The fifth lecture at HITLab, Canterbury University in New Zealand was all about how important it is to run a proper analysis of the qualitative data. We discussed the value in looking at data from individual (phenomenological) perspective versus combined (reductionist) perspective. But we agreed that regardless of the chosen approach it is crucial to look at the data from more than just one perspective to be sure the interpretation is not biased by researcher's on view of the world.

1. qualitative data analysis:
many ways
to understand
user insights
aga szóstek(at)gmail.com

2. the interpretation
of the data is
at least as important
as the data itself

3. qualitative research
Development of concepts
which help to understand
social phenomena
in natural (rather than experimental) settings,
giving due emphasis
to the meanings, experiences
and views of the participants.
Pope & Mays
BMJ 1995; 311:42-45

4. key features of qualitative research
- relatively open-ended, exploratory research design

5. key features of qualitative research
- relatively open-ended, exploratory research design
- collection of unstructured forms of data
- transcripts from interviews
- field-notes from observations
- audio- or video-recordings
- written documents of various kinds
- photographs, drawings
- electronic data from virtual interactions

6. key features of qualitative research
- relatively open-ended, exploratory research design
- collection of unstructured forms of data
- transcripts from interviews
- field-notes from observations
- audio- or video-recordings
- written documents of various kinds
- photographs, drawings
- electronic data from virtual interactions
- plans for data collection and analysis, and even research
questions themselves, may change during the course of inquiry

7. principles of qualitative research
- people differ in their experience and understanding of
reality

8. principles of qualitative research
- people differ in their experience and understanding of
reality
- social phenomenon cannot be understood outside its
own context

9. principles of qualitative research
- people differ in their experience and understanding of
reality
- social phenomenon cannot be understood outside its
own context
- qualitative research is used to describe a given
phenomenon or generate theory grounded in data

10. principles of qualitative research
- people differ in their experience and understanding of
reality
- social phenomenon cannot be understood outside its
own context
- qualitative research is used to describe a given
phenomenon or generate theory grounded in data
- understanding human behaviour emerges slowly and
non-linearly

11. principles of qualitative research
- people differ in their experience and understanding of
reality
- social phenomenon cannot be understood outside its
own context
- qualitative research is used to describe a given
phenomenon or generate theory grounded in data
- understanding human behaviour emerges slowly and
non-linearly
- some cases may yield insights in to a problem or new
idea for further inquiry

12. types of
qualitative analysis

13. Case studies are attempts
to shed light on a phenomenon
by studying in depth
a single case example of that phenomenon.
The case can be an individual person,
an event, a group or an institution.

14. Ethnographic research focuses on
the sociology of meaning
through close field observation
of sociocultural phenomena.
Typically, the ethnographer
focuses on a community.

15. Grounded theory is
an analytic induction method
where an examination of the data starts
with a single case from a‘pre-defined’population
with the goal to formulate
a general statement about a population,
a concept or a hypothesis.
Then all subsequent cases are compared
to see if they fit the initial hypothesis.

16. Phenomenological analysis describes
the structures of experience
as they present themselves to consciousness,
without recourse to theory, deduction,
or assumptions from other disciplines.
.

17. Content analysis is a procedure
for the categorization of
verbal or behavioural data
for the purpose of classification,
summarization and tabulation.

18. Narrative analysis focuses on narratives as
transcribed experiences.
The researcher aims to sort out
and reflect upon these narratives,
enhance them
and present them in a revised shape.

19. Discourse analysis is a method of analyzing
a naturally spoken interaction
and all types of written texts.
It focuses on how people produce
and make sense of everyday social life.

20. Historic analysis is a systematic collection
and objective evaluation of data
related to past occurrences
in order to test hypotheses
concerning causes, effects or trends
of these events that may help to explain
present events and anticipate future events.

21. Framework analysis is a method
of finding patterns and interrelations
in the collected data
in order to develop, expand,
confirm or reject an initial research hypothesis,
or to find a systematic answer
to a given research question.

22. so, we have data
– what now?

23. 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.
http://onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php

24. - data is related to concepts,
opinions, values and
behaviours of people in
context and thus chaotic
- issues seem too large and
complex to grasp and order
- data that are not easily
reduced to numbers

25. characteristics of qualitative data analysis
- circular and not linear
- iterative and progressive
- close interaction with the data
- different levels of analysis
- different ways of sorting data

26. answering research questions
- descriptions of people and their attitudes,
dispositions, patterns of behaviour, and of places
and activities that take place in the context of the
study
- explanations for the identified patterns along with
evidence showing the presence of the causal factors
and their effects for the studied contexts

27. data analysis
- iterative process of data exploration
- art of finding patterns
- beginning with general open-ended questions, moving toward
greater precision as more information emerges
- pre-defined variables are not identified in advance
- checking reliability of assumptions, interpretations and
conclusions
- integrating data of multiple kinds (from observations,
interviews, photographs, etc).

28. quality in the study
http://onlineqda.hud.ac.uk/Intro_QDA/qualitative_analysis.php

29. deduction versus induction

31. stages of data analysis
- coding the data: generating categories from the data by
finding what is‘in’the data with regards to the research
questions posed

32. stages of data analysis
- coding the data: generating categories from the data by
finding what is‘in’the data with regards to the research
questions posed
- identifying framework: determining categories and
subcategories, and sorting data into the framework

33. stages of data analysis
- coding the data: generating categories from the data by
finding what is‘in’the data with regards to the research
questions posed
- identifying framework: determining categories and
subcategories, and sorting data into the framework
- using the framework for descriptive analysis: comparing data
placed in the same conceptual category to clarify and
develop ideas about each category and its interrelations with
other categories

34. stages of data analysis
- coding the data: generating categories from the data by
finding what is‘in’the data with regards to the research
questions posed
- identifying framework: determining categories and
subcategories, and sorting data into the framework
- using the framework for descriptive analysis: comparing data
placed in the same conceptual category to clarify and
develop ideas about each category and its interrelations with
other categories
- second order analysis: validating the importance and the
dominance of the discovered phenomena

35. strategies for analyzing qualitative data
- chronology
- key events
- different settings
- different people
- types of processes
- types of issues

36. terms used in qualitative data analysis
- theory: set of interrelated concepts, definitions and
propositions presenting a systematic view of the data
- themes: categories emerging from grouping of lower-level data
- characteristic: a single item or event, the smallest unit of
analysis
- coding: the process of attaching labels to lines of text so that
the researcher can group and compare related pieces of
information
- coding sorts: compilation of similarly coded elements from
different sources in to a single file
- indexing: generating a word list comprising all substantive
words and their location within the texts

37. computer assisted qualitative data
analysis (CAQDAS)
- facilitates the coding, storage, and retrieval of data
- worthwhile if dealing with a large amount of data
- popular programs:
- Atlas ti 6.0 (www.atlasti.com)
- HyperRESEARCH 2.8 (www.researchware.com)
- Max QDA (www.maxqda.com)
- The Ethnograph 5.08
- QSR N6 (www.qsrinternational.com)
- QSR Nvivo (www.qsrinternational.com)
- Weft QDA (www.pressure.to/qda)
- Open code 3.4 (www8.umu.se)

38. reporting
qualitative research

39. Qualitative research
generates rich information -
thus deciding
where to focus
and the level of sharing
is very challenging.
http://www.psy.dmu.ac.uk/michael/qual_writing.htm

40. data interpretation
- identifying and explaining the
meaning of the data
- generalizing
- ensuring credibility

41. - choose a format: research report, scientific article, field report,
evaluation report, inspiration report
- determine your focus:
- academic: conceptual frameworks and theories,
methodology and interpretation
- practical: concrete suggestions and recommendations
- general public: describing the problem, suggesting
appropriate practices
- use quotes from the data: illustrative, representing a range of
issues, presenting opposing views
- list the discovered issues in a rank or a sequence order
- describe types: of behaviour, strategies, experiences
- report proportions
- add flow diagrams: decision-making, event sequencing

42. traditional research format
- introduction
- literature review
- goal and motivation of the study
- brief description of the study
- description of study context
- methodology
- results
- discussion and conclusions
- implications
- acknowledgements
- references

43. exemplary analysis method:
affinity diagramming

44. - write each of your insights on a separate Post-It note
- spread notes on the table so they are visible to everyone
- gather the team around the cards
- together look for ideas that are related and place them side
by side
- it is okay to have “loners” that do not fit any group
- if a card seems to belong in two groups, make a second
card with the same finding and put cards in both groups
- once all cards are grouped select a title, a short description
for each group and a representative finding

46. exemplary method of framework analysis:
segmentation by behaviour

47. - for each category look for
extreme behaviours and
attitudes:
positive – negative,
dependent – independent,
extrovert – introvert,
aware – unaware, etc.
- put the extremes on the two
ends of the same axis
- place other behaviours
between the two extremes
- choose behaviours, which you
would like to design your
solution for

48. personas
type of the mobile phone
brand of the phone
looks of the phone
physicality of the phone
choosing the phone
smartphone
feature
phone
important unimportant
important
important unimportant
unimportant
independent requires advice