Docstoc

Routledge

Document Sample
Routledge Powered By Docstoc
					        CHAPTER THIRTEEN



ANALYSING DATA II: QUALITATIVE DATA
            ANALYSIS
STAGES OF QUALITATIVE ANALYSIS

Miles and Huberman (1994) suggest that qualitative data
analysis consists of three procedures:

1. Data reduction. This refers to the process whereby the
mass of qualitative data you may obtain – interview
transcripts, field notes, observations etc. – is reduced and
organised, for example coding, writing summaries,
discarding irrelevant data and so on.

At this stage, try and discard all irrelevant information, but
do ensure that you have access to it later if required, as
unexpected findings may need you to re-examine some
data previously considered unnecessary.
2. Data display. To draw conclusions from the mass of data,
Miles and Huberman suggest that a good display of data, in
the form of tables, charts, networks and other graphical
formats is essential. This is a continual process, rather than
just one to be carried out at the end of the data collection.
3. Conclusion drawing/verification. Your analysis should
allow you to begin to develop conclusions regarding your
study. These initial conclusions can then be verified, that
is their validity examined through reference to your
existing field notes or further data collection.
CODING QUALITATIVE DATA
Coding is the organisation of raw data into conceptual
categories. Each code is effectively a category or ‘bin’
into which a piece of data is placed.


As Miles and Huberman (1994, p.56) note:
Codes are tags or labels for assigning units of meaning to
 the descriptive or inferential information compiled during
a study. Codes are usually attached to ‘chunks’ of varying
  size – words, phrases, sentences or whole paragraphs.
Codes should be:
• Valid, that is they should accurately reflect what is
being researched.
• Mutually exclusive, in that codes should be distinct,
with no overlap.
• Exhaustive, that is all relevant data should fit into a
code.
STAGES OF DATA CODING

1. The data is carefully read, all statements relating to the
research question are identified, and each is assigned a
code, or category.

These codes are then noted, and each relevant statement is
organised under its appropriate code. This is referred to as
open coding.
2. Using the codes developed in stage 1, the researcher
rereads the qualitative data, and searches for statements
that may fit into any of the categories.

Further codes may also be developed in this stage. This is
also referred to as axial coding.
3. Once the first two stages of coding have been
completed, the researcher should become more analytical,
and look for patterns and explanation in the codes.

Questions should be asked such as:

• Can I relate certain codes together under a more general
code?
• Can I organise codes sequentially (for example does
code A happen before code B)?
• Can I identify any causal relationships (does code A
cause code B)?
4. The fourth stage is that of selective coding.

This involves reading through the raw data for cases that
illustrate the analysis, or explain the concepts.

The researcher should also look for data that is
contradictory, as well as confirmatory, as it is important not
to be selective in choosing data.

You must avoid what is referred to as confirmation bias,
or the tendency to seek out and report data that supports
your own ideas about the key findings of the study.
ORGANISING YOUR DATA
Coded data may then be organised as suggested by Biddle
et al. (2001) whereby the data units (statements, sentences,
etc.) are clustered into common themes (essentially the
same as codes), so that similar units are grouped together
into first order themes, and separated away from units with
different meaning.
The same process is then repeated with the first order
themes, which are grouped together into second order
themes.
This is repeated as far as possible as shown…
          Raw data                    Higher order        General
          themes                      themes            dimensions



The ordinary wood… has the
ultimate feel, it feels like it’s a
golf club that you're very
much in control of, rather than
its in control of you.

The whole club swung very             Controllable
well, it felt nice. You felt as if       feel
you were in control.

… just feels as though I'm in
control of the clubhead right
                                                       Club
throughout the shot.
                                                       control
I feel that I've no control over
that clubhead at all.

This feels much more difficult        Uncontrollable
to control…                               feel

…but I could not control it due
to the length and the flex of
the shaft.
At no stage are numbers assigned to any category. As
Krane et al. (1997, p.214) suggest:

Placing a frequency count after a category of experiences
 is tantamount to saying how important it is; thus value is
derived by number. In many cases, rare experiences are
   no less meaningful, useful, or important than common
   ones. In some cases, the rare experience may be the
                  most enlightening one.
WHAT SHOULD I LOOK FOR WHEN I HAVE CODED MY
DATA?

• You should look for patterns or regularities that occur.
• Within each code, look for data units that illustrate or
describe the situation you are interested in.
• Try to identify key words or phrases, such as ‘because’,
‘despite’, ‘in order to’, ‘otherwise’ and so on and try to make
sense of the data.
• Look for statements that not only support your theories, but
also refute them.
• Try to build a comprehensive picture of the topic.
Frankfort-Nachimas and Nachimas (1996) suggest that
you ask yourself a number of questions to assist in your
analysis:

1. What type of behaviour is being demonstrated?
2. What is its structure?
3. How frequent is it?
4. What are its causes?
5. What are its processes?
6. What are its consequences?
7. What are people’s strategies for dealing with the
behaviour?
USING RAW DATA TO SUPPORT YOUR ANALYSIS

You should resist the temptation to over-use quotes.
However, as a rule of thumb, you should use direct quotes
or observations:

• When they describe a phenomenon particularly well.
• To show cases or instances that are unusual.
• To show data that is unexpected.


You should also avoid including quotes without making
clear reference to how such quotes refer to your analysis.
ENSURING THE TRUSTWORTHINESS OF YOUR
ANALYSIS

Holloway and Wheeler (2009) summarise the means by
which you can try to ensure the trustworthiness of your data.

These include:

Member Validation − One particular method of note is to ask
those being investigated to judge the analysis and
interpretation themselves, by providing them with a summary
of the analysis, and asking them to critically comment upon
the adequacy of the findings.
Searching for negative cases and alternative explanations –
Interpretation should not focus on identifying only cases to
support the researcher’s ideas or explanations, but to also
identify and explain cases that contradict.


Triangulation – Combining the analysis with findings from
different data sources is useful as a means to demonstrate
trustworthiness in the analysis.
The audit trail – To ensure reliability all research should
have an audit trail by which others are able to judge the
process through which the research has been conducted,
and the key decisions that have informed the research
process.


Reflexivity – Reflexivity means that researchers critically
reflect on their own role within the whole of the data
collection process, and demonstrate an awareness of this,
and how it may have influenced findings, to the reader.
SUMMARY

1. Although qualitative and quantitative data are different in
nature, the analysis of both involves inference, systematic
analysis and comparison. Both try to seek valid conclusions
and avoid errors.
2. There are a number of ways of approaching qualitative
analysis.
3. Analysing qualitative data should be an ongoing process
throughout, as well as after the collection of data.
4. There are three key stages to qualitative data analysis:
data reduction, data display and conclusion
drawing/verification.


5. Data reduction takes place through the process of
coding. Coding involves assigning units of meaning to data
chunks, and can be open, axial or selective. These codes
can then be displayed or organised to allow the drawing of
conclusions.

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:3
posted:11/2/2012
language:Latin
pages:21