Finding and communicating the story in qualitative information - Lesson 2

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Finding
and

Communica-ng
the
Story

Lesson
2
of
6

Working
with
Qualita-ve

Informa-on

Ray
Poynter

April
2016

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Series
Schedule

•  An
Introduc5on
and
Overview
-‐
Feb
23

•  Working
with
Qualita-ve
Informa-on
–
Apr
5

•  Working
with
Quan5ta5ve
Informa5on

-‐
May
26

•  Working
with
mul5ple
streams
&
big
data
-‐
July
5

•  U5lizing
visualiza5on
–
Sep
13

•  Presen5ng
the
story
-‐
Nov
8

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Agenda

•  Overview
of
the
Frameworks
approach

•  Qualita5ve
informa5on

•  Qualita5ve
analysis

•  Finding
the
story
in
qualita5ve
informa5on

•  Communica5ng
qualita5ve
messages

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

The
Frameworks
Approach

1.  Deﬁne
and
frame
the
problem

2.  Establish
what
is
already
known

–  And,
what
is
believed/expected

3.  Organise
the
data
to
be
analysed

4.  Apply
systema5c
analysis
processes

5.  Extract
and
create
the
story

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Who
is
the
project
for?

_________________

What
is
the
business
issue/problem
that
is
being
addressed?

__________________________________________________

What
does
the
business
want
to
do,
once
it
has
addressed
this
issue?

______________________________________________________

What
do
we
already
know?

Item
Held
by:
Descrip-on

1 

______
______
______________

2 

______
______
______________

3 

______
______
______________

Assump-ons
and
predic-ons

Who
What

1. 

______
______

2. 

______
______

Simpliﬁed

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

What
is
Qualita-ve?

No
single,
perfect
descrip5on

–  Deﬁni5ons
oen
a
ma]er
of
degree

•  Qual
includes
human
judgements
as
part
of
the
analysis

–  Quant
is
algorithmic,
removing
or
minimising
the
human
role

•  Qual
is
about
meaning
and
understanding

–  Quant
is
about
quan5ﬁca5on

•  Qual
deals
with
all
sorts
of
informa5on,
including
unstructured

–  Quant
requires
the
data
to
become
structured/opera5onalised

•  Qual
looks
at
within
case
informa5on
(≈
lots
of
informa5on
about
a
few

people)

–  Quant
looks
at
across
cases
informa5on
(≈
small
amount
of
informa5on
about

lots
of
people)

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

What
is
Qualita-ve?

Which
is
the
best
door
for
our
building?

Focus
Group
or
IDIs

Determine
A
is
preferred

by
le-‐handed
people,
and

B
by
right-‐handed
people.

Perhaps
find
out
that
one

group
is
more
insistent

than
the
other
-‐
Qual

A
B

Ethnographical
approach

Watch
people
tackling
a

variety
of
doors,
plus
other

objects.
Determine
people

who
tend
to
favour
their
le

prefer
A
and
visa
versa
-‐
Qual

Usability
Professional

Assesses
the
op5ons
based
on

experience
and
criteria
-‐
Qual

Or,
apply
a
fixed
scoring
system

-‐
Quant

Survey
People

Discover
90%
prefer
B
–

Quant

Or,
include
le/right

handed
variable,
find
right-‐
handed
people
prefer
B
–

Quant

Or,
include
open-‐ended

ques5on
on
why,
some

people
cite
handedness
–

Quant
with
some
Qual

Picking
the
best
door?
Qual

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Quant
starts
as
Qual

A.  How
many
drinks
did
you
have
today?

–  What
is
a
drink?

2
sips
from
a
bo]le
versus
2
sips
from
a
fountain?

2
separate
glasses
of
wine
versus
a
glass
of
wine
that

was
topped
up?

B.  Agree
Strong,
Agree,
Neither
Agree
Nor

Disagree,
Disagree,
Disagree
Strongly?

–  In
the
mind
of
the
par5cipant
there
are
no
numbers,

they
pick
an
answer
which
they
believe
best
reﬂects

their
view

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Opera-onalizing

From
Qual
to
Quant

Qual
is
analysed
by
a
human*,
quant
employs
an

algorithm

If
we
code
qual
data
and
count
the
codes,
we
convert

from
qual
to
quant,
via
opera5onalizing

–  Brand
men5ons

–  Likes
and
Dislikes

–  Sen5ment

–  Marking
an
essay

–  Evalua5ng
people
for
mental
health
disorders

Tendency
to
treat
this
quant
as
‘hard’
data,
and
the
underlying
qual
as
‘so’

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Computers
＆ Qualita-ve
Analysis

•  Scissors
&
coloured
pens
è
Word,
Excel
etc

•  CAQDAS
–
Computer
Aided
Qualita5ve
Data
Analysis
Soware,
e.g.

Nvivo

•  Text
analy5cs,
from
word
clouds
to
Leximancer

•  Social
Media
analysis,
e.g.
Brandwatch
&
Radian
6

•  Coding
soware,
e.g.
Ascribe

•  Photos
and
Video
organising,
e.g.
Google
Photos
and
Living
Lens

Your
organisa5on’s
Framework
should
specify
the
tools
to
be
used,
storage
protocols,

and
approaches
to
things
like
memos,
tags,
and
notes.

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

AI
and
Qual

At
some
point
in
the
future,
and
maybe
somewhere
in
the
world
today,
it
might
be
possible

for
qual
data
to
be
analysed
by
AI
instead
of,
or
as
well
as,
humans.

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Organising
Exis-ng
Knowledge

•  Include
qual
and
quant
knowledge

•  Stakeholders
summarise
what
is
known
and

what
they
think
the
research
will
show

•  Make
the
data*
accessible

– Transcripts,
transla5ons,
video
libraries,
photo

galleries

– Consider
computer
tools
like
NVivo

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Qualita-ve
Data?

•  Notes
created
by
researchers

when
observing,
listening,

discussing
with
par5cipants

•  Open-‐ended
comments
in

interviews,
focus
groups,

surveys
etc

•  Posts
in
Social
Media

•  Le]ers

•  Videos,
recordings,
transcripts

•  Art

•  Meals,
clothes,
trash

•  Theatre,
cinema

•  Play,
ac5vi5es,
interac5ons

•  Objects

•  Photographs
&
recordings

•  Observa5on
&
passive
data

Many
of
these
can
also
be
called
artefacts
(ar5facts
in
North
America)

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Symbiosis
of
Collec-on
and
Analysis

Establish
the

Ques5on
and

what
is
Known,

Plan
Research

Do

Research

Analyse

Update

plan

Analyse
Story

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Academic
versus
Commercial

Analysis
of
Qualita-ve
Data

Many
techniques
are
used
by
both,
e.g.
conversa5on

analysis,
grounded
theory,
etc

But!

–  Timelines
vary,
commercial
one
day
to
one
week,

academic
can
be
months

–  Success
can
vary,
commercial
=
be]er
business
decision,

academic
=
advancing
knowledge
(academic
deﬁni5on
of

knowledge)

–  Purity
of
methodology,
academic
more
pure,
commercial

more
pragma5c
(which
oen
means
using
hybrids)

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Common
Analy-cal
Approaches

•  Grounded
Theory
–
created
by
Glaser
&
Strauss
in
the
1960s
adopts

a
formal
approach
to
coding
the
data,
linking
the
codes
into
concepts,

linking
these
into
categories,
and
crea5ng
an
overarching
structure.

Tends
to
require
plenty
of
5me.
Tries
to
ignore
exis5ng
theories
–
increasing

sensi5vity
to
the
content
of
the
data.
Induc5ve
approach,
general
theories
from

speciﬁc
observa5ons.

•  Abduc-ve
Analysis
–
compares
the
data
with
the
theories
and
expecta5ons,

iden5fy
the
non-‐expected
and
leap
(abduct)
from
these
observa5ons
to
a
new

theory
that
is
suﬃcient
and
probably
correct/plausible.

•  Content
Analysis
–
is
popular
both
with
tradi5onal
researchers
and
those

seeking
to
computerise
some
or
all
of
qualita5ve
analysis.
As
with
other

approaches,
the
data
is
coded
and
categorised,
but
in
content
analysis
the

frequency
of
codes
and
categories
and
the
frequency
of
links
between
them
is

taken

into
greater
account
that
with
most
other
methods.
The
use
of
‘’coun5ng’

increases
the
importance
of
sampling
when
using
content
analysis.

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Common
Analy-cal
Approaches

•  Narra-ve
Analysis
–
focuses
on
the
en5re
text,
not
subdivided
components.

Enter
the
text
(coding/memoing),
interpre5ng,
verifying
(e.g.
alterna5ve

explana5ons),
represen5ng
(write
the
plot
of
the
story),
illustra5ng
(e.g.
finding

quotes,
drawing
diagrams).

•  Conversa-on
Analysis
–
is
one
form
of
Discourse
Analysis,
CA,
Conversa5on

Analysis,
was
developed
from
the
work
of
Harvey
Sacks’
work
in
the
1960s
&

1970s.
CA
looks
at
how
people
speak,
the
pa]erns
they
use,
how
they
create

meaning,
for
example:
turn-‐taking,
repairs,
dispreferred
responses.
Conversa5on

analysis
pays
less
a]en5on
to
what
people
say
than
the
way
they
say
it.

•  Thema-c
Analysis
–
the
focus
is
to
generate
themes
from
the
data.
In
par5cular

pa]erns
(e.g.
codes
and
categories)
are
iden5fied
in
the
early
data
(e.g.
the
first

interviews
or
focus
groups)
and
then
used
as
tools
to
analyse
subsequent
data.

One
difference
between
thema5c
and
grounded
theories
is
that
grounded
theory

seeks
to
create
a
broader
theory,
thema5c
analysis
tends
to
be
happy
to
create
a

narra5ve
to
explain
the
data.

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Semio-cs

Semio-cs
was
developed
from
the
work
of
Ferdinand
de
Saussure
from
the
later

19thCentury
onwards.
Semio5cs
is
the
study
of
meaning-‐making
by
looking
at
the
use

of
signs
and
symbols
(which
can
be
any
form
of
data,
including
worlds,
brands,
images,

sounds
etc.)
Semio5cs
does
not
require
the
collec5on
of
data
from
research

par5cipants;
semio5cs
if
frequently
conducted
with
artefacts
that
exist
in
the
‘real

world’
rather
than
in
an
MR
created
world.
However,
semio5cs
can
be
applied
to
MR

data,
just
as
it
can
be
applied
to
any
other
data.

Sign

Signiﬁed

Signiﬁer

Sign

Rose

Sign

Passion

Rose

Sign

Passion

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Overarching
Structure

No
uniform

No
books

Travel
costs

School
fees

Worry

Mind
elsewhere

Tired
in
School

Headaches

Lack
school

materials

Unable
to
pay

school
costs

Worry
about

dependents

Feeling

exhausted

Physically
&

emo5onally

stressed

Can’t

aﬀord

school

These
children

have
tangible

problems

Adapted
from

www.open.edu/openlearnworks/mod/resource/view.php?id=52658

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Deciding
What
to
Believe

and
What
to
Interpret

Less
believable

–  Yes,
I
always
give
my

children
healthy
snacks

–  Yes,
I
will
buy
this
new

product

–  I
always
remember
to
take

my
medicine

–  I
buy
on
value,
not

because
of
the
adver5sing

More
believable

–  I
have
two
children

–  No,
I
did
not
like
it

–  I
think
men
will
like
this

more
than
women

–  Which
of
these
three
is

the
odd
one
out?

–  Why
is
it
the
odd
one
out?

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Popular

Internet
meme

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Why
‘Just
Say
No!’

is
Not
so
Easy

Just
Say
No?
The
Use
of
Conversa5on
Analysis
in
Developing
a
Feminist

Perspec5ve
on
Sexual
Refusal,
Celia
Kitzinger,
1999

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Common
Analy-cal
Elements

•  Saturated
analysis
–
keep
going
un5l
you

stop
finding
new/useful
things

•  Structure
–
find/create
an
architecture
to

what
you
find

•  Make
notes
of
what
you
find,
linking
back

to
the
data,
highligh5ng
examples

•  Look
to
support
AND
break
hypotheses

Conversa5on
Analysis

Q.
What
did
you
take
into
account
when
you
decided
to
buy
this
new

technology?

What
did
we...
we
looked
at
cost,
we
looked
at
reliability
and
we
sort
of,

we
compared
a
few
different
types,
talked
to
some
people
that
had

them.

Q.
When
you
say
you
talked
to
some
people
who
were
they?

Some
dental
colleagues.
There's
a
couple
of
internet
sites
that
we
talked

to
some
people...
people
had
tried
out
some
that
didn't
work
very
well.

Q.
So
in
terms
of
materials
either
preven5ve
materials
or
restora5ve

materials;
what
do
you
take
in
account
when
you
decide
which
one
to

adopt?

Well,
that's
a
good
ques5on.
I
don't
know.
I
suppose
we
[laughs]
look
at

reliability.
I
suppose
I've
been
looking
at
literature
involved
in
it
so
I
quite

like
my
own
li]le
research
about
that,
because
I
don't
really
trust
the

research
that
comes
with
the
product
and
once
again
what
other

den5sts
are
using
and
what
they've
been
using
and
they're
happy
with.

I'm
finding
the
internet,
some
of
those
internet
forums
are
actually
quite

good
for
new
products.

Conversa-on
Analysis

Pauses/Repairs/Disconnects:

Person
is
portraying
that
they
are

not
confident.

Restructured
answer

“Well,
that’s
a
good
ques5on.”
–

Indicates
the
ques5on
was
not
a

good
ques5on,
deals
with
it
by

saying
‘Don’t
know’
and
then

proceeds
to
answer
what
he/she

thinks
the
ques5oner
is
hoping
to

learn.

From
an
example
of
Grounded
Theory

www.biomedcentral.com/imedia/4037816045634649/supp3.doc

Discourse
Analysis

Q.
What
did
you
take
into
account
when
you
decided
to
buy
this
new

technology?

What
did
we...
we
looked
at
cost,
we
looked
at
reliability
and
we
sort
of,

we
compared
a
few
different
types,
talked
to
some
people
that
had

them.

Q.
When
you
say
you
talked
to
some
people
who
were
they?

Some
dental
colleagues.
There's
a
couple
of
internet
sites
that
we

talked
to
some
people...
people
had
tried
out
some
that
didn't
work

very
well.

Q.
So
in
terms
of
materials
either
preven5ve
materials
or
restora5ve

materials;
what
do
you
take
in
account
when
you
decide
which
one
to

adopt?

Well,
that's
a
good
ques5on.
I
don't
know.
I
suppose
we
[laughs]
look
at

reliability.
I
suppose
I've
been
looking
at
literature
involved
in
it
so
I

quite
like
my
own
li]le
research
about
that,
because
I
don't
really
trust

the
research
that
comes
with
the
product
and
once
again
what
other

den5sts
are
using
and
what
they've
been
using
and
they're
happy
with.

I'm
finding
the
internet,
some
of
those
internet
forums
are
actually

quite
good
for
new
products.

DA
-‐
Foo-ng

The
role
the
den5st
is
filling?

Somebody
who
is
not
confident,

and
who
is
doub}ul
about
the

sources
available
to
him/her.

Discourse
Analysis

Q.
What
did
you
take
into
account
when
you
decided
to
buy
this
new

technology?

What
did
we...
we
looked
at
cost,
we
looked
at
reliability
and
we
sort
of,

we
compared
a
few
diﬀerent
types,
talked
to
some
people
that
had

them.

Q.
When
you
say
you
talked
to
some
people
who
were
they?

Some
dental
colleagues.
There's
a
couple
of
internet
sites
that
we

talked
to
some
people...
people
had
tried
out
some
that
didn't
work

very
well.

Q.
So
in
terms
of
materials
either
preven5ve
materials
or
restora5ve

materials;
what
do
you
take
in
account
when
you
decide
which
one
to

adopt?

Well,
that's
a
good
ques5on.
I
don't
know.
I
suppose
we
[laughs]
look
at

reliability.
I
suppose
I've
been
looking
at
literature
involved
in
it
so
I

quite
like
my
own
li]le
research
about
that,
because
I
don't
really
trust

the
research
that
comes
with
the
product
and
once
again
what
other

den5sts
are
using
and
what
they've
been
using
and
they're
happy
with.

I'm
ﬁnding
the
internet,
some
of
those
internet
forums
are
actually

quite
good
for
new
products.

DA
–
Repe--on

Reliability
&
“Internet
sites”

No
repe55on
of
cost.
Cost
is
a

‘preferred
response’
–
it
is
used

and
discarded.

Discourse
Analysis

Q.
What
did
you
take
into
account
when
you
decided
to
buy
this
new

technology?

What
did
we...
we
looked
at
cost,
we
looked
at
reliability
and
we
sort
of,

we
compared
a
few
diﬀerent
types,
talked
to
some
people
that
had

them.

Q.
When
you
say
you
talked
to
some
people
who
were
they?

Some
dental
colleagues.
There's
a
couple
of
internet
sites
that
we

talked
to
some
people...
people
had
tried
out
some
that
didn't
work

very
well.

Q.
So
in
terms
of
materials
either
preven5ve
materials
or
restora5ve

materials;
what
do
you
take
in
account
when
you
decide
which
one
to

adopt?

Well,
that's
a
good
ques5on.
I
don't
know.
I
suppose
we
[laughs]
look
at

reliability.
I
suppose
I've
been
looking
at
literature
involved
in
it
so
I

quite
like
my
own
li]le
research
about
that,
because
I
don't
really
trust

the
research
that
comes
with
the
product
and
once
again
what
other

den5sts
are
using
and
what
they've
been
using
and
they're
happy
with.

I'm
ﬁnding
the
internet,
some
of
those
internet
forums
are
actually

quite
good
for
new
products.

DA
–
Evalua-ve
terms

I
quite
like
my
own
li]le
research

I
don’t
really
trust
the
research
that

comes
with
the
product

Some
of
those
internet
forums
are

actually
quite
good
for
new

products

DA
Thoughts

Q.
What
did
you
take
into
account
when
you
decided
to
buy
this
new

technology?

What
did
we...
we
looked
at
cost,
we
looked
at
reliability
and
we
sort

of,
we
compared
a
few
different
types,
talked
to
some
people
that

had
them.

Q.
When
you
say
you
talked
to
some
people
who
were
they?

Some
dental
colleagues.
There's
a
couple
of
internet
sites
that
we

talked
to
some
people...
people
had
tried
out
some
that
didn't
work

very
well.

Q.
So
in
terms
of
materials
either
preven5ve
materials
or
restora5ve

materials;
what
do
you
take
in
account
when
you
decide
which
one
to

adopt?

Well,
that's
a
good
ques5on.
I
don't
know.
I
suppose
we
[laughs]
look

at
reliability.
I
suppose
I've
been
looking
at
literature
involved
in
it
so
I

quite
like
my
own
li]le
research
about
that,
because
I
don't
really

trust
the
research
that
comes
with
the
product
and
once
again
what

other
den5sts
are
using
and
what
they've
been
using
and
they're

happy
with.
I'm
finding
the
internet,
some
of
those
internet
forums

are
actually
quite
good
for
new
products.

The
story?

The
den5st
lacks
confidence,
he/
she
men5ons
cost,
but
comes
back

to
the
topic
of
reliability.

He/she
distrusts
the
research
from

the
manufacturers,
so
tries
to
do

his/her
own
research,
by

connec5ng
with
people
who
have

used
the
new
products,
via
internet

forums

Sales
Recommenda-on

Connect
this
type
of
den5st
with

happy
users.
Encourage
reliability

tes5monials
and
SM
posts.

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Word
Clouds?

A
weak
form
of

qualita5ve
analysis

Can
be
an
entry
point,

some5mes

Can
be
useful
in

communica5ng
the

story

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Finding
the
Story

•  Use
the
client’s
ques5on
as
the
lens

•  Tag,
code,
memo
the
material
as
you
analyse

•  Challenge
what
is
known/believed

•  Find
the
main
story

•  Find
the
relevant
excep5ons/diﬀerences

•  Create
an
overall
structure,
the
plot

•  Is
it
good
news
or
bad
news?

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Finding
the
Story

•  Use
the
client’s
ques5on
as
the
lens

– What
does
success
look
like?

– What
ac5ons
are
pending
on
the
results?

– What
do
people
think
is
true?

– What
do
people
think
the
results
will
be?

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Good
and
Bad
News

•  There
are
four
typical
stories

–  Good
news

–  Good
news
with
caveats

–  Bad
news
with
some
op5ons

–  Bad
news

•  The
storytelling
for
these
four
cases
is
different

•  Good
news
and
bad
news
is
defined
by
what
the

client
wanted
AND
what
the
research
finds

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Bad
News

•  5
stages
of
grief

–  Anger,
Denial,
Bargaining,
Depression,
Acceptance

•  One
presenta5on/report
rarely
tackles
all
the
stages
of

bad
news

•  ‘Facts’
are
rarely
enough
to
persuade

–  Emo5ons
are
the
key
–
a
customer
video
can
be
more

powerful
than
any
amount
of
analysis

•  Go
back
to
a
point
where
the
expecta5ons
match
the

ﬁndings
and
build
from
there

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Conveying
Confidence

•  Confidence
is
created
by
the
researcher

•  Don’t
convey
more
confidence
than
you
have

–  Don’t
convey
less
confidence

•  U5lise

–  Triangula5on

–  Testable
predic5ons

–  Consistency

–  Coherence

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Case
Study

Calvin
Klein,
semio5cs
study
by
Semio5cs
Analysis

The
problem

– 1980s
success
Obsession

– 1990s
success
Eternity

– 2000s
failure
e.g.
Truth

– Why
and
what
should
CK
do
next?

RW
Connect,
Greg
Rowland,
2014

h]ps://rwconnect.esomar.org/semio5cs-‐the-‐billion-‐dollar-‐case-‐study/

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Case
Study

The
story

– CK
success
based
on
codes
of
modernism

– CK
failure
linked
to
using
industry
codes

– Use
modernism

Good
news?
Bad
news?

– Depends
on
what
CK
believed

– If
they
wanted
modernism,
simply
urge
them
forward

– If
they
liked
the
new
codes,
take
them
back
to
success

and
build
the
story
from
there

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Case
Study

1980s

✔

1990s

✔

2000s

✗

$Billions

✔

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

The
Big
Picture

•  Frameworks
for
reliable
/
effec5ve
stories

•  Define
the
problem

•  Organise
the
data
according
to
the
Framework
–

everybody
using
the
same
tools
and
approaches

•  Find
the
main
story
and
build
out
from
there

•  Is
it
good
or
bad
news,
confirming
or
challenging

expecta5ons/beliefs

•  Engaging,
memorable,
simple
story

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Schedule

•  An
Introduc5on
and
Overview
-‐
Feb
23

•  Working
with
Qualita-ve
Informa-on
–
Apr
5

•  Working
with
Quan5ta5ve
Informa5on

-‐
May
26

•  Working
with
mul5ple
streams
&
big
data
-‐
July
5

•  U5lizing
visualiza5on
–
Sep
13

•  Presen5ng
the
story
-‐
Nov
8

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Thank
You!

Follow
me
on
Twiber
@RayPoynter

Or
sign-‐up
to
receive
our
weekly
mailing
at

hbp://NewMR.org

Finding
and
Communica-ng
the
Story
–
Lesson
2
of
6
–
Qualita-ve
Informa-on

Ray
Poynter,
2016

Q
&
A

Ray
Poynter

The
Future
Place

Finding and communicating the story in qualitative information - Lesson 2

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