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Finding and communicating the story in qualitative information - Lesson 2

This is the second lesson in our series looking at 'Finding and communicating the story'. The lesson looks at how to work with qualitative data.

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Finding and communicating the story in qualitative information - Lesson 2

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 21. Finding  and  Communica-ng  the  Story  –  Lesson  2  of  6  –  Qualita-ve  Informa-on   Ray  Poynter,  2016   Popular   Internet  meme  
  22. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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  

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