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ASSIGNEMENT # 2
Question # 1 Explain with examples, the various kinds of Research.
Ans. Social Research is a collection of methods people use systematically to
produce knowledge. It is an exciting process of discovery, but it requires
persistence, personal integrity, tolerance for ambiguity, interaction with other, and
price in doing quality work.
There are following types of Research
1. BASIC (or theoretical ),
2. APPLIED, and
3. PRACTICAL research.
BASIC RESEARCH is concerned with knowledge for the sake of theory. Its
design is not controlled by the practical usefulness of the findings.
APPLIED RESEARCH is concerned with showing how the findings can be
applied or summarized into some type of teaching methodology.
PRACTICAL RESEARCH goes one step further and applies the findings of
research to a specific "practical" teaching situation.
A useful way to look at the relationships among these three research types is
illustrated in the diagram below. Each of the three different types of research
contributes to the other in helping revise and frame the research from each
category.
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For example, practical research may be based on theory that came from
previously done basic research. Or, theory may be generated by the combination
of results from various practical research projects. The same bidirectional
relationship exists between applied research and basic research or practical
research.
There are many kinds of personnel research. Three dimensions are particularly
important in classifying types of research:
Applied vs Basic research. Applied research is research designed to solve a
particular problem in a particular circumstance, such as determining the cause of
low morale in a given department of an organization. Basic research is designed
to understand the underlying principles behind human behavior. For example, we
might try to understand what motivates people to work hard at their jobs. This
distinction is discussed in more detail in another handout. Click here to read it.
Exploratory vs Confirmatory. Exploratory research is research into the
unknown. It is used when we are investigating something but really don't
understand it all, or are not completely sure what we are looking for. It's sort of
like a journalist whose curiousity is peaked by something and just starts looking
into something without really knowing what they're looking for. Confirmatory
research is where we have a pretty good idea what's going on. That is, we have
a theory (or several theories), and the objective of the research is to find out if the
theory is supported by the facts.
Quantitative vs Qualitative. Quantitative studies measure variables with some
precision using numeric scales. For example, we might measure a person's
height and weight. Or we might construct a survey in which we measure how
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much respondents like President Clinton, using a 1 to 10 scale. Qualitative
studies are based on direct observation of behavior, or on transcripts of
unstructured interviews with informants. For example, we might talk to ten female
executives about their decision-making process behind their choice to have
children or not, and if so, when. We might interview them for several hours, tape-
recording the whole thing, and then transcribe the recordings to written text, and
then analyze the text.
As a general rule (but there are many exceptions), confirmatory studies tend to
be quantitative, while exploratory studies tend to be qualitative.
Research Methods in the Social Sciences
The research methods below are broken up into Qualitative and
Quantitative methods. Quantitative means that we are generating numbers
of some sort, or quantities that can be counted and analyzed; statistics
represent one form of quantitative analysis. Qualitative means that what
we are trying to describe cannot be reduced to numbers. For example, if
we wanted to study memories of childhood, it would be hard to capture
people's memories just by counting, e.g., number of toys they received,
etc.; we would want them to tell we brief (or long) stories within certain
categories we had chosen. Note that some of the methods below, such as
survey/questionnaire, can generate both quantitative as well as qualitative
data, depending on the kinds of questions we ask.
Qualitative
Ethnography/Participant Observation – Sociology, Anthropology, Education
Example: We visit the same class everyday throughout a quarter. Our record
and analyze as much of the classroom culture as we can: how the desks are set
up, how the teacher addresses students, how the material is presented, how
classroom order is maintained, etc. We occasionally talk to the participants.
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Through our observations, we draw conclusions about what teaching methods
produced motivated learning in different kinds of students.
Case Study – Clinical Psychology, Political Science, Sociology, Criminal Justice
Example: We choose a weng Latina from a Spanish-speaking household who is
entering college for the first time. We ask her to record her activities everyday.
We observe her in classroom situations. We get copies of her coursework and
track her progress in her courses. We meet with her and occasionally interview
her about how things are going. Through wer detailed examination of her
student life, we examine factors that make a difference in her success and how
many of them are related to language and culture.
Primary Source Analysis – History, Political Science, Media Studies
Note: Before choosing this research method, make sure we understand
the difference between a primary and secondary source.
Example: We watch and analyze five popular war movies made during the
Vietnam War. We look for similarities/differences in elements of the movie: the
hero, the battle scenes, the soundtrack, and the overall attitude towards war.
Through wer analysis of their content and relative popularity, we draw
conclusions about what kinds of messages and attitudes about war were
popular/common during that time.
Interpretive Argument – History, Political Science, Anthropology, Women's
Studies
Example: We read what other historians have written about the response in the
women’s movement to Roe v. Wade. We read newspaper articles that reported
on it. We watch talk shows from the time that discussed the judgment. We read
organizational memos and brochures produced by women’s groups after the
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judgment. Through this, we develop an interpretive argument about how the
decision affected the direction of the women’s movement in the 1970s.
Quantitative
Survey/Questionnaire – Sociology, Social Psychology, Political Science, Cultural
Studies
Example: We design a survey of twenty questions about what political beliefs
people hold and whom they voted for in a recent election. We distribute it to 100
women between the ages of 27 and 45, in middle-income brackets, with or
without children, married or unmarried. We use this data to determine how
consistent their voting choices are with their political beliefs.
***Note: Surveys can contain both quantitative and qualitative questions. For
example, if we ask a number of respondents whether they "strongly agree, agree,
are neutral, disagree, or strongly disagree" with a particular statement (e.g., "The
tax system is fair"), we will generate quantitative data: 67% of respondents don't
think the system is fair (or some other number). If we ask respondents to
describe their parents experience with paying taxes every year when they were
growing up, we'd be generating qualitative data.
Content Analysis – Communication, Ethnic Studies, Political Science
Example: We record and watch the advertisements run during Monday Night
Football for three weeks. As we watch and analyze them, we categorize how
many ads are for beer, trucks, etc. We record data about the content. Of the
beer ads, how many involve working class men, how many include scantily
dressed women, etc.? We use the data to draw conclusions about what kinds of
audiences the advertisers are targeting.
Field Observation – Sociology, Anthropology, Psychology
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Example: We sit on a street corner in an area where panhandlers hang out. As
they solicit money, we record data about how often and how much money people
give. We record data about who gives and who doesn’t (gender, ethnicity, age,
etc.). We record who has other kinds of responses (anger, an apology, etc.).
We use the data we generate to draw conclusions about whether certain
demographic groups are more or less sympathetic to the plight of the homeless.
Experiment – Psychology, Communication
Example: We choose subjects who will sit in booths with video cameras and
pulse monitors. As they watch one hour of pre-screened MTV, we record their
pulse fluctuations, visual responses, etc. We use the results to draw conclusions
about common responses to certain kinds of content.
Question # What is a questionnaire? What are the general guidelines for
formulating questions for a questionnaire?
A written or electronic survey instrument comprised of a series of questions,
designed to measure a specific item or set of items. The collection of data
through questionnaire is one of the most popular methods used these days. A
questionnaire contains many questions pertaining in the field of inquiry and
provides space for answers. It may be persons, supposed to possess it by
making them record their replies to a number of questions. It is sent to the
informant by post. The informant sends back the questionnaire duly felled in
within the stipulated time mentioned in the covering latter sent with the
questionnaire.
Developing a Good Questionnaire
A good questionnaire is one that helps the researcher to obtain data related to the
objectives of the study. The topic areas to be covered or the content of the questionnaire
value out of what the researcher wants to accomplish from the proposed project. The
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types of questions or items to be included in the questionnaire and the format depend to a
large extent on the types of data sought and the questionnaire design concepts and
alternatives.
The researcher cannot develop a good questionnaire simply by increasing down what he
thinks will provide him the type of data for which he looking.
General guidelines for question:
1. The first several questions should:
o be easy for respondents to understand
o be important to the study's purpose
o engage the attention and interest of your respondent
Do not begin with an open-ended question or one which respondents might feel
has a "right" answer.
2. It is important to get your respondent interested in the survey at the very
beginning. In cases where the topic is already of interest or importance to the
respondent, start with general questions, then funnel to more specific ones. If the
topic is of low importance to respondents, start with specific questions. This gives
respondents a frame of reference; then ask broader, more general questions.
3. Group questions in sections, and position sections or questions in a logical order.
4. Introduce new sections with a sentence or phrase so that participants have a
chance to switch mental gears.
5. Place questions about sensitive issues such as income, sexual habits or drug abuse
toward the end of the document, or section. This helps avoid alienating, taxing or
in other ways worrying participants.
6. Consider lists of similar items carefully. For example, you might ask about
product satisfaction using 10 different attributes (price, availability, delivery,
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color, fit and so on). The first items on a list such as this usually get treated
differently by respondents than the last items do. This is called position bias.
There are several ways to minimize position bias in a self-administered survey.
One good way is to print several versions of the questionnaire, each with a
different ordering of the items on the list. This way is often impractical, however,
because of the high cost involved in printing several different versions of your
questionnaire.
You can also randomly or alphabetically order list items and indicate in the
instructions how they are ordered. This reduces the likelihood that respondents
will see the first items as most important.
For interviewer-administered surveys, have your interviewers modify the order in
which the attributes are presented to the respondent.
7. Put demographic questions at the end of the questionnaire, if possible. There are
at least two reasons for this. First, some demographic questions such as age and
income can be sensitive and should be placed at the end, as discussed in guideline
number 5.
Second, it is better to keep respondents' minds on the purpose of the survey at the
beginning, while you have their attention. Demographic questions rarely require
much thought, so wait until the end when respondents might be tired.
8. Try to minimize the number of times the respondent or the interviewer has to
follow a skip rule. For example "If the answer to question 3 is 'None of the
Above,' skip to question 6." When there are too many skips or when skips become
too complicated, you run the risk of introducing error and confusion.
General guidelines for questionnaire layout
1. Create professional, attractive and uncluttered questionnaires; fonts should be
large enough to avoid eye strain; instructions for completing the survey should be
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easy to understand. If your survey has multiple pages, it should be bound in a
booklet so that pages cannot get mixed up. And, if respondents are to see the
survey, it should have an attractive cover.
2. Make the survey easy to complete; the check boxes or lines easy to see; and the
numbers to be circled far enough apart so the respondent or interviewer will not
inadvertently circle two numbers. If you are using scannable forms, where
bubbles need to be filled in completely and with a specific writing utensil such as
a number 2 pencil, make sure the instructions are clear and easy to find.
3. Number your questions clearly. This will lessen the chance, particularly in longer
surveys, of respondents or interviewers getting lost.
4. Start with a brief introduction describing the survey's purpose, the topics being
covered and how the results will be used. Also, mention any incentive for
completing the survey, such as a drawing entry, the opportunity to have a copy of
the results and so on.
5. If you are conducting a telephone survey, do NOT have the interviewer say "How
are you?" in the introduction. This will irritate respondents who, at such an early
stage in the interview, have been given no reason to want to talk to the
interviewer, much less tell them how they are. Keep the introduction short, polite
and to the point. Normally, the only question that should be asked in the
introduction is if respondents are willing to participate.
6. For interviewer-administered surveys, make any interviewer instructions (that is,
anything that is not supposed to be read to the respondent), easy to distinguish.
Put interviewer instructions in UPPER CASE, color or italics.
7. Leave plenty of room for respondents to write answers to open-ended questions.
Do not supply lines because this could constrain any comments.
Question # 3. Differentiate between cohort analysis, trend studies, and
panel studies. Give examples of your own.
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Cohort Study
A Cohort Study is a study in which subjects who presently have a certain
condition and/or receive a particular treatment are followed over time and
compared with another group who are not affected by the condition under
investigation. For research purposes, a cohort is any group of individuals who are
linked in some way or who have experienced the same significant life event
within a given period. There are many kinds of cohorts, including birth (for
example, all those who born between 1970 and 1975) disease, education,
employment, family formation, etc. Any study in which there are measures of
some characteristic of one or more cohorts at two or more points in time is cohort
analysis.
In some cases, cohort studies are preferred to randomized experimental design.
For instance, since a randomized controlled study to test the effect of smoking on
health would be unethical, a reasonable alternative would be a study that
identifies two groups, a group of people who smoke and a group of people who
do not, and follows them forward through time to see what health problems they
develop.
In general, Cohort analysis attempts to identify cohorts effects: Are changes in
the dependent variable (health problems in this example) due to aging, or are
they present because the sample members belongs to the same cohort (smoking
vs. non smoking)? In other words, cohort studies are about the life histories of
sections of populations and the individuals who comprise them. They can tell us
what circumstances in early life are associated with the population's
characteristics in later life - what encourages the development in particular
directions and what appears to impede it. We can study such developmental
changes across any stage of life in any life domain: education, employment,
housing, family formation, citizenship and health
Advantages
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One of the advantages of cohort analysis is that the study design does not
require strict random assignment of subjects, which is, in many cases, unethical
or improbable. As in the case of smoking vs. non-smoking cohort study, random
assignment is not a feasible or ethical alternative. (Who wants to be assigned to
a smoking group if he/she is non-smoker?). Cohort analysis is an appealing and
useful technique because it is highly flexible. It provides insight into the effects of
maturation and social, cultural, and political change. In addition, it can be used
with either original data or secondary data. In some instances, a cohort analysis
can be less expensive than experiments or surveys.
Disadvantages
One of the most difficult tasks in cohort studies is to assess whether associations
between cohort and dependent variables derived from the studies are of a causal
nature or not. Cohort studies are subject to the influence of factors over which
the investigators most often do not have full control, and that findings from these
studies are more open to threats to validity than those of studies with an
experimental research design
Because of the lack of randominization in the cohort design, the two groups may
differ in ways other than in the variable under study. For example, if the subjects
who smoke tend to have less money than the non-smokers, and thus have less
access to health care, that would exaggerate the difference between the two
groups.
The other problem with cohort studies is that they can end up taking a very long
time, since the researchers have to wait for the conditions of interest to develop.
Researchers are, of course, anxious to have meaningful results as soon as
possible, but another disadvantage with long studies is that things tend to change
over the course of the study. People die, move away, or develop other
conditions, new and promising treatments arise, and so on. If the remaining
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cohort members differ in regard to the variable under they study, the variation in
the cohort study may simply reflect this change.
It is therefore imperative that findings from cohort studies are critically scrutinized
before any judgment of causality is made.
Trend Study
The trend study is probably the most common longitudinal study among others. A
trend study samples different groups of people at different points in time from
the same population. For example, trend studies are common around public
opinion poll. Suppose that 2 months before a year-long gun control campaign, a
sample of adults is drawn: 64% report that they're in favor of a strict gun control
regulation and 34% report that they are not. A year later, a different sample
drawn from the same population shows a change: 75% report that they're in favor
of gun control and 25% report that they are not. This is a sample example of
trend study. Trend studies provide information about net changes at an
aggregate level. In the example we know that in the period under consideration,
the gun control program gained 11% more support. However, we do not know
how many people changed their positions (from con to pro OR from pro to con),
nor do we know how many stayed with their original choice. To determine both
the gross change and the net change, a panel study would be necessary.
Characteristics
Data is collected from the population at more than one point in time. (This
does not always mean that the same subjects are used to collect data at
more than one point in time, but that the subjects are selected from the
population for data at more than one point in time).
There is no experimental manipulation of variables, or more specifically,
the investigator has no control over the independent variable.
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This kind of study involves data collection only. No intervention is made by
the investigator other than his/her method or tool to collect data.
In analyzing the data, the investigator draws conclusions and may attempt
to find correlations between variables. Therefore, trend studies are
uniquely appropriate for assessing change over time and for situation
relating (prediction) questions because variables are measured at more
than one time. However, this method is deficient for situation producing
questions (causal) because there in no manipulation of the independent
variable.
Advantages
Trend studies are valuable in describing long-term changes in a population. They
can establish a pattern over time to detect shifts and changes in some event.
Marketing companies, for example, compile trend studies that chart fluctuations
in consumption levels for a certain product. Among others there are two
important advantages of trend studies.
Flexibility
One advantage of trend study is that they can be based on a comparison
of survey data originally constructed for other purposes. Of course in
utilizing such secondary data, the research needs to recognize any
differences in question wording, contexts, sampling, or analysis
techniques that might differ from one survey to the next.
Cost effectiveness
Since trend studies allow researchers to use secondary data, it saves
time, money, and personnel.
Disadvantages
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Trend analysis is to provide descriptive trends of some topic in a certain period of
time. Therefore, there is less concerns on internal validity because it does not
aim to provide causal inferences as in the case of experimental studies or some
penal studies. However, trend analysis also suffer from similar threats to validity.
If data are unreliable, for example, false trends will show up in the results. If trend
analysis is based on inconsistent measures, the results will be biased just like
instrumentation threat can bias experimental studies. That is, changes in the way
indexes are constructed or the way questions are asked will produce results that
are not comparable over time. In the worst case, the changes in measures alone
can produce a pseudo trend which might fool both the researchers and readers.
Panel Studies
Panel studies measure the same sample of respondents at different points in
time. Unlike trend studies, panel studies can reveal both net change and gross
change in the dependent variable. Additionally, panel studies can reveal shifting
attitudes and patterns of behavior that might go unnoticed with other research
approaches. Depending on the purpose of the study, researchers can use either
a continuous panel, consisting of members who report specific attitudes or
behavior patterns on a regular basis, or an interval panel, whose members agree
to complete a certain number of measurement instruments only when the
information is needed. In general, panel studies provide data suitable for
sophisticated statistical analysis and might enable researcher to predict cause-
effect relationships.
Panel data are particularly useful in predicting long-term or cumulative effects
which are normally hard to analyze in a one-shot case study (or cross-sectional
study). For example, in the early 80s', the National Broadcasting Company
supported a panel study in order to investigate the causal influence of violent TV
viewing on aggression among young people. In brief, the methodology in the
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study involved collecting data on aggression, TV viewing, and a host of
sociological variables from children in several metropolitan cities in the US. About
1,200 boys participated in the study and the variables were measured six times
for 3 year study period. The researchers sought to determine whether TV viewing
at an earlier time added to the predictability of aggression at a later time. After
looking at all the results of data analyses, the investigators concluded that there
was no consistent statistically significant relationship between watching violent
TV programs and later acts of aggression.
Advantages
Panel data are particularly useful in answering questions about the dynamics of
change. For example, under what conditions do voters change political party
affiliation? What are the respective roles of mass media and friends in changing
political attitudes? Additionally, as mentioned above, panel study is useful in
predicting long-term or cumulative effects which are normally hard to analyze in a
one-shot case study (or cross-sectional study). Finally, panel studies help solve
the problems normally encountered when defining a theory on the basis of a one-
shot study. Since the research progresses over a period time, the research can
allow for the influences of competing stimuli on the subject, which might increase
external validity of the study. However, this also causes problems in terms of
achieving internal validity because the study design does not strictly control for
confounding variables.
Disadvantages
On the negative side, panel members are often difficult to recruit because of an
unwillingness to fill out questionnaires or submit to interviews several times.
Once the sample has been secured, the problem of mortality emerges. Some
panel members will drop out for one reason or another. Because the strength of
panel studies lies in interviewing the sample size at different times, this
advantage diminishes as the sample size decreases.
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Another problem related to this mortality issue is that it can hurt internal validity of
the study design. As mentioned above, the panel design provides an opportunity
for the researcher to make statements about the cause-effect relationships
among different variables. There are three necessary conditions for determining
cause and effect. The first is time order. Causation is present if and only if the
cause precedes the effect. Second, causation can occur only if there is
a covariation between the two variables. Third, before effects are attributed to
causes, all other alternative explanations must be ruled out. (for more information
about establishing cause & effect relationship, try this link).
Since the variables are measured over time in panel studies, it is relatively more
valid to make causal inferences with temporal order which might be difficult to get
in cross-sectional studies. However, it only satisfies the two necessary conditions
(time order and covariation). There might be other alternative variable or factor
that is causing outcomes. For example, if the mortality rate in a panel study is
high, the remaining penalists might differ in regard to the variable under they
study. If it is the case, the variation in the panel study may simply reflect this
change.
Another serious problem is that the study design is vulnerable to testing threat
because respondents often become primed to measurement instruments after
repeated interviewing, thus making the sample atypical.
Finally, panel studies are often vulnerable to instrumentation threat if the
researcher in panel studies is not confined to the variables measured in the
original study. In the intervening time, new variables might have been identified
as important, but if those variables were not measured during the original survey,
they are unavailable to the researcher. In some cases, the researcher might want
to modify measurement with different operationalization. In this case, the change
in instrumentation, not the variable of the interest, might lead to the outcome.
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Question # 4. What is meant by Content analysis? How would you identify a
research problem. Determine a relevant population, select a
representative sample, define measurement categories and analyze the
results.
Content analysis (also called: textual analysis) is a standard methodology in the social
sciences on the subject of communication content. Earl Babbie defines it as "the study of
recorded human communications, such as books, web sites, paintings and laws". Harold
Lasswell formulated the core questions of content analysis: "Who says what, to whom,
why, to what extent and with what effect?". Content analysis has also been defined
as a systematic, replicable technique for compressing many words of text into
fewer content categories based on explicit rules of coding (Berelson, 1952;
GAO, 1996; Krippendorff, 1980; and Weber, 1990). Holsti (1969) offers a
broad definition of content analysis as, "any technique for making inferences
by objectively and systematically identifying specified characteristics of
messages" (p. 14). Under Holsti’s definition, the technique of content analysis
is not restricted to the domain of textual analysis, but may be applied to
other areas such as coding student drawings (Wheelock, Haney, & Bebell,
2000), or coding of actions observed in videotaped studies (Stigler, Gonzales,
Kawanaka, Knoll, & Serrano, 1999). In order to allow for replication,
however, the technique can only be applied to data that are durable in
nature.
Content analysis enables researchers to sift through large volumes of data
with relative ease in a systematic fashion (GAO, 1996). It can be a useful
technique for allowing us to discover and describe the focus of individual,
group, institutional, or social attention (Weber, 1990). It also allows
inferences to be made which can then be corroborated using other methods of
data collection. Krippendorff (1980) notes that "[m]uch content analysis
research is motivated by the search for techniques to infer from symbolic data
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what would be either too costly, no longer possible, or too obtrusive by the use
of other techniques".
Practical Applications of Content Analysis
Content analysis can be a powerful tool for determining authorship. For
instance, one technique for determining authorship is to compile a list of
suspected authors, examine their prior writings, and correlate the frequency
of nouns or function words to help build a case for the probability of each
person's authorship of the data of interest. Mosteller and Wallace (1964) used
Bayesian techniques based on word frequency to show that Madison was
indeed the author of the Federalist papers; recently, Foster (1996) used a
more holistic approach in order to determine the identity of the anonymous
author of the 1992 book Primary Colors.
Content analysis is also useful for examining trends and patterns in
documents. For example, Stemler and Bebell (1998) conducted a content
analysis of school mission statements to make some inferences about what
schools hold as their primary reasons for existence. One of the major research
questions was whether the criteria being used to measure program
effectiveness (e.g., academic test scores) were aligned with the overall
program objectives or reason for existence.
Additionally, content analysis provides an empirical basis for monitoring
shifts in public opinion. Data collected from the mission statements project in
the late 1990s can be objectively compared to data collected at some point in
the future to determine if policy changes related to standards-based reform
have manifested themselves in school mission statements.
Conducting a Content Analysis
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According to Krippendorff (1980), six questions must be addressed in every
content analysis:
1) Which data are analyzed?
2) How are they defined?
3) What is the population from which they are drawn?
4) What is the context relative to which the data are analyzed?
5) What are the boundaries of the analysis?
6) What is the target of the inferences?
At least three problems can occur when documents are being assembled for
content analysis. First, when a substantial number of documents from the
population are missing, the content analysis must be abandoned. Second,
inappropriate records (e.g., ones that do not match the definition of the
document required for analysis) should be discarded, but a record should be
kept of the reasons. Finally, some documents might match the requirements
for analysis but just be uncodable because they contain missing passages or
ambiguous content (GAO, 1996).
Analyzing the Data
Perhaps the most common notion in qualitative research is that a content
analysis simply means doing a word-frequency count. The assumption made
is that the words that are mentioned most often are the words that reflect the
greatest concerns. While this may be true in some cases, there are several
counterpoints to consider when using simple word frequency counts to make
inferences about matters of importance.
One thing to consider is that synonyms may be used for stylistic reasons
throughout a document and thus may lead the researchers to underestimate
the importance of a concept (Weber, 1990). Also bear in mind that each word
may not represent a category equally well. Unfortunately, there are no well-
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developed weighting procedures, so for now, using word counts requires the
researcher to be aware of this limitation. Furthermore, Weber reminds us
that, "not all issues are equally difficult to raise. In contemporary America it
may well be easier for political parties to address economic issues such as
trade and deficits than the history and current plight of Native American
living precariously on reservations" (1990, p. 73). Finally, in performing word
frequency counts, one should bear in mind that some words may have
multiple meanings. For instance the word "state" could mean a political body,
a situation, or a verb meaning "to speak."
A good rule of thumb to follow in the analysis is to use word frequency counts
to identify words of potential interest, and then to use a Key Word In Context
(KWIC) search to test for the consistency of usage of words. Most qualitative
research software (e.g., NUD*IST, HyperRESEARCH) allows the researcher
to pull up the sentence in which that word was used so that he or she can see
the word in some context. This procedure will help to strengthen the validity
of the inferences that are being made from the data. Certain software
packages (e.g., the revised General Inquirer) are able to incorporate artificial
intelligence systems that can differentiate between the same word used with
two different meanings based on context (Rosenberg, Schnurr, & Oxman,
1990). There are a number of different software packages available that will
help to facilitate content analyses (see further information at the end of this
paper).
Content analysis extends far beyond simple word counts, however. What
makes the technique particularly rich and meaningful is its reliance on
coding and categorizing of the data. The basics of categorizing can be
summed up in these quotes: "A category is a group of words with similar
meaning or connotations" (Weber, 1990, p. 37). "Categories must be mutually
exclusive and exhaustive" (GAO, 1996, p. 20). Mutually exclusive categories
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exist when no unit falls between two data points, and each unit is
represented by only one data point. The requirement of exhaustive categories
is met when the data language represents all recording units without
exception.
Emergent vs. a priori coding. There are two approaches to coding data
that operate with slightly different rules. With emergent coding, categories
are established following some preliminary examination of the data. The
steps to follow are outlined in Haney, Russell, Gulek, & Fierros (1998) and
will be summarized here. First, two people independently review the material
and come up with a set of features that form a checklist. Second, the
researchers compare notes and reconcile any differences that show up on
their initial checklists. Third, the researchers use a consolidated checklist to
independently apply coding. Fourth, the researchers check the reliability of
the coding (a 95% agreement is suggested; .8 for Cohen's kappa). If the level
of reliability is not acceptable, then the researchers repeat the previous steps.
Once the reliability has been established, the coding is applied on a large-
scale basis. The final stage is a periodic quality control check.
When dealing with a priori coding, the categories are established prior to the
analysis based upon some theory. Professional colleagues agree on the
categories, and the coding is applied to the data. Revisions are made as
necessary, and the categories are tightened up to the point that maximizes
mutual exclusivity and exhaustiveness (Weber, 1990).
Coding units. There are several different ways of defining coding units. The
first way is to define them physically in terms of their natural or intuitive
borders. For instance, newspaper articles, letters, or poems all have natural
boundaries. The second way to define the recording units syntactically, that
is, to use the separations created by the author, such as words, sentences, or
paragraphs. A third way to define them is to use referential units. Referential
22
units refer to the way a unit is represented. For example a paper might refer
to George W. Bush as "President Bush," "the 43rd president of the United
States," or "W." Referential units are useful when we are interested in
making inferences about attitudes, values, or preferences. A fourth method of
defining coding units is by using propositional units. Propositional units are
perhaps the most complex method of defining coding units because they work
by breaking down the text in order to examine underlying assumptions. For
example, in a sentence that would read, "Investors took another hit as the
stock market continued its descent," we would break it down to: The stock
market has been performing poorly recently/Investors have been losing
money (Krippendorff, 1980).
Typically, three kinds of units are employed in content analysis: sampling
units, context units, and recording units.
Sampling units will vary depending on how the researcher makes
meaning; they could be words, sentences, or paragraphs. In the
mission statements project, the sampling unit was the mission
statement.
Context units neither need be independent or separately describable.
They may overlap and contain many recording units. Context units do,
however, set physical limits on what kind of data you are trying to
record. In the mission statements project, the context units are
sentences. This was an arbitrary decision, and the context unit could
just as easily have been paragraphs or entire statements of purpose.
Recording units, by contrast, are rarely defined in terms of physical
boundaries. In the mission statements project, the recording unit was
the idea(s) regarding the purpose of school found in the mission
statements (e.g., develop responsible citizens or promote student self-
23
worth). Thus a sentence that reads "The mission of Jason Lee school is
to enhance students' social skills, develop responsible citizens, and
foster emotional growth" could be coded in three separate recording
units, with each idea belonging to only one category (Krippendorff,
1980).
Reliability. Weber (1990) notes: "To make valid inferences from the text, it
is important that the classification procedure be reliable in the sense of being
consistent: Different people should code the same text in the same way" . As
Weber further notes, "reliability problems usually grow out of the ambiguity
of word meanings, category definitions, or other coding rules". Yet, it is
important to recognize that the people who have developed the coding scheme
have often been working so closely on the project that they have established
shared and hidden meanings of the coding. The obvious result is that the
reliability coefficient they report is artificially inflated (Krippendorff, 1980).
In order to avoid this, one of the most critical steps in content analysis
involves developing a set of explicit recording instructions. These instructions
then allow outside coders to be trained until reliability requirements are met.
Reliability may be discussed in the following terms:
Stability, or intra-rater reliability. Can the same coder get the same
results try after try?
Reproducibility, or inter-rater reliability. Do coding schemes lead to
the same text being coded in the same category by different people?
One way to measure reliability is to measure the percent of agreement
between raters. This involves simply adding up the number of cases that
were coded the same way by the two raters and dividing by the total number
of cases. The problem with a percent agreement approach, however, is that it
does not account for the fact that raters are expected to agree with each other
24
a certain percentage of the time simply based on chance (Cohen, 1960). In
order to combat this shortfall, reliability may be calculated by using Cohen's
Kappa, which approaches 1 as coding is perfectly reliable and goes to 0 when
there is no agreement other than what would be expected by chance (Haney
et al., 1998). Kappa is computed as:
where:
= proportion of units on which the raters agree
= the proportion of units for which agreement is expected by chance.
Table 1 – Example Agreement Matrix
Rater 1 Marginal
Totals
Academic Emotional Physical
Academic .42 (.29)* .10 (.21) .05 (.07) .57
Rater 2 Emotional .07 (.18) .25 (.18) .03 (.05) .35
Physical .01 (.04) .02 (.03) .05 (.01) .08
.50 .37 .13 1.00
*Values in parentheses represent the expected proportions on the basis of
chance associations, i.e. the joint probabilities of the marginal proportions.
25
Given the data in Table 1, a percent agreement calculation can be derived by
summing the values found in the diagonals (i.e., the proportion of times that
the two raters agreed):
By multiplying the marginal values, we can arrive at an expected proportion
for each cell (reported in parentheses in the table). Summing the product of
the marginal values in the diagonal we find that on the basis of chance alone,
we expect an observed agreement value of:
Kappa provides an adjustment for this chance agreement factor. Thus, for the
data in Table 1, kappa would be calculated as:
In practice, this value may be interpreted as the proportion of agreement
between raters after accounting for chance (Cohen, 1960). Crocker & Algina
(1986) point out that a value of does not mean that the coding decisions
are so inconsistent as to be worthless, rather, may be interpreted to
mean that the decisions are no more consistent than we would expect based
on chance, and a negative value of kappa reveals that the observed
agreement is worse than expected on the basis of chance alone. "In his
methodological note on kappa in Psychological Reports, Kvalseth (1989)
suggests that a kappa coefficient of 0.61 represents reasonably good overall
agreement." (Wheelock et al., 2000). In addition, Landis & Koch (1977, p.165)
have suggested the following benchmarks for interpreting kappa:
26
Kappa Statistic Strength of Agreement
<0.00 Poor
0.00 0.20 Slight
0.21 0.40 Fair
0.41 0.60 Moderate
0.61 0.80 Substantial
0.81 1.00 Almost Perfect
Cohen (1960) notes that there are three assumptions to attend to in using
this measure. First, the units of analysis must be independent. For example,
each mission statement that was coded was independent of all others. This
assumption would be violated if in attempting to look at school mission
statements, the same district level mission statement was coded for two
different schools within the same district in the sample.
Second, the categories of the nominal scale must be independent, mutually
exclusive, and exhaustive. Suppose the goal of an analysis was to code the
kinds of courses offered at a particular school. Now suppose that a coding
scheme was devised that had five classification groups: mathematics, science,
literature, biology, and calculus. The categories on the scale would no longer
be independent or mutually exclusive because whenever a biology course is
encountered it also would be coded as a science course. Similarly, a calculus
would always be coded into two categories as well, calculus and mathematics.
Finally, the five categories listed are not mutually exhaustive of all of the
different types of courses that are likely to be offered at a school. For
27
example, a foreign language course could not be adequately described by any
of the five categories.
The third assumption when using kappa is that the raters are operating
independently. In other words, two raters should not be working together to
come to a consensus about what rating they will give.
Validity. It is important to recognize that a methodology is always employed
in the service of a research question. As such, validation of the inferences
made on the basis of data from one analytic approach demands the use of
multiple sources of information. If at all possible, the researcher should try to
have some sort of validation study built into the design. In qualitative
research, validation takes the form of triangulation. Triangulation lends
credibility to the findings by incorporating multiple sources of data, methods,
investigators, or theories (Erlandson, Harris, Skipper, & Allen, 1993).
For example, in the mission statements project, the research question was
aimed at discovering the purpose of school from the perspective of the
institution. In order to cross-validate the findings from a content analysis,
schoolmasters and those making hiring decisions could be interviewed about
the emphasis placed upon the school's mission statement when hiring
prospective teachers to get a sense of the extent to which a school’s values are
truly reflected by mission statements. Another way to validate the inferences
would be to survey students and teachers regarding the mission statement to
see the level of awareness of the aims of the school. A third option would be to
take a look at the degree to which the ideals mentioned in the mission
statement are being implemented in the classrooms.
Shapiro & Markoff (1997) assert that content analysis itself is only valid and
meaningful to the extent that the results are related to other measures. From
this perspective, an exploration of the relationship between average student
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achievement on cognitive measures and the emphasis on cognitive outcomes
stated across school mission statements would enhance the validity of the
findings. For further discussions related to the validity of content analysis
see Roberts (1997), Erlandson et al. (1993), and Denzin & Lincoln (1994).
Conclusion
When used properly, content analysis is a powerful data reduction technique.
Its major benefit comes from the fact that it is a systematic, replicable
technique for compressing many words of text into fewer content categories
based on explicit rules of coding. It has the attractive features of being
unobtrusive, and being useful in dealing with large volumes of data. The
technique of content analysis extends far beyond simple word frequency
counts. Many limitations of word counts have been discussed and methods of
extending content analysis to enhance the utility of the analysis have been
addressed. Two fatal flaws that destroy the utility of a content analysis are
faulty definitions of categories and non-mutually exclusive and exhaustive
categories.
Q. 5 What do you mean by narrative discourse analysis and case study method? Explain.
A case study is a particular method of qualitative research. Rather than using large
samples and following a rigid protocol to examine a limited number of variables, case
study methods involve an in-depth, longitudinal examination of a single instance or
event: a case. They provide a systematic way of looking at events, collecting data,
analyzing information, and reporting the results. As a result the researcher may gain a
sharpened understanding of why the instance happened as it did, and what might become
important to look at more extensively in future research. Case studies lend themselves to
both generating and testing hypotheses (Flyvbjerg 2006).
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Yin, on the other hand suggests, that case study should be defined as a research strategy,
an empirical inquiry that investigates a phenomenon within its real-life context. Case
study research means single- and multiple case studies, can include quantitative evidence,
relies on multiple sources of evidence and benefits from the prior development of
theoretical propositions. He notes that case studies should not be confused with
qualitative research and points out that they can be based on any mix of quantitative and
qualitative evidence (Yin 2002).
The scope and relevance of case studies
Certain disciplines thrive on case studies: others find them less suitable in given
situations. Compare usage and perceived validity in the humanities, natural sciences,
social sciences, pseudoscience and business.
Rogers, in Business Analysis for Marketing Managers (1978) distinguishes case studies
from case histories and projects. He describes a case history as an event or series of
events set in an organisational framework with or without a related environment. The
events are described in some detail with the main and subsidiary points highlighted.
Actions taken by subjects in the case are described; reactions, responses and effects on
other subjects are related, and events taken to a conclusion or to a point that is
irreversible. Medical cases are typical of the category. Symptoms are described, probable
and possible causes suggested, treatment recommended, prognosis recorded, and the date
when the patient was discharged or buried.
He defined the case study as also describing events in a framework within an
environment. The problems are not always highlighted or even made clear; they emerge
as the case material is subjected to analysis. A conclusion is not necessarily stated nor is
the situation reached in the case irreversible. It is usually possible to ‘take over’
operations at a suitable point in the role of an external adviser or from a position in the
case. Most business cases fall into this category.
The case project is a series of diverse continuous events, set in an organizational
framework and normally in a well-defined environment. Those studying the case are led
30
to a specific point in time and circumstance where they become a ‘participant’ in the
case. They may be asked to assume the role of a person in the case, appointed to a
particular vacancy, or to advise from the position of an external consultant. The role is
made explicit and it is from that viewpoint that analysis, views, arguments and
recommendations must be made; there is thus a behavioural aspect introduced. If placed
in the position of a newly appointed middle manager, responses and suggestions are
likely to be different from those of an external consultant. Rogers developed the case
project in 1966 for the Chartered Institute of Marketing’s diploma final open book
examination. To avoid pre-prepared scripts being submitted, the examination paper
progressed the case by several months from when it was published, introducing new
material. This required candidates to modify the analyses and conclusions already
reached and write a true examination room report.
Types of case study
Illustrative case studies
Illustrative case studies describe a domain; they use one or two instances to analyze a
situation. This helps interpret other data, especially when researchers have reason to
believe that readers know too little about a program. These case studies serve to make the
unfamiliar familiar, and give readers a common language about the topic. The chosen site
should typify important variations and contain a small number of cases to sustain readers'
interest.
The presentation of illustrative case studies may involve some pitfalls. Such studies
require presentation of in-depth information on each illustration; but the researcher may
lack time on-site for in-depth examination. The most serious problem involves the
selection of instances. The case(s) must adequately represent the situation or program.
Where significant diversity exists, no single individual site may cover the field
adequately.
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Exploratory case studies
Exploratory case studies condense the case study process: researchers may undertake
them before implementing a large-scale investigation. Where considerable uncertainty
exists about program operations, goals, and results, exploratory case studies help identify
questions, select measurement constructs, and develop measures; they also serve to
safeguard investment in larger studies.
The greatest pitfall in the exploratory study involves premature conclusions: the findings
may seem convincing enough for inappropriate release as conclusions. Other pitfalls
include the tendency to extend the exploratory phase, and inadequate representation of
diversity.
Critical instance case studies
Critical instance case studies examine one or a few sites for one of two purposes. A very
frequent application involves the examination of a situation of unique interest, with little
or no interest in generalizability. A second, rarer, application entails calling into question
a highly generalized or universal assertion and testing it by examining one instance. This
method particularly suits answering cause-and-effect questions about the instance of
concern.
Inadequate specification of the evaluation question forms the most serious pitfall in this
type of study. Correct application of the critical instance case study crucially involves
probing the underlying concerns in a request.
Program implementation case studies
Program implementation case studies help discern whether implementation complies with
intent. These case studies may also prove useful when concern exists about
implementation problems. Extensive, longitudinal reports of what has happened over
time can set a context for interpreting a finding of implementation variability. In either
32
case, researchers aim for generalization and must carefully negotiate the evaluation
questions with their customer.
Good program implementation case studies must invest sufficient time to obtain
longitudinal data and breadth of information. They typically require multiple sites to
answer program implementation questions; this imposes demands on training and
supervision needed for quality control. The demands of data management, quality
control, validation procedures, and analytic modelling (within site, cross-site, etc.) may
lead to cutting too many corners to maintain quality.
Program effects case studies
Program effects case studies can determine the impact of programs and provide
inferences about reasons for success or failure. As with program implementation case
studies, the evaluation questions usually require generalizability and, for a highly diverse
program, it may become difficult to answer the questions adequately and retain a
manageable number of sites. But methodological solutions to this problem exist. One
approach involves first conducting the case studies in sites chosen for their
representativeness, then verifying these findings through examination of administrative
data, prior reports, or a survey. Another solution involves using other methods first. After
identifying findings of specific interest, researchers may then implement case studies in
selected sites to maximize the usefulness of the information.
Prospective case studies
Case studies can be used not only for inductive theory development, but also as quazi-
experiments in deductive theory testing. In a prospective case study design, the researcher
formulates a set of theory-based hypotheses in respect to the evolution of an on-going
social or cultural process and then tests these hypotheses at a pre-determined follow-up
time in the future by comparing these hypotheses with the observed process outcomes
using "pattern matching" (Campbell, 1966; Trochim, 1989) or a similar technique. This
prospective research design consists of (1) a baseline case study, which is used to
formulate a set of hypotheses in respect to the evolving social process (i.e., "What
33
predictions would a given theory make in respect to this process?"), establish the follow-
up time, follow-up study methodology, and outcome evaluation criteria; and (2) of a
follow-up case study conducted at the predetermined follow-up time. In this follow-up
study, the formulated hypotheses are compared to the observed outcomes of the social
process, and the predictive power of the theory is, thus, evaluated.
Cumulative case studies
Cumulative case studies aggregate information from several sites collected at different
times. The cumulative case study can have a retrospective focus, collecting information
across studies done in the past, or a prospective outlook, structuring a series of
investigations for different times in the future. Retrospective cumulation allows
generalization without cost and time of conducting numerous new case studies;
prospective cumulation also allows generalization without unmanageably large numbers
of cases in process at any one time.
The techniques for ensuring sufficient comparability and quality and for aggregating the
information constitute the "cumulative" part of the methodology. Features of the
cumulative case study include the case survey method (used as a means of aggregating
findings) and backfill techniques. The latter aid in retrospective cumulation as a means of
obtaining information from authors that permits use of otherwise insufficiently detailed
case studies.
Opinions vary as to the credibility of cumulative case studies for answering program
implementation and effects questions. One authority notes that publication biases may
favor programs that seem to work, which could lead to a misleading positive view
(Berger, 1983). Others raise concerns about problems in verifying the quality of the
original data and analyses (Yin, 1989).
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Narrative case studies
Case studies that present findings in a narrative format are called narrative case studies.
This involves presenting the case study as events in an unfolding plot with actors and
actions.
Medical case studies
In medical science case studies are considered "Class V" evidence, and are thus the least
suggestive of all forms of medical evidence.
Case selection
When selecting a case for a case study, researchers often use information-oriented
sampling, as opposed to random sampling (Flyvbjerg 2006). This is because the typical
or average case is often not the richest in information. Extreme or atypical cases often
reveal more information because they activate more basic mechanisms and more actors in
the situation studied. In addition, from both an understanding-oriented and an action-
oriented perspective, it is often more important to clarify the deeper causes behind a
given problem and its consequences than to describe the symptoms of the problem and
how frequently they occur. Random samples emphasizing representativeness will seldom
be able to produce this kind of insight; it is more appropriate to select some few cases
chosen for their validity.
The following three types of information-oriented cases may be distinguished: (1)
Extreme or deviant cases, (2) Critical cases, and (3) Paradigmatic cases. The extreme
case can be well-suited for getting a point across in an especially dramatic way, which
often occurs for well-known case studies such as Freud’s ‘Wolf-Man.’
A critical case can be defined as having strategic importance in relation to the general
problem. For example, an occupational medicine clinic wanted to investigate whether
people working with organic solvents suffered brain damage. Instead of choosing a
representative sample among all those enterprises in the clinic’s area that used organic
35
solvents, the clinic strategically located a single workplace where all safety regulations
on cleanliness, air quality, and the like, had been fulfilled. This model enterprise became
a critical case: if brain damage related to organic solvents could be found at this
particular facility, then it was likely that the same problem would exist at other
enterprises which were less careful with safety regulations for organic solvents. Via this
type of strategic sampling, one can save both time and money in researching a given
problem. Another example of critical case sampling is the strategic selection of lead and
feather for the test of whether different objects fall with equal velocity. The selection of
materials provided the possibility to formulate a generalization characteristic of critical
cases, a generalization of the sort, ‘If it is valid for this case, it is valid for all (or many)
cases.’ In its negative form, the generalization would be, ‘If it is not valid for this case,
then it is not valid for any (or only few) cases.’
A paradigmatic case may be defined as an exemplar or prototype. Thomas Kuhn has
shown that the basic skills, or background practices, of natural scientists are organized in
terms of ‘exemplars’ or 'paradigms' the role of which in the scientific process can be
analyzed. Similarly, scholars like Clifford Geertz and Michel Foucault have often
organized their research around specific cultural paradigms: a paradigm for Geertz lay for
instance in the ‘deep play’ of the Balinese cockfight, while for Foucault, European
prisons and the ‘Panopticon’ are examples. Both instances are examples of paradigmatic
cases, that is, cases that highlight more general characteristics of the societies or issues in
question. Kuhn has shown that scientific paradigms cannot be expressed as rules or
theories. There exists no predictive theory for how predictive theory comes about. A
scientific activity is acknowledged or rejected as good science by how close it is to one or
more exemplars; that is, practical prototypes of good scientific work. A paradigmatic case
of how scientists do science is precisely such a prototype. It operates as a reference point
and may function as a focus for the founding of schools of thought.
Generalizing from case studies
The case study is effective for generalizing using the type of test that Karl Popper called
falsification, which forms part of critical reflexivity (flyvbjerg 2006). Falsification is one
36
of the most rigorous tests to which a scientific proposition can be subjected: if just one
observation does not fit with the proposition it is considered not valid generally and must
therefore be either revised or rejected. Popper himself used the now famous example of,
"All swans are white," and proposed that just one observation of a single black swan
would falsify this proposition and in this way have general significance and stimulate
further investigations and theory-building. The case study is well suited for identifying
"black swans" because of its in-depth approach: what appears to be "white" often turns
out on closer examination to be "black."
For instance, Galileo’s rejection of Aristotle’s law of gravity was based on a case study
selected by information-oriented sampling and not random sampling. The rejection
consisted primarily of a conceptual experiment and later on of a practical one. These
experiments, with the benefit of hindsight, are self-evident. Nevertheless, Aristotle’s
incorrect view of gravity dominated scientific inquiry for nearly two thousand years
before it was falsified. In his experimental thinking, Galileo reasoned as follows: if two
objects with the same weight are released from the same height at the same time, they
will hit the ground simultaneously, having fallen at the same speed. If the two objects are
then stuck together into one, this object will have double the weight and will according to
the Aristotelian view therefore fall faster than the two individual objects. This conclusion
seemed contradictory to Galileo. The only way to avoid the contradiction was to
eliminate weight as a determinant factor for acceleration in free fall. And that was what
Galileo did. Historians of science continue to discuss whether Galileo actually carried out
the famous experiment from the leaning tower of Pisa, or whether it is simply a myth. In
any event, Galileo’s experimentalism did not involve a large random sample of trials of
objects falling from a wide range of randomly selected heights under varying wind
conditions, and so on. Rather, it was a matter of a single experiment, that is, a case study,
if any experiment was conducted at all.
Galileo’s view continued to be subjected to doubt, however, and the Aristotelian view
was not finally rejected until half a century later, with the invention of the air pump. The
air pump made it possible to conduct the ultimate experiment, known by every pupil,
whereby a coin or a piece of lead inside a vacuum tube falls with the same speed as a
37
feather. After this experiment, Aristotle’s view could be maintained no longer. What is
especially worth noting, however, is that the matter was settled by an individual case due
to the clever choice of the extremes of metal and feather. One might call it a critical case:
for if Galileo’s thesis held for these materials, it could be expected to be valid for all or a
large range of materials. Random and large samples were at no time part of the picture.
Most skilled scientists simply do not work this way with this type of problem.
By selecting cases strategically in this manner one may arrive at case studies that allow
generalization.
History of the case study
As a distinct approach to research, use of the case study originated only in the early 20th
century. The Oxford English Dictionary traces the phrase case study or case-study back
as far as 1934, after the establishment of the concept of a case history in medicine.
The use of case studies for the creation of new theory in social sciences has been further
developed by the sociologists Barney Glaser and Anselm Strauss who presented their
research method, Grounded theory, in 1967.
The popularity of case studies as research tools has developed only in recent decades.
One of the areas in which case studies have been gaining popularity is education and in
particular educational evaluation. Some of the prominent scholars in educational case
study are Robert Stake and Jan Nespor (see references). Case studies have, of course, also
been used as a teaching method and as part of professional development. They are well-
known in business and legal education. The problem-based learning (PBL) movement is
one of the examples. When used in (non-business) education and professional
development, case studies are often referred to as critical incidents (see David Tripp in
references).
History of Business Cases. - When the Harvard Business School was started, the faculty
quickly realized that there were no textbooks suitable to a graduate program in business.
Their first solution to this problem was to interview leading practioners of business and to
38
write detailed accounts of what these managers were doing. Of course the professors
could not present these cases as practices to be emulated because there were no criteria
available for determining what would succeed and what would not succeed. So the
professors instructed their students to read the cases and to come to class prepared to
discuss the cases and to offer recommendations for appropriate courses of action.
Basically that is the model still being used. See a critique of this approach.
Conclusions
The case study offers a method of learning about a complex instance through extensive
description and contextual analysis. The product articulates why the instance occurred as
it did, and what one might usefully explore in similar situations.
Case studies can generate a great deal of data that may defy straightforward analysis. For
details on conducting a case study, especially with regard to data collection and analysis,
see the references listed below.
