Approaches to qualitative data analysis Dr Ayaz Afsar 1
Transcript
Slide 1
Approaches to qualitative data analysis Dr Ayaz Afsar 1
Slide 2
Introduction Qualitative data analysis involves organizing,
accounting for and explaining the data; in short, making sense of
data in terms of the participants denitions of the situation,
noting patterns, themes, categories and regularities. In this
lecture I will discuss several forms of qualitative data analysis.
There is no one single or correct way to analyse and present
qualitative data; how one does it should abide by the issue of
tness for purpose. Further, qualitative data analysis, as we shall
see here, is often heavy on interpretation, and one has to note
that there are frequently multiple interpretations to be made of
qualitative data that is their glory and their headache! In abiding
by the principle of tness for purpose, the researcher must be clear
what he or she wants the data analysis to do as this will determine
the kind of analysis that is undertaken. The researcher can set
out, for example: 2
Slide 3
ContIntroduction to describe to portray to summarize to
interpret to discover patterns to generate themes to understand
individuals and idiographic features to understand groups and
nomothetic features (e.g. frequencies, norms, patterns, laws) to
raise issues to prove or demonstrate to explain and seek causality
to explore 3
Slide 4
ContIntroduction to test to discover commonalities, differences
and similarities to examine the application and operation of the
same issues in different contexts. The signicance of deciding the
purpose is that it will determine the kind of analysis performed on
the data. This, in turn, will inuence the way in which the analysis
is written up. The data analysis will also be inuenced by the kind
of qualitative study that is being undertaken. For example, a
biography and a case study may be most suitably written as
descriptive narrative, often chronologically, with issues raised
throughout. 4
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ContIntroduction An ethnography may be written as narrative or
stories, with issues raised, but not necessarily conforming to a
chronology of events, and including description, analysis,
interpretation and explanation of the key features of a group or
culture. A grounded theory and content analysis will proceed
through a systematic series of analyses, including coding and
categorization, until theory emerges that explains the phenomena
being studied or which can be used for predictive purposes. The
analysis will also be inuenced by the number of data sets and
people from whom data have been collected. Qualitative data often
focus on smaller numbers of people than quantitative data, yet the
data tend to be detailed and rich. Researchers will need to decide,
for example, whether to present data individual by individual, and
then, if desired, to amalgamate key issues emerging across the
individuals, or whether to proceed by working within a largely
predetermined analytical frame of issues that crosses the
individuals concerned. 5
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ContIntroduction Some qualitative studies (e.g. Ball 1990;
1994a; Bowe et al. 1992) deliberately focus on individuals and the
responses of signicant players in a particular scenario, often
quoting verbatim responses in the nal account; others are content
to summarize issues without necessarily identifying exactly from
whom the specic data were derived. Later on here we discuss methods
to be used with respect to people and issues. Some studies include
a lot of verbatim conversations; others use fewer verbatim data.
Some researchers feel that it is important to keep the avour of the
original data, so they report direct phrases and sentences, not
only because they are often more illuminative and direct than the
researchers own words, but also because they feel that it is
important to be faithful to the exact words used. Indeed, as
reported in the example later, direct conversations can be
immensely rich in data and detail. Ball (1990) and Bowe et al.
(1992) use a lot of verbatim data, not least because those whom
they interviewed were powerful people and justice needed to be done
to the exact words that they used. 6
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ContIntroduction By contrast Walford (2001: 92), commenting on
the fetish of transcription, admits that he rarely fully
transcribed more than a few interviews for any of [his] research
studies, not least because of the time that it took for
transcription (Walford suggests a ratio of ve to one ve hours to
transcribe one hour of interviews, though it can take much longer
than this). At a practical level, qualitative research rapidly
amasses huge amounts of data, and early analysis reduces the
problem of data overload by selecting out signicant features for
future focus. Miles and Huberman (1984) suggest that careful data
display is an important element of data reduction and selection.
Progressive focusing, according to Parlett and Hamilton (1976),
starts with the researcher taking a wide angle lens to gather data,
and then, by sifting, sorting, reviewing and reecting on them, the
salient features of the situation emerge. These are then used as
the agenda for subsequent focusing. The process is akin to
funnelling from the wide to the narrow. 7
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ContIntroduction At a theoretical level, a major feature of
qualitative research is that analysis often begins early on in the
data collection process so that theory generation can be undertaken
(LeCompte and Preissle 1993: 238). Researchers should set out the
main outlines of the phenomena that are under investigation. They
should then assemble blocks or groups of data, putting them
together to make a coherent whole (e.g. through writing summaries
of what has been found). Then they should painstakingly take apart
their eld notes, matching, contrasting, aggregating, comparing and
ordering notes made. The intention is to move from description to
explanation and theory generation. 8
Slide 9
Tabulating data I outline several examples of data analysis and
presentation in this session. The rst of these illustrates simple
summary and clear, tabulated data presentation and commentary. It
derives from a doctorate thesis. Chinese children learning English:
an example of analysing and presenting interview data. The
interview data are presented question by question. In what follows,
where the data for respondents in each age phase are similar they
are grouped into a single set of responses by row; where there are
dissimilar responses they are kept separate. The left-hand column
in each table indicates the number of the respondent (112) and the
level which the respondent taught (e.g. P1,F3 etc.), so, for
example, 13: P 1 means the responses of respondents 13, who taught
P1 classes; the right-hand column indicates the responses. In many
cases it can be seen that respondents all gave similar responses in
terms of the actual items mentioned and the coverage of items
specied. A brief summary comment is provided after each table.
9
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ContTabulating data The data here derive from a doctorate
thesis concerning the problems that school children experience in
learning English in China. The data set reproduced is incomplete
and has been selected for illustrative purposes only. Note that the
data are not verbatim, but have already been summarized by the
researcher, i.e. what is presented here is not the rst stage of the
data analysis, as the rst stage would be transcription. The coding
is as follows: P1P6 = Primary forms (16), P1 = Year One, P2 = Year
Two etc. F1F5 = Secondary forms (15), F1 = Form One (rst year of
secondary school), F2 = Form Two (second year of secondary school
etc.) 10
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The effectiveness of English teaching 11
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ContTabulating data The numbers preceding each letter in the
left hand column refer to the number ascribed to the teacher (Box
1). There were twelve teachers in all, six from primary and six
from secondary schools. English teaching and learning at school
have not really achieved their intended purposes.Students are poor
at understanding written or spoken English, speaking, reading,
listening and writing; this limits their abilities, regardless of
the number of years of learning English; low- level memorization
model leads to supercial learning; teaching and learning are poor;
students can enter university, even though their standard is poor,
as there are many universities to take students; students do not
require English to gain employment. 12
Slide 13
ContTabulating data Comment: the primary English teachers had a
wider range of views than the secondary teachers; there was greater
unanimity between the primary teachers in comparison with the
secondary teachers; all the Form Three secondary teachers were
unanimous in their comments, and all the Form Five secondary
teachers had different views. Box 2 indicates that the strengths of
English teaching were that students start to learn English very
young, and schools had autonomy over the design of syllabuses. The
weaknesses in English teaching were that insufcient emphasis was
placed on understanding, students were too young to learn English,
and syllabuses were unrealistic in their demands, being too rich,
leading teachers to a spoon-feeding mentality in their teaching.
Also undue pressure was put on teachers and students because of the
demands of the syllabus and English had to compete with other
languages for curriculum space. Hence students did not learn well,
despite years of learning English. 13
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ContTabulating data The strengths and weaknesses of English
language teaching 14
Slide 15
ContTabulating data Comment: apart from one primary teacher,
the other eleven teachers, drawn from both primary and secondary
schools, were unanimous in the comments they gave. It was clear
that high class size (between 30 and 50 students, rising to 60) and
tight syllabuses exerted a signicant impact on teaching methods and
restrictions of class activities, because of control issues (Box
3). The nature of this inuence is to adopt largely didactic and
grammar- translation methods, with little extended recourse to
using or thinking in English. Teaching utilized some group
activity, but this was very limited. Teachers used Chinese to
explain English. 15
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Teaching methods 16
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Teaching methods Comment: all the teachers here were unanimous
in their comments which fell mainly into two sets of points.
Students contributed signicantly to their own success or failure in
learning English (Box 4). They were shy, afraid of making mistakes
and of losing face, and had little interest in learning at all, let
alone English; they were overloaded with other subjects, a
situation exacerbated by their poor time management; they held
negative attitudes to the bookish nature of learning English and
its unrelatedness to other curriculum subjects, had too many other
distractions and had limited abilities in English; they had little
incentive to learn fast as they could repeat courses, gave little
priority to English, had poor foundations for learning English and
had limited motivation or positive attitudes to learning English;
they were given limited direction in their learning and had limited
incentive to learn English well, as universities required only a
low standard of English. 17
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Student-related factors 18
Slide 19
ContTabulating data Comment: there was a great variety of
comments here. There were degrees of agreement: the teachers of the
younger primary children agreed with each other; the teachers of
the older primary children agreed with each other; and the teachers
of the older secondary children agreed with each other. The
teachers of the younger secondary children raised different points
from each other. However, the four groups of teachers (younger
primary, older primary, younger secondary and older secondary)
raised different points from each other. 19
Slide 20
Summary of the interview data The issues that emerge from the
interview data are striking in several ways. What characterizes the
data is the widespread agreement of the respondents on the issues.
For example: There was absolute unanimity in the responses to
questions 9, 12. There was very considerable, though not absolute,
unanimity on question 11. In addition to the unanimity already
observed, there was additional unanimity among the primary teachers
in respect of question 11. In addition to the considerable, though
not absolute, unanimity already observed, there was much unanimity
among the primary teachers concerning question 6. Such a degree of
unanimity gives considerable power to the results, even though,
because of the sampling used, they cannot be said to be
representative of the wider population. However, the sample of
experienced teachers was deliberately selected to provide an
informed overview of the key issues to be faced. It must be
remembered that, though the unanimity is useful, the main purpose
of the interview data was to identify key issues, regardless of
unanimity, convergence or frequency of mention. That the
respondents articulated similar issues, however, signals that these
may be important elements. 20
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ContSummary of the interview data Further, the issues
themselves are seen to lie in a huge diversity of elds, such that
there is no single or simplistic set of problems or solutions.
Hence, to complement the considerable unanimity of voice is a
similar consensus in identifying the scope of the problem, yet the
range of the problems is vast. Both singly and together, the issues
of English language teaching, learning and achievement in Macau are
complex. The messages are clear in respect of Form Five students
and their English teaching and learning. First, English performance
is weak in all its aspects reading, writing, speaking and listening
but it is particularly weak in speaking and writing. Second, local
cultural factors exert an inuence on learning English: 21
Slide 22
ContSummary of the interview data Students do not wish to lose
face in public (and the Chinese emphasis on gaining and maintaining
face is powerful). Students are shy and afraid of making mistakes.
The pressure of examination success is universal and severe. The
local culture is not English; it is Chinese and, if anything else,
is Portuguese rather than English, although this latter is very
limited; there is little need for people to speak or use English at
present; 22
Slide 23
ContSummary of the interview data In some quarters, knowledge
of English culture is seen to be an important element in learning
English; this was refuted by the teachers in this sample. The third
main message is that English is seen instrumentally, but this
message has to be qualied, as many students gain employment and
university entrance even though their English is weak. The fact of
English being an international language has limited effect on
student motivation or achievement. Finally, poor teaching and
learning are signicant contributors to poor performance, in several
areas: 23
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ContSummary of the interview data There is great emphasis on
drill, rote learning and memorization. There is a predominance of
passive rather than active learning, with teaching as the delivery
of facts rather than the promotion of learning and understanding.
Traditional didactic methods are used. There is reliance on a very
limited range of teaching and learning styles. The limited subject
and pedagogical knowledge of English teachers are compounded by the
lack of adequate initial and post-initial teacher education.
Frequently the careful laying of foundations of English teaching
and learning is absent. Students use so much Chinese during English
lessons that they have little chance to think in English they
translate rather than think in English. 24
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ContSummary of the interview data From the interview data it
can be seen that the size of the problems and issues to be faced in
English language teaching and learning is vast. In this example,
tables are carefully laid out to draw together similar sets of
responses. The tables enable the reader to see, at a glance, where
similarities and differences lie between the two groups of
respondents. Note also that after each table there is a summary of
the main points to which the researcher wishes to draw the readers
attention, and that these comprise both substantive and overall
comments (e.g. on the topic in hand and on the similarities and
differences between the groups of respondents respectively).
Finally, note that an overall summary of key messages has been
provided at the end of all the tables and their commentaries. This
is a very abridged and selective example, and justice has not been
done to the whole of the data that the original researcher used.
Nevertheless the point is clearly illustrated here that summarizing
and presenting data in tabular form can address the twin issues of
qualitative research: data reduction through careful data display
and commentary. 25
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Five ways of organizing and presenting data analysis We present
ve ways of organizing and presenting analysis as follows: the rst
two methods are by people, and the next two methods are by issue,
and the nal method is by instrument. One can observe in the example
of teaching English in Macau that the data have been organized and
presented by respondents, in response to particular issues. Indeed,
where the respondents said the same, they have been organized by
groups of respondents in relation to a given issue. The groups of
respondents were also organized by their membership of different
strata in a stratied sample: teachers of younger primary children,
older primary children, younger secondary children and older
secondary children. This is only one way of organizing a
qualitative data analysis by groups. The advantage of this method
is that it automatically groups the data and enables themes,
patterns and similar to be seen at a glance. While this is a useful
method for summarizing similar responses, the collective responses
of an individual participant are dispersed across many categories
and groups of people, and the integrity and coherence of the
individual respondent risks being lost to a collective summary.
26
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ContFive ways of organizing and presenting data analysis
Further, this method is often used in relation to a
single-instrument approach, otherwise it becomes unwieldy (for
example, trying to put together the data derived from qualitative
questionnaires, interviews and observations could be very
cumbersome in this approach). So, researchers may nd it helpful to
use this approach instrument by instrument. A second way of
organizing the data analysis is by individuals. Here the total
responses of a single participant are presented, and then the
analysis moves on to the next individual. This preserves the
coherence and integrity of the individuals response and enables a
whole picture of that person to be presented, which may be
important for the researcher. However, this integrity exacts its
price, in that, unless the researcher is interested only in
individual responses, it often requires him or her then to put
together the issues arising across the individuals (a second level
of analysis) in order to look for themes, shared responses,
patterns of response, agreement and disagreement, to compare
individuals and issues that each of them has raised, i.e. to
summarize the data. 27
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ContFive ways of organizing and presenting data analysis While
approaches that are concerned with people strive to be faithful to
those involved in terms of the completeness of the picture of them
qua people, unless case study approaches are deemed to be driving
the research, they are usually accompanied by a second round of
analysis, which is of the issues that arise from the people, and it
is to the matter of issues that I turn now. A third way of
organizing data is to present all the data that are relevant to a
particular issue. This is the method that was used in the example
of Chinese students learning English. While it is economical in
making comparisons across respondents (the issue of data reduction
through careful data display, mentioned earlier), again the
wholeness, coherence and integrity of each individual respondent
risks being lost. 28
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ContFive ways of organizing and presenting data analysis The
derivation of the issue for which data are gathered needs to be
claried. For example, it could be that the issue has been decided
pre- ordinately, in advance of the data collection. Then all the
relevant data for that issue are simply collected together into
that single basket the issue in question. While this is an
economical approach to handling, summarizing and presenting data,
it raises three main concerns: The integrity and wholeness of each
individual can be lost, such that comparisons across the whole
picture from each individual is almost impossible. The data can
become decontextualized. This may occur in two ways: rst, in terms
of their place in the emerging sequence and content of the
interview or the questionnaire (e.g. some data may require an
understanding of what preceded a particular comment or set of
comments), and second, in terms of the overall picture of the
relatedness of the issues, as this approach can fragment the data
into relatively discrete chunks, thereby losing their
interconnectedness. 29
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ContFive ways of organizing and presenting data analysis Having
had its framework and areas of interest already decided pre-
ordinately, the analysis may be unresponsive to additional relevant
factors that could emerge responsively in the data. It is akin to
lowering a magnet onto data the magnet picks up relevant data for
the issue in question but it also leaves behind data not deemed
relevant and these risk being lost. The researcher, therefore, has
to trawl through the residual data to see if there are other
important issues that have emerged that have not been caught in the
pre-ordinate selection of categories and issues for attention. The
researcher, therefore, has to be mindful of the strengths and
weaknesses not only of pre-ordinate categorization (and, by
implication, include responsive categorization), but also the
researcher must decide whether it is or is not important to
consider the whole set of responses of an individual, i.e. to
decide whether the data analysis is driven by people/respondents or
by issues. 30
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ContFive ways of organizing and presenting data analysis A
fourth method of organizing the analysis is by research question.
This is a very useful way of organizing data, as it draws together
all the relevant data for the exact issue of concern to the
researcher, and preserves the coherence of the material. It returns
the reader to the driving concerns of the research, thereby closing
the loop on the research questions that typically were raised in
the early part of an inquiry. In this approach all the relevant
data from various data streams (interviews, observations,
questionnaires etc.) are collated to provide a collective answer to
a research question. There is usually a degree of systematization
here, in that, for example, the numerical data for a particular
research question will be presented, followed by the qualitative
data, or vice versa. This enables patterns, relationships,
comparisons and qualications across data types to be explored
conveniently and clearly. 31
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ContFive ways of organizing and presenting data analysis A fth
method of organizing the data is by instrument. Typically this
approach is often used in conjunction with another approach, e.g.
by issue or by people. Here the results of each instrument are
presented, e.g. all the interview data are presented and organized,
and then all the data from questionnaires are presented, followed
by all the documentary data and eld notes, and so on. While this
approach retains delity to the coherence of the instrument and
enables the reader to see clearly which data derive from which
instrument, one has to observe that the instrument is often only a
means to an end, and that further analysis will be required to
analyse the content of the responses by issue and by people. Hence
if it is important to know from which instrument the data are
derived then this is a useful method; however, if that is not
important then this could be adding an unnecessary level of
analysis to the data. Further, connections between data could be
lost if the data are presented instrument by instrument rather than
across instruments. 32
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ContFive ways of organizing and presenting data analysis In
analysing qualitative data, a major tension may arise from using
contrasting holistic and fragmentary/atomistic modes of analysis.
The example of teaching English in Macau is clearly atomistic,
breaking down the analysis into smaller sections and units. It
could be argued that this violates the wholeness of the respondents
evidence, and there is some truth to this, though one has to ask
whether this is a problem or not. Sectionalizing and fragmenting
the analysis can make for easy reading. On the other hand, holistic
approaches to qualitative data presentation will want to catch the
wholeness of individuals and groups, and this may lead to a more
narrative, almost case study or story style of reporting with
issues emerging as they arise during the narrative! Neither
approach is better than the other; researchers need to decide how
to present data with respect to their aims and intended readership.
33
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Systematic approaches to data analysis Data analysis can be
very systematic. Becker and Geer (1960) indicate how this might
proceed: comparing different groups simultaneously and over time
matching the responses given in interviews to observed behaviour
analysing deviant and negative cases calculating frequencies of
occurrences and responses assembling and providing sufcient data
that keeps separate raw data from analysis. 34
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Systematic approaches to data analysis In qualitative data the
analysis here is almost inevitably interpretive, hence the data
analysis is less a completely accurate representation (as in the
numerical, positivist tradition) but more of a reexive, reactive
interaction between the researcher and the decontextualized data
that are already interpretations of a social encounter. Indeed
reexivity is an important feature of qualitative data analysis. The
issue here is that the researcher brings to the data his or her own
preconceptions, interests, biases, preferences, biography,
background and agenda. As Walford (2001: 98) writes: all research
is researching yourself. In practical terms it means that the
researcher may b selective in his or her focus, or that the
research may be inuenced by the subjective features of the
researcher. Robson (1993: 3745) and Lincoln and Guba (1985: 3545)
suggest that these can include: 35
Slide 36
ContSystematic approaches to data analysis data overload
(humans may be unable to handle large amounts of data) rst
impressions (early data analysis may affect later data collection
and analysis) availability of people (e.g. how representative these
are and how to know if missing people and data might be important)
information availability (easily accessible information may receive
greater attention than hard-to-obtain data) positive instances
(researchers may over-emphasize conrming data and under emphasize
disconrming data). internal consistency (the unusual, unexpected or
novel may be under treated). uneven reliability (the researcher may
overlook the fact that some sources are more reliable or unreliable
than others). missing data (that issues for which there is
incomplete data may be overlooked or neglected) revision of
hypotheses (researchers may over-react or under-react to new data)
36
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ContSystematic approaches to data analysis condence in
judgement (researchers may have greater condence in their nal
judgements than is tenable) co-occurrence may be mistaken for
association inconsistency (subsequent analyses of the same data may
yield different results); a notable example of this is Bennett
(1976) and Aitkin et al. (1981). The issue here is that great
caution and self- awareness must be exercised by the researcher in
conducting qualitative data analysis, for the analysis and the
ndings may say more about the researcher than about the data. For
example, it is the researcher who sets the codes and categories for
analysis, be they pre-ordinate or responsive (decided in advance of
or in response to the data analysis respectively). It is the
researchers agenda that drives the research and the researcher who
chooses the methodology. 37
Slide 38
ContSystematic approaches to data analysis As the researcher
analyses data, he or she will have ideas, insights, comments,
reections to make on data. These can be noted down in memos and,
indeed, these can become data themselves in the process of
reexivity (though they should be kept separate from the primary
data themselves). Glaser (1978) and Robson (1993: 387) argue that
memos are not data in themselves but help the process of data
analysis. This is debatable: if reexivity is part of the data
analysis process then memos may become legitimate secondary data in
the process or journey of data analysis. Many computer packages for
qualitative data analysis (discussed later) have a facility not
only for the researcher to write a memo, but also to attach it to a
particular piece of datum. 38
Slide 39
There is no single nature or format of a memo; it can include
subjective thoughts about the data, with ideas, theories,
reections, comments, opinions, personal responses, suggestions for
future and new lines of research, reminders, observations,
evaluations, critiques, judgements, conclusions, explanations,
considerations, implications, speculations, predictions, hunches,
theories, connections, relationships between codes and categories,
insights and so on. Memos can be reections on the past, present and
the future, thereby beginning to examine the issue of causality.
There is no required minimum or maximum length, though memos should
be dated not only for ease of reference but also for a marking of
the development of the researcher as well as of the research.
39
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ContSystematic approaches to data analysis Memos are an
important part of the self-conscious reection on the data and have
considerable potential to inform the data collection, analysis and
theorizing processes. They should be written whenever they strike
the researcher as important during and after analysis. They can be
written any time; indeed some researchers deliberately carry a pen
and paper with them wherever they go, so that ideas that occur can
be written down before they are forgotten. The great tension in
data analysis is between maintaining a sense of the holism of the
data the text and the tendency for analysis to atomize and fragment
the data to separate them into constituent elements, thereby losing
the synergy of the whole, and often the whole is greater than the
sum of the parts. There are several stages in analysis, for
example: 40
Slide 41
ContSystematic approaches to data analysis generating natural
units of meaning classifying, categorizing and ordering these units
of meaning structuring narratives to describe the contents
interpreting the data. These are comparatively generalized stages.
Miles and Huberman (1994) suggest twelve tactics for generating
meaning from transcribed data: counting frequencies of occurrence
(of ideas, themes, pieces of data, words) noting patterns and
themes (Gestalts), which may stem from repeated themes and causes
or explanations or constructs seeing plausibility: trying to make
good sense of data, using informed intuition to reach a conclusion
clustering: setting items into categories, types, behaviours and
classications making metaphors: using gurative and connotative
language rather than literal and denotative language, bringing data
to life, thereby reducing data, making patterns, decentring the
data, and connecting data with theory 41
Slide 42
ContSystematic approaches to data analysis splitting variables
to elaborate, differentiate and unpack ideas, i.e. to move away
from the drive towards integration and the blurring of data
subsuming particulars into the general (akin to Glasers (1978)
notion of constant comparison: a move towards clarifying key
concepts factoring: bringing a large number of variables under a
smaller number of (frequently) unobserved hypothetical variables
identifying and noting relations between variables nding
intervening variables: looking for other variables that appear to
be getting in the way of accounting for what one would expect to be
strong relationships between variables building a logical chain of
evidence: noting causality and making inferences making
conceptual/theoretical coherence: moving from metaphors to
constructs, to theories to explain the phenomena. 42
Slide 43
ContSystematic approaches to data analysis This progression,
though perhaps positivist in its tone, is a useful way of moving
from the specic to the general in data analysis. Running through
the suggestions from Miles and Huberman (1994) is the importance
that they attach to coding of data, partially as a way of reducing
what is typically data overload from qualitative data. Miles and
Huberman (1994) suggest that analysis through coding can be
performed both within-site and cross-site, enabling causal chains,
networks and matrices to be established, all of these addressing
what they see as the major issue of reducing data overload through
careful data display. Content analysis involves reading and
judgement; Brenner et al. (1985) set out several steps in
undertaking a content analysis of open- ended data: 43
Slide 44
ContSystematic approaches to data analysis brieng:
understanding the problem and its context in detail sampling: of
people, including the types of sample sought Associating: with
other work that has been done Hypothesis development hypothesis
testing Immersion: in the data collected, to pick up all the clues
Categorizing: in which the categories and their labels must reect
the purpose of the research, be exhaustive and be mutually
exclusive Incubation: e.g. reecting on data and developing
interpretations and meanings Synthesis: involving a review of the
rationale for coding and an identication of the emerging patterns
and themes Culling: condensing, excising and even reinterpreting
the data so that they can be written up intelligibly
Interpretation: making meaning of the data Writing: including
giving clear guidance on the incidence of occurrence; proving an
indication of direction and intentionality of feelings; being aware
of what is not said as well as what is said silences; indicating
salience (to the readers and respondents) Rethinking. 44
Slide 45
ContSystematic approaches to data analysis Content analysis
requires researchers to address several factors: Understand the
research brief thoroughly. Evaluate the relevance of the sample for
the research project. Associate their own experiences with the
problem, looking for clues from the past. Develop testable
hypotheses as the basis for the content analysis. Test the
hypotheses throughout the interviewing and analysis process. Stay
immersed in the data throughout the study. Categorize the data in
the Concept Book, creating labels and codes. Incubate the data
before writing up. Synthesize the data in the Concept Book, looking
for key concepts. 45
Slide 46
ContSystematic approaches to data analysis Cull the data; being
selective is important because it is impossible to report
everything that happened. Interpret the data, identifying its
meaning and implication. Write up the report. Rethink and rewrite:
have the research objectives been met? 46
Slide 47
ContSystematic approaches to data analysis Hycner (1985) sets
out procedures that can be followed when phenomenologically
analysing interview data. The phenomenologist advocates the study
of direct experience taken at face value and sees behaviour as
determined by the phenomena of experience rather than by external,
objective and physically described reality. Hycner (1985) points
out that there is a reluctance on the part of phenomenologists to
focus too much on specic steps in research methods for fear that
they will become reied. The steps suggested by Hycner, however,
offer a possible way of analysing data which allays such fears. As
he himself explains, his guidelines have arisen out of a number of
years of teaching phenomenological research classes to graduate
psychology students and trying to be true to the phenomenon of
interview data while also providing concrete guidelines (Hycner
1985). In summary, the guidelines are as follows: 47
Slide 48
Brief view of guidelines Transcription: having the interview
tape transcribed, noting not only the literal statements but also
non-verbal and paralinguistic communication. Bracketing and
phenomenological reduction: this means suspending (bracketing) as
much as possible the researchers meaning and interpretations and
entering into the world of the unique individual who was
interviewed. The researcher thus sets out to understand what the
interviewee is saying rather than what the researcher expects that
person to say. Listening to the interview for a sense of the whole:
this involves listening to the entire tape several times and
reading the transcription a number of times in order to provide a
context for the emergence of specic units of meaning and themes
later on. Delineating units of general meaning: this entails a
thorough scrutiny of both verbal and non-verbal gestures to elicit
the participants meaning. It is a crystallization and condensation
of what the participant has said, still using as much as possible
the literal words of the participant (Hycner 1985). 48
Slide 49
ContBrief view of guidelines Delineating units of meaning
relevant to the research question: once the units of general
meaning have been noted, they are then reduced to units of meaning
relevant to the research question. Training independent judges to
verify the units of relevant meaning: ndings can be veried by using
other researchers to carry out the above procedures. Hycners own
experience in working with graduate students well trained in this
type of research is that there are rarely signicant differences in
the ndings. Eliminating redundancies: at this stage, the researcher
checks the lists of relevant meaning and eliminates those clearly
redundant to others previously listed. Clustering units of relevant
meaning: the researcher now tries to determine if any of the units
of relevant meaning naturally cluster together; whether there seems
to be some common theme or essence that unites several discrete
units of relevant meaning. 49
Slide 50
ContBrief view of guidelines Determining themes from clusters
of meaning: the researcher examines all the clusters of meaning to
determine if there is one (or more) central theme(s) which
expresses the essence of these clusters. Writing a summary of each
individual interview: it is useful at this point, Hycner suggests,
to go back to the interview transcription and write up a summary of
the interview incorporating the themes that have been elicited from
the data. Return to the participant with the summary and themes,
conducting a second interview: this is a check to see whether the
essence of the rst interview has been accurately and fully
captured. 50
Slide 51
ContBrief view of guidelines Modifying themes and summary: with
the new data from the second interview, the researcher looks at all
the data as a whole and modies them or adds themes as necessary.
Identifying general and unique themes for all the interviews: the
researcher now looks for the themes common to most or all of the
interviews as well as the individual variations. The rst step is to
note if there are themes common to all or most of the interviews.
The second step is to note when there are themes that are unique to
a single interview or a minority of the interviews.
Contextualization of themes: at this point it is helpful to place
these themes back within the overall contexts or horizons from
which they emerged. 51
Slide 52
Composite summary: Hycner (1985) considers it useful to write
up a composite summary of all the interviews which would accurately
capture the essence of the phenomenon being investigated. Such a
composite summary describes the world in general, as experienced by
the participants. At the end of such a summary the researcher might
want to note signicant individual differences (Hycner 1985).
52
Slide 53
Methodological tools for analysing qualitative data There are
several procedural tools for analysing qualitative data. LeCompte
and Preissle (1993:253) see analytic induction, constant
comparison, typological analysis and enumeration as valuable
techniques for the qualitative researcher to use in analysing data
and generating theory. 53
Slide 54
Analytic induction Analytic induction is a term and process
that was introduced by Znaniecki (1934) in deliberate opposition to
statistical methods of data analysis. LeCompte and Preissle (1993:
254) suggest that the process is akin to the several steps set out
above, in that data are scanned to generate categories of
phenomena, relationships between these categories are sought and
working typologies and summaries are written on the basis of the
data examined. These are then rened by subsequent cases and
analysis; negative and discrepant cases are deliberately sought to
modify, enlarge or restrict the original explanation or theory.
Denzin (1970b: 192) uses the term analytical induction to describe
the broad strategy of participant observation that is set out
below: 54
Slide 55
ContAnalytic induction A rough denition of the phenomenon to be
explained is formulated. A hypothetical explanation of that
phenomenon is formulated. One case is studied in the light of the
hypothesis, with the object of determining whether or not the
hypothesis ts the facts in that case. If the hypothesis does not t
the facts, either the hypothesis is reformulated or the phenomenon
to be explained is redened, so that the case is excluded. Practical
certainty may be attained after a small number of cases has been
examined, but the discovery of negative cases disproves the
explanation and requires a reformulation. This procedure of
examining cases, redening the phenomenon, and reformulating the
hypothesis is continued until a universal relationship is
established, each negative case calling for a redenition of a
reformulation. 55
Slide 56
ContAnalytic induction A more deliberate seeking of disconrming
cases is advocated by Bogdan and Biklen (1992: 72) where they
enumerate ve main elements in analytic induction: In the early
stages of the research a rough denition and explanation of the
particular phenomenon is developed. This denition and explanation
is examined in the light of the data that are being collected
during the research. If the denition and/or explanation that have
been generated need modication in the light of new data (e.g. if
the data do not t the explanation or denition) then this is
undertaken. A deliberate attempt is made to nd cases that may not t
into the explanation or denition. The process of redenition and
reformulation is repeated until the explanation is reached that
embraces all the data, and until a generalized relationship has
been established, which will also embrace the negative cases.
56
Slide 57
Constant comparison In constant comparison the researcher
compares newly acquired data with existing data and categories and
theories that have been devised and which are emerging, in order to
achieve a perfect t between these and the data. Hence negative
cases or data which challenge these existing categories or theories
lead to their modication until they can fully accommodate all the
data. 57
Slide 58
Typological analysis Typological analysis is essentially a
classicatory process (LeCompte and Preissle 1993: 257) wherein data
are put into groups, subsets or categories on the basis of some
clear criterion (e.g. acts, behaviour, meanings, nature of
participation, relationships, settings, activities). It is the
process of secondary coding (Miles and Huberman 1984) where
descriptive codes are then drawn together and put into subsets.
Typologies are a set of phenomena that represent subtypes of a more
general set or category (Loand 1970). Lazarsfeld and Barton (1951)
suggest that a typology can be developed in terms of an underlying
dimension or key characteristic. In creating typologies Loand
(1970) insists that the researcher must deliberately assemble all
the data on how a participant addresses a particular issue what
strategies are being employed; disaggregate and separate out the
variations between the ranges of instances of strategies; classify
these into sets and subsets; and present them in an ordered, named
and numbered way for the reader 58
Slide 59
Enumeration The process of enumeration is one in which
categories and the frequencies of codes, units of analysis, terms,
words or ideas are counted. This enables incidence to be recorded,
and, indeed statistical analysis of the frequencies to be
undertaken (e.g. Monge and Contractor 2003). This is a method used
in conventional forms of content analysis. This lecture has
suggested several approaches to analysing and presenting
qualitative data.. 59