What is qualitative data analysis?
Different approaches to analysing qualitative
data
By the end of the session you will…
• Have an understanding of what qualitative data sets look like
and the common forms in which they are presented
• Have an understanding of the different data analysis methods
that are available
• Be able to prepare, manage and code your data, identify
themes and make recommendations
• Understand the issues surrounding validity and reliability when
analysing qualitative data sets
• Know how to produce a qualitative analysis data report based
on your findings
What is qualitative research?
‘Qualitative Data Analysis (QDA) is the range
of processes and procedures whereby we
move from the qualitative data that have been
collected, into some form of explanation,
understanding or interpretation of the people
and situations we are investigating’.
QDA is usually based on an interpretative philosophy. The
idea is to examine the meaningful and symbolic content of
qualitative data. By analysing interview data, the researcher
may be attempting to identify any or all of:
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Someone's interpretation of the world,
Why they have that point of view,
How they came to that view,
What they have been doing,
How they conveyed their view of their situation,
How they identify or classify themselves and others in
what they say,
Types of Qualitative data sets
• Semiotic analysis: Analysing signs and symbols (e.g. A
participant captures an image and is filmed whilst talking
through what the image means to them– it is important
to note that the analysis is of the picture and its meaning
in a particular social context)
• Thematic analysis – An account from the participant is
captured by an interviewer driving the conversation
through asking questions.
• Conversational analysis – capturing a conversation
between two people without any prompts.
• Narrative analysis – A deep analysis of the dimensions of
social life. This is one person giving an account of an
issue, similar to ‘story telling’.
Different analysis methods
You can apply different analytic methods to a data set depending
on how the data has been collected.
Framework analysis
You have a pre-existing research question(s) and you are looking
to prove this through analysing the data set.
VS
Grounded theory
Analyse the data set looking for themes derived from the content
and make a decision as to whether 1 theme is worth exploring
more than other. Grounded theory is the basis of thematic analysis
and state that the theory emerges out of the data.
Principles of Grounded theory
‘Naming, defining and understanding what people say’
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Theory emerges out of the data rather than being developed in
advance
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Begin with as few pre-determined ideas as possible
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Coding is shaped by the researchers interpretation of the data
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Characterised by different types and levels of code
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Categories of data are created and refined through constant
comparison
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Analysis and data collection are iterative
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End point is theoretical saturation
Thematic analysis: moving between
levels of codes
Things to consider before you start…
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Make field notes throughout the entire data collection and analysis process.
Reflecting on the process of analysis during your fieldwork is key.
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Be aware of researcher effects: For example, if you are analysing interview
data; did you put words in participant’s mouth? ‘Did that make you feel
guilty?’ – Avoid this!
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Important to consider your research design before you collect research as
this will make the analysing part easier (we will come back to this later!)
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Keep memos: They are useful to bridge the gap between the start of the
project and the end of the data analysis. You can find something completely
different at the end but memos allow you to keep evaluating what you are
doing and what you would do different if you were to run research again?
Validity & Reliability
Validity
How can we be sure we are measuring what our we set
out to? To what extent are we describing and measuring
what our research question set out?
As qualitative researchers we need to be able to account
for how inferences were made in our analysis and our
conclusions reached. This is the only way our findings
are credible.
Create an analysis group – through having different
people analysing the data set, your validity will increase.
Reliability
If we repeated the research, would we find the same results?
Objectivity needs to be embedded into the research process.
Demonstrating reliability through keeping detailed records of the
links between the claims you are making and the evidence on
which it is based.
‘Careful Scholarship- Seale 2006’
Be aware of the boundaries of the claims that you make from
analysing your qualitative data and remain transparent throughout
your analysis about all of the processes that you have used.
Are there any instances that contradict the claim that you are
making? Disruptions can exist and not contradict your argument.
Be upfront about these anomalies and this will help your reliability.
Preparing and managing your data set
• Leave large margins for annotations
• Do not tidy the language – although it is
tempting to correct language and make it more
your own style…RESIST!
• Keep your field notes to hand when analysing
your data set.
Coding your data set
Formal definition
‘The identification of passages of text (or other meaningful
phenomena, such as parts of images) and applying labels to
them that indicate they are examples of some thematic
idea.
At its simplest, this labelling or coding process enables
researchers quickly to retrieve and collect together all the
text and other data that they have associated with some
thematic idea so that they can be examined together and
different cases can be compared in that respect’.
Stage 1 : Initial coding
‘Define, understand and explain what is happening in
the data’
1. Number each line in the data
2. Code every new concept – keep this basic and do not
attribute a feeling to the code. Simple codes such as
‘bereavement’. To code ‘loss’ would come later because
attributing an emotion here.
3. Code everything else, not just content in the data. For
example, when the data was collected, where it was
collected or if there were any external influences?
Stage 2 : Focussed coding
Synthesise the most significant/frequently used codes
across cases to create categories/themes/conceptual
similarities.
1. Move from initial descriptive codes, onto groups of
codes. This is where ‘loss’ would become relevant.
2. Compare your data to see if the same codes are
appearing.
Stage 3 : Selective/theoretical coding
For example, if you had some initial codes of Money,
Income, Receipts. The selective code could be Finance.
Ensure you hold coding meetings. Having more
than 1 person help with coding adds creativity and
validity to your analysis.
Keep a ‘coding book’ throughout the entire coding
process. Include this in your appendix when writing
your report.
Example…
‘X’ is an overall outstanding lecturer. One, if not the ONLY lecturer on my
course that I feel has my best interests at heart and takes an interest in
her students. ‘X’ is always networking and engaging with PR practitioners
and agencies to ensure we get the best chances of securing Work
Experience or future job vacancies. ‘X’ always encourages us to raise our
chances for employability and helps us with this through her own personal
contacts as well as arranging regular CIPR networking events - dedicating
her time outside of class. ‘X’ is fair and organised - which the whole class
appreciates - and she is able to communicate her messages well. She
explains why we are being taught a certain topic and how this will add to
either our employability or personal development. She is always looking for
new ways to enhance these two areas, and often drafts in live clients and
professionals - which enables us to experience real life simulations, as well
as development of our abilities. I could have nominated ‘X’ for 'Open door',
'outstanding feedback' or 'strongest supervision' because she really is a
fantastic influence. I think ‘X’ is a credit to Solent and I know I would have
dropped out in my second year, if it wasn’t for her support.
Stage 1: Initial coding
1. Outstanding
2. Best interest
3. Takes an interest
4. Networking agencies
5. Securing work experience
6. Raises chances of employability
7. Helps make new contacts
8. Arranges networking events
9. Dedicates time outside class
10. Organised
11. Communicates well
12. Explains why
13. Personal development
14. Develops abilities
15. Fantastic influence
16. Credit to university
17. Would have dropped out if not for lecturer
Stage 2&3…
Stage 2: Focussed coding
Code 1: Organisation/style of teaching: 8, 10, 11, 12, 14, 3, 7, and 2
Code 2: Skills: 1, 5, 7, 10, 11, 12, 14, and 17
Code 3: Outside of classroom behaviour: 4, 5, 6, 7, 8, 9, 17, and 13
Code 4: Employability opportunities: 4, 5, 6, 7, and 8
Code 5: Admiration of lecturer: 15, 16
Stage 3: Selective coding
Improving employability prospects (Code 4)
Adopting a style of teaching that moves beyond standard academic
practices/teaching (Code 1, 2, 3)
Admiration/Respect for lecturer (Code 5)
Theoretical saturation
This is where you reach a point where no new
information is coming out of the data. Be realistic
about how much time and resource you have and
stop analysis at a sensible point.
Writing up your findings
What have you found? In many cases what you
write may be analytic ideas. In other cases it may
be some form of précis or summary of the data,
though this usually contains some analytic ideas.
Make it your own!
Ensure you evidence reliability and validity and
include your coding book in your appendix
Thank you, any questions?