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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	
  

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Finding and communicating the story in qualitative information - Lesson 2

  • 1. 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  
  • 2. 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    
  • 3. 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  
  • 4. 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  
  • 5. 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  
  • 6. 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)  
  • 7. 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  
  • 8. 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  
  • 9. 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’  
  • 10. 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.  
  • 11. 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.  
  • 12. 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  
  • 13. 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)  
  • 14. 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  
  • 15. 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)  
  • 16. 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.  
  • 17. 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.  
  • 18. 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        
  • 19. 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  
  • 20. 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?  
  • 21. Finding  and  Communica-ng  the  Story  –  Lesson  2  of  6  –  Qualita-ve  Informa-on   Ray  Poynter,  2016   Popular   Internet  meme  
  • 22. 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  
  • 23. 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  
  • 24. 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  
  • 25. 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.  
  • 26. 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.  
  • 27. 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  
  • 28. 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.  
  • 29. 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  
  • 30. 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?  
  • 31. 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?  
  • 32. 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  
  • 33. 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  
  • 34. 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  
  • 35. 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/  
  • 36. 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  
  • 37. Finding  and  Communica-ng  the  Story  –  Lesson  2  of  6  –  Qualita-ve  Informa-on   Ray  Poynter,  2016   Case  Study   1980s   ✔   1990s   ✔   2000s   ✗   $Billions   ✔  
  • 38. 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  
  • 39. 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    
  • 40. 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      
  • 41. Finding  and  Communica-ng  the  Story  –  Lesson  2  of  6  –  Qualita-ve  Informa-on   Ray  Poynter,  2016   Q  &  A   Ray  Poynter   The  Future  Place