Samenvatting literatuur week 1 voor data analyse voor gezondheid en maatschappij (YRM21806)
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Course
Data Analysis for Health and Society (YRM21806)
Institution
Wageningen University (WUR)
Book
Qualitative Data Analysis
samenvatting van de literatuur van week 1 van het vak data analyse voor gezondheid en maatschappij jaar 2. Dit is gebaseerd op wat er gelezen moet worden volgens de studiewijzer. Het boek van Miles, Huberman, en Sadana en het boek van Field
Van miles, huberman & saldaña: hoofdstuk 1 en hoofdstuk 4 tot en met pagina 86. van fields : hoofdst
May 10, 2021
21
2020/2021
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Data Analysis for Health and Society (YRM21806)
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LITERATUUR WEEK 1
MILES, HUBERMAN & SALDANA
CHAPTER 1 QUALITATIVE RESEARCH
GENRES OF QUALITATIVE RESEARCH
Saldana describes more than 20 different qualitative research genres out of many more available to
investigators, ranging from well-established traditions such as ethnography , grounded theory ,
phenomenology and case study to more progressive genres of qualitative research , such as poetic inquiry,
narrative inquiry, ethnodrama, autoethnography and duo ethnography. Each approach will generally employ
particular forms of analysis with their data.
The primary methodology of social anthropology , ethnography, stays close to the naturalist form of inquiry.
That is a extended contact withing a given community, concern for mundane day-to-day events as well as for
unusual ones, direct or indirect participation in local activities with particular care given to the description of
local particularities, a focus on individual’s perspectives and interpretations of their world, relatively little pre-
structured instrumentation and more purposeful observation than in other traditions.
Ethnographic methods tend toward the descriptive. The analysis task is to reach across multiple data sources
and to condense them. Genres such as content analysis, conversation analysis, and discourse analysis pay
meticulous attention to the nuances and embedded meanings of literally every single word in a data corpus.
Genres such as visual arts-based research and photovoice place primacy on the power of the I mage to
represent human experience. Autoethnography examines the self, while duo ethnography examines the self in
relationship with another – who is also examining one’s self.
AN APPROACH TO QUALITATIVE DATA ANALYSIS
The data-analytics methods and techniques we’ve employed over the past few decades have been a little bit of
this and a little bit of that, used on an ‘as needed’ basis. The analytic sequence of this book is probably closest
to ethnographic methods, with some borrowed techniques from grounded theory. It moves from one inductive
inference to another by selectively collecting data, comparing and contrasting this material in the quest for
patterns or regularities, seeking out more data to support or qualify these emerging clusters and gradually
drawing inferences from the links between other new data segments and the cumulative set of
conceptualizations.
ANALYTIC METHODS: SOME COMMON FEATURES
We’ve observed features that recur in many approaches to qualitative analysis. Some analytic practices may be
used across different qualitative research types:
- Assigning codes or themes to a set of field notes, interview transcripts or documents
- Sorting and sifting through these coded materials to identify similar phases, relationships between
variables, pattern, themes, categories, distinct differences between subgroups and common
sequences
- Isolating these patterns and processes and commonalities and differences and taking them out to the
field in the next wave of data collection
- Noting reflections or other remarks in jottings, journals and analytic memos
- Gradually elaborating a small set of assertions, propositions and generalizations that cover the
consistencies discerned in the database
- Comparing those generalizations with a formalized body of knowledge in the form of constructs or
theories.
,The analytic challenge for all qualitative researchers is finding coherent descriptions and explanations that still
include all of the gaps, inconsistencies and contradictions inherent in personal and social life. The risk is forcing
the logic, the order and the plausibility that constitute theory making on the uneven, sometimes random ,
nature of social life.
THE NATURE OF QUALITATIVE DATA
GENERAL NATURE
The words we collect and analyse are based on observations, interviews , documents and artifacts. These data
collection activities typically are accrued out in close proximity to a local setting for a sustained period of time.
Such data requires some type or processing like the expanding and typing up of raw field notes and audio
recordings which need to be transcribed. But the words we attach to fieldwork experiences are inevitably
framed by our implicit concepts, and the processing of field notes is itself problematic. The words we choose to
document what we see and hear can never truly be ‘objective’ they can only be our interpretation of what we
experience. The influence of the researcher’s personal values, attitudes and beliefs from and toward fieldwork
is not unavoidable.
STRENGTHS OF QUALITATIVE DATA
One major feature of well-collected qualitative data is that they focus on naturally occurring, ordinary events in
natural settings. So that we have a strong handle on what ‘real life’ is like. The confidence is buttressed by local
groundedness, the fact that the data were collected in close proximity to a specific situation.
Another feature is their richness and holism, with strong potential for revealing complexity.
Furthermore , the fact that such data are typically collected over a sustained period makes them powerful for
studying any process. And the flexibility of qualitative studies gives further confidence that we really
understand what is going on.
Qualitative data with their emphasis on people’s lived experience, are fundamentally well suited for locating
the meanings people place on the events, processes and structures of their lives and for connecting these
meaning to the social world around them.
OUR VIEW ON QUALITATIVE DATA ANALYSIS
We see analysis as three concurrent flows of activities : data condensation, data display and conclusion drawing
/ verification.
DATA CONDENSATION
This refers to the process of selecting data, focusing, simplifying, abstracting, and / or transcribing the data that
appear in the full corpus ( body ) of written up field notes, interview transcripts, documents and other empirical
materials. By condensing, we’re making data stronger. Data condensation is a form of analysis that sharpens,
sorts, focuses, discards and organizes data in such a way that ‘final’ conclusions can be drawn and verified.
DATA DISPLAY
Generically, a display is an organized, compressed assembly of information that allows conclusion drawing and
action. In daily life, displays vary from gasoline gauges to newspapers and Facebook status updates. Looking at
displays helps us understand what is happening.
DRAWING AND VERIFYING CONCLUSIONS
, From the start of data collection , the
qualitative analyst interprets what
things mean by noting patterns ,
explanations, causal flows and
propositions. The competent
researcher holds these conclusions
lightly , maintaining openness and
scepticism, but the conclusions are
still there, vague at first, then
increasingly explicit and grounded.
‘final’ conclusions may not appear
until data collection is over.
Conclusion drawing, is only half of a Gemini configuration. Conclusions are also verified as the analyst proceeds.
Verification may be as brief as a fleeting second through crossing the mind during the witing or with extensive
efforts to replicate a finding in another data set.
CHAPTER 4 FUNDAMENTALS OF QUALITATIVE DATA ANALYSIS
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