common features of qualitative data analysis

2. • Qualitative analysis which is interpretive aim to go further than
descriptive analysis, unpicking the accounts that are give and asking
questions like ‘What is going on here?’ and ‘How can we make sense of
these account?’
• It tries to gain a deeper understanding of the data that have been
gathered, and often looks ‘beneath the surface’ of the data, as it were,
try to understand how and why the particular accounts were generated
and to provide a conceptual account of the data, and/or some sort of
theorising around this.

3. • The analysis of qualitative data essentially begins with a process of ‘immersion’ in the
data – to become intimately familiar with your dataset’s content, and to begin to
notice thins that might be relevant to your research question.
• This normally involve:
- textual data: reading and re-reading each data item
- audio data: repeated listening
• By doing so, you may start to notice things of interest – e.g. loose overall impression
of the data, a conceptual idea you have about the data, or more concrete and specific
issues.

4. • Familiarisation is not a passive process of just understanding the words (or images). It
is about starting to read data as data – not simply absorbing the surface meaning of
the words, but reading the word actively, analytically and critically, starting to think
about what the data mean.
• This involves asking questions like:
- How does participant make sense of their experiences?
- Why might they be making sense of their experiences in this way (and not in
the other way?)
- In what different ways do they make sense of the topic discussed?
- How ‘common-sense’ is their story?
- What assumptions do they make in talking about the world?
- What kind of world is revealed through their account?
• An analytic sensibility is essential for moving beyond a surface, summative reading of
the data and questions like the above will help in developing an analytic sensibility.

5. • It is good to keep a record of these ‘noticing’ and record them in a place you can refer back to
(often called a analytical memo or research diary).
• What to write in analytical memo?
Reflect on:
- what the data is telling you
- reflects on the words or phrases
- record your ideas about an emergent concept or theme
- your research question, note your assumptions, issues for further investigation &
hunches to check out
- your analysis, eg. codes & their operational definition
- possible relationships among the codes, patterns, categories, themes, concepts and
assertion problems with the study
- personal or ethical dilemas
- emergent or related existing theory
- personal relation with the participant or the phenomenon
- and anything that helps you with your analysis.

6. • Coding – an abstract representation of an object or phenomenon.
• In other words, coding is a way of indexing or categorizing the text in order to
establish a framework of thematic ideas about it.
• A code is a word of brief phrases that captures the essence of why you think a
particular bit of data maybe useful. Codes provide a building blocks of analysis.
• Coding is not an exclusive process – any data extract can an should be coded as
many ways as fit the purpose.
• However, the way you code will depend on the analytical approach that you
• In broad terms, code can either reflect the semantic content of the data (called data-
derived or semantic codes) or more conceptual or theoretical interpretations of the
data (called researcher-derived or latent codes).
• New qualitative researchers tend to initially generate mostly data-driven codes as
are easier to identify, and rely less on having conceptual and theoretical knowledge
through which to make sense of data.
• The ability to generate researcher-derived codes develops with experience, as they
require deeper level of engagement with the data and with fields of scholarship and
theorising.

7. Data-derived codes Researcher-derived codes
• Provide a succinct summary of the explicit
content of the data
• Semantic codes – based on semantic
meaning of the data.
• They mirror participants’ language &
concept – we haven’t put an interpretative
frame around their words.
• Example:
Code: modern technology facilitate obesity
Passage: “I think modern technology, like allows you to
be lazy as well cos you don’t have to do things for
yourself. You get machines and stuff to do things for
you”
• Go beyond the explicit content of the data.
• They are latent codes – invokes researcher’s
conceptual and theoretical frameworks to
identify implicit meaning within data (i.e. the
assumptions an frameworks that underpin
what is said in the data).
• Example:
Code: human as naturally lazy
Passage: “..erm you know we didn’t all have cars. So like
my mum used to walk two to three miles to go to the
train station to go another ten miles to work, you know,
was like there wasa lot more impact. There was no bus
her so she had to walk. Nowadays we think ‘oh I can’t do
that’ can’t miles to go and do that’. Yeah take the cars”

8. Theme 1
(wall)
Theme 2
(roof)
Theme 3
(wall)
Code
(each individual brick)
Theory/Framework

9. • Involves identifying a corpus of ‘instances’ of phenomenon that you are
interested in, and then selecting those out.
• The purpose here is one of ‘data reduction’.
• Often seen as a pre-analytics process, the pragmatic selection of your data corpus,
rather than part of your analysis.
• However it does inevitably have an analytic element, in that you need to work out
what counts as an instance of what you are looking for, and where the instance starts
and finishes.
• It also requires pre-existing theoretical and analytic knowledge that enables you to
identify the analytic concepts that you are looking for.
• The process of reading and familiarisation may be more involved and take longer with
this approach than for a complete coding approach.
• Selective coding is normally use for narrative, discursive and conversation analytic
approaches as well as pattern base discourse analysis to build a corpus of instances of
the phenomenon you are interested in.
a) Selective Coding

10. • Involves identifying a corpus of ‘instances’ of phenomenon that you are interested in,
and then selecting those out.
a) Selective Coding

11. • Aim to identify anything and everything of interest or relevance to answering
your research question, within the entire dataset.
• You code all the data that is relevant to your research question, and it’s only later in
the analytic process that you become more selective.
b) Complete Coding

12. • Begin with your first data item, and systematically work through the whole item,
looking for chunks of data that potentially address your research question.
• If you are starting with a very broad research question, which you may refine during
the analytic process, you want to code widely and comprehensively.
• If you have a very specific research question, you may fin that large section of the
data are not relevant and don’t need to be coded.
• The process continues in the same way for the rest of the data item. For each new bit
of text you code, you have to decide whether you can apply a code you have
used, or whether a new code is needed in order to capture what it is you have
identified.
• Basically, every time you identify something potentially relevant, code it.
• What important is that coding is inclusive, thorough and systematic.
• As your coding progresses and you start to understand the shape and texture of
data a bit more, you will likely modify existing codes to incorporate new material.
• Once you have finished the first coding of the dataset, it is worth revisiting the whole
thing, as your codes will probably have developed during coding.

13. • What makes a good code:
- Codes should be as concise as possible
- Codes should work when separated from data.
- Codes should be distinct in some way.
• Ultimately you want a comprehensive set of codes that differentiates between
different concepts, issues and ideas in the data, which has been applied consistently
to the dataset.
• The final stage of complete coding is collating the coded data (the instances of text
where that codes appear in the dataset).

14. • To do selective coding, you need to know what you are looking to code before you
begin.
• Data familiarisation is thus particularly vital.
• The basic elements of selective coding include:
a) identifying what you’re coding for – normally focus on researcher-derived
codes (define it, look for it and mark it)
b) determining the boundaries of instances – deciding when an instance
begins and ends.
c) collating instances – compile all instances into a single file.
• Often additional coding occurs throughout the development of analysis, as the shape
of analysis take form. This means some instances will be rejected as no longer
relevant, and other data may need to be collated to fully develop and complete the
analysis.

15. Item Example
Specific acts, behaviours – what people do or say Avoiding the question. Getting the opinion of friends
Event- these are usually brief, one-off events or things someone
has done.
Being rejected at job interview.
Movie into a homeless hostel.
Activities – these are of longer duration than acts & often take
place in a particular setting & may have several people involved.
Going dancing.
Taking a training course.
Working a part time job.
Strategies, practices or tactics – activities aimed towards some
goal.
Using word-of-mouth to find jobs.
Getting divorced for financial reasons.
States – general conditions experienced by people or found in
organizations.
Resignation.
Working extra hours to get the job done.
Meanings – a wide range of phenomena at the core of much
qualitative analysis.
• What concepts do participants use to understand their world? What
norms, values, rules guide their actions?
• What meaning or significance does it have for participants? How do
they construe event? What are their feeling?
• What symbols do people use to understand their situation? What
do they use for objects, events, persons roles, settings or equipment?
• The idea of ‘on-sight climbing’ amongst rock climbers to
describe a climb without inspection, artificial aids, pre-placed
protection.
• Blame. E.g. ‘His letter made me feel I was to blame’
• Delivery van referred to as ‘the old bus’
Teaching referred to as ‘work at the chalkface’
Source: Gibbs, G. (2011). Analyzing Qualitative Data. London: Sage Publication Ltd.

16. Item Example
Participation – people’s involvement or adaptation to a setting. Adjusting to a new job. E.g. ‘I find I have to be careful
what I say now because I know about things before they
are finalized’
Relationship or interaction – between people, considered
simultaneously.
Enjoying the family. E.g. ‘…they’re 26 and 21 and most
boys of that age are married, but mine aren’t and they
to come home, have friends to stay. I like that’.
Conditions or constraints – the precursor to or cause of events or
actions, things that restrict behaviour or actions.
Firm’s loss of market (before lay off)
Divorce (before financial difficulties)
Consequences – what happens if…. Experience gets job. E.g. ‘So what you get is, people that
haven’t got no qualification, but have got few months’
experience are walking into jobs’.
Settings – the entire context of the events under study Hostel for the homeless.
Day care centre.
Reflexive – the researchers’ role in the process. How intervention
generated the data.
Expressing sympathy, e.g. “it must be hard for you in that
situation’.
Source: Gibbs, G. (2011). Analyzing Qualitative Data. London: Sage Publication Ltd.

17. a) ‘Seeing as’ – generating conceptual codes
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• Identify: What is interesting? Highlight the passage
• Ask: Why is it interesting? This may generate a useful descriptive code or perhaps
interpretive code. It may also warrant a comment in a memo.
• Then ask: Why am I interested in that? This will ‘lift you off the page’ to generate a
more abstract and generally applicable concept. Moving from description to analysis.

18. b) A priori (or Theoretically derived codes)
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• Codes derived from literature, past experiences or background knowledge.
• While having a list of a priori codes can be useful (especially in focused or time-limited
study), it can confine your reading of the text.
• So it is advisable to ‘hang loose’, feel free to change or develop what you have set up,
as you delve into the data.

19. c) In vivo (or Indiginous codes)
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• Codes derived directly from the data – capturing an actual expression of a
participant as the title for a code.
• Look for local terms, especially those that may sound unfamiliar or are used in
unfamiliar ways.
• Use the language of the participants to label the typological concepts.

20. d) Repetition or Regularities
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• People repeat ideas that are of significance for them. Repetition therefore suggest
useful concepts to use as a basis for nodes.
e) Ask questions
• Use questions of the text to generate codes – who, what, when, why, how, how much,
what for, what if, or with what results?
• Each aspect then warrants a separate code.

21. f) Compare and Contrast
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• Compare one segment of text with another – think about the ways in which they are
both similar and different.
• Comparative technique help move attention from factual description to increase
sensitivity to the dimension of the concepts being derived from the data, as well as to
overcome analytic block.
• One consequence of doing so is that you may create codes (nodes in Nvivo) simply to
hold ideas. If these nodes never acquire any data, then that is of interest too.

22. g) Record narrative structure & mechanisms
Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.
• How things are said and the way in which text was structured by the interviewee
(discourse & narrative features o the text) are also revealing
• Particular features that you might note include:
o Transition and turning point in the narrative, signifying a change of theme or a subject to be avoided.
o Inconsistencies, endings, omissions, repetitions and silences
o Denotation in time and tenses in verbs
o The use of metaphors and analogies
o Repetitive use of a word or phrase
o Structural aspects of turn taking and other ‘rules’ naturally occurring in conversation.
o The broader discursive construction or framework within which the discourse is set, eg. Biomedical,
romantic, gender etc.
o Narrative (story) components within a longer non-narrative text.
o Use of particular articles or pronouns pointing to particularized or generalized referents.

23. a) Manually – Pen & Paper

24. b) Word processor –
annotation/comments

25. a) CAQDAS – e.g. Nvivo, Atlas TI etc.

26. From codes to themes
• A theme ‘capture something important about the data in relation to the research
question, represents some level of patterned response or meaning within the dataset’.
• It is typically broader than a code in that it contains many facets.
• A good code will capture one idea, a theme has a central organising concept, but will
contain lots of different ideas or aspects related to the central organising concept –
meaningful, something about how, and in what way, that concept appears in the data.
• Theme tells us something meaningful in relation to our research question.

27. From codes to themes
Theme: ‘Modern life is rubbish’
i.e. modern lifestyles encourage obesity
Availability of convenient food No time for cooking
Ubiquitous advertising of ‘unhealthy’ foodAvailability of fast food chain

28. How to identify themes?
• Developing theme from coded data is an active process – the researcher examines the
codes and coded data, and start to create potential patterns.
• Involves reviewing the codes and collated data relating to each code, with the
identifying similarity and overlap between codes.
• Basically you want to identify a number of themes that capture the most salient
patterns in the data relevant to answering your research question.

29. How to identify themes?
Good questions to ask in developing theme:
• Is it a theme (is it a code or just a subtheme)?
• Is there a central organising concept that unifies the data extract?
• What is the quality of this theme? Does the central organising concept tell me something
meaningful about a pattern in the data, in relation to my research question?
• Can I identify the boundaries of this theme? What does it include and exclude?
• Are there enough (meaningful) data to support this theme? Is the theme too ‘thin’?
• Is there too much going on in the theme, so that it lacks coherence? Are the data too diverse
and wide-ranging? Would using subthemes resolves this problem? Or should it be better split
into two or more themes, each with their own central organising concept?
• How does this (potential)theme relate to other (potential) themes? Is the relationship between
(potential) themes hierarchical or linear?
• What’s the overall story of my analysis? How does this theme contribute to overall story?
• Is the central organising concept reflected in the title I have given to the theme?

30. How to identify themes?
Important things to remember:
• After initial analysis, you normally come up with some candidate themes
These themes will be revised or refined through the developing analysis.
• It is also important to remember that themes are not determine in some quantitative
fashion and there is no magical equation or cut-off point to determine what counts
counts as a theme across dataset, and what doesn’t.
• Your theme doesn’t have to cover everything in the data – they should be about
addressing the research question, and since you are reporting patterned meaning,
some less patterned or irrelevant codes will be excluded.
• If you are doing your analysis with anyone else involved (supervisor or co-
it is important to realise that some coding and analytic differences are likely. The key
is to work out whether the differences are problematic, and if so, work out where
are coming from (perhaps different theoretical perspective) and how to resolve them.