This is the lecture Dr Igor Calzada delivered on Ethnography in order to tackle the issues regarding data analysis in qualitative research in addition to the writing process of ethnographic method. That was delivered as a mean to achieve social innovation projects.

1. Becoming
a
Social
Scien-st
Sociology
&
Policy
Department
TP2
Developing
Research
Skills
and
Prac-ce
Week
5:
Ethnography
II
Data
Analysis
and
Wri-ng
Ethnography
Dr
Igor
Calzada

2. Lecture
Outline:
REMINDER:
Lecture’s
sequence,
Previous
Lecture
Week
4
&
Key
Readings.
‘Ethnography
I:
What
is
It
Where
It
Came
From
How
To
Do
It
’
LECTURE
Week
5:
·∙
1.
Qualita-ve
research
·∙
2.
Making
sense
of
qualita-ve
data:
theory
and
data
analysis
·∙
3.
Qualita-ve
data
analysis
·∙
4.
Grounded
Theory:
key
issues,
prac-ces
and
cri-cisms
·∙
5.
CAQDAS
&
NVivo
·∙
6.
Analysing
images
·∙
7.
Key
ethical
issues
in
social
research

3.
REMINDER:
Lecture’s
sequence,
Previous
Lecture
Week
4
&
Key
Readings
‘Ethnography
I:
What
is
It
Where
It
Came
From
How
To
Do
It
’

4. Lecture’s
Sequence
• Reminder
of
the
Quan-ta-ve
Methods:
• Week
2:
Quan-ta-ve
Data
Gathering
I.
Surveys,
What
The
Are
and
How
to
Do
with
Them.
• Week
3:
Quan-ta-ve
Data
Gathering
II.
Sta-s-cs
and
Content
Analysis.
Cross
the
bridge!
QUALItaQve
• Quan-ta-ve
Methods:
Ethnography
• Week
4:
Ethnography
I.
What
it
is,
Where
It
Came
From
and
How
to
Do
It.
• Week
5:
Ethnography
II.
Data
Analysis
and
WriQng
Ethnography.
• Week
7:
Ethnography
III.
Theory
and
Reflexive
Ethnography.

5. • Reminder
of
the
Quan-ta-ve
Methods
• Quan-ta-ve
Methods:
Ethnography
Reminder

6. • Reminder
of
the
Quan-ta-ve
Methods
• Quan-ta-ve
Methods:
Ethnography
Reminder

8. OBSERVE!

9. Quan-ta-ve
Qualita-ve
Numbers
Words
Researcher
Viewpoint
Par-cipant
Viewpoint
Hard ,
reliable
data
Rich
and
thick
data
Sta-c/Snapshot
Process/Change
Structured
Unstructured
Test
Theory
Emergent
Theory
Generalizable
Context
Specific
Researcher
Distant
Researcher
Close
Macro
Micro
Behaviour
Meaning
Ar-ficial
Secngs
Natural
Secng
Contras-ng
Quan-ta-ve
and
Qualita-ve
Research

13. The
Ethnographer

14. Main
defini-ons>
Characteris-cs
• Contextual:
• The
research
is
carried
out
in
the
context
in
which
the
subjects
normally
live
and
work.
• Unobtrusive:
• The
research
avoids
manipula-ng
the
phenomena
under
inves-ga-on.
• Longitudinal:
• The
research
is
rela-vely
long.

15. Main
defini-ons>
Characteris-cs
• Collabora-ve:
• The
research
involves
the
par-cipa-on
of
stakeholders
other
than
the
researcher.
• Interpreta-ve:
• The
researcher
carries
out
interpreta-ve
analyses
of
the
data.
• Organic:
• There
is
interac-on
between
ques-ons/hypotheses
and
data
collec-on/interpreta-on.

16. How
to
proceed
• Research
Procedure:
• The
design
of
an
ethnographic
research
is
decep-vely
simple.
• It
appears
to
require
only
one
‘act
naturally’-­‐
• Then
again,
looking
beyond,
conduc-ng
an
ethnographic
research
is
a
process
of
discovery.
It
is
something
that
cannot
be
programmed.
• It
is
not
a
maher
of
following
methodological
rules
but
a
prac-cal
ac-vity
requiring
the
exercise
of
one’s
judgement.

17. How
to
proceed
• Data
Collec-on:
• Typical
ethnographic
research
employs
three
kinds
of
data
collec-on:
interviews,
observa-on,
and
documents.
This
in
turn
produces
three
kinds
of
data:
quota-ons,
descrip-ons,
and
excerpts
of
documents,
resul-ng
in
one
product:
narra-ve
descrip-on.
• Watching
what
happens.
• Listening
to
what
is
said.
• Asking
ques-ons
through
informal
and
formal
interviews.
• Collec-ng
documents
and
ar-facts.

18. How
to
proceed
• Data
Collec-on:
• The
data
collected
include,
in
addi-on
to
the
rich
descrip-ve
accounts,
photographs,
maps,
figures,
tables,
texts,
audio
and
video
records,
and
transcrip-ons.
The
most
common
types
of
method
used
in
data
collec-on
are
interviews
[both
formal
and
informal],
documents
[also
both
formal
and
informal/offical],
and
through
observa-on.

19. How
to
proceed
• Ethical
Concerns:
• In
conduc-ng
an
ethnographic
research,
there
are
also
certain
ethical
concerns
that
are
being
raised
every
now
and
then.
Over-­‐all,
they
can
be
summarised
as:
• Informed
consent
• Privacy
• Harm
• exploita-on

20. Checklist
for
an
Ethnographer
• Always
listen
more
than
you
speak
• Remember
that
it
is
your
responsibility
to
be
true
for
the
thoughts,
behaviour
and
expressions
of
people
you
are
studying.
• Conduct
the
research
in
the
natural
context
of
the
topic
you
are
studying
and
try
to
create
a
fun
and
welcoming
atmosphere,
if
appropriate.
• Start
the
interview
with
a
general
descrip-on
of
the
goal
of
the
study,
but
don’t
provide
a
too
narrow
focus
as
that
might
limit
the
responses
you
will
get.
• Encourage
people
to
share
their
thoughts
and
go
about
their
business
freely,
while
you
follow
along.
• Avoid
leading
ques-ons
and
ques-ons
that
can
be
answered
with
only
yes/no
answers.
Ask
follow
up
ques-ons.
• Prepare
an
outline
of
the
interview
ques-ons
you
would
like
to
ask
beforehand,
but
don’t
be
afraid
to
stray
from
it.
• Be
a
shuherbug
and
snap
photos
of
interes-ng
things
and
behaviors.
• Keep
your
ears
and
eyes
open
also
aler
the
recorder
stops,
this
is
olen
the
moment
when
you
get
valuable
revela-ons.

21. When
Conduc-ng
Ethnographic
Research
Remember
DOs:
• Be
unobtrusive
(observer
discreetly)
• Use
your
eyes
–
non-­‐verbal
cues
(observe
the
environment
and
how
the
customers
interact
in
that
space).
• Use
your
ears
–
verbal
cues
(listen
to
what
is
said)
• Preserve
objec-vity
–
create
a
persona
for
yourself
(away
from
your
demographics/brand)
to
remove
any
preconceived
no-ons.
• Find
themes
among
behaviours/paherns
(even
in
unexpected
paherns)
• Work
with
other
researchers/ethnographers
on
the
floor

22. When
Conduc-ng
Ethnographic
Research
Remember
DON’Ts:
• Be
obvious
(when
talking
pictures/recording
videos)
• Be
too
concerned
with
note-­‐taking
(instead
focus
on
data
naturally
occurring)
• Follow
only
one
person
(instead
observe
different
customers/situa-ons)
• Be
biased
(focusing
on
past
knowledge
can
alter
results
instead
keep
an
open
mind)
• Make
observa-ons
with
answers
in
mind,
do
not
make
valida-on
a
goal
(use
ethnography
to
gain
deeper
understanding
of
the
bigger
picture)
• Generalize
ac-ons
of
individuals
to
reflect
a
larger
majority

23. Key
Readings
·∙
Banks,
M.
(2007),
Using
Visual
Data
in
Qualita.ve
Research,
London:
Sage.
Esp.
Chap
3.
·∙
Bri-sh
Sociological
Associa-on’s
Statement
of
Ethical
Prac-ce:
hhp://www.britsoc.co.uk/media/27107/StatementofEthicalPrac-ce/pdf
·∙
Crang,
M.
and
Cook,
I.
(2007),
Doing
Ethnographies,
London:
Sage.
Chap
8.
·∙
Coffey,
A.
et
al,
(1996),
‘Qualita-ve
Data
Analysis:
Technologies
and
Representa-ons’,
Sociological
Research
Online,
1
(1)
·∙
Pole,
C.
and
Lampard,
R.
(2002),
Prac.cal
Social
Inves.ga.on:
Qualita.ve
and
Quan.ta.ve
Methods
in
Social
Research,
London:
Pren-ce
Hall.
Chap
8.
·∙
Prosser,
J.
(2000),
‘The
Moral
Maze
of
Image
Ethics’,
Simons,
H.
and
Usher,
R.
(eds),
Situated
Ethics,
London:
Routledge

24.
1.-­‐
Lecture
Week
5
Ethnography
II
Data
Analysis
&
WriQng
Ethnography

25. LECTURE
Week
5:
·∙
1.
Qualita-ve
research
&
data
collec-on
·∙
2.
Making
sense
of
qualita-ve
data:
theory
&
data
analysis
·∙
3.
Qualita-ve
data
analysis
·∙
4.
Grounded
Theory:
key
issues,
prac-ces
&
cri-cisms
·∙
5.
CAQDAS
&
NVivo
·∙
6.
Analysing
images
·∙
7.
Key
ethical
issues
in
social
research!

26. 1.
Qualita-ve
research
&
data
collec-on
• Ethnography: immersion in a case study!
• ‘Rich and thick’ description; attention to the minutiae of
everyday life!
• A variety of data sources:!
• Words: observations, listening, note-taking, interviews,
conversations, anecdotes!
• Pictures: still and moving!
• Can produce extensive data: e.g. one field note can easily be
5k words; one 45 minute interview can be 10k word transcript;
image making is cheap and easy!
• ‘It’s a horrible but inescapable fact that it takes more time to
organise, write and present material well than it does to gather
it … ‘ (Wax, quoted in Crang and Cook 2007, p.131)!

27. 2.
Making
Sense
of
Research:

28. 3.
Qualita-ve
Data
Analysis
Theory!
Research
Questions!
Data
Collection!
Analysis!
Conclusion!
Qualitative research has a more ambiguous
relationship to theory/data analysis; it is no less
theoretical/analytical; but it is far less prescriptive as
to what this relationship should be.!

29. 3.
Qualita-ve
Data
Analysis
• Crea-ng
analy-cal
pathways
through
‘rich
and
thick’
data
• No
set
rules
for
codifica-on
and
analysis
• InformaQon
Overload:
Be
careful!
• Broad
guidelines
provided
by:
• Analy-c
induc-on
(rarely
used
today)
• Grounded
theory
(dominant
approach)
• Extended
case
method
(week
8)

30. 3.
Qualita-ve
Data
Analysis

31. 3.
Qualita-ve
Data
Analysis

32. 3.
Qualita-ve
Data
Analysis

33. 3.
Qualita-ve
Data
Analysis

34. 3.
Qualita-ve
Data
Analysis

35. 3.
Qualita-ve
Data
Analysis

36. Analy-c
Induc-on
Research
Question!
Hypothesis
(refinement of
research
question)!
Case Study
Data
Collection!
Inconsistent
data:
reformulate
hypothesis to
exclude case!
Inconsistent
data:
reformulate
hypothesis!
Analy-c
induc-on
is
essen-ally
posi-vis-c
(hypothesis
tes-ng)
for
qualita-ve
research
and
so
has
fallen
into
disuse

37. 4.
Grounded
Theory
Data
Collection!
Analysis!
Data
Collection!
Analysis!
Data
Collection!
Analysis!
Data
Collection!
Analysis!
Glaser and Strauss 1967!

38. 4.
Grounded
Theory
key
issues
• Theory
develops
out
of
data
through
an
iteraQve
(i.e.
repe--ve)
rela-onship
(Crang
and
Crook
2007)
• This
is
a
crea-ve,
ac-ve
process
of
interpreta-on
• Analysis
is
not
a
separate,
post-­‐fieldwork
stage
of
research
• Analysis
and
data
collec-on
proceed
together,
constantly
referring
back
to
one
another
• We
begin
with
broadly
drawn
research
ques-ons/theories
and
refine
these
through
data
collec-on
• Key
points:
• Theore-cal
sampling:
data
collec-on
(who
to
speak
to,
what
to
observe)
controlled
by
emerging
theory
and
is
ongoing
• Data
coding:
data
(interview
transcript,
field
note,
pix)
broken
down
into
component
parts
and
given
names.
• Theore-cal
satura-on:
con-nue
theore-cal
sampling
un-l
a
category
is
‘saturated’
i.e.
no
new
data
seems
relevant
and/or
the
category
is
well-­‐developed
• Constant
comparison:
maintain
closeness
between
data
collec-on
and
genera-on
of
concepts

39. 4.
Grounded
Theory
–
data
coding
• Data
analysis
–
chop
up,
(re)order,
(re)assemble
data
• Create
codes:
codes
are
shorthand
devices
to
label,
separate,
compile
and
organise
data
• Not
just
data
management,
but
interpreta-on
and
analysis
• The
beginnings
of
developing
concepts
• Types
of
codes:
• Open
coding:
breaking
data
down
(this
can
be
detailed
and
extensive)
• Axial
coding:
reconnec-ng
data
in
new
ways
• Selec-ve
coding:
iden-fying
core
categories
(the
‘storyline’
that
frames
an
event’
(Bryman
2012).
Key
themes.
• Coding
by:
‘cut
and
paste’,
index
cards,
word
processing
files,
CAQDAS
(see
below)
• Memos:
notes
on
concepts
and
categories
(notes
that
draw
across
the
data)

40. INT. What is your name?!
B: Bless […]!
INT. How old are you?!
B: 9 years.!
INT. Where do you come from?!
B: I’m from a village near Kpando.!
INT. Have you been to school before?!
B: Yes, please I was in class 3 before I left school.!
INT. Did you stop attending school?!
B: No, I didn’t stop going to school it was my mother who didn’t allow me to go further because
she had a fight with my father which resulted in their separation. So my mother took me with her
when she was leaving, but never sent me back to school when we left my father. Anytime my
father comes for me and sends me to school, she will also come and take me away so I ended
up not going to school even though I begged her to allow me because I wanted to also grow
and become someone great in future…One day my father came to visit me so that he could give
me money for my upkeep. My mother fought with him again. So my father decided to divorce
her and she took me to Kasoa (a suburb of Accra) to my grandfather who is my mother’s father.
But I didn’t like the place because I was always being beaten up, so I decided to leave and
came to Kaneshie. I had made a few friends in Kaneshie. One day one of them came to me
with a certain man, I didn’t know, but my friend said he knew him. The man asked us to follow
him, but I was somehow afraid of the man so I told my friend that, when we get to a point, we will
run away but we couldn’t because the man was smarter and he dragged us into a taxi going to
Accra. We didn’t know that the man had a gun on him. So when we got to Bubuashi the taxi
stopped but we didn’t get out of the taxi and it took us to Kaneshie traffic light where the taxi
stopped and the man told us that we were thieves so he took us to the police station for us to be
interrogated. The policemen arrested us and told us that until our parents came to bail us, they
won’t release us. Nobody came so they decided to send us to our parent themselves. When
we got to Kasoa my father refused to take me because he saw that there were policemen with
me, instead he ran away. Before then, they asked me what my father does for a living. I told
them that he smokes marijuana sometimes and drinks lots of alcohol when he is drunk, beats
me up and throws me out of the house. The policemen brought me back to Kaneshie but didn’t
know what to do with me so they left me there and went away. It was then that I went to CAS.
The people at CAS didn’t know that I had been arrested before until some of the boys told them.
So they decided to send me back to my hometown the following week; then I met one of my
brothers whom I use to sleep with. He told me that he would buy me something to send home
so I agreed to that… !
9 yrs old. Very young. Our youngest
yet?!
!
Kpando. Volta Region. Approx. 200
km from Accra, 30KM from Togo
border.!
!
Little schooling. Attenuated
education.!
!
Family tension/conflict. Link to
education.!
Mother left. Family dissolution.!
!
Child commitment to school.!
!
Access to resources.!
!
Family tension. Divorce. !
Care from grandparent.!
Child choice to leave. Child Agency.!
Street friends.!
Contact with (un)known adults.!
Anxiety about adults.!
!
Adult force.!
!
Near Kaneshie market.!
Main junction near market.!
Identification with crime.!
Contact with police.!
Assumed family care/return to family.!
Kasoa – Accra suburb?!
Rejection by family.!
Fear or authority.!
Family drugs, alcohol.!
Family tensions. Child vulnerability.!
Expulsion from family.!
Limits of public care.!
Contact with NGO. Contact with
police.!
Return home.!
Segment of Transcript with ‘Bless’, age 9, Kaneshie Market, 26.05.2006!
Open codes!
Selective codes!

41. 4. Grounded Theory -
Criticisms!
• Time
consuming!
• Theory
produc-on
–
concepts
yes
but
theory?
• Data
fragmenta-on
–
how
to
keep
the
big
picture
in
mind;
where
is
the
narra-ve?
• Data
is
never
‘raw’
–
is
it
possible
to
suspend
awareness
of
theories/concepts
un-l
data
analysis?
• We
begin
with
ideas/theories
and
these
shape
what
we
see
(reflexivity
and
subject
posi-on
–
week
8)
• Objec-vist?
Do
categories
exist
in
the
data
or
are
they
constructed.

42. 5.
CAQDAS
&
NVivo
• Computer
Aided
QualitaQve
Data
Analysis
So_ware
e.g.
QSR
Nvivo
8
• Solware
designed
explicitly
for
managing
and
analysing
qualita-ve
data
e.g.
interviews,
group
discussion,
observa-ons,
field
notes,
field
dairies,
s-ll
and
moving
images
• Automates/simplifies
many
basic
clerical
func-ons:
• Coding
data
• Retrieving
data
• Coding
must
s-ll
be
undertaken
by
researcher
but
CAQDAS
assists
making
codes,
copying
transcripts/notes,
collec-ng
coded
data
together
(no
physical
cut
and
paste
or
index
cards)
• Some
problems:
• Encourages
quan-fica-on
• Ease
of
coding
leads
to
fragmenta-on
• Fragmenta-on
produces
decontextualised
data
• Word
processing
files
just
as
good
• Encourages
grounded
theory
as
orthodoxy
• Probably
not
good
for
small
projects,
but
useful
for
larger
ones
• High
start-­‐up
investment

44. 6.
Analysing
(Photographic)
Images
• Considering
(photographic)
images
as
data
• Images
as
evidence
…
of
what?
• Objects
containing
pictorial
informa-on/data:
• ‘Internal
narra-ve’
(Banks
2007):
what
a
picture
shows
–
its
content/story
• ‘External
narra-ve’:
what
a
picture
is/does
–
an
object
that
is
made
object,
exchanged,
put
to
work
• A
way
of
seeing:
how
some
people
look
at
‘others’,
pictorial
forms
of
representa-on,
power
rela-ons
• Avoid
naïve/simple
realism,
but
not
a
cri-cal
realism?
(week
8)

45. Analysing an Image![Picture this],… four children, their ages difficult to
tell, are laid out on matting spaced across a
doorway. Beyond them it is possible that there lay
other companions, but in the dimness of the
photograph’s periphery it is impossible to tell. The
darkness circling these subjects nevertheless
reinforces what is otherwise clearly evident: that this
is an image of sleeping children. All are clothed to
some degree but their stained garments, bare feet
and lack of visible possessions allude to a sparse
existence. An arrangement neither neat nor
pleasant, it is one nonetheless which exudes design
and purpose. The sleeping mat, a discarded
sleeping cloth, makeshift pillow, a just-visible water
sachet, the parallel sleeping positions, each of these
provides a sign of clear intent. Less obvious but no
less significant is a physical connectedness that
binds these sleeping children together. The child
closest to the photographer (a boy) lies with the sole
of his left foot against the top of the foot of the
second closest child (a boy), who in return rests his
upper right arm against the first child’s torso; the
first and the third closest child (a girl) are united by
touching elbows just above the head of the second
child; the last visible child (a girl) embraces the
back of the third child in the crook of her bent right
leg, while her left leg bends backwards to also hold
close her sleeping companion. This physical
connection of children asleep in a doorway is an
allusion to something of greater significance. (Mizen
and Ofosu-Kusi 2012).!

46. 7.
Ethics
in
Social
Research
• Social
research
involves
research
with
human
beings
• The
values
and
principles
that
guide
research
conduct:
• How
should
be
treat
people
on/with
whom
we
conduct
research?
• What
is
(in)appropriate
to
engage
in
when
research
involves
human
subjects?
• No
easy
or
simple
answers,
perhaps
no
universals
or
rule-­‐based
approaches
• A
need
to
reflect
on
the
specifics
of
each
situa-on
and
make
considered
judgements
• Ethics
review
boards/ethical
governance:
UofW
Ethical
Scru-ny
process:
hhp://www2.warwick.ac.uk/services/rss/researchgovernance_ethics/
research_code_of_prac-ce/researchethicscommihees
• BSA
statement
of
ethical
prac-ce:
hhp://www.britsoc.co.uk/media/27107/StatementofEthicalPrac-ce.pdf
• Ethics
is
not
an
irrita-ng
hurdle.
It
is
integral
to
the
conduct
of
high
quality
research

47. 7.
Promo-ng
Ethical
Research:
Key
Issues
1. Harm
to
respondents:
• experience
of
being
researched
(anxiety,
stress,
trauma)
• Consequences
of
being
research
• Ensure
confiden-ality
and
anonymity:
e.g.
pseudonyms
(what
about
pix?)
2. Informed
consent
• Informing
par-cipants
so
as
to
allow
them
to
make
informed
judgements
• Covert
and
overt
research
• Is
covert
research
ever
jus-ficable?
• Is
(really)
informed
consent
ever
possible
(e.g.
do
we
know
what
research
will
lead
to?)

48. 7.
Promo-ng
Ethical
Research:
Key
Issues
3. Invasion
of
privacy
• Jus-fying
the
intruding
on
someone’s
privacy
• Nego-a-ng
consent:
necessarily
con-ngent
or
par-al
• The
public
and
private
• To
what
extent
can
visual
methods
involve
unacceptable
invasions
of
privacy?
4. Decep-on
• How
do
researchers
present
themselves?
• Can
researchers
ever
be
truly
faithful?
• Is
some
measure
of
decep-on
commonplace
in
social
research?
• Can
covert
research
ever
be
jus-fied?

49. Conclusions
• Rela-ng
theory
to
data:
guided
by
research
ques-ons
but
refined
through
analy-cal
itera-on
• Qualita-ve
data
analysis
–
coding
and
concept
building:
theory
emerges
from
data
(grounded
theory)
• Approaches
to
analysing
images
• Qualita-ve
data
analysis
solware
• Ethics
are
integral
to
high
quality
research

50. Thanks for your attention!