Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Predictive Analytics with UX Research Data: Yes We Can!

1,520 views

Published on

Mike Fritz & Paul Berger's presentations from the UXPA Boston 2015 Conference

Published in: Design
  • Be the first to comment

Predictive Analytics with UX Research Data: Yes We Can!

  1. 1. Predic've  Analy'cs  with  UX     Research  Data:   Yes  We  Can!   Mike  Fritz   Paul  Berger   UXPA  BOSTON  2015   1  
  2. 2. Paul  Berger   Visi'ng  Scholar  and  Professor  of   Marke'ng,  and  Academic  Director  of   Master  of  Science  in  Marke'ng  Analy'cs,   Bentley  University   Ph.D.  Sloan  School,  MIT   Mike  Fritz   Manager  of  Usability  and  User   Experience  Research   PeopleFluent   MS  in  Human  Factors  in  Informa'on   Design  Bentley  University       Who  We  Are   2  
  3. 3. Our  book:    March  2015   3  
  4. 4. What  we’re  going  to  discuss  today   •  Basic  (and  not  so  basic)  predic've  analy'cs  you  can  apply  to  the  data   you’re  collec'ng  today!     •  We’ll  show  examples  using  data  garnered  from  moderated  and   unmoderated  usability  tests  and  surveys.   •  Confidence  Intervals     •  Correla'on   •  Simple  Linear  Regression   •  Stepwise  Regression   •  We’re  going  to  concentrate  on  usability  and  survey  data,  but  you  can   apply  these  techniques  to  all  kind  of  data  that  you  might  collect  using   different  methods:  interviews,  focus  groups,  card  sor'ng,  contextual   inquiries,  and  even  physiological  tes'ng,  such  as  eye  tracking,  heart   rate  variance  and  skin  conductance.       4  
  5. 5. Confidence     Intervals     5  
  6. 6.     Confidence  Intervals:    A  good  way  to  depict  them:     6   Put  simply,  a  confidence  interval  is  an  interval  which   contains  a  popula'on  value,  such  as  the  popula'on  mean,   with  some  specified  probability,  usually,  0.95  or  95%.    
  7. 7.     Confidence  Intervals   7   •  Confidence  intervals  are  extremely  useful—and   even  cri'cal—to  any  UX  researcher.     •  In  fact,  it’s  easy  to  make  a  case  that  construc'ng   a  confidence  interval  is  even  more  important   when  you  have  a  small  sample  size.     •  And,  indeed,  that’s  exactly  what  we  have  in  most   usability  datasets.     •  However…consider  the  following:    
  8. 8.     How  is  preparing  for  a  usability  test  and  making  a  big   bowl  of  chili  for  Sunday’s  football  game*  the  same?       *with  or  without  Tom  Brady  
  9. 9. It’s  almost  the  same  amount  of  work   gehng  ready  for  4….or  8.       9   So  you  might  as  well  “serve”  8,  because……  
  10. 10. You  will  be  able  to   report  out  your   findings  with  a  LOT   more  sta's'cal   authority!   10  
  11. 11. Usability  Tes'ng:    It’s  all  about  the  prep  'me……   •  The  prepara'on  for  crea'ng  and  preparing  a  test  for   4  versus  8  is  almost  the  same.    That  is,  it’s  the  same   amount  of  work  to  write  up  a  test  plan,  define  the   tasks,  get  consensus  on  the  tasks,  and  coordinate  the   assets  for  the  test  whether  you’re  tes'ng  for  4  or  8.     •  Admiledly,  it’s  going  to  take  longer  to  recruit  and   actually  run  the  tests,  but  it’s  probably  a  difference  of   only  one  day  of  tes'ng.     11  
  12. 12. Example:    Likert  Scale  with  8  par'cipants   12   •  Let’s  assume  you’ve  just  finished  running  a  usability  test  for   an  online  shoe  store.         •  Aner  the  test,  par'cipants  are  asked  to  rate  their  agreement   with  the  statement  “Finding  running  shoes  in  my  size  is  easy”   on  a  scale  of  1  to  5,  where  1  =  Strongly  Disagree  and  5  =   Strongly  Agree.     •  Let’s  assume  that  there  was  an  even  split  between  “3”s  and   “4”s  (4  each)  for  an  average  of  3.5.   •   The  resul'ng  95%  confidence  interval  for  the  true  mean   ra'ng  of  3.5  ±  0.45.      
  13. 13. Example:  Likert  Scale  with  4  par'cipants   13   •  Now  assume  that  you  ran  the  same  test  with  only  4  par'cipants.     •  Again,  aner  the  test,  par'cipants  are  asked  to  rate  their  agreement  with   the  statement  “Finding  running  shoes  in  my  size  is  easy”  on  a  scale  of  1   to  5,  where  1  =  Strongly  Disagree  and  5  =  Strongly  Agree.     •  Again,  let’s  assume  that  there  was  an  even  split  between  “3”s  and  “4”s   (2  each).       This  &me,  you  s&ll  have  an  average  of  3.5,  but  now   your  confidence  interval  has  more  than  doubled  in   width  to  3.5  ±  0.92!  
  14. 14. Confidence  Intervals:    Sample  Size  of  4  versus  8   14  
  15. 15. Correla'on   15  
  16. 16. Correla'on   16   •  The  “correla'on  coefficient”  reflects  the   rela'onship  between  two  variables.     •  Specifically,    it  measures  the  strength  of  a   straight-­‐line  rela'onship  between  two  variables,   and  also  tells  you  the  direc'on  of  the   rela'onship,  if  any.  It  is  a  numerical  value  that   ranges  between  −1  and  +1  and  is  typically   denoted  by  “r”:       −  1  ≤  r  ≤  +  1  
  17. 17. Correla'on  Scenario     Scenario     •  You’re  a  usability  researcher  at  Behemoth.com,  an   employment  Web  site.     •  The  main  source  of  Behemoth’s  income  is  from   employers  who  post  jobs  on  the  site  and  buy  access  to   its  enormous  database  of  over  a  million  resumes  to   search  for  good  candidates  to  fill  those  jobs.       •  The  candidate  search  engine  is  not  great,  and  is  only   effec've  for  those  savvy  recruiters  who  know  how  to   construct  clever  Boolean  search  strings  that  yield  results   that  get  them  what  they  want.       17  
  18. 18. Correla'on  Scenario     Scenario(cont.)     • You  hear  from  the  grapevine  that  Behemoth  is  about  to   spend  80  million  dollars  on  a  brand  new  “Turbo   Search”  (built  by  a  Palo  Alto  start-­‐up)  that  will   “fundamentally  change  the  way  recruiters  search  for   candidates  through  its  algorithm  that  searches  for  people,   not  keywords.”     • What’s  the  rub?  Turbo  will  kill  Boolean  search!     Dissension  in  the  ranks:     “Will  recruiters  abandon  us  if  we  abandon  Boolean  search?”     18  
  19. 19.   19  
  20. 20. Correla'on  Scenario     Your  Challenge:     Determine  whether  killing   Boolean  capability  is  a  mistake   before  Behemoth  blows  $80   million  on  a  new  search   engine!       20  
  21. 21. Correla'on  Methodology     1. Launch  unmoderated  usability  test  of  the  current   Behemoth  search  engine  to  about  300  recruiters.  All  the   respondents  are  tasked  with  finding  good  candidates   for  the  same  three  requisi'ons.     2. Aner  comple'ng  the  tasks  of  finding  candidates  for   the  three  posi'ons,  the  par'cipants  are  asked  to  rate   their  percep'on  of  usefulness  for  each  of  the  15  fields   in  the  search  engine,  on  a  scale  of  1–5,  where  1  =  not  at   all  useful  and  5  =  extremely  useful.     3.   Calculate  the  correla'on  coefficient  between  the   usefulness  of  the  ability  to  perform  a  Boolean  Search   and  the  likelihood  of  adop'on  of  the  Search  engine.       21  
  22. 22. Rated  Search  Engine  Components   1.  Ability  to  search  by  job  'tle   2.  Ability  to  search  by  years  of  experience   3.  Ability  to  search  by  loca'on   4.  Ability  to  search  by  schools  alended   5.  Ability  to  search  candidates  by  date  of  updated  resume   6.  Ability  to  search  candidates  by  level  of  educa'on   7.  Ability  to  search  by  skills   8.  Ability  to  search  candidates  by  average  length  of  employment  at  each  company   9.  Ability  to  search  candidates  by  maximum  salary    10.Ability  to  search  candidates  by  job  type  he/she  is  looking  for:  full  'me,  part  'me,   temporary/contract,  per  diem,  intern    11.Ability  to  search  candidates  by  companies  in  which  they  have  worked    12.Ability  to  search  candidates  by  willingness  to  travel.  (Expressed  as  “no  travel  ability   required,”  “up  to  25%,”  “up  to  50%,”  “up  to  75%,”  “up  to  100%”)    13.Ability  to  search  candidates  by  willingness  to  relocate    14.Ability  to  search  candidates  by  security  clearance.  (Ac've  Confiden'al,  Inac've  Con-­‐ fiden'al,  Ac've  Secret,  Inac've  Secret,  Ac've  Top  Secret,  Inac've  Top  Secret,  Ac've  Secret/ SCI,  Inac've  Top  Secret/SCI)    15.  Ability  to  perform  a  Boolean  search     22  
  23. 23. Dependent  Variable   Methodology:       3.  At  the  very  end  of  the  survey  ra'ng,  you  insert  the   dependent  variable  ques'on(y):       “Imagine  that  this  search  engine  is  available  to  you  at   no  cost  to  find  qualified  candidates  using  the  candidate   databases  you  currently  employ.  Rate  your  likelihood  of   adopGng  this  candidate  search  engine  on  a  scale  of  1–5,   where  1  =  not  at  all  likely  and  5  =  extremely  likely.”         23  
  24. 24. Correla'on  Methodology:  Excel  Screen  Shot   24  
  25. 25. Correla'on  Methodology:  Excel  Screen  Shot   25   •  We  can  see  that  the  correla'on  coefficient  is  +0.449.  What  this  tells  us  is  that  a   higher  sense  of  usefulness  of  a  Boolean  search  capability  is  associated  with  a   higher  likelihood  of  adop'on  of  the  search  engine.     •  This  also  says  that  20.2%  (100  *  (.449)  ^  2)  of  the  variability  in  likelihood  of   adop'on  of  the  search  engine  is  explained  by  a  recruiter’s  assessment  of  the   usefulness  of  having  a  Boolean  search  available.       We  can  also  determine  the  specific  rela'onship  between  the  2  variables:    
  26. 26. Linear     Regression   26  
  27. 27. Linear  Regression     27   •  The  fundamental  purpose  of  regression  analysis  is  to   study  the  rela'onship  between  a  “dependent   variable”  (which  can  be  thought  of  as  an  output  variable)   and  one  or  more  “independent  variables”  (which  can  be   thought  of  as  input  variables).     •  A  linear  regression  analysis  will  determine  the  best  fihng   slope  and  intercept  of  a  linear  rela'onship.     •  In  this  scenario,  we  will  have  one  independent  variable— this  form  of  regression  is  called  “simple  regression.”       •  In  our  next  scenario,  we  will  have  several  input/ independent  variables  (i.e.,  X’s)—this  will  be  called   “mul'ple  regression.”  
  28. 28. Linear  Regression  Methodology:  Excel  Screen  Shot   28  
  29. 29. Linear  Regression  Methodology:  Excel  Screen  Shot   29  
  30. 30. Results   •  The  very  low  p-­‐value(less  than  once  chance  in  a   billion!)  indicates  that  there  is  virtually  no  doubt  that   there  is  a  posiGve  linear  relaGonship  between  the   usefulness  of  the  Ability  to  do  a  Boolean  search,  and   the  Likelihood  of  AdopGon  of  the  search  engine.   •   Furthermore(and  as  noted  earlier),    the  r-­‐square  value   of  0.202  means  we  es'mate  that  the  usefulness  of   Boolean,  by  itself,  explains  more  than  20%  of  the   responder’s  choice  for  the  Likelihood  of  Adop'on  of   the  search  engine  query.   •  The  best  fihng  (or  “least  squares”)  line  is     Yp=2.4566  +  0.460  *  X     Example:    if  X=3,  Yp=3.84     30  
  31. 31. Example  2   Stepwise  Regression       31  
  32. 32. Stepwise  Regression     • Your  results  trickle  upwards  in  the  managerial   chain.  Your  CEO,  Joey  Vellucci,    exasperated  by  all   the  nega've  news  that  always  comes  from  the   usability  lab,  proclaims  to  his  VP  of  development:     “These  UX  folks  remind  me  of  Agnew’s   ‘naUering  nabobs  of  negaGvism’.  Why  don’t   they  come  up  with  their  own  ideal  search   engine  instead  of  just  finding  problems  all   the  Gme  in  the  lab?”   32  
  33. 33. Stepwise  Regression   Challenge  Accepted!     Stepwise  Regression   to  the  Rescue!   33  
  34. 34. For  those  of  you  born  way  aner  Watergate:         34  
  35. 35. Linear  Regression  Methodology   To  refresh  your  memory:     1.   You  launched  an  unmoderated  usability  test  of  the   current  Behemoth  search  engine  to  about  300   recruiters.  All  the  respondents  were  tasked  with  finding   good  candidates  for  the  same  three  requisi'ons.     2.   Aner  comple'ng  the  tasks  of  finding  candidates  for   the  three  posi'ons,  the  par'cipants  are  asked  to  rate   their  percep'on  of  usefulness  for  each  of  the  15  fields   in  the  search  engine,  on  a  scale  of  1–5,  where  1  =  not  at   all  useful  and  5  =  extremely  useful.           35  
  36. 36. Stepwise  Regression  Example     3.  At  the  very  end  of  the  survey  ra'ng,  you  insert  the   moment  of  truth  ques'on:       “Imagine  that  this  search  engine  is  available  to  you  at   no  cost  to  find  qualified  candidates  using  the  candidate   databases  you  currently  employ.  Rate  your  likelihood  of   adopGng  this  candidate  search  engine  on  a  scale  of  1–5,   where  1  =  not  at  all  likely  and  5  =  extremely  likely.”         36  
  37. 37. Stepwise  Regression  Example   •  Stepwise  regression  is  a  varia'on  of  regular   mul'ple  regression  that  was  invented  to   specifically  address  the  issue  of  variables   that  overlap  a  lot  in  the  informa'on  they   provide  about  the  “Y”  (the  output  variable).     •  It’s  an  automated  process  that  brings   variables  in  (and  once  in  a  while  out)  of  the   equa'on  one  at  a  'me.     37  
  38. 38. The  beauty  of  stepwise  regression!     Stepwise  regression  has  2  excellent   quali'es:     1)All  variables  in  the  final  equa'on  are   sta's'cally  significant.     2)It  is  guaranteed  that  there  are  no   variables  not  in  the  equa'on  that  would   be  sta's'cally  significant.       38  
  39. 39. Stepwise  Regression  Example:    SPSS  Screen  Shots   39  
  40. 40. Stepwise  Regression  Example:    Screen  Shots   40  
  41. 41. Stepwise  Regression  Example:    Screen  Shots   41  
  42. 42. Sta's'cally  significant  variables     42   •  Ability  to  perform  a  Boolean  search   •  Ability  to  search  by  skills   •  Ability  to  search  by  job  'tle   •  Ability  to  search  candidates  by  companies  in   which  they  have  worked   •  Ability  to  search  by  loca'on   •  Ability  to  search  by  years  of  experience   •  Ability  to  search  candidates  by  level  of  educa'on      
  43. 43. Stepwise  Regression  Example   Yc  =  0.528  +  0.311  *  X15  +  0.177  *  X7  +  0.121  *  X11  +  0.153  *  X1  +  0.106  *  X   +  0.106  *  X2  +  0.055  *  X6,     or,  if  we  order  the  variables  by  subscript,     Yc  =  528  +  0.153  *  X1  +  0.106  *  X2  +  0.106  *  X3  +  0.055  *  X6  +  0.177  *  X7   +  0.121  *  X11  +  0.311  *  X15.     In  other  words,  this  equa'on  says  that  if  we  plug  in  a   person’s  value  for  X1,  X3,  X6,  X7,  X11,  and  X15,  we  get   our  “best”  model  for  predicGng  what  the  person  will   choose  for  Y,  the  likelihood  on  the  5-­‐point  scale  that  he/ she  will  adopt  the  search  engine.  AND,  NOTE  THAT  ALL   THE  COEFFICIENTS  ARE  POSITIVE!!   43  
  44. 44. Stepwise  Regression  Example:    Recommenda'ons   For  your  recommenda'ons,  you  produce  a  wireframe   that  illustrates  the  user  interface  for  a  new  search  home   page:     1.   Your  new  design  shows  a  two-­‐'ered  system;  a  “basic   search”  includes  the  top  seven  variables  iden'fied  as   significant  in  your  stepwise  regression  analysis.     2.   If  desired,  the  user  can  click  on  “Advanced”  search  to   reveal  the  remaining  eight  variables.    Even  though  they   were  not  staGsGcally  significant,  and  cannot  be  said  to   “add  to  the  story,”  they  nevertheless  might  be  useful  for   certain  recruiters  looking  for  a  very  specific  set  of   qualifica'ons.     44  
  45. 45. Stepwise  Regression  Example:      CEO  LIKES  IT!   45  
  46. 46. Our  book:    March  2015   46  
  47. 47. What  we  show  you  how  to  do  in  the  book.     •  Prac'cal  Advice  on  choosing  the  right  data  analysis  technique   for  each  project   •  A  step-­‐by-­‐step  methodology  for  applying  each  technique,   including  examples  and  scenarios  drawn  from  the  UX  field.   •  Detailed  screen  shots  and  instruc'ons  for  performing  the   techniques  using  Excel(both  for  PC  and  Mac)  and  SPSS     •  Clear  and  concise  guidance  on  interpre'ng  the  data  output   •  Exercises  to  prac'ce  the  techniques,  along  with  access  to   sample  data  on  the  companion  website.     47  
  48. 48. Don’t  fear  the  future…   48  
  49. 49. Embrace  it!   49  
  50. 50. Predic've  Analy'cs  with  UX     Research  Data:   Yes  We  Can!   Mike  Fritz   Paul  Berger   UXPA  BOSTON  2015   50   mike.fritz@peoplefluent.com   pberger@bentley.edu   QUESTIONS?  

×