Transcript Qualitative data analysis

Research Methods
Qualitative data analysis
Session outline
Qualitative data analysis: what it is,
aims, initial thoughts and common
mistakes
 Approaches to data analysis: Theoretical
propositions, Triangulation, Grounded
theory, Content analysis
 4 key steps: Speculative analysis, Data
Reduction, Data Display, Drawing and
verifying conclusions

Types of
qualitative data

field notes descriptions of
observations, events
 interview transcripts
 documents - eg.
policies, syllabuses
 open ended
questionnaire
answers
2 messages:

Decide how to
analyse your data
BEFORE you collect
it!
 NOT easier than
quantitative data
analysis - but
different
Data Analysis
Initial thoughts:
* fatigue - time/energy in collecting data
* I’ve done the hard bit
* where do I start??
* writers block
Go back to research questions…
*what did the study aim to do
*what were your initial hunches
Aims/purposes of analysis
central to the production of meaningful
and valid research
 from which you draw your conclusions
 process by which you convince your
readers of these
 therefore needs to be open, public
process - how did you get there?

Preliminary considerations

No specified
 Preliminary and
conventions for
ongoing analysis
analysing qualitative
should be engaged
data in the way that
in as data is
exists for quantitative
collected
data.
 distinction data
 Nevertheless, still
collection and
need systematic
analysis less sharp
approach to
examining and
for qualitative studies
interpreting the
than for quantitative.
results of the data
gathering.
Qualitative is not as easy as it
looks…
 Qualitative analysis is ‘as much a test of the
enquirer as it is a test of the data:
 first and foremost, analysis is a test of
the...ability to think - to process information in
a meaningful and useful manner...qualitative
analysis remains much closer to codified
common-sense than the complexities of
statistical analysis of quantitative data’
(Robson 1995: 374).
 As a result it is important to recognise some
common failings.
Common mistakes
(Robson, 1995: 374-5)




data overload - too much
to process and remember
information availability information which is
difficult to get hold of
(wrongly) gets less
attention
positive instances - a
tendency to ignore
evidence which conflicts
with hypotheses
uneven reliability - that
some sources are more
reliable than others




fictional base - tendency
to compare with a base
or average when no
base data is available
confidence in judgement
- excessive confidence
once a judgement is
made
co-occurrence interpreted as strong
evidence of correlation
inconsistency - repeated
evaluations of the same
data which differ
Approaches to analysis
Theoretical propositions
 Triangulation
 Grounded theory
 Content analysis

Theoretical propositions

The task of analysing data can be more
systematic if the study is based on
particular theoretical propositions (or a
conceptual framework).
Triangulation
Triangulation involves gathering data on
the same theme from a variety of
sources.
 It can be useful in data analysis whether
or not there are correspondences or
discrepancies.

Grounded theory
Provides an ‘open’ approach to data analysis
where the structure is derived from the data.
This means systematically analysing it so as
to tease out themes, patterns and categories.
 Jones (1987 cited in Easterby-Smith et al
1993: 108) argues that ‘rather than forcing
data within logico-deductively derived
assumptions and categories, research should
be used to generate grounded theory, which
‘fits’ and ‘works’ because it is derived from the
concepts and categories used by social
actors themselves to interpret and organise
their worlds’.

Grounded theory analysis:
Seven main stages (1/2)
(Easterby-Smith et al,1987)
familiarisation - re-reading transcripts
and preliminary impressions
 reflection - questions posed might
include ‘does it support existing
knowledge?’ ‘does it challenge it?’.
Cataloguing is important. There may be
a range of ideas, hypotheses or
explanations.
 conceptualisation - a set of concepts or
variables are identified as important

Grounded theory analysis:
Seven main stages (2/2)
(Easterby-Smith et al,1987)

cataloguing concepts - the main concepts are
organised and recorded
 recoding - the concepts are examined by
revisiting the data to understand how they may
apply in different contexts
 linking - the analytical framework and
explanations become more refined. Findings
and ideas may be discussed with others
 re-evaluation - refinement in the light of
comment by others and reflection
Content analysis and
hermeneutics: what it is
Detailed analysis of the contents of a
certain body of texts.
 Useful to analyse marketing materials
such as adverts and brochures, District
Council tourism strategies, and other
written documents.
 A research method in its own right, and
also a data analysis technique for indepth interview transcripts, focus
groups…

Content analysis: what it does
Some authors emphasize frequency
(e.g. number of times particular items are
mentioned) but this is very mechanistic
and content analysis can go further and
analyse other components and also data
in ways other than counting.
 Frequency can be preliminary analysis to
then allow other research

Qualitative data analysis:
4 Key Steps
1.
2.
3.
4.
Speculative analysis
Data Reduction
Data Display
Drawing and verifying conclusions
1. Speculative analysis
on going throughout the data collection,
and confirmed through data transcription
 initial hunches, thoughts, reactions
 often recorded alongside the actual data
* eg before and after an interview- what
did you think?

2. Data Reduction

reduce data to
manageable and
meaningful amounts
 reduction is NOT
ignoring/getting rid of
data
 involves coding or
categorising of the
data
 categories need to be
exclusive

a code is a means of
classifying events,
actions, ideas
 a means of quickly
retrieving data
 a means to develop
an analytical
framework
 need to code data
using theoretical
sensitivity
Data Reduction
(Robson,1995: 401)

counting - categorising data and measuring
frequency
 patterning - noting recurring patterns or themes
 clustering - groupings of objects, persons, activities,
settings etc. with similar characteristics
 factoring - grouping of variables into a small number
of hypothetical factors
 relating variables - explaining the type of relationship
between two variables (if any)
 building causal networks - chains or webs of linkages
between variables
 relating findings to general theoretical frameworks find general propositions that account for the
particular findings in the study
Theoretical Sensitivity
‘Theoretical sensitivity is the ability to
recognise what is important in the data
and to give it meaning’ (Hitchcock and
Hughes, 1995, p.298)
 thorough understanding of theoretical
and research literature and personal/
professional experience
 continual interaction with the data

Data - Theory relationship
in Qualitative Research
quantitative
data
 theory- data
collection to
test - confirm
or refute the
theory
qualitative data
 start with the
data - builds
theory
 ‘grounded
theory’
 more openended, creative
process
3. Data Display
clear to the reader
 two key ways:
 narrative text - discussion of key themes,
or issues, illustrated with data (eg.
quotations, q’naire answers etc)
 Visual presentation - eg. pie diagrams,
tables etc
 not either/or, can use a mixture...

Narrative Text
Key themes identified by reading and rereading data
 sifting and sorting into categories
 describing and analysing the data is
intertwined, drawing on theoretical
framework of the study)
 (analysis of data consider the
implications of the data -why is what you
have found important and in what ways?)

Visual Presentation
presents a clear visual image of
patterns/themes
 titles, scales, and reference to the
diagrams in text is IMPORTANT
 avoid using % if numbers are small
 avoid describing what is in a table in the
text..explore the implications instead

Presentation of qualitative data
4. Drawing and verifying
conclusions

representativeness of sample - don’t over
generalise

your interpretation of events 

triangulation of data collection?
claiming things that are not there!

present evidence from your data to support
interpretations eg. quotations, observations
Conclusions

admit limitations in data collection
strength, not weakness
 contributes to the research validity
 demonstrates your reflexivity as researcher


Finally ...Good qualitative research
depends upon good analysis not just
description.