University of

Bedfordshire

ASS113-2: Research Exploring Data

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

Student comms PAT Feedback Week

This week, the grades from your semester 1 assessments will be reported to the Exam
Board and confirmed on SITS. If you have failed any units and have referrals to take,
you will be advised of this outcome and the new submission details for April 22nd.

However, all of you will have seen the comments made by your markers in the feedback
box on your BREO unit shell. This feedback is crucial for your on-going professional
and academic development, regardless of how well or poorly you performed in the
assessments. You must not ignore it, but act on it to ensure you can evidence your
growth and achieve your successful outcome. You will shortly receive an email from
your Personal Academic Tutor, (PAT) inviting you to come to a meeting to go through
this feedback. You will learn how you can improve your performance and get the
support you need, and for those of you doing well, how you can get even better marks
going forwards.

To make this process the most useful it can be, we are asking you to complete the
attached Feedback Tracker by cut and pasting the feedback from each unit into a box
on this document so you can discuss it in the PAT session and keep on top of your
progress. You need to get value from your degree and this is a great support
mechanism to ensure just that. Don’t miss this opportunity, so look out for the email
coming soon.
Thanks, Fiona
Introduction to Qualitative data
analysis.

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 Qualitative research:
De ve lop a rich un ders tan din g of th e local
Develop a rich understanding le laws al
of th e loc
co nte xt rathe r than ge nera lisab ws
context rather than generalisable20la 07, p291) llis et
(Wiilli al, 2007, p291)
(W s et al,

m e c o m b i n a t io n o
o f
f
ic a lly i n v o lv e s s o
o m e c o m b i n a t i o n
yp
Ty
T pically invo,lveensqsuiring or exa
g x a m
m in
in i
i n
n g
g
p e r i e n c in qu ir in g o r e
ex
e xperiencingh,een
t s o c ia l w o r ld …
… Q
Q u
u a
a lit
li t a
a t
t iv
iv e
e
fe a t u rre s o
o f
f t h e s o c i a l wo r ld e
fea t u e s u r e s a t t h e h e a r t ,
, t
th
h e
t a a n a ly s is fe a t r e s a t t h e h e a r t
da
d ata analysis featu
t io n o f o b s e rv a tio n
n s
s … but these positive attributes
no
not io n o f o b s e rv a tio (B… rgbut
in , 2 0 1
these
ergin, 2018, p130)8 , p 1 30 )
positive attributes
e
(Balso
also mean mean it it is
is not
not always
always
straightforward
straightforward to to analyse…
analyse…
‘attractive
‘attractive nuisance’ nuisance’ (Miles 1979)
(Miles 1979)

Str en gt hs of qu alita tiv e re se arch is th

h is thee
Strengths of qualitative researcit pr od uc es…
de pth an d ric hn es s of th e da ta
depth and richness of the data it produces… (Bryman,, 2021, p524))
(Bryman 20 21, p524
ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite
 Qualitative research:
Generally takes an interpretivist
perspective - asserting there is no
single objective reality, as all
knowledge is socially constructed.
Aim:
Aim: illuminating
illuminating the
the perspectives
perspectives of
of research
research participants
participants

Criticism qualitative research is less

scientific/unreliable

Mindful:
Mindful: recognise
recognise && avoid
avoid bias,
bias,
Validity
Validity critically
critically analyse
analyse situations
situations with
with
rigour…
rigour…
Triangulation
Triangulation

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 Revision?

What methods can we use to

collect qualitative data?

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 Qualitative research:
Methods
Methods of
of collecting
collecting data:
data:

• Interviews
(structured/semi-structured/unstructured & ethnographic)
• Focus groups
• Life histories
• Surveys
an aly si
iss
Offtte n an aly s • Ethnographic methods
O enerative::
iis iitterative • Participant observations
s
pe tiittiiv e
re v e • Document based analysis*
repeyt between
inte rp la y b e t we en
pla ction &
intecro • Secondary data analysis*
llle
e ct i o n &
l
co nalysis
analysis
a • Visual methods.

ASS113-2: Lecture Slides: Jonny Hunt - Week 3

 Qualitative research:

General
General strategies
strategies of
of data
data analysis:
analysis:

• Grounded theory • Discourse

• Thematic analysis
analysis • Conversation
• Narrative analysis
analysis • Content analysis
By ‘general strategy’ of analysis we simply
By ‘general strategy’ of analysis we simply
mean an established set of principles and
mean an established set of principles and
practices that guide coding and analysis of data
practices that guide coding and analysis of data

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 Terms to remember…
Grounded
Grounded
theory
theory

Thematic
Thematic
analysis
analysis

Content
Content
analysis
analysis

Discourse
Discourse
analysis
analysis

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 Coding
Coding
Coding -- indexing
indexing
Reviewing
Reviewing field
field notes
notes oror transcripts
transcripts and
and
giving
giving labels
labels to
to component
component parts
parts that
that
seem
seem toto be
be of
of significance.
significance.
C
Cood
dininggh he
elp
lpssaa re
res
s e
e a
a rc
rch
h I notice that the grand
I notice that the grand
dis ti n
distingu g uis
ishh ra
rawwddaa ta
ta fr
fr o
om
m
majority of homes
majority of homes
“n o is
“noise” e have chain link fences
” have chain link fences
in front of them. There
(Gläser & Laudel, 2013) in front of them. There
(Gläser & Laudel, 2013) are many dogs (mostly
are many dogs (mostly
Co de s se rv e as sh ort
or ha
th nd
an d German Shepherds)
Codes serve as sh German Shepherds)
with signs on fences
de vic es to la be l, se pa
pa ra
ra te
te,, with signs on fences
devices to label, se that say:
comp ile an d orga ni se
e da
da ta
ta. . that say:
compile and or ga nis “Beware of the Dog”.
(Charmez, 1983,) “Beware of the Dog”.
(Charmez, 1983,)

Codes can be predetermined or can emerge spontaneously from the data

Codes can be predetermined or can emerge spontaneously from the data
itself
itself ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite
- Week 3
1.
1. Familiarise
Familiarise yourself
yourself
with
with the
the data:
data: read the
read the
Coding
whole transcript through to get a
whole transcript through to get a
sense of it. Write general notes,
sense of it. Write general notes,
about what struck you as
about what struck you as
especially important when
especially important when
finished.
finished.
2.
2. Re-read
Re-read your
your data
data &
& write
write initial
initial memos
memos begin to make
begin to make
initial descriptive memos, what is going on, significant remarks or
initial descriptive memos, what is going on, significant remarks or
observations. Make as many as possible. Initially these will be vary basic
observations. Make as many as possible. Initially these will be vary basic
- perhaps key words, or names you give to themes
- perhaps key words, or names you give to themes

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

 finished.
“I mean erm, a friend of mine, erm, he was out of work actually
1 for

about nine months and he came in one day and said, ‘give us a
2
job’,
Friend asks for a job
and he’d done bits and pieces for me over the years, I, erm, his,
3
he
used to be big into his old cars, so Saturday used to be time
4 Friend into cars
spent
on his motor, erm, he, he preferred his Mark 2 Jags and he,
5 F worked on classic cars
going
back some years now and er, he did a lot of work and I helped
6
him
through with the cars here, if he’d got a problem he could ask
7 Helping F work on car
me
and, and he just, he’d be in here or something, one day he said
8 Support whilst unemployed
to
me he said, ‘you don’t half seem to have a lot of female
9 F notices Female customers
Customers’,
I’d never really noticed in fairness, well yeah you know, but I
10 D - not noticed Females
mean,
whatever, and I used to do his sister’s car for him and er, he
11 D does F’s sister’s car
said, he
was on about, it came up again in conversation and I said, ‘don’t
12
you
ask me’, he said, ‘it’s funny’, he said, ‘cause I was talking to
13 F talks to sister about D
[name]’
that was his sister, ‘the weekend and er’, I think he must have
14
asked
her and he said, ‘well why do you take the car to Dick, as a
15
woman,
F’s sister say D doesn’t treat her like a fool
why do you go to Dick?’ and she said, ‘because he doesn’t treat
16
me
like a fool’, well okay and I think that’s why, probably so many
17
female
Customers, if you, women, no disrespect, are open in a Garage Women can be fooled Mechanics scam women
18
to - women don’t know cars
be taken to the cleaners, because they don’t know
ASS113-2: the
Lecture ins
Slides: and
Jonny Hunt - Week 3
19
outs
1.
1. Familiarise
Familiarise yourself
yourself
with
with the
the data:
data: read the
read the Coding
whole transcript through to get a
whole transcript through to get a
sense of it. Write general notes,
sense of it. Write general notes,
about what struck you as
about what struck you as
especially important when
especially important when
finished.
finished.
2.
2. Re-read
Re-read your
your data
data &
& write
write initial
initial memos:
memos: begin to
begin to
make initial descriptive memos, what is going on, significant remarks or
make initial descriptive memos, what is going on, significant remarks or
observations. Make as many as possible. Initially these will be vary basic
observations. Make as many as possible. Initially these will be vary basic
- perhaps key words, or names you give to themes
- perhaps key words, or names you give to themes

3.
3. Review
Review your
your codes:
codes: Focused (analytic) coding: Identify similar
Focused (analytic) coding: Identify similar
codes within and across interviews and work out what they are telling us
codes within and across interviews and work out what they are telling us
(e.g. trustworthiness, generosity, stupidity, word of mouth recommendations)
(e.g. trustworthiness, generosity, stupidity, word of mouth recommendations)
Axial (refining) coding: Group related codes into categories
Axial (refining) coding: Group related codes into categories
(e.g. honesty, work practices, skilled labour, gender assumptions, social networks)
(e.g. honesty, work practices, skilled labour, gender assumptions, social networks)

4. Consider more general theoretical ideas: outline

4. Consider more general theoretical ideas: outline
connections between the concepts and categories you are developing.
connections between the concepts and categories you are developing.
Start to theories and make connections to existing research literature.
Start to theories and make connections
ASS113-2: Lecture Slides: Jonny to
Hunt - existing
Week 3 research literature.
 finished.
“I mean erm, a friend of mine, erm, he was out of work actually for
1

2 about nine months and he came in one day and said, ‘give us a job’,
Friend asks for a job
3 and he’d done bits and pieces for me over the years, I, erm, his, he
4 used to be big into his old cars, so Saturday used to be time spent Friend into cars
Mechanic work as fun
5 on his motor, erm, he, he preferred his Mark 2 Jags and he, going F worked on classic cars
6 back some years now and er, he did a lot of work and I helped him
7 through with the cars here, if he’d got a problem he could ask me Helping F work on car Networks > jobs
and, and he just, he’d be in here or something, one day he said to Support whilst
8
unemployed
me he said, ‘you don’t half seem to have a lot of female Customers’, F notices Female
9
customers
10 I’d never really noticed in fairness, well yeah you know, but I mean, D - not noticed Females
11 whatever, and I used to do his sister’s car for him and er, he said, he D does F’s sister’s car
12 was on about, it came up again in conversation and I said, ‘don’t you
13 ask me’, he said, ‘it’s funny’, he said, ‘cause I was talking to [name]’ F talks to sister about D
14 that was his sister, ‘the weekend and er’, I think he must have asked
15 her and he said, ‘well why do you take the car to Dick, as a woman, F’s sister say D doesn’t
Evidence of honesty
16 why do you go to Dick?’ and she said, ‘because he doesn’t treat me treat her like a fool

17 like a fool’, well okay and I think that’s why, probably so many female Women can be fooled Women as dupes
Mechanics scam women
18 Customers, if you, women, no disrespect, are open in a Garage to
- women don’t know Dishonest mechanics
19 be taken to the cleaners, because they don’t know the ins and outs cars
20 of a Motorcar. It’s not their forte, some are interested, to a point,
21 some aren’t, so you can tell them, if you want to, they come in with a Women can be fooled
Evidence of honesty
22 problem, you can tell them whatever and they won’t know what it is Women don’t know cars
or how much it’s likely to cost, but if you’re honest and up front they Women appreciate
23
honesty
24 come back and they go, ‘ooh go and see him’, I mean I’ve got four Women recommend
ASS113-2: Lecture Slides: Jonny Hunt - Week 3 Recommendations
25 girls sharing a house, the one came from a recommendation from honest mechanics

26 the girl who used to be in the house...” Women recommend me Recommendations

 Coding

Initial,
Initial, Focused
Focused and
and Axial
Axial codes
codes

Honesty
Honesty

Evidence
Evidence of
of Dishonest
Dishonest
Recommendations
Recommendations honesty mechanics
honesty mechanics
F’s sister
Women F’s sister
Women says that D Mechanics
recommend Women D does F’s says that D Women can Mechanics
recommend Women D does F’s doesn’t Women can scam
honest recommend sister’s car doesn’t be fooled scam
honest recommend sister’s car treat her be fooled women
mechanics me treat her women
mechanics me like a fool
like a fool

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

-Week 3
 Coding

Create
Create a
a code
code
book/list
book/list
•
• Keep
Keep aa list
list of
of the
the
codes as you
codes as you
develop
develop them
them
•
• Note
Note
definition/scope
definition/scope ofof
each
each code
code
•
• Edit as you
Edit as you
merge/delete/add
merge/delete/add
new
new codes
codes
ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite
- Week 3
 Questions…?

Email: bari-ika.vite@beds.ac.uk
Email: bari-ika.vite@beds.ac.uk

ASS113-2: Lecture Slides: Adopted by Dr Bati-ika Vite

- Week 3