case
may be used to explore a particular phenomenon.
Indeed, there appears to have been a great deal of
faulty case study research because there are few
guidelines by which to select the case. In fact, the
choice must always involve certain critical decisions in
advance of the data collection and analysis.
First and foremost, the researcher must decide the
grounds for selecting a particular unit for study. Since
such selection is never based upon issues of statistical
inference, the theoretical reasons for selecting the case
must always be made clear—or, at least, as clear as
possible—in advance of the research. For example, the
sociologist R. Stephen Warner believed that broad
societal changes might have a deep influence on the life
of churches in America. Thus, he chose to focus on a
single church, and to examine how the internal
organization of the church changed over several
decades during which there was marked change in
American society. Second, the researcher should
choose those cases that will furnish the clearest tests of
the theory, or argument, in question. If a researcher is
interested in the causes of revolution, then a society
should be chosen where there has been a substantial
and marked revolution, to examine the possible
causes.
3.1 What Qualifies as a Case?
Unlike certain forms of research method, such as the
sample survey, case study research can employ very
different units of analysis on which to focus its
theoretical attention. The earliest examples of case
studies were often in-depth studies of particular social
roles, designed to explore the nature of such roles.
Other famous case studies have been done of entire
cities, including the range of different people and
organizations in such cities. More recently, social
researchers have studied specific organizations using
the case study format; specific locales designed to
understand the nature of social groups in those locales;
and even specific indiiduals whose traits exemplify
special qualities for investigation (Stake 1995). In
addition, the case is sometimes used very effectively to
explore in rich detail the nature of social processes at
work. One of the best illustrations of using the case
study in this manner was done by the political scientist,
Matthew Crenson (1971). He furnished a number of
important insights into the nature and exercise of
power, including the dynamics of agenda-setting,
simply from the study of how political decisions were
made in a single city.
4. The Case Study as a Strategy of Research
By comparison with other research methods, the case
study has certain limitations. It does not permit the
easy and refined manipulation of variables in the same
way as an experiment does. Nor does it permit a
researcher to investigate various configurations of
variables that might be associated with particular
outcomes, or dependent variables, like the sample
survey. Most often, the case study is done using
qualitative methods, thus resulting in some confusion
between case studies and qualitative research (Merriam
1998). Yet there are important examples in which
quantitative data also have been collected to provide
key information about the single case. For example,
some researchers have used time series data on a single
country to examine it in depth.
4.1 The Case Study as an Inductie Tool
The case study has proven most useful for the
generation of new theory. Because a single case can be
examined in great depth, and because it can be studied
with a variety of research tools, researchers can mine it
for a great deal of information. At the same time, the
careful examination of such information can furnish
insights not easily acquired by other research methods.
Therefore the case study is perhaps at its strongest
when it is used as an inductive research instrument,
allowing the researcher to construct an explanation
for the phenomenon under investigation (Glaser and
Strauss 1967).
4.2 The Case Study as a Deductie Tool
Yet, the study of the single case can also furnish an
important tool for those observers who wish to test, or
apply, deductive theories. One of the most famous
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Case Study: Logic
illustrations of this occurs in the classic study of the
InternationalTypographers Union(Lipset etal. 1956).
The authors had concluded, both on empirical and
theoretical grounds, that trade unions were characterized
by very rigid and tightly closed political
administrations, with one clique maintaining its rule in
office over a long period of time. They then chose
deliberately to examine the ITU in detail, because it
appeared to be the exception to the general situation.
What was it about the ITU, they asked, that produced
empirically disparate results from all other trade
unions? This was a powerful, well-refined question. In
effect, the ITU provided a comparison to all other
cases: if its outcome was different, so too must have
been the set of elements that brought about such an
outcome. The answer, furnished through a variety of
evidence, suggested that, because of their level of
education, skill, and commitment to the ITU, union
members of this union, as compared to others, simply
were more likely to take an active role in union affairs.
The one consideration that is overlooked in using
the case study as a deductive tool is that researchers
may fail to specify the nature of the null hypothesis, or
the likely outcome if the theory is incorrect. While
mostcasestudieshavefailedtoconsiderthis important
feature of theory construction and testing, Yin (1994)
argues that, if done carefully, such a strategy can be
incorporated easily into the analysis of single, and
even multiple, cases.
5. The Case Study: Seminal Illustrations
Over the years there have been a number of very
significant case studies. Two, in particular, illustrate
the wide variety of uses to which case study analysis
can be put in the study of social phenomena.
One of the first, and surely most famous, case
studies was done of the city of Muncie, Indiana (Lynd
and Lynd 1929). Conducted during the course of the
1920s, the investigators and their research team
wanted to uncover the full nature of a typical American
city over the course of its lifetime. Part of their
work involved tracing the history of the city. A large
part, however, involved the collection of a wide range
of information on the city’s current residents, organizations,
and its internal workings.
There were a variety of important discoveries,
including discoveries about the nature of work life and
religious life in the community, as well as about the
dominance of a leading industrial family over the
entirerangeofcivicandpoliticallifeofthecommunity.
It might be noted that sample surveys of city residents,
or demographic data about them, would never have
uncovered the full and complex portrait the Lynds
were able to construct through their pioneering case
study research.
A second famous case study was done of men who
lived in the South End of Boston (Whyte 1943). This
study, also conducted in the 1930s, was ostensibly an
examination of a group of impoverished men, and
how they adapted to life during the Great Depression.
In fact, as Whyte so brilliantly showed, it really was a
case study of a small group of men, and the way in
which the nature of stratification emerged within the
group, and how it influenced the behavior of individuals.
It was done with great insight and care by
Whyte, making it still one of the best and most
influential case studies of all time.
6. Issues of Theoretical Specification: The Use of
Singular Cases to Illuminate General Principles
Sometimes the study of the single case is done
specifically to reveal the importance of a special
configuration, or confluence, of elements that together
help to explain the unique outcome of a case, and its
difference from the general pattern (Campbell 1975).
The logic underlying the analyst’s concern is that the
set of variables together constitutes the sufficient,
perhaps even singular, explanation for a particular
outcome, or effect. The research on the International
Typographers Union by Lipset et al. (1956), for
instance, pointed to the special configuration of
conditions in the ITU that helped to promote internal
democracy. It suggested that the special confluence of
factors promoted democracy, leading to the obvious
conclusion that only under certain special social and
historical circumstances could such variables again
come together in such a specific manner.
7. Issues of Theoretical Generalization and
Refinement: The Use of Parallel and Different
Cases
Because the analysis of a single case can only take the
analyst so far, the best rule to follow, if possible, is to
examine at least three or four pertinent cases in depth.
Multiple cases permit the researcher to refine and
develop theoretical arguments, and they do so precisely
because case studies permit the researcher to
examine cases deeply and, ultimately, to make careful
comparisons among those cases.
One example will illustrate the advantages of using
several cases and examining them in depth. Orum
(1995) was interested in examining various theories
about the origins of urban growth. Popular theories
had suggested that such growth came about because of
special alliances among the leading figures in different
major institutional spheres, particularly local government,
business, and the media. Orum suggested that
such theories were limited only to contemporary
circumstances and that they did not help much to
explain the sources of growth in the past. Using several
different cases, and studying them in great depth
permitted him to come up with several important
discoveries bearing upon issues of urban growth and
expansion.
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Case Study: Logic
The first case examined was that of Milwaukee,
Wisconsin. Tracing its origins back to the early
nineteenth century, Orum discovered that the city
seemed to go through certain stages in its growth, and
that one could not simply rely on explanations of
alliances among leaders of different institutional
spheres. Thus, based upon intensive study, he argued
that, in the earliest years, the growth of the city relied
heavily on local entrepreneurs, people who promoted
the city and who, among other things, sold land for it.
But over time this motive force for growth changed,
and the main forces turned to new economic institutions,
ones that brought in growth by attracting new
residents. A third stage happened when the impetus
for growth shifted from the local economic enterprises,
which had grown somewhat stagnant, to local government.
Government sought to expand the community
by adding new territory. Among other things, it also
became clear in this stage that a single conception of
urban growth was inappropriate; growth could take
the form of new population, but also new territory.
Such an insight would have been impossible had Orum
relied only on the study of this single case in depth. The
fourth and final phase happened as the city began to
decline. In large part, the decline took place when
many of its original industries left for greener pastures
in other cities. Under these circumstances, a heavy
burden was placed upon local government to devise
ways to promote growth, either by expanding its
borders or by attracting new industry through various
incentives.
In effect, then, Orum’s explanation for Milwaukee’s
growth suggested that such expansion happened by
stages, and that at each stage there was a different
configuration of local business and political forces
responsible for such growth, and that the principal
form of growth itself could vary between stages. Next,
heaskedwhetherthis patternheldonlyfor Milwaukee,
or whether it could also describe the trajectory of
growth and decline in other parallel industrial cities.
Thus, he made comparisons in the pattern and stages
of growth in Milwaukee with those in Cleveland,
Ohio, a city which, on the face of it, seemed similar in
many respects. In fact, the stages and forces of growth
at each stage were virtually identical in Cleveland and
Milwaukee. He thus argued that among American
industrial cities, the nature of growth, and the forces
responsible for it, showed important parallels, underscoring
his argument on behalf of a stage theory of
urban growth.
Finally, Orum pushed the argument one step further,
wondering whether the pattern found was only
characteristic of industrial growth, and cities, in
America. Could the pattern of stages also apply to
cities that grew up in the postindustrial era and were
themselves postindustrial cities? Here he made a
comparison between the patterns in Milwaukee and
those in Austin, Texas, a postindustrial city he also
had come to know intimately. This comparison
showed that the first three stages were very similar in
terms of the rapidity of growth, and in the role of
entrepreneurs, local business, and local government in
promoting such growth in Milwaukee and Austin
alike. In the end, then, he refined his overall argument
to suggest that the stage view of urban growth in
America fit not only industrial-era cities, but also cities
in the postindustrial era. This suggested that there
appeared to be an underlying structural pattern to the
growth of American cities, regardless of era. This last
comparison also enabled him to draw out differences
between Milwaukee and Austin, allowing for further
understanding of the nature of urban growth, and
difference, in the United States.
In brief, then, as the example of the various cities is
intended to illustrate, the careful and systematic study
of several cases can permit the researcher both to
substantiate important theoretical generalizations and
to refine and extend them. Again, this can be done
because of the deep acquaintance the researcher gains
from the study of cases, an acquaintance unlike that to
be obtained with methods like surveys or experiments.
8. Conclusion
The use of a single case to develop, or to test,
theoretical insights remains a very important strategy
in the social sciences at the start of the twenty-first
century. Indeed, it would appear to be growing in
significance. But it must be executed with great care,
and with a special sensitivity to theoretical issues. The
major limitation in the study of the single case, or even
a handful of cases, lies not in the limited number of
empirical units, but in the ability of the researcher to
be sensitive to issues of theory development and the
gathering of relevant evidence to develop and refine
that theory.
See also: Case Study: Methods and Analysis; Caseoriented
Research; Human–Environment Relationship:
Comparative Case Studies; Psychotherapy: Case
Study; Single-subject Designs: Methodology; Time
Series: General
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Crenson M 1971 The Unpolitics of Air Pollution. Johns Hopkins
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Feagin J R, Orum A M, Sjoberg G (eds.) 1991 A Case for the
Case Study. University of North Carolina Press, Chapel Hill,
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Glaser B G, Strauss A L 1967 The Discoery of Grounded
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Case Study: Methods and Analysis
A. M. Orum
Case study methods have been around as long as
recorded history, and they presently account for a
large proportion of the books and articles in anthropology,
biology, economics, history, political science,
psychology, sociology, and even the medical sciences.
The logic of case study methods, much like that of any
historian’s or detective’s efforts to make inferences
from patterns within cases and comparisons between
them, is more intuitive than the logic of statistical
inference. Until relatively recently, however, the lack
of formalization of the logic of case study methods
inhibited them achieving their full potential for contributing
to the progressive and cumulative development
of theories. It is only in the last three decades
that scholars have formalized case study methods and
linkedthemtounderlyingargumentsinthe philosophy
of science. Ironically, statistical methods, though less
intuitive, were standardized earlier, so that attempts to
formalize case study methods often misappropriated
terms and concepts from statistics (McKeown 1999).
More recently, case study methods have evolved from
being defined ‘negatively,’ via contrasts to statistical
methods, to being defined ‘positively,’ by their distinctive
logic, techniques, and comparative advantages.
This continuing evolution remains a contested
process, but there is growing consensus on the proper
procedures for carrying out case studies and the
strengths and limits of such studies. It is becoming
increasingly clear through this process that the comparative
advantages of case study and statistical
methods are largely complementary and that the two
methods can thus achieve far more scientific progress
together than either could alone.
1. The Definition of ‘Case’ and ‘Case Study’
Early efforts to define ‘case studies’ relied on distinctions
between the study of a small vs. a large number
of instances of a phenomenon. Case studies thus
became characterized as ‘small n’ studies, in contrast
to ‘large N’ statistical studies. Related to this, one
early definition stated that a ‘case’ is a ‘phenomenon
for which we report and interpret only a single measure
on any pertinent variable’ (Eckstein 1975). Case study
researchers have increasingly rejected this definition,
however, because it wrongly implies, in the language
of statistics, that there is an inherent ‘degrees of
freedom’ problem in case studies. In other words, this
definition of case studies suggests that with a greater
number of variables than observations on the dependent
variable, case studies provided no basis for
causal inference. In the view of case study researchers,
however, each case includes a potentially large number
of observations on intervening variables and qualitative
measures of different aspects of the dependent
variable, so there is not just a ‘single measure’ of the
variables or an inherent degrees of freedom problem.
This point is increasingly recognized by researchers
from the statistical tradition (King et al. 1994).
In addition, the ‘small nlarge N’ distinction implies
that large N methods are always preferable whenever
sufficient data is available. As argued below, however,
case studies can serve useful theory building purposes,
such as the inductive generation of new hypotheses,
even when instances of a phenomenon are sufficiently
numerous to allow the application of statistical
methods.
For present purposes, then, a case is defined as an
instance of a class of eents (George 1979a, 1979b).
The term ‘class of events’ refers here to a phenomenon
of scientific interest, such as revolutions, types of
governmental regime, kinds of economic system, or
personality types. A case study is thus a well-defined
aspect of a historical happening that the inestigator
selects for analysis, rather than a historical happening
itself. The Cuban Missile Crisis, for example, is a
historical instance of many different classes of events:
cases of deterrence, coercive diplomacy, crisis management,
and so on. In deciding which class of events
to study and which theories to use, the researcher
decides what data from the Cuban Missile Crisis is
relevant to their case study of it. Of course, even if one
accepts the present definition of a case, there is still
room in the context of particular studies to debate
such questions as: ‘what is this event a case of’ and
‘given this phenomenon, is this event a case of it?’
(Ragin and Becker 1992).
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There is potential for confusion among the terms
‘comparative methods,’ ‘case study methods,’ and
‘qualitative methods.’ In one view the comparative
method, or the use of comparisons among a small
number of cases, is distinct from the case study
method, which in this view involves the internal
examination of single cases (see Comparatie Studies:
Method and Design). For the present purposes, however,
case study methods are defined to include both
within-case analysis of single cases and comparisons
between or among a small number of cases. This is not
an effort to claim wider meaning for the term ‘case
studies,’ but an outgrowth of the growing consensus
that the strongest means of drawing inferences from
case studies is the use of a combination of within-case
analysis and cross-case comparisons within a single
study or research program, although single case
studies can also play a role in theory development. As
for the term ‘qualitative methods,’ this is sometimes
used to encompass both case studies carried out with a
positivist view of the philosophy of science and those
implemented with a postmodern or interpretive view.
Thispresentarticlehewstothetraditionalterminology
in focusing on ‘case studies’ as that subset of qualitative
methods that has adopted a largely positivist
framework.
2. The Historical Deelopment of Case Study
Methods
Case study methods have developed through several
phases over the last three decades. Prior to the 1970s,
‘case studies’ consisted primarily of historical studies
of particular events, countries, or phenomena, with
littleeffort to cumulateresults or progressively develop
theories (Verba 1967). Throughout the 1970s, however,
scholars who were dissatisfied with the state of
case study methods, and encouraged by the example of
the formalization of statistical methods, began to
formalize case study methods.
First, Adam Przeworski and Henry Teune (1970)
clarified the logic of ‘most similar’ and ‘least similar’
case comparisons. In the former comparison, which
draws on the logic of John Stuart Mill’s method of
difference and mimics the experimental method, the
researcher compares two cases that are similar in all
but one independent variable and that differ in the
outcome variable. Such a comparison may be consistent
with the inference that the difference in the
single independent variable that varies between the
cases accounts for the difference in the dependent
variable (although for a variety of reasons discussed
below, this inference may be spurious). In a comparison
of least similar cases, which draws on Mill’s
method of agreement, the researcher compares two
cases that differ in all but one independent variable but
that have the same value on the dependent variable. If,
Table 1
Types of case studies
Lijphart
Eckstein
atheoretical configurative-ideographic
interpretative disciplined-configurative
hypothesis generating heuristic
deviant ?
theory-confirming
infirming
crucial, most-likely,
least-likely
for example, we find that teenagers are ‘difficult’ in
both tribal societies and industrialized societies, we
might be tempted to infer that it is the nature of
teenagers rather than the nature of society that
accounts for the difficulty of teenagers.
Arend Lijphart (1971) and Harry Eckstein (1975)
contributed further to the formalization of case study
methods by clarifying the differences among various
types of case study research designs and theorybuilding
goals. These authors identified similar types,
although their terminology differs and Lijphart adds
an important type, the ‘deviant case,’ for which
Eckstein does not make explicit provision. Their types
of case studies correspond as shown in Table 1.
The atheoretical or configurative-ideographic case
study takes the form of a detailed narrative or ‘story’
presented in the form of a chronicle that purports to
illuminate how an event came about. Such a narrative
is highly specific and makes no explicit use of theory or
theory-related variables. Most case studies, however,
do have an explanatory purpose. These studies generally
fall into the category of ‘disciplined-configurative’
or ‘interpretive’ case studies, in which general
propositions are used, often implicitly, to explain
specific historical cases. Another variant of such case
studies is the use of cases as examples that illustrate a
theory.
Heuristic case studies seek to generate new hypotheses
inductively from the study of particular cases.
Notably, statistical methods lack this capacity for
inductively generating hypotheses, and they typically
rely instead on hypotheses derived deductively or
borrowed from case study research. An especially
important type of case study for developing new
hypotheses is the ‘deviant’ case study. This is the study
of a case whose outcome is not predicted or explained
adequately by existing theories. Unless the outcome of
a deviant case turns out to be a consequence of
measurement error, the case is likely to be useful for
identifying variables that have been left out of existing
theories. Finally, researchers can use case studies to
test whether the outcomes and processes that theories
predict in particular cases are in fact evident.
Eckstein’s and Lijphart’s contributions demonstrated
that there was not just a single type of case
study, but many kinds of case study research designs
and many different theory-building purposes that they
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Case Study: Methods and Analysis
could serve. Their treatments differed, however, in
that Lijphart relied greatly on statistical concepts and
language. He was thus skeptical of the value of single
case studies for building social science theories, and,
consistent with the widespread preference at the time
for ‘large N’ over ‘small n’ methods, he urged researchers
to consider several means of either decreasing
the number of variables in their models or
increasing the number of cases to be studied in order to
make use of statistical rather than case study methods.
This advice, however, raised the risk ‘conceptual
stretching’ (Sartori 1970), or of lumping together
dissimilar cases under the same definitions. Possibly
for this reason, Lijphart later placed greater emphasis
instead on the controlled comparison of most similar
to cases as a basis for causal inference (Lijphart 1975,
Collier 1993).
Eckstein, in contrast, focused on the use of case
studies for theory testing and argued that even single
case studies could provide tests that might strongly
support or impugn theories. In so doing, Eckstein
developed the idea of a ‘crucial case,’ or a case that
‘must closely fit a theory if one is to have confidence in
thetheory’svalidity,or,conversely,mustnotfitequally
well any rule contrary to that proposed’ (Eckstein
1975, his emphasis). Eckstein argued that true crucial
cases are rare, so he pointed to the alternative of ‘most
likely’ and ‘least likely’ cases. A most likely case is one
that is almost certain to fit a theory if the theory is true
for any cases at all. The failure of a theory to explain
a most likely case greatly undermines our confidence
in the theory. A least likely case, conversely, is a tough
test for a theory because it is a case in which the theory
makes only a weak prediction. A theory’s ability to
explain a least likely case is strong evidence in favor of
the theory. In this way, Eckstein argued, even single
case studies could greatly increase or decrease our
confidence in a theory or require that we alter its scope
conditions.
Alexander George (1979a, 1979b) further developed
case study methods by refining ‘within-case’
analysis and cross-case comparisons in ways that help
each method compensate for the limits of the other.
George argued, as Mill himself had, that the ‘method
of difference’ and the corresponding practice of comparison
of most similar cases could lead to spurious
inferences. One reason for this is that no two nonexperimental
cases achieve the ideal of being similar in
all respects but one independent variable and the
outcome. Thus, there is always the danger that left-out
variables or residual differences in the values of the
independent variables account for the difference in
the outcomes of similar cases of (see Human–
Enironment Relationship: Comparatie Case Studies).
In addition, as Mill recognized, phenomena might be
characterized by what general systems theorists have
termed ‘equifinality,’ or the condition in which the
same outcome can arise through different causal
pathways or combinations of variables. Thus, there
might be no single necessary or sufficient variable for a
phenomenon: it might be that either ABC or DEF
causes Y, and that none of the variables A–F is itself
sufficient to cause Y (see Human–Enironment Relationship:
Comparatie Case Studies). In such circumstances,
pair-wise comparisons of cases might wrongly
reject variables that contribute to the outcome of
interest in conjunction with some contexts but not
with others, and might also accept as causal variables
that are in fact spurious.
To compensate for these limits of controlled comparison,
George developed the ‘within case’ methods
of ‘congruence testing’ and ‘process tracing’ as means
of checking on whether inferences arrived at through
case comparisons were spurious (see Pattern Matching:
Methodology). In congruence testing, the researcher
checks whether the prediction a theory makes
in a case, in view of the values of the case’s independent
variables, is congruent with the actual outcome in the
case. In process tracing, the researcher examines
whether the causal process a theory hypothesizes in a
case is in fact evident in the sequence and values of the
intervening variables in that case. Thus, process
tracing might be used to test whether the residual
differences between two similar cases were causal or
spurious in producing a difference in these cases’
outcomes. Process tracing can perform a heuristic
function as well, generating new variables or hypotheses
on the basis of sequences of events observed
inductively in cases.
George (1979a, 1979b) also systematized case study
procedures by developing what he called the method
of ‘structured focused comparison.’ In this method,
the researcher systematically: (a) specifies the research
problem and the class of events to be studied; (b)
defines the independent, dependent, and intervening
variables of the relevant theories; (c) selects the cases
to be studied and compared; (d) decides how best to
characterize variance in the independent and dependent
variables; and (e) formulates a detailed set of
standard questions to be applied to each case. In
addition, consistent with his emphasis on equifinality,
George argued that case studies could be especially
useful in developing what he called ‘typological
theories,’ or contingent generalizations on ‘the ariety
of different causal patterns that can occur for the
phenomena in question … [and] the conditions under
which each distinctie type of causal patterns occurs’
(George 1979a, his emphasis). He thus advocated a
kind of ‘building block’ approach to the development
of theories in which each case, while rendered in terms
of theoretical variables, might prove to be a distinctive
causal pathway to the outcome of interest.
In the 1980s and 1990s, thousands of books and
articles made use of these improvements in case study
methods in a wide variety of social science research
programs.Meanwhile,scholarscontinuedto elaborate
case study methods and articulate the ways in which
they differed from statistical methods. David Collier,
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Case Study: Methods and Analysis
reviewing the development of case study and comparative
methods, argued that these methods have
advantages in defining and measuring qualitative
variables in conceptually valid ways and forestalling
the problem of conceptual stretching (Collier
1993). Charles Ragin argued that qualitative methods
were also better than statistical methods at accounting
for equifinality and complex interaction effects. Although
statistical methods can model several kinds of
interaction effects, Ragin noted, they can do so only at
the cost of requiring a larger sample size, and models
of nonlinear interactions rapidly become complex and
difficulttointerpret.Raginalsointroducedthe method
of Qualitative Comparative Analysis, which uses
Boolean algebra to reduce a series of comparisons of
cases to the minimum number of logical statements or
hypotheses that entail the results of all the cases
compared (Ragin 1987). This method, he argues,
makes comparisons among cases in ways that treat
them inherently as configurations of variables, and
that thus allow for the possibility of equifinality and
complex interactions (see Configurational Analysis).
Both Collier and Ragin also noted the limitations of
case study methods, including the potential for indeterminacy
when attempting to sort out rival explanations
in a small number of cases, the difficulty of
attaining a detailed understanding of more than a few
cases, and the inability to make broad generalizations
on the basis of small numbers of cases.
3. New Deelopments in Case Study Methods
The thousands of applications of case study methods
in the last two decades have provided fertile ground
for further methodological refinements. Three key
recent developments include the strengthening of
linkages between case study methods and the philosophy
of science, the elaboration of the concept of
typological theories, and the emergence of elements of
consensus on the comparative advantages and limitations
of case study methods.
3.1 Case Studies and the Philosophy of Science
With regard to the philosophy of science, the ‘scientific
realist’ school of thought has emphasized that causal
mechanisms, or independent stable factors that under
certain conditions link causes to effects, are important
to causal explanation (Little 1998). This has resonated
with case study researchers’ use of process tracing to
uncover evidence of causal mechanisms at work. It has
also provided a philosophical counterpoint to attempts
by researchers from the statistical tradition to
place ‘causal effects,’ or the expected difference in
outcomes brought about by the change in a single
independent variable, at the center of causal explanation
(King et al. 1994). Case study researchers
have argued that both causal mechanisms, which are
more easily addressed by case studies, and causal
effects, which are best assessed through statistical
means, are essential to the development of causal
theories and causal explanations (George and Bennett
2001).
Another relevant development in the philosophy of
science has been the resurgence of interest in Bayesian
logic, or the logic of using new data to update prior
confidence levels assigned to hypotheses. Bayesian
logic differs from that of most statistics, which eschew
reliance on prior probabilities. Eckstein’s crucial, most
likely, and least likely case study designs implicitly use
a Bayesian logic, assigning prior probabilities to the
likelihood of particular outcomes (McKeown 1999).
One new development here is the refinement of
Eckstein’s approach, taking into consideration the
likelihood of an outcome not just in view of one
theory, but in the presence of alternative hypotheses.
If a case is ‘most likely’ for a theory, and if the
alternative hypotheses make the same prediction, then
the theory will be strongly impugned if the prediction
does not prove true. The failure of the theory cannot
be blamed on the influence of the variables highlighted
by the alternative hypotheses. Conversely, if a theory
makes only a weak prediction in a ‘least likely’ case,
the alternative hypotheses make a different prediction,
but if the first theory’s prediction proves true, this is
the strongest possible evidence in favor of the theory
(Van Evera 1997). This helps address the central
problemofaBayesianapproach—thatofassigningand
justifying prior probabilities—even if it does not fully
resolve it.
The continuing development of the logic of hypothesis
testing has also been relevant to case study
methods (see Hypothesis Testing: Methodology and
Limitations). On this topic, Imre Lakatos argued that
a theory can be considered progressive only if it
predicts and later corroborates ‘new facts,’ or novel
empirical content not anticipated by other theories
(Lakatos 1976). This criterion helps provide a standard
for judging whether process tracing, the designation
of new subtypes, and the proposal of new
theories from heuristic case studies are being done in a
progressive or regressive way. It also provides a
philosophical basis for arguing that a hypothesis can
be derived from one set of observations within a case
and then to some extent tested against the ‘new facts’
or previously unexamined or unexpected data that it
predicts within that same case, although independent
corroboration in other cases is usually advisable as
well (Collier 1993).
3.2 Typological Theories and ‘Fuzzy Logic’
A second recent development in case study methods
has been the elaboration of the concept of typological
theory. Typological theories occupy a middle ground
between covering laws, or highly general abstract
propositions, and causal mechanisms. Typological
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Case Study: Methods and Analysis
theories identify recurring conjunctions of mechanisms
and provide hypotheses on the pathways through
which they produce effects. Thus, like QCA, typological
theories treat cases as configurations. Unlike
QCA, they do not attempt to reduce the number of
theoretical statements about the variables, but retain a
diverse and admittedly complex set of contingent
generalizations, with potentially one generalization
per type. Consequently, typological theories are well
suited to modeling equifinality.
To construct typological theories, researchers first
specify the variables and use them to define the
typological space, or the set of all mathematically
possible combinations of the variables (this is sometimestermedatruthtableinthephilosophyofscience).
At first this may seem to produce an unmanageably
large number of combinations: a model with five
dichotomous variables, for example, would have 32
possible types. However, once the researcher begins to
categorize extant cases in a preliminary way into
particular types, it often becomes possible to narrow
the range of cases of interest for study. Many types
may remain empty, with no extant cases. Some types
may be overdetermined for the outcome of interest,
and hence not worthy of study unless they have an
unexpected outcome. From among the cases and types
that remain, the researcher can use the preliminary
categorization of cases within the typological space to
help identify most likely, least likely, most similar,
least similar, and crucial cases for study. Cases in the
typological space with unexpected outcomes, or deviant
cases, can help identify new causal pathways that
can be added to the existing theory in a kind of
‘building block’ approach (George and Bennett
2001). A related development concerns the concept of
‘fuzzy logic’ (Ragin 2000). Fuzzy logic treats cases as
configurations but rather than using dichotomous or
trichotomous variables and categorizations of cases, it
allows the use of scaling to give a score on the extent to
which a case fits into a certain type. In other respects,
the use of fuzzy logic proceeds in ways much like those
of typological theories.
3.3 The Emerging Consensus on the Strengths and
Limits of Case Study Methods
A third development is that while several debates on
case study methods continue, others have moved
toward synthesis or even closure, and the overall
picture is of an emerging consensus on the advantages
limitations of case study methods. As noted above,
researchers from a variety of methodological traditions
have recognized that because case studies can
include many observations, they do not suffer from an
inherent degrees of freedom problem. At the same
time, it is also widely agreed that particular case
studies may suffer from indeterminacy, or an inability
to exclude all but one explanation on the basis of
available process tracing evidence (Njolstad 1990).
When this occurs, it may still be possible to narrow the
number of plausible explanations, and it is also
important to indicate as clearly as possible the extent
to which the remaining hypotheses appear to be
complementary, competing, and incommensurate in
explaining the case.
Second, most case study researchers have readily
acknowledged the limits of Mill’s methods. Ragin’s
alternative of qualitative comparative analysis makes
less restrictive assumptions, but its results are highly
sensitive to changes in the measurement or coding of a
single case (Goldthorpe 1997). There has thus been a
movementtowardtypologicaltheoriesandfuzzylogic,
which make still less restrictive assumptions than QCA
and are not so sensitive to the results of a single case.
In addition, there is growing consensus that the use of
within-case methods of analysis helps provide a check
on the potential spuriousness of cross-case comparisons
(Collier 1993, Mahoney 1999, George and
Bennett 2001). Case study researchers consequently
seldom if ever rely on case comparisons alone.
Third, there is growing recognition that the case
selection criteria necessary for statistical studies are in
some respects inappropriate for case studies. Random
selection in a case study research design, for example,
can result in worse biases than intentional selection
(King et al. 1994). There is also increasing understanding
that, consistent with the reliance of some case
study designs on a Bayesian logic, case studies are
sometimes intentionally selected not to be representative
of some wide population but to provide the
strongest possible inferences on particular theories
(McKeown 1999). There is still disagreement between
those who warn against any selection on the dependent
variable (King et al. 1994) and those who argue
that selection on the dependent variable is appropriate
for some research objectives (Collier and Mahoney
1996, Ragin 2000, George and Bennett 2001). Related
to this is a continuing disagreement over whether
single case studies can make only limited contributions
to theory building (King et al. 1994), or whether
single case studies have indeed reshaped entire research
programs (Rogowski 1995). There is wider
agreement, however, that selection bias is potentially
more severe in case studies than statistical studies
because biased selection of case studies can overstate
as well as understate the relationship between the
independent and dependent variables (Collier and
Mahoney 1996).
On the whole discussions of these issues have
moved toward an emerging consensus on the comparative
advantages and limitations of case study
methods. These methods’ advantages include the
conceptualization, operationalization, and measurement
of qualitative variables (conceptual validity), the
avoidance of conceptual stretching, the heuristic identification
of new variables and hypotheses (often
through study of deviant cases), the assessment of
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Case Study: Methods and Analysis
whether statistical generalizations offer plausible or
spurious explanations of individual cases, the incorporation
of equifinality and complex interactions
effects, and the inferences made possible by combining
within-case and cross-case analyses (Collier 1993,
Munck 1998, George and Bennett 2001). It is possible
that new statistical methods may be able to improve
upon the statistical treatment of equifinality and
interaction effects, and at least narrow the gap in the
treatment of this issue, but the other comparative
advantages of case study methods appear to be
inherent in their differences from statistical methods.
The limits of case study methods include their
inappropriateness for judging the relative frequency or
representativeness of cases, their weakness at performing
partial correlations and establishing causal
effects or causal weight, the necessarily narrow and
contingent nature of their generalizations, and the
danger that selection bias can be more catastrophic
than in statistical studies (Collier 1993, King et al.
1994, Munck 1998, George and Bennett 2001). Fortunately,
these are precisely the strengths of statistical
studies.
4. Future Directions in Case Study Methods
Just as the formalization of case study methods in the
1970s inspired a generation of more sophisticated
research, the recent further refinements in these
methods are likely to lead to still greater sophistication
in their use in the social, biological, and even physical
sciences. The increasingly evident complementarity of
case study and statistical methods is likely to lead
toward more collaborative work by scholars using
various methods. The recent interest among rational
choice theorists in using case studies to test their
theories, for example, is an important step in this
direction (Bates et al. 1998). Because case studies,
statistical methods, and formal modeling are all
increasingly sophisticated, however, it is becoming less
likely that a single researcher can be adept at more
than one set of methods while also attaining a cuttingedge
theoretical and empirical knowledge of their
field. Collaboration might therefore take the form of
several researchers working together using different
methods, or of researchers more self-consciously
building on the findings generated by those using
different methods. In either form, effective collaboration
requires that even as they become expert in
one methodological approach, scholars must also
becomeconversantwithalternativeapproaches,aware
of their strengths and limits, and capable of an
informed reading of their substantive results.
See also: Biographical Methodology: Psychological
Perspectives; Case Study: Logic; Case-oriented
Research; Configurational Analysis; Human–Environment
Relationship: Comparative Case Studies;
Psychotherapy: Case Study; Single-case Experimental
Designs in Clinical Settings; Single-subject Designs:
Methodology
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Case-oriented Research
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Case-oriented Research
1. Introduction
A. Bennett
Case-oriented research focuses on interconnections
among parts and aspects within single cases. In this
approach, the researcher attempts to make sense of
each case as a singular, interpretable entity. In-depth
knowledge of the cases included in a study is considered
a prerequisite for the examination of patterns
that might be observed across cases. Case-oriented
researchers often study one case at a time, but they
may also study multiple instances of a given phenomenon
(e.g., comparable instances of ethnic conflict).
The distinctiveness of case-oriented research is apparent
when this approach is contrasted with the
variable-oriented approach, where researchers focus
more exclusively on cross-case patterns, without first
gaining an understanding of each case.
2. Goals of Case-oriented Research
Today social scientists tend to identify case-oriented
research with specific techniques of data collection
linked to the observation and analysis of singular cases
(e.g., direct observation of individuals at the micro
level and archival research on nation-states at the
macro level). While generally useful, the identity of
case-oriented research with specific techniques of data
collection is unfortunate, for it obscures basic differences
between case-oriented research and conventional
variable-oriented research. More fundamental
than differences in methods of data collection is the
contrast between goals (Ragin 1987). Case-oriented
strategies are distinctive in that they are centrally
concerned with making sense of a relatively small
number of cases, selected because they are substantivelyortheoreticallysignificantinsomeway(Eckstein
1975). Conventional variable-oriented strategies, by
contrast, are centrally concerned with the problem of
assessing the relationship between aspects of cases
across a large number of generic ‘observations,’
usually with the goal of inferring general patterns that
hold for a population.
For example, a researcher might use a case-oriented
approach in order to study a small number of firms in
an in-depth manner. Suppose these firms were all
thought to be unusually successful in retaining their
best employees while at the same time investing in
them and thus enhancing their potential value to
competing firms. To find out how they do it, a
researcher would have to conduct an in-depth study of
the firms in question. By contrast, a variable-oriented
researcher might study the predictors of variation in
rates of ‘employee retention’ across a large sample of
firms. Is it more a matter of firm or industry characteristics?
Do these two sets of factors interact? Useful
answers to these questions would be based on careful
analysis of relationships between variables, using data
drawn from a survey of a large number of firms—the
more (and the more varied), the better.
As these two examples show, what matters most is
the researcher’s starting point: does the researcher
seek to understand specific cases or to document
general patterns characterizing a population? This
contrast follows a longstanding division in all of
science, not just social science. Georg Henrik von
Wright argues in Explanation and Understanding
(1971) that there are two main traditions in the history
of ideas regarding the conditions an explanation must
satisfy in order to be considered scientifically respectable.
One tradition, which he calls ‘finalistic,’ is
anchored in the problem of making facts understandable.
The other is called ‘causal-mechanistic’ and is
anchored in the problem of prediction. The contrast
between case-oriented and variable-oriented research
closely parallels this fundamental division. In the two
examples just described, the first researcher uses the
case-oriented approach in order to make certain facts
understandable, for example, the spectacular success
of a handful of firms in retaining their most valuable
employees; the second researcher uses the variableoriented
approach in order to derive an equation
predicting levels of retention, based on a large sample
of firms, and to draw inferences from this equation to
an entire population.
Once the distinction between case-oriented and
variable-oriented research is established and their
contrasting goals acknowledged, it is clear that the
importance of techniques of data collection as bearers
of the ‘case-oriented vs. variable-oriented’ distinction
begins to fade. For example, it is clear that a
researcher using case-oriented methods to study a
handful of firms might benefit from conducting surveys
of their employees and performing a conventional
variable-oriented analysis of these data. The results of
the survey would contribute to this researcher’s depth
of knowledge about the firms in question, just as
interviewing their top executives or studying their
archives would contribute useful information. Likewise,
it is clear that the researcher using variableoriented
methods to predict rates of retention could
benefit from interviews of top executives or personnel
1519