Research Designs: Statnotes, from North Carolina State University, Public Administration Program
Research Designs
Overview
Research designs fall into two broad classes:
quasi-experimental and experimental.
Experimental studies are characterized by the
ability to randomize subjects into treatment and
control groups. This randomization goes a long
way toward controlling for variables which are
not included explicitly in the study. Because
comparison groups are not true, randomized
control groups in quasi-experimental studies,
this type of study has to control for confounding
variables explicitly through statistical
techniques. For this reason, quasi-experimental
studies are sometimes labeled
correlational
designs
.
As discussed below, not much can be concluded
from one-point-in-time studies of a single
group. It is much better to have a comparison
(control) group, and better yet to have
measurements before (pretest) and after
(posttest) the treatment (the change in the
causal variable or variables), and best of all to
have multiple pretests and posttests.
Any given method of data collection, such as
survey research, could be used in an
experimental or a quasi-experimental design,
depending on whether there was a treatment
and a control group, and if the participants were
randomly assigned. Similarly, analysis of
variance (ANOVA) studies may be experimental
or quasi-experimental (but originated in the
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Contents
Key
concepts
and terms
Experimental
designs
Quasiexperimental
designs
Nonexperimental
designss
Assumptions
Frequently
asked
questions
Bibliography
Research Designs: Statnotes, from North Carolina State University, Public Administration Program
former). More on research design may be found
in the sections on ANOVA, ANCOVA, MANOVA,
and MANCOVA. In practical terms, however,
some methods of data collection, such as case
studies, are used in non-experimental designs.
Key Concepts and Terms
■
Experimental Designs
A design is experimental if subjects are randomly assigned to treatment groups
and to control (comparison) groups. Cook and Campbell (1979) mention ten
types of experimental design. Note that the control group may receive no
treatment, or it may be a group receiving a standard treatment (ex., students
receiving computer-supported classes versus those receiving conventional
instruction). That is, the control group is not necessarily one to be labeled "no
treatment."
1. Classic experimental designs: randomization of subjects into control
and treatment groups is a classic experimental method, amenable to a
variety of ANOVA designs discussed separately. The two broad classes of
classic experimental design are:
■
Between subjects designs:. In this type of design, the
researcher is comparing between subjects who experience
different treatments. There are different subjects for each level of
the independent variable(s) (ex., for each different type of media
exposure in a study of the effect of political advertising). Any given
subject is exposed to only one level and comparisons are made
between subjects' reactions or effects. The researcher relies on
randomization of subjects among the treatment groups to control
for unmeasured variables, though sometimes stratification of
subjects is employed to guarantee proportions on certain key
variables (ex., race).
■
Factorial designs
use categorical independent variables to establish groups.
For instance in a two factor design, the independent
variables might be information type (fiction, non-fiction) and
media type (television, print, Internet ), generating 2 times
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3 = 6 categories. An equal number of subjects would be
assigned randomly to each of the six possible groups (ex.,
to the fiction-television group). One might then measure
subjects on information retention. A null outcome would be
indicated by the average retention score being the same for
all six groups of the factorial design. Unequal mean
retention scores would indicate a main effect of information
type or media type, and/or an interaction effect of both.
■
■
Fully-crossed
vs. incomplete
factorial
designs
. A design is fully crossed if
there is a study group for every possible combination
of factors (independent variables). An incomplete
factorial design, leaving out some of the groups, may
be preferred if some combinations of values of factors
are non-sensical or of no theoretical interest. Also,
when one of the factors is treatment vs. control (no
treatment) and another factor is types/levels of
treatment, the control subjects by definition will not
receive types/levels of treatment so those cells in the
factorial design remain empty.
Randomized block
designs
stratify the subjects and for each
strata, a factorial design is run. This is typically done when
the researcher is aware of nuisance factors that need to be
controlled (example, there might be an air conditioned room
stratum and a no air conditioning stratum) or if there were
other mitigating structural factors known in advance (ex.,
strata might be different cities). That is, the blocking
variables which stratify the sample are factors which are
considered to be control variables, not independent
variables as they would be in a simple factorial design.
Randomized block designs seek to control for the effects of
main factors and their interactions, controlling for the
blocking variable(s).
■
In SPSS:
Consider city to be the
blocking variable and information type and media
type to be the main factors. In a simple factorial
design, city would be an additional factor and in SPSS
one would ask for Analyze, General Linear Model,
Univariate; the dependent variable would be retention
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score; city, information type, and media type would
be fixed factors; the model would be "full
factorial" (the default). In a randomized block design,
one would ask for Analyze, General Linear Model,
Univariate; the dependent variable would be retention
score; information type, and media type would be
fixed factors; the blocking variable, city, would be
entered as a random factor; click Model and select
Custom, then set "Build Term(s)" to "Main Effects"
and move all three factors over to the "Model:" box;
uncheck "Include Intercept in Model."; Continue; OK.
Note that this procedure reflects the fact that in a
randomized block design there are no interaction
effects, just main effects. Later, for multiple
comparisons, repeat this procedure but click the Post
Hoc button and enter the main factors in the Post Hoc
Tests box; also check the type of test wanted (ex.,
Tukey's HSD).
■
■
Within subjects (repeated measures) designs: In this type of
design, the researcher is comparing measures for the same
subjects (hence, "within subjects"). The same subjects are used
for each level of the independent variable, as in before-after
studies or panel studies. Since the subjects are the same for all
levels of the independent variable(s), they are their own controls
(that is, subject variables are controlled). However, there is
greater danger to validity in the form of carryover effects due to
exposure to earlier levels in the treatment sequence (ex., practice,
fatigue, attention) and there is danger of attrition in the sample.
Counterbalancing
is a
common strategy to address carryover effects: ex., half the
subjects get treatment A first, then B, while the other half get B
first, then A, so that the carryover effect washes out in the sense
that it is counterbalanced in the overall sample. Keep in mind that
counterbalancing does not remove all effects - for instance, if
there is a practice effect in a test situation, with higher scores for
the second-taken test, on the average both tests will score higher
in the overall sample than they would otherwise, since for both
tests half the sample had the benefit of a practice effect.
Counterbalancing in this situation only seeks that both test scores
are biased equally upward, not that bias in absolute scores is
eliminated.
Matched pairs designs. Compared to between-subjects designs,
within-subjects designs control for subject variables better but at
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the expense of greater threat to validity in the form of
contamination from influences arising from subjects going from
one experimental level (condition) to another. Another type of
repeated measures design is matched pairs, where the repeated
measurement is not of the same subjects but of very similar
subjects matched to have like key attributes. While matched pairs
designs avoid some types of invalidity of within subjects designs,
such as the threat of subject fatigue across repeated tests,
matched pairs designs control only for the matched attributes
whereas same-subject within-subjects designs control for both
explicit and unmeasured subject variables.
2. Lottery designs: used when lotteries are expected, as in some
communities' manner of assignment of students to magnet schools, this
eliminates a major impediment to randomization in social science
situations, where it is frequently considered unethical or even illegal, or
contrary to community standards, to offer benefits (a public policy
treatment) to some citizens but not to others.
3. Mandated control designs: sometimes, as in the military, control
levels are sufficiently high that random assignment to treatment and
control conditions will be accepted as a matter of course.
4. Waiting list designs: used when demand outstrips supply, which also
legitimates giving treatment to some citizens but not others, since
offering treatment to all is impossible due to limited supply (ex., studies
of clients vs. a waiting-list group).
5. Equivalent time series designs: used when treatment cannot be
delivered simultaneously to all, also legitimating giving treatment to
some citizens but, temporarily, not to others, as when all employees are
to receive training, but in rotations, such that different types of training
can be delivered to different groups.
6. Spatial separation designs: when treatment groups are separated
and have no experiment-relevant intercommunication, as when
participative management is tried with a treatment group of new,
randomly assigned employees in one location but not in another.
7. Mandated change/unknown solution designs: when change is
required but there is no one clear solution, random assignment of
subjects can gain acceptance, as in random assignment of students to
classes with alternative textbooks.
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8. Tie-breaking designs: in cases where receiving treatment is meritbased, as in some academic situations, those receiving tied scores on
merit-based exams can be randomly assigned to a treatment and a
control group.
9. Indifference curve designs: the attractiveness of the treatment can
sometimes be adjusted to a point where some people will be indifferent
toward receiving or not receiving it, and thus may be randomly assigned
to a treatment or a control group.
10. New organizations designs: when a new organization is established,
such as one providing job training, opportunities may well arise which
permit random assignment of clients to a control and a treatment
organization.
■
Quasi-Experimental Designs
■
Nonequivalent Control Group Designs
A design is quasi-experimental if subjects are not randomly assigned to
groups but statistical controls are used instead. There may still be a
control or comparison group. While subjects are not randomly
assigned
, they are either randomly
selected
(sampled) or are all the relevant cases.
For instance, a random sample of cities with council–manager
governments may be compared with a random sample of cities with
mayor–council governments. Cook and Campbell (1979) outline 11
nonequivalent control group research designs. In each case, due to the
nonequivalency of the comparison group, threats to validity are much
more possible than in a randomized design and the researcher should
consider checklist-style all the types of validity threats.
1. One-Group Posttest-Only Design: Sometimes called the "oneshot case study," this design lacks a pretest baseline or a
comparison group, making it impossible to come to valid
conclusions about a treatment effect because only posttest
information is available. The level of the dependent variable may
be due to treatment, or may be due to any number of causes of
invalidity such as history (other events coexisting with treatment),
maturation (changes in subjects which would have occurred
anyway), experimenter expectation (subjects seeking to provide
responses known to be desired or simply reacting to the attention
of being tested), or other biases discussed in the section on
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validity. If this design is used, information must be gathered on
pretest conditions, if only through respondent recollections, which
are often subjective and unreliable.
2. Posttest-Only Design with Nonequivalent Comparison
Groups Design: In this common social science design, it is also
impossible to come to valid conclusions about treatment effect
based solely on posttest information on two nonequivalent groups
since effects may be due to treatment or to nonequivalencies
between the groups. Strategies for improving validity center on
trying to create equivalency between groups by random
assignment of subjects or matched-pair assignment to groups.
When such assignment is impossible, then attempts may be made
to control statistically by measuring and using as covariates all
variables thought to affect the dependent variable. Nonetheless,
many of the same threats to validity exist as in one-group posttestonly designs: history (concurrent events affect the two groups
differently), maturation (the two groups would have evolved
differently anyway), testing (the two groups have different
reactions to testing itself), regression to the mean (the two groups
tend to revert to their respective means if starting from extreme
levels), etc.
3. Posttest-Only Design with Predicted Higher-Order
Interactions: Sometimes the expectation of the treatment effect
interacts with a third variable. Instead of the expectation that
treatment group subjects will be higher on the dependent, one has
the expectation that the subjects will be higher if in the upper half
of third variable Y but lower (or not as high) if in the bottom half
of Y. For instance, training may lead to greater productivity for
high education employees but not for low education employees on
the same tasks. The interaction creates two or more expectations
compared to the simple one-expectation one-group posttest only
design. Because there are more expectations, there is greater
verification of the treatment effect. However, this design is still
subject to possible challenges to validity due to such factors as
history (subjects high in education had different experiences) -- it
is just that the counter-argument has to be more complex to
account for the interaction, and therefore may be somewhat less
likely to be credible.
4. One-Group Pretest-Posttest Design: This is a common but
flawed design in social science. It is subject to such threats to
validity as history (events intervening between pretest and
posttest), maturation (changes in the subjects that would have
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occurred anyway), regression toward the mean (the tendency of
extremes to revert toward averages), testing (the learning effect
on the posttest of having taken the pretest), and most challenges
discussed in the separate section on validity. Sometimes the
pretest data is collected at the same time as the posttest data, as
when the researcher asks for recollection data of the "before"
state. This is know as a proxy
pretest-posttest
design
and has additional validity problems since
the pretest data are usually significantly less reliable.
5. Two-Group Pretest-Posttest Design Using an Untreated
Control Group (separate pretest-posttest samples design): If a
comparison group which does not receive treatment is added to
what otherwise would be a one-group pretest-posttest design,
threats to validity are greatly reduced. This is the classic
experimental design. Since the groups are not equivalent, there is
still the possibility of selection (observed changes are due to
selection of subjects, such as working with more motivated
volunteers in a treatment group -- see two-stage least squares for
a discussion of testing for selection bias). Much depends on the
outcome. For instance, if the treatment group starts below the
comparison group and ends up above after treatment, a stronger
inference of a treatment effect exists than if both groups rise in
performance, but the treatment group more so (this might well be
due to selection). A strongly recommended modification to this
design is to have more than one pre-test. Multiple pretests (at the
same interval as between the last pretest and the posttest) help
establish the performance trends in both the treatment group and
the control group, and treatment should be revealed by a change
in the trend line for the treatment group but not the control group.
6. Double pretest designs. One can strengthen pretest-posttest
designs by having two (or more) pretest measures. This can
establish if there is a trend in the data independent of the
treatment effect measured by the posttest. By seeing if there is a
posttest effect over and above the trend, one controls for
maturation threats to study validity.
7. Four-group Design with Pretest-Posttest and PosttestOnly Groups. Also known as the "Solomon four-group design,"
this design has a treatment and control group with both pretests
and postests and has treatment and control groups with posttests
only. This design strengthens the two-group pretest-posttest
design because, if the same effect difference is found for
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treatment vs. control groups in the pretest-posttest set as for the
posttest-only set, then the researcher may rule out threats to
validity having to do with repeated measurement (ex., learning
effects from having taken the test before).
8. Nonequivalent Dependent Variables Pretest-Posttest
Design: In this design, the researcher identifes dependent
variables related to the treatment-related variable, but where
treatment is predicted to have no effect. Then, if the variable
thought to be affected by treatment does in fact change in the
predicted direction, but there is no change in the other related
dependent variables, again as predicted, then the inference is
made that the change in question is due to treatment, not some
confounding cause such as test experience from the pretest.
9. Removed-Treatment Pretest-Posttest Design: In some
situations it is possible not only to introduce a treatment but also
to remove it. If the dependent variable goes up after treatment
and then goes down when treatment is removed, this is some
evidence for the effect of treatment. Of course, if the variable goes
up after treatment, it might come down on its own anyway due to
a declining return or attrition effect. Cook and Campbell (1979)
therefore recommend at least two posttests after treatment and
before removal of treatment, in order to establish trend effects
after treatment. The researcher also needs to beware of
resentment effects due to treatment removal, as these also might
cause a decline in the variable measured, depending on the
situation.
10. Repeated-Treatment Design: This design is similar to the
preceding one but follows a pretest-treatment-posttest-removal of
treatment-posttest-restoration of treatment-posttest pattern. The
expected treatment effect is for the dependent variable to increase
after treatment, decline after removal of treatment, then increase
again with restoration of treatment. Even if this outcome occurs,
inference is not foolproof as the decline phase may be due to
resentment at removal of treatment rather than direct adverse
affects of removal of treatment, and the subsequent rise may be
due not to restoration of treatment but removal of the source of
resentment. Also, subjects may more easily become aware of
experimenter expectations in this design, and may seek to meet
(or react against) expectations, thereby contaminating the study.
11. Switching Replications Designs. In this research design, there
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are two comparison groups and three measures. Both groups are
measured under pretest conditions. The treatment is given to one
group but not the control group, and a first post-test measure
taken. Then the treatment is given to the control group but not
the first group, and a second post-test measure is taken.
12. Reversed-Treatment Pretest-Posttest Nonequivalent
Comparison Groups Design. This design is one in which the
nonequivalent comparison group receives the opposite treatment
(ex., the treatment group receives participative leadership while
the comparison group receives autocratic leadership). The
expectation is that the posttest will show increase for the
treatment group and decrease for the comparison group. Cook and
Campbell (1979) suggest adding a no-treatment group and even a
placebo group where appropriate. Multiple pretests will improve
this design by showing preexisting trends in the treatment and
nonequivalent comparison group.
13. Cohort Designs with Cyclical Turnover: This design refers to
the study of groups as they evolve over time, as in the study of a
fourth-grade class in year 1, the corresponding fifth grade class in
year two, etc. The expectation is that the class average will
increase in the posttest after treatment. This design is liable to the
same challenges to validity as simple prettest-posttest designs, but
it can be strengthened by
partitioning
the cohort into
subgroups according to their exposure to the treatment. In a study
of the effects of television violence, for instance, the cohort may
be divided into groups of high, medium, and low exposure to
violent television shows. The expectation is that the partitions
exposed more will show more change on the dependent variable.
Where partitioning is not possible, having multiple prettests and
posttests can establish trends to rebut "it would have happened
anyway" arguments about the validity of conclusions under this
design.
14. Regression-Discontinuity Design: One might hypothesize that
if there is a treatment effect, then the slope of the regression line
relating scores before and after treatment would be the same, but
there would be a discontinuous jump in magnitude on the
dependent variable immediately after treatment. This test requires
verification that the relationship between prettest and posttest
scores is linear, as two linear regressions (one before, one after
treatment) on a curvilinear underlying relationship could spuriously
appear to meet this test. Also, there may be a treatment effect
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taking the form of a steeper regression slope but no discontinuity
at the point of treatment. Such a treatment effect is very difficult
to differentiate from a simple curvilinear relationship.
15. Regression Point Displacement Design. In this design there is
a treatment group (ex., a county) and a large number of
comparison groups (ex., other counties in the state). Consider the
case where we wish to estimate the effect of an after-school
intervention on juvenile crime. In the pretest condition we regress
juvenile crime rates on, say, median income level and we note the
position of the test county in the regression scattergram. In the
posttest condition, after we have implemented the intervention
program, we re-run the regression. If the location of the test
county is displaced on the regression scattergram, we conclude
that the intervention had an effect.
■
Interrupted Time Series
Above, in the discussion of non-equivalent control group designs, it was
suggested that pretest-posttest versions could be improved by having at
least two pretests to establish linear tendencies apart from treatment.
Cook and Campbell (1979) list six interrupted time series designs which
extend this suggestion by having multiple pretests and posttests.
1. Simple Interrupted Time Series Design. This is the one-group
pretest-posttest design augmented with multiple pretests and
posttests. The trend found in multiple pretests can be compared to
the trend found in multiple posttests to assess whether apparent
post-treatment improvement may simply be an extrapolation of a
maturation effect which was leading toward improvement anyway.
Since there is no control group, however, the researcher cannot
assess other confounding factors such as history-type challenges
to validity ( the possibility that other factors historically
coterminous with the treatment actually led to the observed
effect). There may be other problems such as failure to seasonally
adjust data, confounding a seasonal effect with a treatment effect;
selection bias, as due to non-random attrition of subjects in the
posttest; instrumentation bias (the posttest is not equivalent to the
pretest); and testing (there may be a learning effect from the
pretest such that the observed effect is one a test artifact rather
than a treatment effect).
2. Interrupted Time Series with a Nonequivalent NoTreatment Comparison Group : This is the two-group pretest-
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posttest design using an untreated control group, but with multiple
pretests and posttests. By having a comparison group, even if
nonequivalent (not randomized), the same threats to validity can
occur, but they usually occur in a more complex and hence more
easily disproved way. For instance, if this design shows an
improvement in the treatment but not comparison group, it may
still be true that there is historical bias, but such biasing history
factors must be unique to the treatment group for some reason
not experienced by the comparison group. There could be
seasonal bias, but only if the seasonal factors were thought to be
uniquely associated with treatment. The researcher's main validity
challenge is to show the two groups were equivalent on all
causally important variables prior to treatmen (ex., in a study of a
rehabilitation program's effect on recidivism, to show the two
groups of prisoners were similar in crime record, age, etc.).. An
alternative strategy, which only works for stronger effects, is to
select a treatment group which would be expected to measure
worse on posttreatment (ex., prisoners with worse criminal records
than the comparison group, in a study of recidivism), on the
theory that if the effect shows in spite of an adverse starting point
for the treatment group, the treatment has an effect.
3. Interrupted Time Series with Nonequivalent Dependent
Variables : This is the nonequivalent dependent variables pretestposttest design with multiple pretests and posttests. The object is
to find dependent variables related to the dependent being
studied, but where the related variables are not thought to be
correlated with the treatment variable. Cook and Campbell (1979)
give the example of influence on accident rates (the dependent) of
breathalyzer tests (the treatment variable) given by police when
bars are open weekend nights, but not given at other times. The
dependent variable of interest is accident rates on weekend nights.
The related dependents are accident rates on weekday nights
when bars are open, and accident rates at times when bars are
not open. The expectation was that accident rates would be
significantly lower on weekend nights because of the presence of
the treatment. Counter-explanations for lower accident rates (ex.,
safer cars, stricter court treatment of offenders) must explain not
only the lower accident rate on weekend nights, but also the lack
of effect at other times. Of course, confounding factors may well
exist, but they must be unique to the dependent variable of
interest.
4. Interrupted Time Series with Removed Treatment : This is
the removed-treatment pretest-posttest design with multiple
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pretests and posttests, including ones in between the original
treatment and its removal, and hence is a more powerful test. For
instance, the threat of history is reduced because any historical
forces coincident with treatment would also have increase after
treatment and decrease after removal, an unlikely circumstance.
Ideally removal of treatment does not occur until enough
observations have been taken to rule out any seasonal or other
cyclical effects.
5. Interrupted Time Series with Multiple Replications . This is
simply the interrupted time series with removed treatment design,
except that treatment and removal occur multiple times on a
schedule. Circumstances rarely permit such a design, but it is
stronger yet. By timing the replications randomly, the researcher is
able to minimize contamination from cyclical factors. This design
assumes one is dealing with a treatment effect which dissipates in
a timely manner before the next replication, without carryover
effects (otherwise there is "multiple treatment interference,"
meaning that receiving earlier treatments adds to or multiplies the
effect of receiving later treatments).
6. Interrupted Time Series with Switching Replications . This
is a further refinement in which there are two groups, each serving
as either the treatment or comparison group on an alternating
basis, through multiple replications of treatment and removal.This
requires an even higher level of control over subjects by the
researcher but is a particularly strong design in ruling out threats
to validity. It does not lend itself to studies where the treatment
intervention has been gradual, or where treatment effect does not
decay well.
■
Non-Experimental Designs
A design is non-experimental if the subjects are neither randomly assigned nor
randomly selected. There may still be comparison groups.
■
■
■
Case study designs are discussed in a separate section.
Content analysis is discussed in a separate section. Under some
circumstances, content analysis may also be part of a quasi-experimental
design.
Ethnography is discussed in a separate section.
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Focus groups are discussed in a separate section.
■
Narrative analysis is discussed in a separate section.
■
Network analysis and sociometry are discussed in a separate section.
■
Participant observation is discussed in a separate section.
Assumptions
■
■
■
The researcher is assumed to have a research design!
The researcher is assumed to have considered all threats to validity associated
with the design.
In the case of experimental designs, it is assumed that randomization of
subjects controls for all unmeasured variables. However, the smaller the sample
size, the less likely this is to be true.
Frequently Asked Questions
■
■
■
Is a quasi-experimental design ever preferable to an experimental
design?
How do I handle the problem of sample attrition in designs which
involve observations at two or more time periods?
Is a quasi-experimental design ever preferable to an experimental
design?
In principle, no. However, it frequently occurs that an experimental
design is not feasible, ethical, or even lawful. Data may need to be
analyzed on the basis of existing archival information, it may be
impossible to randomize subjects, and pretest data may be absent. Even
when randomized experiments are undertaken, they may become flawed
due to such factors as attrition in the treatment group, in which case the
wise researcher will have a "fall-back" quasi-experimental design for
purposes of analysis.
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How do I handle the problem of sample attrition in designs which
involve observations at two or more time periods?
There is no statistical "fix" for this problem. The researcher must report
and discuss differences in means and variances for key variables,
including demographic variables, which differentiate the samples at each
observation time point. The researcher may not be able to generalize
findings to the original sample as first intended. With larger sample sizes
it may be possible to model attrition, such as by methods discussed by
Duncan and Duncan (1994). The researcher may also wish to consider
the pros and cons of weightinglater samples in light of distributional
characteristics of the initial sample.
Bibliography
■
■
■
■
■
■
Cook, Thomas D. and Donald T. Campbell, Quasiexperimentation:
Design and analysis
issues for field
settings
. Boston: Houghton-Mifflin, 1979. A leading
classic. See Shadish, Cook, and Campbell (2002).
Creswell, John W. (2002). Research
design: Qualitative,
quantitative, and
mixed methods
approaches.
Thousand Oaks, CA: Sage Publications.
Duncan, S. C. and T. E. Duncan (1994). Modeling incomplete longitudinal
substance use using latent growth curve methodology.
Mulitvariate
Behavioral Research
, 29:
Leedy, Paul and Jeanne Ellis Ormrod (2004).
Practical research :
Planning and design
(8th Edition)
. A leading text.
Levin, Irwin P. (1999). Relating
statistics and
experimental design
. Thousand
Oaks, CA: Sage Publications. Quantitative Applications in the Social Sciences
series #125. Elementary introduction covers t-tests and various simple ANOVA
designs. Some additional discussion of chi-square, significance tests for
correlation and regression. and non-parametric tests such as the runs test,
median test, and Mann-Whitney U test.
Pedhazur, E.J. and L. P. Schmelkin. (1991).
Measurement, design,
and analysis: An
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Research Designs: Statnotes, from North Carolina State University, Public Administration Program
■
integrated approach.
Lawrence Erlbaum Assoc. A widely used textbook.
Shadish, W.R., Thomas D. Cook, and Donald T. Campbell, D.T. (2002).
Experimental and quasiexperimental designs
for generalized causal
inference
. Boston: Houghton-Mifflin. An update of a
classic by a third author.
Copyright 1998, 2008 by G. David Garson..
Last update 4/4/2008.
Back
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PA 765: Case Studies
Case Studies
Case study research is a time-honored, traditional approach to the study of topics in social science
and management. Because only a few instances are normally studied, the case researcher will
typically uncover more variables than he or she has data points, making statistical control (ex.,
through multiple regression) an impossibility. This, however, may be considered a strength of case
study research: it has the capability of uncovering causal paths and mechanisms, and through
richness of detail, identifying causal influences and interaction effects which might not be treated
as operationalized variables in a statistical study, As such it may be particularly helpful in
generating hypotheses and theories in developing fields of inquiry.
In recent years there has been increased attention to implementation of case studies in a
systematic, stand-alone manner which increases the validity of associated findings. However,
although case study research may be used in its own right, it is more often recommended as part
of a multimethod approach ("triangulation") in which the same dependent variable is investigated
using multiple additional procedures (ex., also grounded theory, survey research, sociometry and
network analysis, focus groups, content analysis, ethnography, participant observation, narrative
analysis, archival data, or others).
Key Concepts and Terms
●
Types of case studies. Jensen and Rodgers (2001: 237-239) set forth a typology of case
studies, including these types:
: Detailed,
1. Snapshot case studies
objective study of one research entity at one point in time. Hypothesis-testing by
comparing patterns across sub-entities (ex., comparing departments within the case
study agency).
2. Longitudinal case
studies
. Quantitative and/or qualitative study of one research
entity at multiple time points.
3. Pre-post case studies
. Study of
one research entity at two time points separated by a critical event. A critical event is
one which on the basis of a theory under study would be expected to impact case
observations significantly.
. A set
4. Patchwork case studies
of multiple case studies of the same research entity, using snapshot, longitudinal, and/
or pre-post designs.This multi-design approach is intended to provide a more holistic
view of the dynamics of the research subject.
5. Comparative case
studies
. A set of multiple case studies of multiple research entities
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PA 765: Case Studies
for the purpose of cross-unit comparison. Both qualitative and quantitative
comparisons are generally made.
●
●
Representativeness. Unlike random sample surveys, case studies are not representative
of entire populations, nor do they claim to be. The case study researcher should take care
not to generalize beyond cases similar to the one(s) studied. Provided the researcher
refrains from over-generalization, case study research is not methodologically invalid simply
because selected cases cannot be presumed to be representative of entire populations. Put
another way, in statistical analysis one is generalizing to a population based on a sample
which is representative of that population. In case studies, in comparison, one is
generalizing to a theory based on cases selected to represent dimensions of that theory.
Case selection should be theory-driven. When theories are associated with causal
typologies, the researcher should select at least one case which falls in each category. That
cases are not quantitative does not relieve the case researcher from identifying what
dependent variable(s) are to be explained and what independent variables may be relevant.
Not only should observation of these variables be part of the case study, but ideally the
researcher would study at least one case for every causal path in the model suggested by
theory. Where this is not possible, often the case, the researcher should be explicit about
which causal types of cases are omitted from analysis. Cases cited in the literature as
counter-cases to the selected theory should not be omitted. In public administration, "best
practices" lists may provide cases for selection, though it is necessary also to pick
contrasting cases.
❍
❍
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Cross-theoretic case
selection.
As multiple theories can conform to a given set
of data, particularly sparse data as in case study research, the case research design is
strengthened if the focus of the study concerns two or more clearly contrasting
theories. This enables the researcher to derive and then test contrasting expectations
about what would happen under each theory in the case setting(s) at hand.
Other selection
criteria
: Yin (1984) points out that researchers may select
cases not only when they are critical (to testing a theory), but also when they are
revelatory (reveal relationships which cannot be studied by other means) or unusual
(throws light on extreme cases).
Pattern matching is the attempt of the case researcher to establish that a preponderance
of cases are not inconsistent with each of the links in the theoretical model which drives the
case study. For instance, in a study of juvenile delinquency in a school setting, bearing on
the theory that broken homes lead to juvenile delinquency, cases should not display a high
level of broken homes and simultaneously a low level of delinquency. That is, the researcher
attempts to find qualitative or quantitative evidence in the case that the effect association
for each causal path in the theoretical model under consideration was of non-zero value and
was of the expected sign.
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PA 765: Case Studies
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Process tracing is the a more systematic approach to pattern matching in which the
researcher attempts, for each case studied, to find evidence not only that patterns in
the cases match theoretical expectations but also that (1) that there is some
qualitative or quantitative evidence that the effect association which was upheld by
pattern matching was, in fact, the result of a causal process and does not merely
reflect spurious association; and (2) that each link in the theory-based causal model
also was of the effect magnitude predicted by theory. While process tracing cannot
(selecting among
resolve indeterminancy
alternative models, all consistent with case information), it can establish in which
types of cases the model does not apply.
■
■
■
Controlled
observation
is the most common form of process
tracing. Its name derives from the fact that the researcher attempts to control
for effects by looking for model units of analysis (ex. people, in the case of
hypotheses about people) which shift substantially in magnitude or even
valence, on key variables in the model being investigated. In a study of prison
culture, for instance, in the course of a case study an individual may shift from
being free to being incarcerated; or in a study of organizational culture, an
individual may shift from being a rank-and-file employee to being a supervisor).
Such shifts can be examined to see if associated shifts in other variables (ex.,
opinions) also change as predicted by the model. Controlled observation as a
technique dictates that the case study (1) be long enough in time to chronicle
such shifts, and (2) favor case selection of cases where shifts are known to
have occurred or are likely to occur.
Time series analysis
is a
special and more rigorous case of process tracing, in which the researcher also
attempts to establish not only the existence, sign, and magnitude of each
model link is as expected, but also the temporal sequence of events relating the
variables in the model. This requires observations at multiple points in time, not
just before-after observations, in order to establish that the magnitude of a
given effect is outside the range of normal fluctuation of the time series.
Critical incident
. CIT is a method of case
technique (CIT)
selection and analysis developed in the 1950s by the Air Force at the University
of Pittsburgh (see Flanagan, 1954). It is a methodology associated with the
American Institutes for Research (AIR), founded by John C. Flanagan as a more
empirical data collection method thought to be superior to conventional survey
research. AIR has provided an extensive bibliography on CIT. Cases are
selected based on their being important, significant, and critical to determining
either an effective or ineffective outcome. Effective outcomes are those which
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solve a problem or resolve a situation. Ineffective outcomes are those which
only partially solve problems or resolve situations, but also create new problems
or new needs for resolution. By collecting a large number of brief, factual
reports on critical incidents, researchers attempt to identify common factors
associated with effective outcomes. These factors may be set forth in "critical
requirements" or "acceptable performance" standards for the organization.
❍
●
●
Congruence testing is an even more systematic approach to pattern matching
which requires the selection of pairs of cases which are identical in causal type, except
for the difference of one independent variable. Differences in the dependent variable
are attributed to incongruency on the independent. Where there are a large number
of cases, it may be possible to replace congruence testing with statistical methods of
correlation and control.
Explanation-building is an alternative or supplement to pattern matching. Under
explanation-building, the researcher does not start out with a theory to be investigated.
Rather, the researcher attempts to induce theory from case examples chosen to represent
diversity on some dependent variable (ex., cities with different outcomes on reducing
welfare rolls). A list of possible causes of the dependent variable is constructed through
literature review and brainstorming, and information is gathered on each cause for each
selected case. The researcher then inventories causal attributes which are common to all
cases, common only to cases high on the dependent variable, and common only to cases
low on the dependent variable. The researcher comes to a provisional conclusion that the
differentiating attributes are the significant causes, while those common to all cases are not.
Explanation-building is particularly compelling when there are plausible rival explanations
which can be rebutted by this method. Explanation-building can also be a supplement to
pattern matching, as when it is used to generate a new, more plausible model after pattern
matching disconfirms an initial model.
Meta-Analysis is a particular methodology for extending grounded theory to a number of
case studies. In meta-analysis the researcher creates a metaanalytic schedule
, which is a cross-case
summary table in which the rows are case studies and the columns are variable-related
findings or other study attributes (ex., time frame, research entity, case study design type,
number and selection method for interviewees, threats to validity like researcher
involvement in the research entity). The cell entries may be simple checkmarks indicating a
given study supported a given variable relationship, or the cell entries may be brief
summaries of findings on a given relationship or brief description of study attributes. The
purpose of meta-analysis is to allow the researcher to use the summary of case studies
reflected in the meta-analytic table to make theoretical generalizations. In doing so,
sometimes the researcher will weight the cases according to the number of research entities
studied, since some case studies may examine multiple entities. See Hodson (1999); Jensen
and Rodgers (2001: 239 ff.). Hodson (1999: 74-80) reproduces an example of a metaanalytic schedule for the topic of workplace ethnography.
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Problems of meta-analysis include what even case study advocates admit is the "formidible
challenge" (Jensen and Rodgers, 2001: 241) involved in developing a standardized metaanalytic schedule which fits the myriad dimensions of any sizeable number of case studies.
No widely accepted "standardized" schedules exist. Moreover, for any given proposed
schedule, many or most specific case studies will simply not report findings in one or more
of the column categories, forcing meta-analysts either to accept a great deal of missing data
or to have to do additional research by contacting case authors or even case subjects.
Considerations in implementing meta-analytic schedules:
1. Variables
: In addition to substantive variables particular to the
researcher's subject, methodological variables should be collected, such as date of
data collection, subject pool, and methodological techniques employed.
2. Coder training
. It is customary to provide formal
training for coders, who ideally should not be the researchers so that data collection is
separated from data interpretation.
3. Reliability
. The researcher must establish inter-rater
reliability, which in turn implies there must be multiple raters. Reliability is generally
increased through rater debriefing sessions in which raters are encouraged to discuss
coding challenges. Duplicate coding (allowing 10% or so of records to be coded by
two coders rather than one) is also used to track reliability. In larger projects, rating
may be cross-validated across two or more groups of coders.
4. Data weighting
. Meta-analysis often involves
statistical analysis of results, where cases are studies. The researcher must decide
whether cases based on a larger sample size should be weighted more in any
statistical analysis. In general, weighting is appropriate when cases are drawn from
the same population to which the researcher wishes to generalize.
5. Handling missing data
. Dropping
cases where some variables have missing data is generally unacceptable unless there
are only a very small number of such cases as (1) it is more likely that missing-data
cases are related to the variables of the study than that they are randomly distributed,
and (2) dropping cases when the number of cases is not large (as is typical of metaanalytic studies) diminishes the power of any statistical analysis. There is no good
solution for missing data. See the separate section on data imputation, but maximum
likelihood estimation of missing values carries fewer assumptions about data
distribution than using regression estimates or substituting means. SPSS supports MLE
imputation.
6. Outliers
. Metapanalysis often involves results coded from a
relatively small number of cases (ex., < 100). Consequently, any statistical analysis
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PA 765: Case Studies
may be affected strongly by the presence of outlier cases.
Sensitivity analysis
should be
conducted to understand the difference in statistical conclusions with and without the
outlier cases included. The researcher may decide that deviant
may be appropriate, based on a finding
case analysis
that relationships among the variables operate differently for outlier cases.
7. Spatial autocorrelation
.
It is possible that central tendencies and conclusions emerging from meta-analytic
studies will be biased because cases cluster spatially. If many cases are from a
spatially neighboring area and if the relationships being studied are spatially related,
then generalization to a larger reference area will be biased. If the researcher has
included longitude and latitude (or some other spatial indicator) as variables, then
many geographic information systems packages and some statistical packages can
check for spatial autocorrelation (see Land and Deane, 1992). However, a visual
approach of mapping cases to identify clusters, then comparing in-cluster and out-ofcluster statistical results usually is a sufficient check on spatial autocorrelation.
Assumptions
●
●
●
●
Cases selected based on dimensions of a theory (pattern-matching) or on diversity on a
dependent phenomenon (explanation-building).
No generalization to a population beyond cases similar to those studied.
Conclusions should be phrased in terms of model elimination, not model validation.
Numerous alternative theories may be consistent with data gathered from a case study.
Case study approaches have difficulty in terms of evaluation of low-probability causal paths
in a model as any given case selected for study may fail to display such a path, even when it
exists in the larger population of potential cases.
Frequently Asked Questions
●
●
●
●
●
●
What are common standards for case studies based dissertations?
Is case study research a social science substitute for scientific experimentation?
Aren't case studies unscientific because they cannot be replicated?
Aren't case studies unscientific because findings cannot be generalized?
Where can I find out more about case study research?
What is NUD*IST?
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PA 765: Case Studies
●
What are common standards for case studies based dissertations?
Doctoral programs do not dictate methodology but rather leave issues of research
design and empirical procedure to dissertation committees. For this reason few
programs articulate formal bars to any particular methodology, case studies included.
Although it is true that skepticism toward the case study method is widespread,
prompted by fears of low quality, the great majority of doctoral programs in public
administration and policy nonetheless allow case studies. Many, however, do so with
guidelines or stipulations. Based on replies to a survey of 35 doctoral programs in
public administration and public policy, a composite set of guidelines has been
constructed below.
Common Guidelines for Case Study Based Dissertations
Case study dissertations should represent original research, be analytic, well-written,
insightful, systematic, explicitly related to the literature of the field, and should cover
their focus in depth. This focus must test propositions which are relevant to significant
theoretical issues. Theoretical issues may be political-theoretic, decision-theoretic,
economic or market-theoretic, or public policy or action-theoretic, to name some of
the possible dimensions of theory. In this way the criteria for acceptable case study
dissertations do not differ from those for other types of dissertations.
To test propositions derived from theory, one must have some variance in the
dependent variable(s) under study, which in turn requires there be some type of
comparison such as might be provided by before-after studies of a policy intervention
or by examining a phenomenon in a public compared to a private setting. That is,
case study dissertations must have a longitudinal, cross-sectional, or other
comparative perspective. In some but not all dissertations, it may be necessary to
study multiple cases to achieve the requisite variance in the object of study. Nonlongitudinal "single shot" case studies of a given organization or policy event do not
provide a basis for comparison and testing of propositions and are not acceptable no
matter how detailed the description. In fact, description not directly germane to the
theoretical concerns of the thesis should be relegated to appendices or dropped from
the dissertation altogether.
Because case study dissertations seek to provide theoretical or policy insight based on
a small number of cases or even on a single case, a "triangulation" approach to
validation is strongly recommended. Such a rigorous approach involves a multimethod design in which key constructs and processes are traced using more than a
single methodology. Multiple methods may include structured and unstructured
interviews, sample surveys, focus groups, narrative analysis, phenomenological
research, ethnography, symbolic action research, network analysis, advocacy coalition
research (Sabatier), content analysis, participant observation, examination of archival
records, secondary data analysis, experiments, quasi-experiments, and other
methods. Testing the same propositions through data gathered by multiple methods
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helps address some of the validation problems in case study designs.
The standard reference for public administration and public policy graduate students
doing case study research, formally recommended by many programs, is Robert Yin's
Case Study Research: Design and Methods (1984, 1994, 2002). Other references
which were cited by survey respondents as the basis for standards for certain types of
case study research included Goetz and LeCompte (1984), Ragin (1987), Strauss and
Corbin (1990), Sabatier (1993), and Morgan (2001).
●
●
●
●
Is case study research a social science substitute for scientific experimentation?
It is interesting to note that case study research plays an important role in the natural
sciences as well as social sciences. Many scientific fields, such as astronomy, geology,
and human biology, do not lend themselves to scientific investigation through
traditional controlled experiments. Darwin's theory of evolution was based, in essence,
on case study research, not experimentation, for instance.
Aren't case studies unscientific because they cannot be replicated?
It is true that a later researcher using case methods will of necessity be studying a
different case, if only because he or she comes later, and therefore may come to
different conclusions. Similarly, in experimental and quasi-experimental research the
subjects will differ, meaning relationships may differ. What makes research replicable
in either case study or experimental research is not the units of analysis but whether
the research has been theory-driven. If the case researcher has developed and tested
a model of hypothesized relationships, then a future case researcher can replicate the
initial case study simply by selecting cases on the basis of the same theories, then
testing the theories through pattern matching. If pattern matching fails to uphold
theories supported by the first case researcher, the second case researcher may
engage in explanation building, as discussed above, to put forward a new model.
Aren't case studies unscientific because findings cannot be generalized?
Generalizability of findings is a function of the range and diversity of settings in which
a theory is tested, not of the testing methodology per se. It is true that randomization
of subjects in experimental research and random sampling in quasi-experimental
research, along with larger sample sizes, mean that research of this type can more
easily lay claim to range and diversity than can case study research projects.
Nonetheless, judicious case selection to identify cases illustrating the range of a
theory (ex., a theory about causes of divorce) may result in more generalizable
research than, say, the attempt to test the same theory based on a random sample of
students in one university. Moreover, if case research is replicated (discussed above),
generalization of case-based findings can be enhanced further.
Where can I find out more about case study research?
❍
Introduction to case study, by Winston Tellis, The
Qualitative Report
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, Vol. 3, No. 2, July,
PA 765: Case Studies
❍
❍
●
1997
Text Analysis Software Links
More Content Analysis Software Links
What is NUD*IST (N6)?
NUD*IST is software standing for Non-numerical Unstructured Data Indexing,
Searching, and Theorizing, which can be used in conjunction with grounded theory to
create and analyze theories and provide a framework for understanding. At this
writing, the latest version is called simply N6 . It can handle coding categories and
sub-categories, supporting hierarchical indexing; browse and code documents and
indexing databases; search for words and word patterns and combine them in
indexes; "memo link" emerging codes and categories with their associated
documents; and create new indexing categories out of existing ones. See Richards
and Richards (1991). Information on NUD*IST and other content analytic software is
linked from http://www.qsr.com.au/products/productoverview/product_overview.htm.
Bibliography
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Allison, Graham T. (1971). Essence of
decison: Explaining the
Cuban Missile Crisis
. Boston: Little, Brown,
and Co. Considered a classic model case study.
Annells, M. P. (1997). Grounded theory method, part 1: within the five moments of
qualitative research," Nursing Inquiry
, Vol. 4:120-129;
and Grounded theory method, part II; options for users of the method,
Nursing Inquiry
, Vol. 4: 176-180. A good two-part
historical overview of the grounded theory method, relating it to theory, methodological
issues, and practical procedures.
Bailey, Mary Timney (1992). Do physicists use case studies? Thoughts on public
administration research. Public
Administration Review
, Vol. 52 (Jan./
Feb.): 47-55. Much-cited article defends the case study approach as meeting scientific
standards of generalizability, transferability, and replicability.
Benbasat; Izak; David K. Goldstein; & Melissa Mead (1987). The case research strategy in
studies of information systems. MIS Quarterly
11(3), 369386.
Bock, Edwin a., ed. (1962). Essays on the case
method
. NY: Inter-University Case Program.
Campbell, Donald (1975). 'Degrees of freedom' and the case study.
Comparative Political
Studies
8( 2): 178-193. Classic defense of the case study method.
Clarke, Adele (2005). Situational
analysis: Grounded theory
http://www2.chass.ncsu.edu/garson/pa765/cases.htm (9 of 12) [5/22/2008 7:17:03 AM]
PA 765: Case Studies
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after the postmodern turn
.
Thousand Oaks, CA: Sage Publications. Based on Anselm Straus's ecological social worlds/
arenas.discourses framework. Treats development of situational maps, social worlds/arenas
maps, and positional maps.
Denzin, Norman K. and Yvonna Lincoln (2003). Strategies
of qualitative inquiry,
Second edition
. Thousand Oaks, CA: Sage Publications.
Includes a chapter on case studies by Robert Stake.
Eisenhardt, K.M. (1989). Building theories from case study research.
Academy of Management
, 14(4): 532-550.
Review
Flanagan, J. C. (1954). The critical incident technique.
Psychological Bulletin
5(4), 327358.
Glaser, Barney G. and Anselm Strauss (1967). The
discovery of grounded
theory: Strategies for
. Chicago, IL:
qualitative research.
Aldine Publishing Co. The seminal work in grounded theory.
Goetz, J. P. and M. D. LeCompte (1984). Ethnography
and qualitative design in
educational research
. London: Academic
Press.
Hamel, Jacques (1993). Case study methods
..
Thousand Oaks, CA: Sage Publications. Covers history of the case study method.
Hodson, Randy (1999). Analyzing
documentary accounts
. Thousand Oaks,
CA: Sage Publications. Quantitative Applications in the Social Sciences Series No. 128.
Describes random sampling of ethnographic field studies as a basis for applying a metaanalytic schedule. Hodson covers both coding issues and subsequent use of statistical
techniques.
Hoyle, R. H. (1999). Statistical
strategies for small
sample research
. Thousand Oaks, CA: Sage
Publications. Not on case studies per se, but relevant.
Kennedy, Mary M. (1979). Generalizing from single case studies.
Evaluation Quarterly
, 3: 661-78.
Land, K. C. and G. Deane (1992). On the large-sample estimation of regression models with
spatial- or network-effects terms. In P. V. Marsden, ed. (1992: 221-248).
Sociological Methodology
.
Washington, DD: American Sociological Association.
Lee, Allen S. (1989). A scientific methodology for MIS case studies. MIS
Quarterly
, March: 33-50. Using management information systems as
a focus, Lee addresses problems of and remedies for case study research.
Lucas, W. (1974). The case survey method of aggregating case experience. Santa Monica,
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PA 765: Case Studies
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Calif.: Rand.
Morgan, David L. (2001). Combining
qualitative and
quantitative methods
. Portland, OR:
Portland State University.
Naumes, William and Margaret J. Naumes (1999). The art and craft of case writing.
Thousand Oaks, CA: Sage Publications. The authors have led case writing workshops for the
Academy of Management and the Decision Sciences Institute, and use their experiences to
illustrate issues in case stduy research.
Ragin, Charles (1981). Comparative sociology and the comparative method,
International Journal of
Comparative Sociology
, 22(1-2): 102120.
Ragin, Charles (1987). The comparative method: Moving beyond qualitative and quantitative
strategies. Berkeley: University of California Press. Ragin is a distinguished sociologist noted
for his defense of the case study method.
Ragin, Charles, and David Zaret (1983). Theory and method in comparative research: Two
strategies. Social Forces
, 61(3): 731-754.
Richards, Tom J. and Lynn Richards (1991). The NUDIST qualitative data analysis system.
Qualitative Sociology
, Vol. 14: 307324.
Rhodes, Terrel R. (2002). The public manager
case book
. Thousand Oaks, CA: Sage Publications. Eight decisionmaking focused case studies.
Sabatier, Paul C. (1993). Policy change and
learning: An advocacy
coalition approach
. Boulder, CO: Westview
Press.
Sabatier, Paul C., ed. (1999). Theories of the
policy process:
Theoretical lenses on
public policy
. Boulder, CO: Westview Press.
Scholz, Roland W. and Olaf Tietje (2002). Embedded case
study methods:
Integrating quantitative
and qualitative knowledge
.
Thousand Oaks, CA: Sage Publications.
Soy, Susan K. (1997). The case study as a research method. Unpublished paper. Retrieved
10/1/07 from http://www.gslis.utexas.edu/~ssoy/usesusers/l391d1b.htm.
Stake, Robert E. (1995). The art of case
study research
. Thousand Oaks, CA: Sage Publications.
Focuses on actual case (Harper School) to discuss case selection, generalization issues, and
case interpretation.
Strauss, Anselm and Juliet Corbin (1990). Basics of
qualitative research:
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Grounded theory
.
procedures and techniques
Newbury Park, CA: Sage Publications. Probably now the leading contemporary treatment of
grounded theory.
Strauss, Anselm and Juliet Corbin, eds. (1997). Grounded
theory in practice
. London: Sage Publications.
Travers, Max (2001). Qualitative
research through case
studies
. Thousand Oaks, CA: Sage Publications. Discusses grounded
theory, dramaturgical analysis, ethnomethodology, and conversation analysis.
U. S. General Accounting Office (1990). Case study
evaluations
. Washington, DC: USGPO. GAO/PEMD-91-10.1.9.
Available online in .pdf format. Government manual for doing evaluation research using case
studies.
Yin, Robert K. (1984). Case study
research, design, and
methods. 3rd edition
. Thousand Oaks, CA:
Sage Publications. Second ed., 1994. Third ed., 2002.
Yin, Robert K. (2002). Applications of
case study research, 2nd
edition
. Thousand Oaks, CA: Sage Publications. Completed case studies
discussed in relation to themes in Yin (1984 ff.).
Yin, Robert K. and Karen A. Heald (1975). Using the case survey method to analyze policy
studies. Administrative Science
Quarterly
20(3): 371-381.
Copyright 2002, 2008 by G. David Garson.
Back
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Quantitative Methods in Public Administration
Content Analysis and Qualitative Research
Overview
Content analysis is the manual or automated coding of documents, transcripts, newspapers,
or even of audio of video media to obtain counts of words, phrases, or word-phrase clusters
for purposes of statistical analysis. Typically the researcher creates a dictionary which
clusters words and phrases into conceptual categories for purposes of counting. Various
constraints may filter the count, such as the constraint that one concept be or not be within
so many words of another concept. While content analysis is normally focused on the
analysis of print media and media transcripts, it is applicable to any form of communication,
as, for instance, in the study by DuRant et al. (1997) on "Tobacco and alcohol use behaviors
portrayed in music videos."
There are a large number of reasons for conducting content analysis, many enumerated by
Berelson (1952) over half a century ago:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
To
To
To
To
To
To
To
To
To
To
describe trends in content over time
describe the relative focus of attention for a set of topics
compare international differences in content
compare group differences in content
compare individual differences in communication style
trace conceptual development in intellectual history
compare actual content with intended content
expose use of biased terms in propaganda research
test hypotheses about cultural and symbolic use of terms
code open-ended survey items
Related information is contained in the sections on case study research and on ethnography.
Key Concepts
Krippendorf (2004) identifies five key processes inherent to content analysis:
1. Unitizing. The researcher must establish the unit of analysis (word, meaning, sentence,
paragraph, article, news clip, document, etc.).
2. Sampling. Usually the universe of interest is too large to study the content of all units of
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Quantitative Methods in Public Administration
analysis, and instead units must be sampled. Sampling involves counting, which may require
the researcher to develop thesauruses (so different terms with like meanings will be counted
under the same construct) and expert systems or other rule engines (so the proper
contextual valence is assigned to each counted construct).
3. Reducing. Content data must be reduced in complexity, usually by employing conventional
summary statistical measures. Coding and statistical analysis is covered by Hodson (1999).
4. Inferring. Contextual phenomena must be analyzed to provide the context for findings.
5. Narrating. Conclusions in the content analytic tradition are usually communicated using
narrative traditions and discursive conventions.
Software Resources
Listed alphabetically
●
●
●
:
ATLAS.ti
is software for text analysis and model building. It handles
graphical, audio, and video data files as well as text. With this package one can code and/or
annotate text or media segments in a variety of ways, search/select segments by code
(using proximity, Boolean, or semantic thesaurus methods), create hotlinks connecting
segments, and display relationships among segments in diagrammatic format. An automatic
coding mode codes all similar segments according to defined patterns. Video segments can
be as small as frames and likewise audio segments can be detailed. Network diagrams,
created with the built-in semantic network editor, can be exported to graphics and word
processing packages and a built-in HTML generator creates web pages for sharing work with
collaborators. Visually, annotations and links are made in a margin area of the computer
display. Data can be generated in SPSS format for further analysis. However,
ATLAS.ti
is not a content analysis package per se, but rather a text
management package lacking fundamental content analysis statistical functions.
ATLAS.ti
is available from Scolari Software, of Sage Publications, Inc.
The General Inquirer
is the classic package
for content analysis, now web-enabled by psychologist Phil Stone (Harvard University). It
contains large content dictionaries (Lasswell Value Dictionary; Harvard Psycho-Sociological
Dictionary) which are used in conjunction with text scanning software to establish patterns
in the meaning of words. The General
Inquirer
is now being distributed by the Zentrum fuer Umfragen,
Methoden, und Analysen (ZUMA, Mannheim); for more information, contact Dr. Peter Ph.
Mohler, O05@DHDURZ2.
Intext
TextQuest
and TextQuest
.
is tje Windows version of the Intext
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content analysis software developed by Harald Klein, with a website at http://www.intext.de.
The software produces word lists, word sequence lists, word permutations, cross-references,
and basic content analysis functions.
●
●
●
NUD*IST
is a leading content analysis package, discussed by Richards and
Richards (1991). It allows authors to establish lexical and conceptual relations among words,
to index text files, and to conduct pattern matching and searching operations using Boolean
co-occurrences of nodes in the text. NUD*IST
is available from Scolari
Software, of Sage Publications, Inc. Scolari also publishes a variety of other text analysis
software packages.
QUALRUS is a general-purpose qualitative analysis program which supports text and
multimedia sources. It offers intelligent suggestions throughout the coding process and
comes with a number of tools to help with analysis of data once it has already been coded.
Users can customize and automate many tasks by taking advantage of Qualrus's scripting
language. A free, functional demo version is available. More information on Qualrus is
available at its homepage, http://www.qualrus.com.
TextSmart
is SPSS's module for coding and analyzing open-ended
survey questions. It supports text management, seaching, and some forms of text analysis.
Its "Import Wizard" brings text data into a tab-delimited ASCII file format, on the fly filtering
responses by automated stemming (a linguistic engine which identifies word stems to
combine terms), aliasing (grouping synonyms), and excluding trivial words. The automatic
categorization option automatically clusters terms that tend to occur together in responses,
to create meaningful categories automatically. Some categorization parameters are usercontrollable and the researcher can create his or her own categories by combining
categories using Boolean logic. Output can be to an SPSS or a tab-delimited ASCII file, and
categorization parameters can be saved for future TextSmart
runs.
Because TextSmart
is "dictionary-free," the researcher is freed of the
burden of creating a coding scheme or concept dictionary prior to beginning analysis. By the
same token, if the control which comes with a user-defined dictionary is wanted,
TextSmart
is not the appropriate tool. Online information is available
from SPSS, Inc.
Assumptions
●
●
Sampling. Content analysis is subject to all the usual biases and problems of sampling.
Contextual bias. Particularly in automated content analysis, crucial context for word and
meaning counts may be flawed.
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Frequently Asked Questions
●
●
What is the address for the online discussion list about content analysis?
The CONTENT list is at content@sphinx.gsu.edu. To join, send the message
"subscribe CONTENT yourfirstname yourlastname" (without quotes) to
listproc@listproc.gsu.edu. The list editor has been William Evans, who maintains an
archive site with additional resources at http://www.gsu.edu/~wwwcom/content.html.
Where else can I find out about content analysis software?
Harald Klein, author of INTEXT
, maintains an overview page at http://
www.intext.de/ENGLISH.HTM . He also has a page on various other forms of text
analysis software at http://www.intext.de/TEXTANAE.HTM. See also William Evans'
website mentioned in the previous question/answer segment.
Bibliography
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Berelson, B. (1952). Content Analysis in Communication Research. Glencoe, Ill: Free Press.
DuRant,R. H.; E S Rome, M Rich, E Allred, S J Emans & E R Woods (1997). Tobacco and
alcohol use behaviors portrayed in music videos: a content analysis.
American Journal of Public
Health
87(7), 1131-1135.
Franzosi, Roberto (1990). Computer-assisted coding of textual data. An application to
semantic grammars. Sociological
, 19/2: 225-257.
Methods and Research
Gottschalk, Louis A. (1995). Content analysis
of verbal behavior: New
findings and clinical
applications
. Hillsdale, NJ: Lawrence Erlbaum.
Hodson, Randy (1999). Analyzing
. Thousand Oaks,
documentary accounts
CA: Sage Publications. Quantitative Applications in the Social Sciences Series No. 128.
Describes random sampling of ethnographic field studies as a basis for applying a metaanalytic schedule. Hodson covers both coding issues and subsequent use of statistical
techniques.
Ilo, Saidat (2005). Research in public administration: A content analysis of applied research
projects completed from 1999-2005 at Texas State University in the Masters of Public
Administration Program. San Marcos, TX: Texas State University. Retrieved 9/27/07 from
http://ecommons.txstate.edu/cgi/viewcontent.cgi?article=1010&context=arp. Provides a
review of the use of content analysis in relation to analyzing MPA education.
Klein, Harald (1991). "INTEXT/PC - A program package for the analysis of texts in the
humanities and social sciences." Literary and
Linguistic Computing
, 6/2: 108-111.
Krippendorf, Klaus (2004). Content analysis:
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An introduction to its
methodology
. 2nd ed. Thousand Oaks, CA: Sage Publications.
Neuendorf, Kimberly A. (2002). The content
analysis handbook
. Thousand Oaks, CA: Sage
Publications. Covers history of content analysis, sampling message units, handling variables,
reliability, and use of NEXIS for text acquisition. Also covers PRAM, software for reliability
assessment with multiple coders.
Nissan, Ephraim, and Klaus Schmidt, eds. (1995). From
information to knowledge:
Conceptual and content
. London: Intellect.
analysis by computer
Phillips, Nelson and Cynthia Hardy (2002). Discourse
analysis: Investigating
processes of social
construction
. Thousand Oaks, CA: Sage Publications.
Perhaps the first full-length book on discourse analysis.
Popping, Roel (1999). Computer-assisted
text analysis
. Thousand Oaks, CA: Sage.
Richards, Thomas J. and Lyn Richards (1991), "The NUD.IST qualitative data analysis
system", Qualitative Sociology
14(4),
307-24.
Riffe, Daniel, Stephen Lacy, and Frederick G. Fico. (1998). Analyzing
media messages: Using
quantitative content
Mahwah, NJ:
analysis in research.
Lawrence Erlbaum, 1998.
Roberts, Carl W. and Roel Popping (1993). "Computer-supported content analysis: Some
recent developments." Social Science
Computer Review
, 11: 283-291.
Roberts, Carl W., ed. (1997). Text analysis for
the social sciences:
Methods for drawing
inferences from texts and
transcripts.
Mahwah, NJ: Lawrence Erlbaum.
Smith, Charles P., ed. (1992). Motivation and
personality: Handbook of
thematic content
New York: Cambridge University Press.
analysis.
Stemler, Steve (2001). An overview of content analysis. Practical
Assessment, Research &
Evaluation
7(17). Available online: http://edresearch.org/pare/
getvn.asp?v=7&n=17.
Stone, Philip J., Dexter C. Dunphy; Marshall S. Smith, and Daniel M. Ogilvie (1966).
General Inquirer: A
computer approach to
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●
●
content analysis
. Cambridge, MA: MIT Press. The
original work popularizing The General
Inquirer
.
Weber, Robert P. (1990). Basic content
Newbury Park, CA: Sage
analysis. Second ed.
Publications. A standard introductory overview.
Weitzman, Eben. A.; Miles, Matthew B. (1998): Computer
programs for qualitative
data analysis. A software
sourcebook. Second ed.
Thousand Oaks,
CA: Sage Publications.
Back
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Ethnographic Research: Statnotes, from North Carolina State University, Public Administration Program
Ethnographic Research
Overview
Ethnography is a form of research focusing on the sociology of meaning through close field
observation of sociocultural phenomena. Typically, the ethnographer focuses on a
community (not necessarily geographic, considering also work, leisure, and other
communities), selecting informants who are known to have an overview of the activities of
the community. Such informants are asked to identify other informants representative of the
community, using chain sampling to obtain a saturation of informants in all empirical areas
of investigation. Informants are interviewed multiple times, using information from previous
informants to elicit clarification and deeper responses upon re-interview. This process is
intended to reveal common cultural understandings related to the phenomena under study.
These subjective but collective understandings on a subject (ex., stratification) are often
interpreted to be more significant than objective data (ex., income differentials).
It should be noted that ethnography may be approached from the point of view of art and
cultural preservation, and as a descriptive rather than analytic endeavor. The comments
here, however, focus on social science analytic aspects. In this focus, ethnography is a
branch of cultural anthropology.
Related information is contained in the sections on content analysis and on case study
research.
Key Concepts and Terms
●
The ethnographic method starts with selection of a culture, review of the literature
pertaining to the culture, and identification of variables of interest -- typically variables
perceived as significant by members of the culture. The ethnographer then goes about
gaining entrance, which in turn sets the stage for cultural
immersion
of the ethnographer in the culture. It is not unusual for
ethnographers to live in the culture for months or even years. The middle stages of the
ethnographic method involve gaining informants, using them to gain yet more informants in
a chaining process, and gathering of data in the form of observational transcripts and
interview recordings. Data analysis and theory development come at the end, though
theories may emerge from cultural immersion and theory-articulation by members of the
culture. However, the ethnographic researcher strives to avoid theoretical preconceptions
and instead to induce theory from the perspectives of the members of the culture and from
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observation. The researcher may seek validation of induced theories by going back to
members of the culture for their reaction.
Definition.
A popular definition of ethnography is found in
Hammersley and Atkinson (1995: 1), who write of ethnography, "We see the term as
referring primarily to a particular method or sets of methods. In its most characteristic form
it involves the ethnographer participating, overtly or covertly, in people's lives for an
extended period of time, watching what happens, listening to what is said, asking questions
—in fact, collecting whatever data are available to throw light on the issues that are the
focus of the research. More recently, Johnson (2000: 111) defines ethnography as "a
descriptive account of social life and culture in a particular social system based on detailed
observations of what people actually do."
Ethnographic methodologies vary and some ethnographers advocate use of structured
observation schedules by which one may code observed behaviors or cultural artifacts for
purposes of later statistical analysis. Coding and subsequent statistical analysis is treated in
Hodson (1999). See also Denzin and Lincoln (1994).
●
●
●
●
●
●
Macro-ethnography is the study of broadly-defined cultural groupings, such as "the
English" or "New Yorkers."
Micro-ethnography is the study of narrowly-defined cultural groupings, such as "local
government GIS specialists" or "members of Congress."
Emic perspective is the ethnographic research approach to the way the members of the
given culture perceive their world. The emic perspective is usually the main focus of
ethnography.
Etic perspective, is the ethnographic research approach to the way non-members
(outsiders) perceive and interpret behaviors and phenomena associated with a given culture.
Situational reduction refers to the view of ethnographers that social structures and social
dynamics emerge from and may be reduced analytically to the accumulated effects of
microsituational interactions (Collins 1981, 1988). Put another way, the cosmos is best
understood in microcosm. Situational reduction, Collins (1981b: 93) wrote, ". . . produces an
empirically stronger theory, on any level of analysis, by displaying the real-life situations and
behaviors that make up its phenomena. In particular, it introduces empirically real causal
forces in the shape of human beings expending energy. It enables us to discover which
macro-concepts and explanations are empirically groundable, and which are not..."
Symbols, always a focus of ethnographic research, are any material artifact of a culture,
such as art, clothing, or even technology. The ethnographer strives to understand the
cultural connotations associated with symbols. Technology, for instance, may be interpreted
in terms of how it relates to an implied plan to bring about a different desired state for the
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culture.
●
●
Cultural patterning is the observation of cultural patterns forming relationships involving
, believing that
two or more symbols. Ethnographic research is holistic
symbols cannot be understood in isolation but instead are elements of a whole. One method
of patterning is conceptual mapping
, using the
terms of members of the culture themselves to relate symbols across varied forms of
behavior and in varied contexts. Another method is to focus on
learning processes
, in order to understand
how a culture transmits what it perceives to be important across generations. A third
method is to focus on sanctioning
processes
, in order to understand which cultural elements are
formally (ex., legally) prescribed or proscribed and which are informally prescribed or
proscribed, and of these which are enforced through sanction and which are unenforced.
Tacit knowledge is deeply-embedded cultural beliefs which are assumed in a culture's way
of perceiving the world, so much so that such knowledge is rarely or never discussed
explicitly by members of the culture, but rather must be inferred by the ethnographer.
Assumptions
●
●
●
●
Ethnography assumes the principal research interest is primarily affected by community
cultural understandings. The methodology virtually assures that common cultural
understandings will be identified for the research interest at hand. Interpretation is apt to
place great weight on the causal importance of such cultural understandings. There is a
possibility that an ethnographic focus will overestimate the role of cultural perceptions and
underestimate the causal role of objective forces.
Ethnography assumes an ability to identify the relevant community of interest. In some
settings, this can be difficult. Community, formal organization, informal group, and individuallevel perceptions may all play a causal role in the subject under study, and the importance
of these may vary by time, place, and issue. There is a possibility that an ethnographic focus
may overestimate the role of community culture and underestimate the causal role of
individual psychological or of sub-community (or for that matter, extra-community) forces.
Ethnography assumes the researcher is capable of understanding the cultural mores of the
population under study, has mastered the language or technical jargon of the culture, and
has based findings on comprehensive knowledge of the culture. There is a danger that the
researcher may introduce bias toward perspectives of his or her own culture.
While not inherent to the method, cross-cultural ethnographic research runs the risk of
falsely assuming that given measures have the same meaning across cultures.
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Frequently Asked Questions
●
●
Isn't ethnography a subjective rather than scientific social science research
method?
Selection of informants is not based on the researcher's personal judgments but on
identifications made by community members. Likewise, conclusions about cultural
understandings of the phenomena of interests are not personal insights of the
researcher, or even of particular community members, but are views cross-validated
through repeated, in-depth interviews with a broad cross-section of representative
informants. Ethnographers may also validate findings through conventional archival
research, consultation with experts, use of surveys, and other techniques not unique
to ethnography. At the same time, ethnographic interviews are far more in-depth than
survey research. Ethnographers respond to charges of subjectivity by emphasizing
that their approach eschews preconceived frameworks and derives meaning from the
community informants themselves, whereas survey instruments often reflect the
conceptual categories preconceived by the researcher prior to actual encounter with
respondents.
What are the Human Relations Area Files (HRAF)?
The Human Relations Area Files (HRAF), based at Yale University, are a large
collection of pre-coded ethnographic field studies of some 350 cultures. Originally
available only on microfiche, collection subsets are now available on CD-ROM.
Examples of coded subjects include marriage, family, crime, education, religion, and
warfare. The researcher must code variables of interest to go beyond the precoding
done by HRAF. Hundreds of articles have been based on the HRAF cultural database,
and collections of coding schemes are documented in Barry and Schlegel, eds. (1980).
The HRAF database is suitable for ethnographic coding methods as described in
Hodson (1999).
Bibliography
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●
●
Agar, Michael (1996). Professional
stranger: An informal
introduction to
ethnography, second
edition.
Academic Press, ISBN 0120444704 . Emphasizes continuity in
century-old tradition of ethnographic research. A second edition of a widely used modern
classic.
Barry, H. III and A. Schlegel, eds. (1980). Crosscultural samples and
codes
. Pittsburgh, PA: University of Pittsburgh Press.
Clifford, J. (1999). On ethnographic authority. Ch. 11 in Alan Bryman and Robert Burgess,
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●
●
eds., Methods of qualitative research, Vol. III. Thousand Oaks, CA: Sage Publications.
Clough, Patricia Ticineto (1998). The end(s) of ethnography: Now and then.
Qualitative Inquiry
, Vol. 4, No. 1 (March): 314. A concise recent summary by the author of The end(s) of
ethnography: From realism
to social criticism
(1992; 2nd ed., 1998).
Thousand Oaks, CA: Sage Publications. Her interests are in poststructural cultural criticism
(ex., feminist theory, postcolonial theory, Marxist cultural studies, impacts of
telecommunications technology on culture, and critical theory regarding race, ethnicity, and
class). Seealso C. Nelson and L. Grossberg, eds., Marxism and
the interpretation of
. Urbana, IL: University of Illinois PRess, 1988.
culture
Coffey, Amanda (1999). The ethnographic self: Fieldwork and the representation of identity.
Thousand Oaks, CA: Sage Publications. Treats "locating the self," the interaction of the
researcher and the field, and the sexualization of the field and the self.
Collins, R. (1981a). On the microfoundations of macrosociology.
American Journal of
Sociology
86(5), 984-1014.
Collins, R. (1981b). Micro-translation as a theory building strategy. Pp. 81-108 in KnorrCetina, K. & Cicourel, A. V., eds. Advances in
social theory and
methodology: Toward an
integration of micro- and
macro- sociologies
. Boston: Routledge & Kegan
Paul.
Collins, R. (1988). The micro contribution to macro sociology.
Sociological Theory
6(2), 242-253.
Denzin, N. K. and Y. S. Lincoln (1994). Handbook of
qualitative research
. Thousand Oaks,
CA: Sage Publications.
Fetterman, David M. (1998). Ethnography stepby-step, second edition
. Thousand
Oaks, CA: Sage Publications. Treats interviewing by "chatting," use of the Internet, research
ethics, report-writing, and more.
GAO (2003). Federal programs:
Ethnographic studies can
inform agencies' actions
GAO-03455, March 2003. Available at http://www.gao.gov/cgi-bin/getrpt?GAO-03-455. Numerous
case examples of federal agencies' use of ethnographic research.
Gold, Raymond L. (1997). The ethnographic method in sociology.
Qualitative Inquiry
, Vol. 3, No. 4
(December): 388-402. Gold writes this summary near the end of his 50-year career in
ethnographic research. The article discusses the requirements of ethnographic research,
validity, reliability, sampling, and systematic data collection.
Hammersley, Martyn, & Atkinson, Paul (1995). Ethnography:
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●
●
●
●
●
●
●
Principles in practice,
. London: Routledge.
Second Ed.
Hodson, Randy (1999). Analyzing
. Thousand Oaks,
documentary accounts
CA: Sage Publications. Quantitative Applications in the Social Sciences Series No. 128.
Describes random sampling of ethnographic field studies as a basis for applying a metaanalytic schedule. Hodson covers both coding issues and subsequent use of statistical
techniques.
Johnson, Allan G. (2000). The Blackwell
Dictionary of Sociology,
Oxford, UK: Blackwell.
Second ed.
Kvale, Steinar (1996). Interviews: An introduction to qualitative research interviewing.
Thousand Oaks, CA: Sage Publications. Not specifically ethnographic, but treats approaches
to interviews and surveys from the concerns of phenomenology, hermeneutics, and
postmodernism.
Lareau, Annette & Schultz, Jeffrey, eds. (1996). Journeys
Through ethnography:
Realistic accounts of
. Boulder, CO: Westview Press.
field work
Madison, D. Soyini (2005). Critical
ethnography: Method,
ethics, and performance
. Thousand
Oaks, CA: Sage Publications.
Puente, Manuel de la (2000). Ethnographic
research at the U.S.
Census Bureau the
enumeration of border
communities along the US/
Mexico border during
Census 2000
. Chapel Hill, NC: UNC-CH, School of Public Health. Minority
Health Project. Discusses ethnographic research at the U.S. Census Bureau dating back to
1971 and illustrates how ethnographic techniques are used at the Census Bureau, using the
example of ethnographic research on border communities during Census 2000. See http://
www.census.gov/srd/papers/pdf/mdp9501.pdf.
Sanday, Peggy R. (1979). The ethnographic paradigm(s).
Administrative Science
Quarterly
, 24: 527-38.
@c 2006, 2008 G. David Garson
last update: 1/14/08.
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Quantitative Methods in Public Administration: Focus Groups
Focus Group Research
Overview
Focus group research is based on facilitating an organized discussion with a group of individuals
selected because they were believed to be representative of some class (ex., the class of
consumers of a product, the class of voters). Discussion is used to bring out insights and
understandings in ways which simple questionnaire items may not be able to tap. Focus group
research has long been prominent in marketing studies (Morgan, 1988), in part because market
researchers seek to tap emotional and unconscious motivations not amenable to the structured
questions of conventional survey research. The interaction among focus group participants brings
out differing perspectives through the language that is used by the discussants. People get caught
up in the spirit of group discussion and may reveal more than they would in the more formal
interview setting. As discussants ask questions of each other, new avenues of exploration are
opened. In discussions, multiple meanings are revealed as different discussants interpret topics of
discussions in different ways. Interaction is the key to successful focus groups. In an interactive
setting, discussants draw each other out, sparking new ideas. The reactions of each person spark
ideas in others, and one person may fill in a gap left by others. One may even find a form of
collaborative mental work, as discussants build on each other to come to a consensus that no one
individual would have articulated on their own.
Key Concepts and Terms
●
●
Focus group research vs. group interviewing. In group interviewing a standard survey
instrument is administered to respondents simultaneously. In focusgroup studies, in
contrast, there is no standard instrument, only a topic to be explored through the exchange
of group discussion. For instance, a start-up topic question might be, "What are the present
satisfactions and dissatisfactions with X and those of Y?" The discussants have a strong
influence on the subtopics which are examined and the insights which are yielded. Where
survey research, even group survey research, requires a priori theory or at least a list of
subtopics as a guide for selection of items to be included in the survey instrument, there is
no a priori theory in focus group research. Focus groups are a method of choice where the
dynamics which determine outcomes are not well known and surprises are expected, as in
marketing research where focus groups are brought together to react to product or
candidate ads.
Role of the moderator. The role of the focus group moderator is to facilitate, not
dominate discussion. The moderator encourages the participation of everyone and seeks to
limit the domination of discussion by a few discussants. The moderator may also give
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prompting questions ("probes") to elicit expansion on interesting subtopics, such as "Give us
an example of ...," "Tell us more about that," "Keep talking," or "Can someone summarize
what we've been saying." The moderator will no ask closed-ended, yes-no questions, such
as "Do you prefer X?", instead always using non-directive prompts like "What is your
reaction to X?" The moderator may also seek to return conversation to the topic at hand.
Finally, the moderator may take notes or record the conversation of the group, though often
that role is left to an assistant moderator. The moderator must record not only overt
statements, but must also be sensitive to omissions, choice of words. non-verbal
communications, expressions of emotion, energy levels, and the roles played by the
discussant. Because of the strong role of the moderator, usually the same one is used if
there are multiple groups, in an attempt to control for the influence of the moderator.
●
Implementation. In terms of implementation, some recommend a focus group size of 6 10, though examples can be found both smaller and larger. Focus group facilitators,
however, usually regard even 10 as becoming unwieldy and counterproductive. Usually the
participants do not know each other. Most studies bring the focus group together for one
session, but a sequence of meetings is also possible, usually for one or two hours each.
Tape excerpts from one meeting may be played back to a subsequent group to obtain
reactions. The broader and more ambitious the purposes of doing focus group research, the
more groups are necessary. A study of "women's attitudes toward health services" will
require many more groups than one on "Boston consumers' preferences for detergent," for
instance. Use of follow-up groups, bringing back past participants, can be very fruitful.
The number of topics explored per meeting is usually at most three (often just one), with
subtopics under each. Meetings are usually held in neutral locations such as hotel meeting
rooms (not, for instance, in the workplace in a study involving employees). Participants may
be selected at random or through information, using a snowball reference technique in
which the first informant recommends others, who in turn recommend yet others.
Participants should be informed of the purposes of the focus group study. Often they are
encouraged to participate on a first-name basis, which encourages informality and openness
while suggesting greater anonymity. The ethics guidelines regarding use of human subjects,
discussed in a previous chapter, apply to focus groups.
❍
Ice-breaking exercises are often used to start a focus group discussion. One
example is the "eyes closed exercise," in which the moderator asks members of the
focus group to close their eyes, imagine the last time they were involved with the
subject of the study, and to articulate their remembrance of this. This sharing of
experiences while eyes are closed is intended to break down formalities and get
conversation rolling as discussants feel closer as a result of the sharing experience.
Another example of an ice-breaking exercise is to have the discussants draw a cartoon
or picture of the best of worst aspects of X, then share their work with the group. A
third example is word association, asking discussants quickly and without thinking to
finish prompts like "The worst thing about X is ...." or "People who like X tend to
be ....". Participants write their answers on a sheet of paper, then share them with the
group. Many other projective exercises are possible.
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●
Drawbacks of the focus group method include the potentially strong influence, one way
or the other, of the discussion moderator; the difficulty of separating individual viewpoints
from the collective group viewpoint; and the difficulty of obtaining a representative sample
within a small focus group. In a group context, individuals may be less willing to reveal
sensitive information because assurance of confidentiality is lost, in spite of the practice of
urging participants not to reveal discussions of the group. The focus group method may also
have positive or negative effects on the subjects, particularly when members of the group
are associated in work or other social contexts.
Assumptions
●
Focus groups are not a panacea for tapping "true" feelings. People often do not themselves
understand their own motivations and preferences and thus cannot articulate them well.
People have complex, even conflicting motivations which may come together in
unpredictable ways given only slightly varying ways of presenting a stimulus. People may
give acceptable or politically correct responses in front of peers, and they may act differently
in real situations compared with hypothetical ones. They may be aware of the study's
sponsorship and tell the researcher what they believe he or she wants to hear. People tend
to express views which enhance their own image of themselves, and they also may
formulate opinions "on the spot," lacking any real commitment to what they say. And people
lie.
Frequently Asked Questions
●
●
When is the focus group approach not recommended?
When is the focus group approach not recommended?
Focus groups are generally a poor choice when quantitative information is desired
(ex., when one wants to know the percentage of people who will buy product X or
vote for candidate X). The small size of focus groups makes any estimates of
quantitative proportions unreliable, even if the members of the focus group are
representative of the target population. By the same token, focus group research is a
poor choice for multivariate research, where one again needs the stability of large
random samples to be ably to disaggregate the effects of explanatory variables
through statistical techniques. Finally, focus group research is a poor choice for
predicting future action in settings yet to emerge since focus group discussants will
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articulate their views in terms of their own present experiences.
Bibliography
●
●
●
●
●
●
Flores, J. G. and C. G.Alonso (1995). Using focus groups in educational research.
Evaluation Review
19(1): 84-101.
Kreuger Richard A. (1988). Focus groups: a
practical guide for
applied research.
London: Sage.
Krueger, Richard A. and Mary Anne Casey (2000). Focus
groups : A practical guide
for applied research
Sage Publications.
. Thousand Oaks, CA:
Merton R.K. and P. L. Kendall (1946). The focused interview. American
Journal of Sociology
(51): 541-557.
Morgan D.L. (1997). Focus groups as
qualitative research,
Second Edition
1988.
. London: Sage Publications. First edituibm
Stewart D.W. and P. N. Shamdasani (1992). Focus groups:
theory and practice
Publications.
Back
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. London: Sage
PA 765: Narrative Analysis
Narrative Analysis
Narrative analysis is analysis of a chronologically told story, with a focus on how elements are
sequenced, why some elements are evaluated differently from others, how the past shapes
perceptions of the present, how the present shapes perceptions of the past, and how both shape
perceptions of the future. Narrative analysis is seen as a more in-depth alternative to survey
research using psychological scales. Some advocates see it as an "empowering" social science
methodology insofar as it gives respondents the venue to articulate their own viewpoints and
evaluative standards.
Note, however, a different branch of narrative analysis is quantitative and uses non-metric
multidimensional scaling, such as smallest space analysis. See also: content analysis.
Key Concepts and Terms
●
●
Scripts are the referential core of personal narratives (Labov and Waletzky, 1967) or the
"canonical events" (Bruner, 1990) used as a basis for understanding new, unexpected
elements. That is, scripts are predictive frames by which a culture interprets particular
instances of behavior associated with that script. Scripts do not require an evaluative
component.
Stories expand on generalized scripts by incorporating particularistic (non-canonical)
events, adding evaluative elements which reveal the narrator's viewpoint regarding these
particulars. Thus stories will evaluate a script as good, bad, successful, tragic, surprising,
and so on.
❍
❍
●
The life story method
of narrative
analysis involves interviewing a subject and then retelling their story as if written by
them (Reissman, 1993).
Metaphors
may be identified, by which subjects organize their
stories. Different metaphors throw light on new meanings in the stories being told.
Interviews as discursive acts. Narratives are gathered through interviewing, which is
understood as a discursive act (Mishler, 1986) in which the interviewer and the respondent
"are engaged in creating the meaning of the questions and answers that constitute the
narrative as they negotiate understanding through language" (Alvarez & Urla, 2002: 40 ).
The interviewer and respondent joint create the narrative framework.
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●
●
●
●
Patterns are recurring forms of patter
which are discerned in narrative
transcripts. Polkinghome (1988: 153) notes that during interviews “people strive to organize
their temporal experience into meaningful wholes and to use the narrative form as a pattern
for uniting the events of their lives.”
Themes are sets of patterns. There is no agreed-upon methodology in narrative analysis to
derive themes from patterns. One practice, however, is to use a research team, with
"themes" being whatever the team reaches consenses on, based on discussion of transcripts
and analysis of patter and patterns. Labov (1972) encourages researchers to look for
sequences of core phrases which are repeated across interviews as indicators of themes.
Coding. As in content analysis, after transcription, narratives may be coded according to
categories deemed theoretically important by the researcher. This labeling of the narrative
structure might, for instance, use a set of structural/functional categories to label each
segment as an AB= Abstract statement segment, OR= Orientation segment, CA=
complicating action, EV= evaluation, RE= resolution, or CO= coda. Many, many coding
schemas are possible.
Temporal organization of the narrative. Frequently the researcher finds it helpful to
organize the narrative according to temporal sequence (see Labov, 1997). Some researchers
add subscripts to clauses in the narrative, with a left subscript indicating how many
anteceding narrative clauses the given clause is simultaneous with, and a right subscript
indicating how many following clauses the given clause is simultaneous with. Inter-rater
reliability in temporally organizing the narrative is important as changes in temporal
organization can radically shift the meaning of the narrative. The work of Labov (1972,
1982, 1997; see also Labov & Waletsky, 1967) pioneered narrative analysis as a primarily
chronological self-account of past events.
❍
●
Plot lines
may be analyzed between subjects to determine
common and divergent elements. Points where expected plot lines are disrupted
illuminate the perceptions of that subject or group of subjects (Burck, 2005). The
focus on plot represents a more recent emphasis in narrative analysis, associated with
Reissman (1993), for whom stories are narratives wtih a beginning, with protagonists,
and with a culminating event, though Reissman acknowledges narratives may lack
culminative events ("habitual narratives") and may even relate events which might
have happened but did not ("hypothetical narratives").
Contextual analysis. As noted by Labov and Waletzky (1967), narratives, and particularly
the evaluative elements of narratives, are a social phenomena. As a social phenomena,
narratives vary by social context (home, school, work, etc.) and evaluative data extracted
from narratives will vary by the social context within which they are collected. Consequently,
it may be fruitful to gather narratives on the same reference objects from otherwise similar
respondents in varying social contexts. Likewise, gathering narratives on the same objects
from the same respondents at different points in some development process (ex., different
career points) will yield differences in evaluative components and consequent insight into
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PA 765: Narrative Analysis
the process.
●
Focus groups. Though not integral to narrative analysis, researchers such as Labov (1997)
have found that "the most important data ... gathered on narrative is not drawn from the
observation of speech production or controlled experiments, but from the reactions of
audiences to the narratives." Thus the exposure of focus groups to narratives and the
comparison of reactions among groups of different composition can be a method of further
extending the anecdotal richness of the narrative method.
❍
●
Retelling narratives. A particular technique further extending group reactions to
narratives is to ask various types of respondents to memorize a short narrative (ex,,
12 - 20 lines) and then retell it. The researcher notes omissions and improvisations,
which further illuminate how various types of respondents react to given types of
narratives. Retelling, when there is a progressively increased time lapse between
exposure and retelling, is also used to rank the perceived centrality of narrative
elements: most central elements are retained longest.
Facet theory. Facet theory methodology (see Shye and Elizur, 1994; Borg and Shye, 1995)
developed by Louis Guttman (1968) may be used in narrative analysis (McAdams, 1993).
Facet theory includes non-metric multidimensional scaling procedures, such as smallest
space analysis (SSA), partial-order scalogram analysis (POSA), and multiple scaling analysis
(MSA). These techniques have been popular in psychology, intelligence testing, and criminal
analysis. See Canter, Kaouri, & Ioannou (2003) for an application to criminal analysis. See
also the reading list on facet theory maintained by Prof. Canter.
❍
Facets
refer to categories in a conceptual spatial diagram partitioned
based on prior theory. Some researchers use exploratory factor analysis to assign
survey items to facets, which correspond to factors, but then use SSA as a
confirmatory procedure. Shye (1991, 1994) has developed software,
Faceted Smallest Space
Analysis (FSSA)
, to map and mathematically
partition conceptual maps. Specifically, FSSA produces a spatial map with each item
located in a position which reflects its strength of relation with all other items. For the
items associated with each facet, FSSA attempts to partition conceptual space in three
ways: with parallel lines, with radial lines, and with concentric circles. FSSA also
produces a separation index
which varies
from 0 to 1 and is used as a goodness-of-fit measure for the partitioning.
Assumptions
●
Subjectivity. By giving totally free rein to subjective story-telling the narrative analyst taps
a rich vein of anecdotal information at the expense of all the usual social scientific
considerations (representative sampling, operationalization of terms, use of controls,
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PA 765: Narrative Analysis
multivariate causal analysis). As Labov (1997) notes, "The discussion of narrative and other
speech events at the discourse level rarely allows us to prove anything. It is essentially a
hermeneutic study, where continual engagement with the discourse as it was delivered gains
entrance to the perspective of the speaker and the audience, tracing the transfer of
information and experience in a way that deepens our own understandings of what
language and social life are all about."
Frequently Asked Questions
●
●
●
For what is narrative analysis useful?
Narrative analysis is best used for exploratory purposes, sensitizing the researcher,
illustrating but not by itself validating theory. A common focus is the exploration of
ethical, moral, and cultural ambiguities. As one illustration, Alvarez and Urla (2002)
argue that in the field of information systems implementation, narrative analysis
provides richer data than is obtained by conventional sytems requirements analysis:
"This paper argues that interview-generated narratives are representational forms
that provide valuable data about work practices and individual worker perspectives, as
well as the larger organizational political and cultural contexts that, for the most part,
have been excluded from requirements determinations" (p. 40).
What are examples of the use of narrative analysis in public administration?
❍
Richard J. Herzog and Ronald G. Claunch (1997). "Stories citizens tell and how
administrators use types of knowledge." Public
, Vol. 57,
Administration Review
No. 5 (Sept./Oct.): 374-379.
❍
Hummel, Ralph (1991). Stories managers tell: Why they are as valid as science."
Public Administration
Review
, Vol. 51, No. 1 (Jan./Feb.): 31-34.
What are some other recent examples of narrative analysis?
❍
Bochner, Arthur P. (1997). It's about time: Narrative and the divided self.
Qualitative Inquiry
, Vol. 3, No. 4
(December): 418-438. This is a narrative analysis about social research methodology.
❍
Ellis, C. and A. Bochner (1992). Telling and performing personal stories: The
constraints of choice in abortion. Pp. 79-101 in C. Ellis and M. Flaherty, eds.,
Investigating
subjectivity: Research
on lived experience
. Thousand Oaks,
CA: Sage Publications.
Bibliography
●
Alvarez, Rosario & Urla, Jaqueline (2002). Tell me a good story: Using narrative analysis to
examine information requirements interviews during the ERP implementation. The
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Database for Advances in
33(1), 38-52.
Information Systems
Atkinson, Robert (1998). The life story
. Thousand Oaks, CA: Sage Publications.
interview
Barthes, R. (1966). Introduction to the structural analysis of narratives. In Sontag, S. (eds.)
A Barthes Reader
, Vintage, 1993.
Borg, I., and S. Shye (1995). Facet theory: Form
and content.
Thousand Oaks, CA: Sage.
Bruner, J. (1990). Acts of meaning
. Cambridge, MA:
Harvard University Press.
Burck, Charlotte (2005). Comparing qualitative research methodologies for systemic
research: The use pf grounded theory, discourse analysis and narrative analysis.
Journal of Family Therapy
27(3):
237-262.
Canter, D., Kaouri, C., Ioannou, M. (2003). The facet structure of criminal narratives. Pp. 2738 in S Levy & D Elizur, eds. Facet theory:
Towards cumulative social
science
. Ljubljana: Center for Educational Development.
Cortazzi, M. (1999). Narrative analysis. Ch. 23 in Alan Bryman and Robert Burgess, eds.,
Methods of qualitative research, Viol. II. Thousand Oaks, CA: Sage Publications.
Czarniawska, Barbara (1997). A narrative
approach to organization
studies
. Thousand Oaks, CA: Sage Publications. Contains illustrations as
well as explanation of the approach.
Daiute, Colette and Cynthia Lightfoot, eds. (2004). Narrative
analysis: Studying the
development of
individuals in society
. Thousand
Oaks, CA: Sage Publications. Interdisciplinary collection.
Dancer, L.S. (1990). Introduction to facet theory and its application.
Applied Psychology: An
International Review,
39: 365-377.
Elliott, Jane (2005). Using narrative in
social research:
Qualitative and
quantitative approaches
.
Thousand Oaks, CA: Sage Publications. Intro text.
Guttman, Louis A. (1968). A general nonmetric technique for finding the smallest coordinate
space for a configuration of points. Psychometrika
, 3: 469506.
Josselson, R. and A. Lieblich (series of volumes, starting in 1993).
Interpreting experience:
The narrative study of
lives
. Thousand Oaks, CA: Sage Publications.
Kreps, G. L. (1994). Gender differences in the critical incidences reported by elderly health
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PA 765: Narrative Analysis
●
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care residents: A narrative analysis. Pp. 27-34 in H. Sterk & L. Turner, eds.
Differences that make a
difference: Examining the
assumptions of research in
communication, language,
and gender
. Westport, CT: Bergin and Garvey.
Labov, W. (1972). The transformation of experience in narrative syntax. Pp. 354--396 in
William Labov, ed., Language in the
inner city: Studies in
Black English vernacular
.
Philadelphia, PA: University of Philadelphia Press.
Labov, W. (1982). Speech actions and reactions in personal narrative. In D. Tannen, ed.,
Analyzing discourse: Text
and talk.
Washington, DC: Georgetown University Press.
Labov, William (1997). Some further steps in narrative analysis. The
Journal of Narrative and
. Available online at http://www.ling.upenn.edu/
Life History
~labov/sfs.html.
Labov, W., & Waletzky, J. (1967). Narrative analysis: Oral versions of personal experience.
Pp. 12-44 in J. Helm (Ed.), Essays on the
verbal and visual arts
. Seattle, WA:
University of Washington Press. Classic work focused on the importance of evaluative
statements in first-person narratives. Available online at http://www.clarku.edu/~mbamberg/
Labov&Waletzky.htm.
Lieblich, Amia, Rivka Tuval-Mashiach, and Tamar Zilber (1998).
Narrative research
Reading, analysis and
interpretation
. Thousand Oaks, CA: Sage
Publications. Focus on classification of written life-story materials and their analysis, which
they divide into holistic-content, holistic-form, categorical-content, and categorical-form
types of reading.
Linde, C. (1993). Life stories: The
creation of coherence
. Oxford: Oxford
University Press.
McAdams, Daniel P. (1988). Power, intimacy,
and the life story :
Personological inquiries
into identity
. Guilford Press. A well-received earlier work
than the author's 1993 best-seller.
McAdams, Daniel P. (1993). The stories we
live by : Personal myths
and the making of the self.
NY: William C. Morrow and Co. McAdams has had an impact in psychology through his
argument that personal understanding must transcend objective data and examine the
myths people create about their lives. This is a seminal work for the emerging field of
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narrative psychology.
Mishler, E. G. (1986). Research
interviewing: Context and
narrative
. Cambridge, MA: Harvard University Press.
Patterson, Molly and Kristen Renwick Monroe (1998). Narrative in political science.
The Annual Review of
Political Science
, Volume 1, 1998. Introduction
to narrative analysis with emphasis on study of the role of culture and the role of narrative
in the construction of social theory.
Polkinghorne, D. E. (1988). Narrative knowing
. Albany, NY:
and the human sciences
State University of New York Press.
Riesman, Catherine K. (1993). Narrative
. Thousand Oaks, CA: Sage Publications. Prize-winning textbook
analysis
on the subject.
Riessman, Catherine K. (2001). Analysis of
. Pp. 695-710 in J. F.
personal narratives
Gurbium & J. A. Holstein, eds., Handbook of
Interview Research
. London: Sage Publications.
Shye, S. (1985). Partial order scalogram analysis. In S. Shye, ed. Theory construction and
data analysis in the behavioural sciences. San Francisco: Jossey Bass: 60-70.
Shye, S. (1992). Faceted small space
analysis. DOS computer
program version 3.01.
Jerusalem: Israel
Institute for Applied Social Research.
Shye, S., and D. Elizur, D., with M. Hoffman (1994).
Introduction to facet
theory.
Thousand Oaks, CA: Sage.
Back
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Network Analysis: Statnotes, from North Carolina State University, Public Administration Program
Network Analysis and Sociometry
Overview
The term network analysis has largely
supplanted the earlier term sociometry, but
both refer to the analysis of social networks
in part utilizing graphical methods. While
some use the term "sociometry" to refer to
all research using quantitative scales, it is
used here in its narrower meaning,
sometimes called sociography, which is a
method of presenting data about complex
individual relationships and networks in
graph form. Sociometry was developed by
Jacob L. Moreno in the 1930s and became
closely associated with small group research
and a focus on interpersonal choices. As a
largely subjective but empirical,
phenomenological approach to the study of
group structure, sociometry can serve as a
contrast or complement to the formal study
of group structure through archival records.
Contents
Key
concepts
and terms
Assumptions
Frequently
asked
questions
Bibliography
In addition to its social scientific purposes,
discussed below, sociometric assessment of
interpersonal choices also plays a role in
therapy by helping facilitate constructive
change in individuals and groups through
greater interpersonal awareness. For this
reasons, in some circles the term sociometry
refers to a form of therapy related to
psychodrama.
See also: network theory, actor-network
theory.
Key Concepts and Terms
❍
Sociometric tests are simply surveys which are administered to subjects (typically all subjects in
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Network Analysis: Statnotes, from North Carolina State University, Public Administration Program
a small group) to determine the direction of certain relationships, although sometimes this can be
assessed through simple observation. For instance, a sociometric test might ask members of a
political group which other members they are most/least likely to consult on political questions;
whom they hold in highest/lowest esteem; or with whom they most/least likely to see face-to-face
during the previous week.
❍
Sociometric representation refers to various graphical methods of data presentation. Some
common sociometric representations are illustrated in the figure below. In each representation,
individuals are depicted as points. The star
representation is made up of several lines
pointing toward or away from a central point, which represents an individual who is involved in
realationships with each of the other points, and where the arrows represent relatinships such as
reciprocation, ignoring, or rejection. Other common representations include the chain
(a mutually reciprocated relationship); the
(a series of relationships); the pair
power
(short for "power behind the throne," who is the object of attraction of a few
very sociometrically attractive individuals; and the isolate
(a subject not
chosen by anyone).
■
■
Inclusiveness is the percentage of non-isolated points in a sociometric diagram. A 25point graph with five isolated points has an inclusiveness of 0.80.
Density is the number of lines as a percentage of total lines when all points are connected.
Total lines, based on probability, is nC2 = n!/(n - 2)!2!. For example, the number of lines
connecting 6 points taken 2 at a time is 6!/[(6-2)!2!] = 720/(24*2) = 15. If a sociometric
diagram had 10 lines, out of the possible total of 15, its density would be .67. This assumes
a non-directed graph in which the lines are bi-directional or non-directional. For a directed
graph in which all lines are one-directional, the total possible number of lines would be
twice the calculation above (30) and thus the density would be half as much (.33).
❍
Sociometric diagrams ("sociograms") map the relationships revealed by sociometric tests, as
illustrated in the figure below While sociometric relationships for a five-person group such as
depicted below could easily be described verbally, as the size of the group increases to dozens of
subjects, sociometric diagrams are increasingly helpful in conveying the relationship structure.
While sociometric studies are often static measurements at one point in time, there is nothing
inherent in sociometry to prevent the recording of panel data over time. Moreover, sociometric
representation can now be animated on the web to provide dynamic data diagrams.
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Sociometric matrices are an alternative form of data representation, used primarily when group
size makes sociometric diagrams too complex. The matrix is an n-b-n square table representing
subjects both across the top and down the side. For each possible pair, the range of the criterion
is shown. For instance, for the criterion "With whom do you most like to work?", the ranges might
be attraction = +1, indifference=0, dislike = -1. This sociometric information may be used to
create an index of popularity by group by comparing the proportion of members chosen as
desirable work partners in one group divided by group size, compared to a similar index in another
is essentially similar, but cell
group. An adjacency matrix
entries are 1's and 0's, depending on whether the pair of subjects are adjacent by some criterion.
Network data diagrams exist to provide a variety of graphic representations of networks
alternative to classic sociograms (for illustration, see Brandes et al., 1999):
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Path diagrams
, not necessarily based on actual path
analysis, represent variables or groups as circles, relationships (which may be correlations,
communications, formal associations, or other interactions) as arrows, and, often,
magnitude of relationship by thickness of the arrow.
Cluster diagrams
represent variables or groups as
points on one or more two-dimensional scatterplots or polar plots, with the proximity of
points representing their similarity on the dimensions, and clusters of points may be
highlighted by perimeter lines around each cluster (including the possibility of intersecting
perimeters where a point may belong to two or more clusters).
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Cliques
are identified by UCINET software as one of its options. A
clique by clique co-membership matrix can also be output.
Factor plots
similarly represent variables or groups as points
on one or more two-dimensional scatterplots, where the dimensions are factors (see factor
analysis); optionally, factor space may be divided into non-intersecting quadrants to
highlight similarities among points.
Centrality
measures the relative importance of nodes in a
network. ICINET outputs four centrality measures: degree, closeness, betweenness, and
eigenvalue.
■
Centrality degree
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. Degree for a node is
Network Analysis: Statnotes, from North Carolina State University, Public Administration Program
highest when the node has the maximum possible number of direct connections to
other nodes. Degree is thus the number of direct ties to other nodes. Nodes of high
centrality degree have more opportunity of giving and receiving information or other
phenomena under study.
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Centrality closeness
: Closeness
for a node is highest when a node can reach all other nodes in the network.
Mathematically, closeness is the graph-theoretic distance of a node to all other nodes.
In diffusion theory, nodes with high centrality closeness are ones most likely to
receive and transmit innovations.
Centrality betweenness
:
Betweenness for a node is highest when that node is maximally utilized by nodes
connecting to other nodes. That is, betweenness measures how many paths pass
through a node. Nodes high on betweenness have high opportunity to play
gatekeeper, liaison, or broker roles.
Centrality eigenvector
: The
node with the highest centrality eigenvector is the node for which its directly
connected nodes have the highest centrality. Nodes high on centrality eigenvector are
high on the possibility of receiving/transmitting to the most nodes on the network
quickly in one path step.
Centrality plots
are polar plots in which
the heavier the loading of the variable or group on the dimension, the closer it is
located to the center of the plot. Optionally, concentric circles may highlight which
points share a similar degree of centrality on the depicted dimension. Loadings may
reflect factor loadings, path distances, or an index of the author's devising. Centrality
index numbers, if assigned to points, are usually coded such that heavier loadings are
represented as lower numbers. In centrality plots, direction of location with respect to
the center (up/down, left/right) often has no meaning other than aesthetics of
placement, but direction can be used to depict a second and third dimension.
Spatial network diagrams
. In
the context of geographic information systems, software such as ArcView
implement network analysis modules which generate map graphics depicting such things as
shortest route between two objects, optimal route passing through a series of objects, or
service areas (by best time or shortest distance) associated with multiple points.
Assumptions
❍
Measurement inerrancy. Sociometric mapping assumes, of course, that individuals respond
accurately to sociometric surveys or can be assessed accurately through observation. However,
sociometry is open to several common problems. It tends not to record subconscious or illicit
relationships. It may be biased toward recording attractions rather than dislikes because subjects
more easily reveal the former. It is best when subjective sociometric responses can be validated
through external objective measures.
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Small group size. Sociometric diagramming becomes unwieldy and difficult for readers to
interpret for very large groups. Also, sociometric techniques may be biased in larger groups since
subjects tend to confine their choices to their own class range.
Frequently Asked Questions
❍
What computer programs exist to generate sociograms or similar representations?
Probably the leading software for network analysis at present is UCI-Net, which is a
comprehensive program s a comprehensive program supporting centrality measures, dyadic
cohesion measures, positional analysis algorithms, clique finders, stochastic dyad models
(P1), network hypothesis testing procedures (including QAP matrix correlation/regression
and categorical and continuous attribute autocorrelation tests), plus general statistical and
multi-variate analysis tools such as multi-dimensional scaling, correspondence analysis,
factor analysis, cluster analysis, multiple regression, etc. In addition, UCINET provides a
host of data management and transformation tools ranging from graph-theoretic procedures
to a full-featured matrix algebra language. A free evaluation version is available.
A public administration example is Kapucu, Naim (2003).Coordinating without hierarchy:
Public-nonprofit partnerships. International Association of Schools and Institutes of
Administration, Conference on Public Administration: Challenges of Inequality and Exclusion,
Miami (USA), 14-18 September 2003.
Other packages include:
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NetDraw
NetDraw
publisher.
. See http://www.analytictech.com/netdraw.htm.
is a complement to UCI-Net
, from the same
Pajek
. A recent academic package capable of analyzing large networks. Pajek
is described in the text by Nooy, Wouter “de” ; Andrej Mrvar, & Vladimir Batagelj (2005).
Exploratory Social
Network Analysis with
. Cambridge, UK: Cambridge University Press. Pajek software and
Pajek
datasets for all examples are freely available at http://vlado.fmf.uni-lj.si/pub/networks/
pajek/.. A brief tutorial is available at http://www.ccsr.ac.uk/methods/publications/
snacourse/pajek.html.
ArcView
is leading geographic information systems software from ESRI. It
has a "Network Analysis" module, which is for calculating service areas around geographic
points, or calculating shortest routes. This is a different meaning of network analysis from
sociometry.
FatCat
. See http://www.sfu.ca/~richards/. A sociometric matrix
manipulation program. It makes graphic representations of two-dimensional crosstabulation
tables, called "panigrams", which make the information contained in the "who" rows and "to
whom" columns.
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MultiNet
. See http://www.sfu.ca/~richards/Multinet/Pages/multinet.
htm. It gives histograms with frequency distributions, panigrams with crosstabs, 3-d ribbon
plots with ANOVAs, scatterplots with correlations, network displays with graph spectra.
Graph theoretic spectra (eigenvalues and eigenvectors allows the researcher to rotate any
axis, change any axis, color nodes by categories, and examine the network in 1, 2 and 3dimensions, and to define new node variables based on eigenvectors and induced partitions,
which may be used to examine the network for group structures.
NEGOPY
. See http://www.sfu.ca/~richards/Pages/negopy.htm. One of the
oldest network analysis programs, NEGOPY finds cliques, liaisons, and isolates in networks
having up to 1,000 members and 20,000 links. In use at over 100 universities and research
centers around the world.
Walsh's Classroom Sociometrics. Designed to identify classroom cliques, this package prints
out a sociometric survey for classroom distribution. Output includes sociograms, barcharts,
and scatterplots, as well as tab-delimited data files SPSS and other packages can import.
Quickly indicates rejected, isolated, popular, and controversial students. Demo version
available. Site has some links to other sociometry resources.
Viewnet
. See http://www.datashare.com.au/.%5CViewnet.htm. Works
with DataShare software and is geared toward electric and other physical networks.
What is role analysis in sociometry?
One can cluster subjects into roles, such as functional work roles in an organization, then
average data for the many incumbents of each of several roles so as to use the averages
rather than the individual responses when constructing sociometric diagrams and matrices.
This general procedure may be used to relate occupational categories, social classes, age
cohorts, and so on.
Example
❍
Uncloaking Terrorist Networks, by Valdes E. Krebs
Bibliography
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Brandes, Ulrik, Patrick Kenis, Jorg Raab, Volker Schneider, and Dorothea Wagner (1999).
Explorations into the visualization of policy networks. Journal of
11(1): 75-106. Broader
Theoretical Politics
than sociometry but using it as a focus, this article places sociometry in the context of network
analysis for policy research, with numerous alternative modes of graphical representation.
Brandes, Ulrik & Thomas Erlebach, eds. (2005). Network
analysis: Methodological
Berlin, Heidelberg: Springer-Verlag.
foundations.
Breiger, Ronald L. (2004). The analysis of social networks. Pp. 505-526 in Melissa Hardy & Alan
Bryman, eds., Handbook of Data
. London: Sage Publications.
Analysis
Carrington, Peter J., John Scott and Stanley Wasserman, eds. (2005). Models
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and methods in social
New York: Cambridge University
network analysis.
Press.
Degenne, Alain and Michel Forse (1999). Introducing
. Thousand Oaks, CA: Sage Publications.
social networks
Reviews the literature and provides an introductory text. using the paradigm of structural analysis.
Covers graph theory and includes guides for usage of software for network analysis.
Freeman, Linton C. (2004). The development of
social network analysis: A
study in the sociology of
. Vancouver: Empirical Press.
science
Geer, John P. van der (1971). Introduction to
multivariate analysis for
the social sciences
. San Francisco, CA: Freeman.
Chapter 2 discusses use of matrix algebra to process sociometric data.
Hanneman, Robert A. (1999). Introduction to Social Network Methods.
Haythornthwaite, Caroline (2001). Exploring multiplexity: social network analysis in a computersupported distance learning class. The Information
17(3): 211-226. Good example of sociogram use.
Society
Moreno, Jacob L. (1934). Who shall survive?
Washington, DC: Nervous and Mental Disorders Publishing Co. This was the seminal study
popularizing sociometry. It was reprinted in 1953 by Beacon House, Beacon, NY.
Moreno, Jacob L. (1960). The sociometry
. Glencoe, IL: Free Press.
reader
Northway, Mary L. (1967). A primer of
. Toronto, Canada: University of Toronto Press.
sociometry
Scott, John (1992). Social network analysis : A handbook. Thousand Oaks, CA: Sage Publications.
Covers network theory.
Wasserman, Stanley and Katherine Faust (1994). Social network analysis : Methods and
applications (Structural Analysis in the Social Sciences, 8). Cambridge, UK: Cambridge University
Press. A current leading textbook on network analysis.
Wellman, Barry and Berkowitz, S.D. (1988). Social
structures: A network
Cambridge: Cambridge University Press.
approach.
@c 2006, 2008 G. David Garson
Last updated 3/25/2008.
Back
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PA 765: Participant Observation and Action Research
Participant Observation and Action Research
Ostensibly, participant observation is a straightforward technique: by immersing him- or herself in
the subject being studied, usually over a long period of time, the researcher is presumed to gain
understanding, perhaps more deeply than could be obtained, for examply, by questionnaire items.
Arguments in favor of this method include reliance on first-hand information, high face validity of
data, and reliance on relatively simple and inexpensive methods. The downside of participant
observation as a data-gathering technique is increased threat to the objectivity of the researcher,
unsystematic gathering of data, reliance on subjective measurement, and possible observer effects
(observation may distort the observed behavior). Participant observation is particularly appropriate
to studies of interpersonal group processes.
Action research is a subset of participant observation, where the participants (typically
practitioners, such as teachers in a school setting) in some focused change effort (ex., to improve
some organizational function) self-reflect on their experiences in order to improve practice for
themselves or the organization. Action can be undertaken by one individual, by a group of
individuals, or as part of a collegial team approach. If the latter, it may be termed "collaborative
inquiry."
Key Concepts and Terms
●
●
The objectivity issue. Participation is a form of investment of time, energy, and self, and
as such it raises obvious questions of possible bias. However, defenders of participant
observation find greater bias in allegedly neutral instruments such as survey questionnaires.
These, they say, involve the imposition of an externally conceived "scientific" measuring
device (the questionnaire) on individuals who do not perceive reality according to that
external conception (Bruyn, 1966).
The phenomenological approach to participant observation emphasizes
intersubjective understanding and empathy. Bruyn (1966) outlined four elements in this
approach:
1. Awareness of time
: Record the temporal
phases of research according to the sequence of experience of the observer in relation
to the milieu (ex., newcomer, provisional member, categorical member, personalized
rapport, and imminent migrant -- that is, as the researcher is about to leave the
community).
2. Awareness of the
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PA 765: Participant Observation and Action Research
physical environment
: Record the
relations of people to their physical environment as they perceive it, not as the
researcher conceptualizes or even experiences it.
3. Awareness of
contrasting experiences
:
Record the experiences of people under contrasting social circumstances; meanings
cannot be assessed under one set of circumstances because they are relative to the
setting.
4. Awareness of social
openings and barriers
: Record
the changes in meaning as the participant observer is admitted into narrower social
regions, transitioning from stranger to member to insider. Determining vocabulary
concepts is a major focus of participant observation, seeking to illuminate the
intersubjective meanings of critical terms.
In general, in the phenomenological approach, the participant observer seeks out the
meaning of the experiences of the group being studied from each of the many different
perspectives within it.
●
The empirical approach to participant observation emphasizes participation as an
opportunity for in-depth systematic study of a particular group or activity. Zelditch (1962)
outlined three elements of this approach:
1. Enumeration of
of various categories of observed behavior, as
frequencies
in interaction analysis. Often there is an explicit schedule of observation geared to
hypotheses framed in advance of participation. As Reiss (1971) observers,
participation may lead to alteration of hypotheses and observation schedules, the
attempt to observe systematically is ongoing.
2. Informant interviewing
to
establish social rules and statuses. There may be systematic sampline of informants to
be interviewed, content analysis of documents encountered, and even recording of
observations in structured question-and-answer format.
3. Participation
incidents.
to observe and detail illustrative
Where the phenomenological approach emphasizes the participant observer experiencing
meanings through empathy, the empirical approach emphasizes systematic observation and
recording of the milieu. This distinction is, of course, more a matter of emphasis than a
dichotomy.
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PA 765: Participant Observation and Action Research
●
Conceptual mapping , sometimes called definitional mapping, is a related technique which
may be used by either approach to participant observation. The labels that people use for
the types of individuals (or organizations, objects, or concepts) which affect them are often
illuminating indicators of the nature of the group being studied. For instance, in a study of a
prison the observer may encounter such terms as rats, peddlars, toughs, fags, square Johns,
suckers, and so on. Definitional mapping is the systematic tracing of such terms as seen
from each of several viewpoints, associated with different roles in the milieu. For instance, a
participant observer study of the deep South in the 1940's mapped social class structure in
this way. The objective upper-upper class divided the community into old aristocracy, other
aristocracy, respectable people, good people but nobody, 'po whites. The objective lowermiddle class divided the community into old aristocracy, people who think they are
somebody, we poor folk, people poorer than us, and no 'counts. The objective lower-lower
class divided the community into society, way high-ups, snobs trying to push up, and people
just as good as anybody. In general, in definitional mapping, for each objective category
(ex., upper-upper socieconomic status) the researcher determines the subjective
classifications used to cover the range of the objective categories (ex., from upper-upper to
lower-lower SES)..
Assumptions
●
●
●
Subjectivity is inherent to participant observation, with the attendant threat of researcher
bias. That is, the researcher may be biased in what data are gathered and how data are
assigned meaning.
The participant observer may affect the pheonomenon being studied. The researcher must
make clear his or her initial expectations at the outset and guard against imposing
expectations on observations.
Participation, ideally, is real (ex., the participant observer may be required to learn a
language or jargon, live in the setting, defer to local culture, etc.). Clearance to enter the
setting must be secured beforehand, such that entry seems legitimate to group members.
Typically, the researcher relies on honesty, presenting him- or herself as a researcher
interested in recording the history and nature of the organizations and groups in the area.
Frequently Asked Questions
●
What journals cover participant observation and field studies?
A leading journal is Qualitative
Inquiry
, from Sage Publications. A related journal is Action Research,
also from Sage.
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Bibliography
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Adler, Patricia A. and Peter Adler (1994). Observational techniques," In
Handbook of qualitative
research
. Norman Denzin and Yvonna S. Lincon. Newbury Park: Sage,
1994, 377-392.
Becker, Howard S. (1993). Problem of inference and proof in participant observation :
Problem of inference and proof in participant observation, Reprint edition. Irvington Pub;
ISBN: 0829034935.
Bogdan, Robert (1972). Participant Observation in Organizational Settings. Syracuse, NY:
Syracuse University Press. ISBN: 0815680805.
Bruyn, Severyn (1966). The human
perspective in sociology:
The methodology of
.
participant observation
Englewood Cliffs, NJ: Prentice-Hall. A classic defense of participant observation.
Bulmer, M. (1982). When is disguise justified? Alternatives to covert participant observation.
Qualitative Sociology
5(4), 251-264.
Harper, Douglas (1994). On the authority of the image, visual methods at the crossroads. In
Norman Denzin and Yvonna S. Lincon, Handbook of
Qualitative Research
. Newbury Park:
Sage, 1994, 403-412.
Jorgensen, Danny L. (1993). Participant
Observation : A
Methododology for Human
(Applied Social Research Methods, Vol. 15). Thousand Oaks, CA:
Studies
Sage Pubns; ISBN: 0803928777.
McKay, J.A. (1992). Professional development through action research.
Journal of Staff
Development
13(1), 18-21.
Reardon, K.; J. Welsh; B. Kreiswirth; & J. Forester (1993). Participatory action research from
the inside: Community develoipment practice in East St. Louis.
American Sociologist
24(1), 69-91.
Reiss, Albert (1971). Systematic observation of natural phenomena. Pp. 3-33 in Herbert
Costner, ed., Sociological
Methodology 1971
. San Francisco: Jossey-Bass.
Spradley, James P. (1997). Participant
observation
. Holt Rinehart & Winston; ISBN: 0030445019.
Winstein, Raymond M. (1982). "The Mental Hospital from the Patient's Point of View". In
Walter R. Gove, ed., Deviance and Mental
Illness
, Thousand Oaks, CA: Sage Publications. This famous article shows
the pitfalls of participant observation research (ex., Goffman's and Rosenhan's classic
studies) and demonstrates how systematic survey research captures the true experience of
mental hospitalization.
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PA 765: Participant Observation and Action Research
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Zelditch, Morris (1962). Some methodological problems of field studies.
American Journal of
Sociology
, Vol. 67, No. 5: 566-576.
Back
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