Ashley  Quintilone  

Marketing  Tools  and  Applications  Spring  2018  Final    To  understand  the  image  of  Fort  Worth  in  order  to  attract  and  retain  creative  young  professionals    Q1  Data  Examining    In  order  to  decide  the  usefulness  of  the  data,  diagnostic  tests  should  be  run  to  assess  whether  normality  is  present  or  if  any  outliers  will  damage  the  relationships  between  variables.      Although  the  Kolmogorov-­‐Smirnov  and  Shapiro-­‐Wilk  tests  can  be  used  to  check  normality,  the  Central  Limit  Theorem  confirms  that  normality  is  present  when  reaching  over  30  responses.  The  dataset  has  a  total  of  534  responses  indicating  I  can  move  on.      The  survey  question  I  determined  to  be  most  indicative  of  the  “image”  of  Fort  Worth  is  Q19:  “What  is  your  perception  of  Fort  Worth  on  these  city  characteristics?”      The  twelve  variable  characteristics  included:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.        The  variables  are  metric.  Respondents  evaluated  the  variables  on  a  1-­‐5  scale:  (1)  Very  Bad,  (2)  Bad,  (3)  Neither  Good  nor  Bad,  (4)  Good  and  (5)  Very  Good.  The  scale  acts  as  an  indicator  of  what  attributes  should  become  focal  points  moving  forward.  The  cities  analyzed  include  Fort  Worth,  Kansas  City  and  Atlanta.      Looking  at  the  descriptives  below,  I  could  determine  that  the  min  was  -­‐3.99  and  fell  outside  of  the  3.5  standard  deviations  above  or  below  the  mean.  This  indicated  that  outliers  are  present.    

 Using  univariate  detection,  I  found  that  Q19  presented  no  threatening  outliers  to  the  dataset.  All  outliers  fell  within  the  normal  scale  of  1-­‐5.  The  comparison  variable  Q16  indicated  that  only  4  outliers  were  present.  Again,  this  is  not  a  concern  moving  forward  to  analysis.  Excluding  this  data  from  the  dataset  would  compromise  the  integrity  and  originality.      I  can  confirm  that  60  of  the  534  recorded  observations  are  missing.  This  falls  a  bit  higher  than  the  5-­‐10%  missing  value  range  we  like  to  stay  inside,  but  we  can  attribute  the  this  to  skip  logic  

Ashley  Quintilone  

and  incomplete  questions  during  the  survey.    Rather  than  estimate  the  missing  values  through  imputation  or  substitution,  I  chose  to  keep  the  values  in  their  original  forms  to  maintain  the  integrity  of  the  data.      Due  to  assumptions  varying  in  each  analysis,  linearity,  independence,  and  multicollinearity  will  be  assessed  in  later  sections.      I  can  conclude  that  each  variable  is  within  its  valid  range  in  terms  of  outliers,  frequencies,  and  means.  The  Director  of  VisionFW  should  have  no  concern  about  the  variables  or  observations  in  the  dataset.        

Ashley  Quintilone  

Q2  Multiple  Regression    Research  Question  /  Objectives    In  order  to  understand  what  influences  the  likelihood  a  YP  recommends  his/her  city  to  friends,  multiple  regression  is  conducted.  Analysis  will  show  which  factors  among  the  perceptions  of  home  city  influence  their  recommendations.      Analysis  Plan    All  variables  are  metric  and  scaled.  Regression  requires  1  dependent  variable,  the  recommendation,  represented  by  Q16_2  in  the  survey.  The  outcome  variable  is  answered  on  a  1-­‐7  scale  of  how  likely  they  are  to  recommend  to  a  friend:  (1)  Very  Unlikely,  (2)  Unlikely,  (3)  Somewhat  Unlikely,  (4)  Undecided,  (5)  Somewhat  Likely,  (6)  Likely  and  (7)  Very  Likely.    The  independent  variables  are  the  twelve  attributes  in  Q19_1-­‐12:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.        Respondents  evaluate  the  variables  on  a  1-­‐5  scale:  (1)  Very  Bad,  (2)  Bad,  (3)  Neither  Good  nor  Bad,  (4)  Good  and  (5)  Very  Good.      

Every  independent  variable  should  have  at  least  15  observations  when  running  regression.  This  dataset  has  534  respondents,  giving  it  enough  power  to  identify  the  influence  of  variables  and  making  it  representative  of  the  population.    

Model  Assumptions      The  errors  should  be  homoscedastic.  The  errors  for  the  perception  variables  should  all  have  the  same  variance,  resulting  in  a  formation  of  two  parallel  lines  on  the  residual  value  plot.  As  indicated  below,  the  plot  is  predicted  by  the  residuals  and  shows  that  the  variances  are  somewhat  equal.  This  confirms  homoscedasticity.    

Ashley  Quintilone  

No  multicollinearity  is  present.  According  to  the  Coefficients  Table,  all  the  variables  present  a  Variation  Inflation  Factor,  VIF,  of  less  than  10.  This  confirms  that  the  perception  factors  do  not  relate  to  one  another  and  the  x’s  are  fixed  and  independent.      Normality  can  be  tested  by  the  Shapiro-­‐Wilk  test.  All  relevant  variables  had  significance  levels  less  than  0.05,  concluding  that  the  data  is  not  normal.  However,  due  to  the  Central  Limit  Theorem,  we  can  assume  normality  because  the  mean  of  all  samples  from  the  population  should  be  proportionate  with  the  mean  of  the  population.      Independence  of  errors  can  be  conducted  through  both  confirmatory  and  step-­‐wise  methods.  I  chose  to  utilize  the  step-­‐wise  method,  giving  me  a  Durbin-­‐Watson  value  of  2.169.  Ideally,  the  Durbin-­‐Watson  should  be  equal  to  2.169  is  within  the  appropriate  range,  indicating  independence  between  errors.        

   To  test  linearity,  the  correlations  table  should  be  assessed.  The  null  is  that  there  is  no  correlation,  with  the  alternative  being  there  is.  The  significance  values  are  all  less  than  0.05,  represented  by  the  stars  in  the  Pearson  Correlation  row.  Because  the  Pearson  Correlation  is  positive  for  each  attribute,  we  can  confirm  there  is  a  positive  linear  correlation  between  perceptions  and  recommendations.  Public  transportation  is  the  only  variable  that  is  not  significant  at  a  0.01  level,  but  still  falls  within  the  0.05.    

                         

Ashley  Quintilone  

Assess  Model  Fit      In  order  to  assess  model-­‐fit,  I  ran  both  a  confirmatory  and  a  step-­‐wise  model.  Under  the  confirmatory  model,  The  ANOVA  table  indicated  the  results  of  the  f-­‐test.  The  f-­‐test  determines  if  at  least  one  x  has  an  influence  over  y,  or  if  one  perception  factor  has  influence  over  recommendations.  The  significance  value  for  the  confirmatory  model  is  less  than  0.05,  confirming  there  is  at  least  one  variable  with  influence  over  recommendation  of  a  city.  the  model  summary  table  confirms  that  31.8%  of  the  variation  for  or  against  city  recommendations  can  be  explained  by  the  model.  The  adjusted  R2  indicates  that  29.8%  of  the  variation  in  recommendations  can  be  explained  by  the  model  adjusted  for  the  number  of  x’s.  The  model  fits.    

   For  the  purpose  of  cleaner  data,  I  chose  to  rerun  the  dataset  using  the  step-­‐wise  method.  This  method  lets  the  model  decide  by  significance  which  variables  are  preserved  or  disposed  of.  If  the  x  has  no  effect  on  the  y  then  the  variable  is  not  included.  The  ANOVA  table  again  depicts  the  results  of  the  f-­‐test.  The  step-­‐wise  model  shows  F  (4,437)  =  47.756.  The  significance  is  less  than  0.05,  showing  that  at  least  one  city  perception  factor  has  influence  on  recommendations.      

Ashley  Quintilone  

The  step-­‐wise  model  indicates  that  30.6%  of  the  variance  in  perceptions  of  home  cities  can  be  explained  by  four  factors:  safety,  developed  downtown  core,  higher  education,  and  parks  and  recreation.  This  is  not  as  high  as  I  would  like  it  to  be,  but  for  the  purposes  of  predicting  rather  than  explaining  I  will  continue.    

   The  adjusted  R2  takes  into  consideration  the  variables  that  aren’t  significant  in  this  model.  The  adjusted  R2  is  lower  than  the  R2  by  0.006,  indicating  that  a  lower  percentage  of  variables  have  a  predictive  power  in  this  model.      Interpretation      In  order  to  determine  the  variables  that  are  influential  over  a  YP  recommending  his/her  city  to  friends,  I  utilized  the  Coefficient  Table.  Using  the  step-­‐wise  model,  I  concluded  that  safety,  developed  downtown  core,  higher  education,  and  parks  and  recreation  are  significant  to  recommendations.  Increasing  or  decreasing  the  perceptions  of  these  attributes  by  one  unit  will  influence  the  likelihood  of  a  person  recommending  it.    

 Recommendation  (hat)  =  1.691  +  0.323  SAFETY  +  0.326  DEVELOPED  DOWNTOWN  CORE  +  0.279  HIGHER  EDUCATION  

+  0.156  PARKS  AND  RECREATION    

Ashley  Quintilone  

 *If  you  increase  safety  in  city  by  one  unit,  the  likeliness  a  person  will  recommend  the  said  city  will  increase  by  0.323.    *If  you  increase  developed  downtown  core  in  city  by  one  unit,  the  likeliness  a  person  will  recommend  the  said  city  will  increase  by  0.326.    *If  you  increase  higher  education  in  city  by  one  unit,  the  likeliness  a  person  will  recommend  the  said  city  will  increase  by  0.279.    *If  you  increase  parks  and  recreation  in  city  by  one  unit,  the  likeliness  a  person  will  recommend  the  said  city  will  increase  by  0.156.      Part  A  Recommendations    The  multiple  regression  analysis  confirmed  that  the  director  should  focus  on  safety,  developed  downtown  core,  higher  education  and  parks  and  recreation  perceptions  to  increase  how  likely  a  young  professional  is  to  recommend  his/her  home  city.      A  developed  downtown  core  often  relates  to  the  shopping,  dining,  and  entertainment  the  city  has  to  offer.  I  recommend  that  the  director  encourage  more  publicity  for  city  attractions.  The  city  website  is  a  useful  way  to  showcase  to  young  professionals  by  tailoring  to  their  interests.  By  implementing  links  and  blogs  to  local  bars  and  eateries  on  various  forms  of  media,  young  professional’s  will  develop  more  positive  perceptions  of  a  city’s  downtown  core.      To  increase  safety  perceptions,  the  director  should  work  alongside  the  police  department  to  promote  success  stories  in  the  community.  Publicity  of  neighborhood  transgression  and  hate  crimes  can  hinder  the  perception  of  safety  in  large  cities  like  Fort  Worth,  Kansas  City  and  Atlanta.  By  promoting  positivity  and  increasing  security  presence  in  large  metropolitan  areas,  young  professional  recommendations  will  likely  increase.      Perceptions  of  higher  education  can  be  promoted  by  making  local  colleges  more  engaging  to  young  professionals.  Partnering  with  local  universities,  the  director  can  work  to  promote  furthering  education.  The  director  can  also  encourage  young  professionals  to  attend  university-­‐hosted  seminars,  sports  games  or  community  events  to  develop  positive  cognizance  with  the  schools.      Lastly,  if  resource  permitting,  parks  and  recreation  perceptions  should  be  recognized.  Young  professionals  today  are  more  active  and  health  conscious  than  in  the  past,  giving  opportunity  to  utilize  parks  and  recreational  areas  for  their  interests.  I  recommend  revamping  parks  around  the  downtown  areas  to  include  basic  fitness  machines.  Making  parks  more  pet-­‐friendly  would  also  work  to  drive  perceptions  up.      Developed  downtown  core  carries  the  most  weight,  therefore  should  be  the  focal  point  when  implementing  changes.  Safety  and  higher  education  follow  behind  developed  downtown  core,  with  parks  and  recreation  being  the  least  influential  to  young  professional  recommendations.      

Ashley  Quintilone  

Part  B  Kansas  City      In  order  to  assess  recommendations  for  Kansas  City  only,  an  additional  multiple  regression  analysis  must  be  computed.      The  sample  size  for  this  regression  decreases  from  534  to  180,  still  proving  enough  power  to  be  representative  of  the  population.  Despite  this  change,  all  other  assumptions  remain  accurate.    Rerunning  the  data  with  a  selection  variable  through  step-­‐wise,  the  Coefficients  Table  concludes  that  safety  and  social  diversity  are  significant  to  Kansas  City  recommendations.  Increasing  or  decreasing  the  perceptions  of  these  attributes  by  one  unit  will  influence  the  likelihood  of  a  person  recommending  it.  

 Recommendation  (hat)=  1.866  +  0.528  SAFETY  +  0.468  SOCIAL  DIVERSITY  

 *If  you  increase  safety  in  Kansas  City  by  one  unit,  the  likeliness  a  person  will  recommend  Kansas  City  to  a  friend  will  increase  by  0.528.    *If  you  increase  social  diversity  in  Kansas  City  by  one  unit,  the  likeliness  a  person  will  recommend  Kansas  City  to  a  friend  will  increase  by  0.468.      Recommendations    When  looking  at  Kansas  City  alone,  safety  and  social  diversity  become  the  two  perception  attributes.  The  director  should  focus  on  safety  first  to  maximize  likelihood  of  a  young  professional  recommending  his/her  city.  Safety  perceptions  can  be  increased  in  Kansas  City  by  increasing  patrol  around  frequent  crime  areas  like  Seventh  to  18th  street  and  Grandview  Boulevard  to  Pacific  Avenue.  Kansas  City  already  funds  “Project  Art”  in  hopes  of  decreasing  crime  

Ashley  Quintilone  

rates.  I  recommend  publicizing  the  program  more  overtly  to  the  public.  This  would  work  towards  developing  positive  association  between  young  professionals  and  safety.      To  increase  social  diversity  perceptions,  the  director  should  focus  on  public  areas  like  markets,  playground  and  parks.  Utilizing  areas  that  border  different  communities  is  the  most  effective  way  to  target  social  diversity.  By  fostering  a  program  that  encourages  cultural  representation,  safety  among  ethnicities  and  mutual  respect  for  cultures,  young  professionals  will  build  stronger  perceptions  of  Kansas  City’s  social  diversity.            

Ashley  Quintilone  

Q3  Logistic  Regression      Part  A  Findings  /  Recommendations    Research  Question  /  Objectives      The  objective  of  this  research  is  to  understand  what  influences  the  likelihood  a  YP  recommends  his/her  city  to  friends.  The  goal  of  this  logistic  regression  is  to  assess  what  city  attributes  influences  the  probability  of  recommending  or  not  recommending.      Analysis  Plan    Using  the  discrete  and  binary  variable  “Recommendation”  as  the  dependent,  respondents  are  given  the  options  “yes”  or  “no”  to  whether  they  would  recommend  a  city.  The  independent  variables  are  the  twelve  attributes  in  Q19_1-­‐12:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.        Respondents  evaluate  the  variables  on  a  1-­‐5  scale:  (1)  Very  Bad,  (2)  Bad,  (3)  Neither  Good  nor  Bad,  (4)  Good  and  (5)  Very  Good.      Every  independent  variable  should  have  at  least  15  observations  when  running  logistic  regression.  This  dataset  has  534  respondents  with  96  missing  cases.  438  cases  carry  enough  power  to  identify  the  influence  of  variables.  APPENDIX  A    I  chose  to  run  both  a  confirmatory  and  step-­‐wise  model  for  this  logistic  regression.  In  order  to  foster  congruency  between  the  regressions,  I  decided  to  make  recommendations  based  on  a  forward-­‐Wald  step-­‐wise  model.  Step-­‐wise  reduces  the  number  of  predictor  variables  based  on  what  the  data  decides  is  significant  or  not.  This  will  make  recommendations  and  comparing  results  more  veracious.        Model  Assumptions    Through  a  pairwise  correlation  of  x’s,  I  visually  assessed  the  model  for  collinearity.  No  multicollinearity  is  present.    In  logistic  regression,  errors  cannot  be  normal.  No  normality  is  presented  in  this  model.      The  model  needs  to  be  gauged  for  incomplete  information  of  independent  variables,  complete  separation  between  variables  and  overdispersion.  With  438  useful  cases,  there  is  enough  data  on  each  variable.  The  recommend  variable  and  the  twelve  city  attributes  are  completely  separated.  This  is  vital  to  logistic  regression  because  a  y  that  can  perfectly  predict  an  x  or  

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numerous  x’s  results  in  perfect  prediction,  essentially  making  it  inconsequential  to  run  the  model.    Because  there  are  enough  independent  observations,  overdispersion  is  not  an  issue.      Assess  Model  Fit    The  fit  test  and  likelihood  ratio  must  be  assessed  when  running  logistic  regression.  Using  the  Omnibus  Tests  of  Model  Coefficients  table,  the  chi-­‐square  column  identifies  whether  the  independent  variables  have  influence  on  the  dependent  variables  in  the  model.      

Chi-­‐squared4=  125.798    This  is  an  indicator  of  how  much  unexplained  information  there  is  after  the  model  has  been  fitted.  The  four  depicts  how  many  x’s  are  being  represented  in  the  test.  

 Deviance  =  -­‐2(125.798)  

 Deviance  tests  the  model  overall  and  ensures  that  it  follows  a  chi-­‐square  distribution.  Because  the  significance  level  is  less  than  0.05,  the  null  is  rejected.    At  least  one  of  the  independent  variables  in  the  model  has  influence  on  the  “Recommend”  variable.      

   The  Model  Summary  table  is  then  used  to  evaluate  the  Nagelkerke  R  Square.  APPENDIX  B      

Nagelkerke  R2  =  0.336    This  confirms  that  33.6%  of  the  variation  in  probability  of  recommending  his/her  city  can  be  explained  by  their  perceptions  of  the  twelve  city  attributes.  This  percentage  is  not  as  high  as  I  would  like,  but  because  the  goal  is  explaining  and  not  predicting  I  will  continue.    

Ashley  Quintilone  

The  HITT  Ratio  is  utilized  to  determine  if  perceptions  are  a  good  indicator  of  whether  young  professionals  will  recommend  his/her  city  to  friends.  Using  Step  4  of  the  Classification  Table,  the  HITT  ratio,  CMAX  and  CPRO  are  determined.        

    Not  Recommend  

PREDICTED  

Recommend  PREDICTED  

 

Not  Recommend  OBSERVED  

215   40   255  

Recommend  OBSERVED  

77   106   183  

  292   146   438      

HITT  Ratio=  73.3%  CMAX=  0.582  CPRO=  0.513  

 1.25CMAX=  0.7275=  72.75%  1.25  CPRO=  0.64125=  64.13%  

 73.3  HITT  RATIO  >  0.7275  CMAX  

 The  ratios  work  to  assess  if  the  model  is  predicting  well.  Because  HITT  ratio  is  greater  than  the  CMAX,  the  highest  proportion  of  the  two  observed  groups,  I  am  confident  that  this  model  can  predict  well.  Overall,  perceptions  of  cities  attributes  are  a  good  indicator  of  whether  or  not  young  professionals  will  recommend  his/her  city  to  friends.              

Ashley  Quintilone  

Interpretation      The  Variables  in  the  Equation  table  is  used  to  determine  which  attributes  are  significant  under  logistic  regression.  The  attributes  Higher  Education,  Safety,  Developed  Downtown  Core  and  Nightlife  have  a  significance  level  less  than  0.05,  confirming  they  are  influential  on  predicting  the  probability  of  young  professionals  recommending  his/her  city  to  a  friend.      

     P(RECOMMEND)  =                                                                                                                                                  1  

                     1+e-­‐(-­‐9.733  +  0.457  (HIGHER  EDUCATION)  +  0.569  (SAFETY)  +  0.840  (DEVELOPED  DOWNTOWN  CORE)  +  0.445  (NIGHTLIFE))  

 

There  is  no  marginal  effect  in  PRECOMMEND.  Therefore,  there  is  no  way  to  determine  which  attributes  are  more  important.  Because  the  betas  are  all  positive,  however,  increasing  perceptions  of  Higher  Education,  Safety,  Developed  Downtown  Core,  and  Nightlife  will  increase  the  probability  of  a  young  professional  recommending  his/her  city  to  a  friend.          

Recommendations    The  logistic  regression  model  reflects  that  perceptions  of  higher  education,  safety,  developed  downtown  core,  and  nightlife  can  positively  increase  the  probability  that  young  professionals  recommend  his/her  city  to  friends.      To  increase  perceptions  of  higher  education,  the  director  should  partner  with  local  universities  to  promote  higher  education  to  young  professionals.  Specifically,  advocating  for  graduate  programs  in  popular  metropolitan  areas  would  work  towards  building  more  positive  perceptions.  

Ashley  Quintilone  

The  director  can  also  encourage  young  professionals  to  attend  university-­‐hosted  seminars,  sports  games  or  community  events  to  develop  positive  cognizance  with  the  schools.      Safety  perceptions  can  be  fixed  by  working  alongside  the  police  department  to  develop  a  program  that  fosters  community  commitment  to  public  safety.  Promoting  success  stories,  increasing  law  enforcement  presence,  and  keeping  hostile  crimes  out  of  the  press  will  develop  a  sense  of  security.  Working  towards  creating  proactive  publicity  through  media  on  topics  such  as  safety  tactics  will  grab  the  attention  of  young  professionals.      Perceptions  of  a  developed  downtown  core  can  be  increased  by  advertising  the  shopping,  dining,  and  entertainment  the  city  has  to  offer.  Focusing  on  places  that  cater  to  young  professional’s  interests,  such  as  sports,  games  or  outside  patio’s,  would  be  most  beneficial  to  increasing  their  perceptions.  Utilizing  the  city  website,  social  media  platforms  and  local  events,  young  professionals  can  progress  a  more  positive  perception  of  a  city’s  developed  downtown  core.      The  perceptions  of  nightlife  are  built  around  the  personal  experiences  of  young  professionals.  I  recommend  working  with  city  authorities  to  potentially  amend  noise  ordinances.  Encouraging  restaurant  and  bars  to  operate  later  into  the  evening  would  be  beneficial  to  bringing  in  business  after  work.  Increasing  accessible  transportation,  like  train-­‐stops,  bus-­‐stops,  Uber  pick-­‐up  zones  or  overnight  parking  garages,  in  areas  with  nightlife  districts,  cultivate  both  safety  and  practicality.  Lastly,  working  with  high-­‐frequented  bars  and  clubs  to  streamline  wait  times  would  promote  a  more  positive  perception  of  nightlife  for  young  professionals.      Focusing  on  any  of  these  four  attributes  will  increase  the  probability  young  professionals  recommend  his/her  city  to  friends.                                          

Ashley  Quintilone  

Part  B  Multiple  Regression  vs  Logistic  Regression    The  multiple  regression  analysis  confirmed  that  the  director  should  focus  on  safety,  developed  downtown  core,  higher  education  and  parks  and  recreation  perceptions  to  increase  how  likely  a  young  professional  is  to  recommend  his/her  home  city.  Developed  downtown  core  carries  the  most  weight  in  this  model,  therefore  should  be  the  focal  point  if  implementing  changes  based  off  this  strategy.  Safety  and  higher  education  follow  behind  developed  downtown  core,  with  parks  and  recreation  being  the  least  influential  to  a  young  professional  recommendation.      Recommendation  MULTIPLE  REGRESSION  (hat)  =  1.691  +  0.323  SAFETY  +  0.326  DEVELOPED  DOWNTOWN  CORE  +  

0.279  HIGHER  EDUCATION  +  0.156  PARKS  AND  RECREATION    

The  logistic  regression  model  reflects  that  perceptions  of  higher  education,  safety,  developed  downtown  core,  and  nightlife  can  positively  increase  the  probability  that  young  professionals  recommend  his/her  city  to  friends.  Unlike  multiple  regression,  logistic  regression  has  no  marginal  effect  and  cannot  determine  which  variable  is  most  important.      PLOGIT(RECOMMEND)  =                                                                                                                                    1  

                                                         1+e-­‐(-­‐9.733  +  0.457  (HIGHER  ED)  +  0.569  (SAFETY)  +  0.840  (DEVELOPED  DT  CORE)  +  0.445  (NIGHTLIFE))  

   Both  multiple  regression  and  logistic  regression  found  that  safety,  developed  downtown  core  and  higher  education  are  significant  when  young  professionals  recommend  his/her  city  to  friends.  Multiple  regression  concluded  that  parks  and  recreation  was  the  fourth  and  final  attribute.  Parks  and  recreation  also  held  the  least  weight  in  terms  of  influencing  whether  a  young  professional  would  recommend  his/her  city  to  a  friend.  Logistic  regression,  however,  found  nightlife  to  be  the  fourth  and  final  significant  attribute.  Recommendations  for  enhancing  perceptions  towards  parks  and  recreation  differ  greatly  from  those  benefitting  nightlife.  For  this  reason,  I  recommend  following  the  multiple  regression  analysis  model  for  recommendations  and  implementations.      The  multiple  regression  model  confirms  which  attributes  are  the  most  influential,  by  weight,  to  a  young  professional  recommending  his/her  city.  This  will  be  more  resourceful  for  the  director  when  making  modifications  to  the  city  then  having  to  focus  on  four  variables  equally.  Nightlife  can  arguably  fit  in  as  an  alcove  to  developed  downtown  core,  working  synonymously  in  perceptions  as  adjustments  are  made.  Additionally,  parks  and  recreation  is  a  much  easier  attribute  to  revise  perceptions  of  than  nightlife.      That  being  said,  I  recommend  following  the  multiple  regression  model  and  focusing  first  and  foremost  on  developed  downtown  core.  The  developed  downtown  core  is  the  shopping,  dining,  and  entertainment  experiences  the  city  can  use  to  cater  to  young  professionals.  Promoting  eateries  with  trendy  menus,  patio  music,  and  games  will  engage  the  young  professional  

Ashley  Quintilone  

demographic.  Publicizing  city  attractions  on  the  city’s  website  and  social  platforms  will  progress  a  more  positive  perception  of  the  developed  downtown  core.    With  the  lasting  resources,  I  recommend  developing  a  community-­‐outreach  that  fosters  commitment  to  public  safety.  Publicizing  success  stories,  increasing  law  enforcement  presence,  and  keeping  hostile  crimes  out  of  the  press  will  develop  a  sense  of  security.  Once  safety  perceptions  have  been  enhanced,  higher  education  can  be  promoted  by  making  local  universities  more  engaging  to  the  city’s  young  professionals.  I  recommend  partnering  with  local  universities  to  promote  higher  education,  specifically  graduate  programs,  in  congested  metropolitan  areas.  Encouraging  young  professionals  to  attend  university-­‐hosted  seminars,  sports  games  or  community  events  will  develop  positive  cognizance  between  the  young  professionals  and  the  schools.  If  developed  downtown  core,  safety  and  higher  education  are  all  accounted  for,  I  recommend  actuating  young  professionals  to  utilize  parks  and  recreational  areas  by  tailoring  to  their  interests.  Parks  are  often  associated  with  families  and  children.  By  advertising  parks  as  a  place  to  work-­‐out,  hangout  or  spend  an  afternoon  reading  a  book,  young  professionals  would  develop  preferred  perceptions  of  what  already  exists.                

Ashley  Quintilone  

Q4  Factor  Analysis      Part  A      Research  Question  /  Objectives    Factor  analysis  is  an  interdependence  technique  implemented  to  reduce  data  for  variable  summarization.  The  objective  of  exploratory  factor  analysis  is  to  develop  the  twelve  city  attributes  in  Q19_1-­‐12  into  factors.  Utilizing  Principal  Components  Analysis,  the  goal  is  to  minimalize  loss  of  data  by  determining  an  appropriate  number  of  factors  to  explain  the  maximum  amount  of  total  variance  in  the  computed  correlation  mix.  PCA  will  transform  the  twelve  city  attributes  into  a  set  of  linear  components  to  simplify  and  abridge  the  data.      Analysis  Plan    Data  was  collected  from  534  young  professionals:  183  from  Fort  Worth,  180  from  Kansas  City  and  171  from  Atlanta.  The  sample  size  is  sufficient  and  representative  of  the  population  APPENDIX  D.      

The  young  professionals  record  their  perceptions  of  the  twelve  city  attributes:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.      Respondents  evaluate  the  variables  on  a  1-­‐5  scale:  (1)  Very  Bad,  (2)  Bad,  (3)  Neither  Good  nor  Bad,  (4)  Good  and  (5)  Very  Good.  Variables  are  both  metric  and  interval.      Model  Assumptions    No  multicollinearity  and  correlation  are  essential  for  factor  analysis.  Using  the  correlations  among  the  variables  as  model  inputs,  factor  analysis  recognizes  interrelated  variables  and  groups  them  together.      The  Bartlett’s  Test  of  Sphericity  determines  whether  the  correlation  among  variables  is  sufficient  to  proceed.  The  significance  is  less  than  0.05,  so  we  reject  the  null.  Correlation  is  present.  The  Kaiser-­‐Meyer-­‐Olkin  Measure  of  Sampling  Adequacy,  MSA,  test  determines  if  variable  in  the  dataset  can  be  predicted  from  one  another.  MSA  is  greater  than  0.5.  APPENDIX  E    

MSA  =  0.830  0.830  >  0.5  

   The  data  set  has  sufficient  correlation  and  no  multicollinearity.  All  other  assumptions  are  met.        

Ashley  Quintilone  

Assess  Model  Fit      To  assess  the  Principal  Component  Analysis,  the  Total  Variance  Explained  table  shows  the  components  created  to  maximize  variance  in  the  fewest  number  of  factors.  Using  the  VERIMAX  orthogonal  rotation  technique,  three  uncorrelated  factors  are  created.      

  Analyzing  a  screen  plot  or  following  the  latent  root  criterion  will  determine  the  ideal  number  of  factors  for  maximal  variance.  APPENDIX  F    The  Eigenvalues  for  the  factors  are  all  greater  than  one,  confirming  that  three  factors  are  optimal  for  summary.      Factor  1  explains  34.865%  of  variance,  Factor  2  explains  11.744%  of  variance,  and  Factor  3  explains  10.175%  of  variance.      Collectively,  the  three  factors  can  explain  56.784%  of  all  factor  variance.      Interpretation      To  determine  what  variables  a  factor  is  inclusive  of,  the  Rotated  Component  Matrix  table  is  utilized.  Any  variable  with  a  factor  loading  greater  than  |0.4|  is  represented  in  that  component,  or  factor.        The  factors  are  a  weighted  linear  combination  of  the  select  city  attributes.  The  orthogonal  rotation  ensures  that  each  factor  is  made  up  of  different  variables.  The  attributes  with  the  largest  weight,  in  terms  of  absolute  value,  are  the  biggest  contributor  to  the  factor.  The  following  factors  were  derived  from  the  following  table:      

Ashley  Quintilone  

 

   

FACTOR  1:  Culturally  Versatile  Developed  Downtown  Core,  Social  Diversity,  Nightlife,  Public  Transportation,  Cultural  Amenities,  

Parks  and  Recreation    

Culturally  Versatile  =  0.720  DEVELOPED  DOWNTOWN  CORE  +  0.699  SOCIAL  DIVERSITY  +  0.675  NIGHTLIFE  +  0.632PUBLIC  TRANSPORTATION  +  0.621  CULTURAL  AMENITIES  +  0.445  PARKS  AND  RECREATION  

   

FACTOR  2:  Social  Responsibility  Public  Education,  Environmentally-­‐Friendly,  Higher  Education,  Safety  

 Social  Responsibility  =  0.834  PUBLIC  EDUCATION+  0.757  ENVIRONMENTALLY-­‐FRIENDLY  +  0.622  HIGHER  EDUCATION  +  

0.604  SAFETY    

FACTOR  3:  Standard  of  Living  Cost  of  Living,  Employment  

 Standard  of  living  =  0.745  COST  OF  LIVING  +  0.437  EMPLOYMENT  

   To  test  reliability,  each  factor  must  be  computed  separately.  The  Cronbach’s  Alpha  measures  the  correlations  among  the  designated  variables,  determining  if  the  factor  is  producing  appropriate  summarization.  The  alpha  ranges  from  zero,  indicating  no  reliability,  to  one,  complete  reliability.  The  Cronbach’s  Alpha  should  be  greater  than  0.7  to  substantiate  the  integrity  of  the  factor.      

Ashley  Quintilone  

 Culturally  Versatile  =  0.764    

0.76  >  0.7    

Social  Responsibility  =  0.762  0.76  >  0.7  

 Standard  of  Living  =  0.483  

0.48  <  0.7    The  Culturally  Versatile  and  Social  Responsibility  factors  both  express  sufficient  reliability.  The  Standard  of  Living  factor,  however,  has  a  low  reliability  statistic.  I  would  not  recommend  moving  forward  with  this  factor  in  terms  of  recommendations.                                    

Ashley  Quintilone  

Part  B  Two-­‐Factor  /  Perceptual  Map      To  summarize  the  city  attributes  in  the  most  simplistic  way,  I  ran  a  two-­‐factor  orthogonal  analysis  on  Q19_1-­‐12.  This  forced  the  model  to  develop  only  two  factors,  allowing  the  opportunity  to  plot  them  on  a  90-­‐degree  perceptual  map.    

 The  Total  Variance  Table  concludes  that  both  factors  have  sufficient  Eigenvalues  for  summary.  I  recognize  that  “Component  3”  has  an  Eigenvalue  >  1,  however,  for  the  purpose  of  simplifying  the  data,  I  will  continue  with  two  factors.  By  doing  so,  the  factors  can  be  mapped  in  a  clear  and  easily  understood  way.      Factor  1  explains  34.865%  of  variance  and  Factor  2  explains  11.744%  of  variance.  Both  factors  collectively  explain  46.609%  of  all  variable  variance.      The  following  factors  were  derived  from  the  Rotated  Component  Matrix:  APPENDIX  H      

FACTOR  1:  Amenability  Public  Education,  Safety,  Environmentally-­‐friendly,  Higher  Education,  Parks  and  Recreation,  

Employment    

Amenability  =  0.771  PUBLIC  EDUCATION  +  0.738  SAFETY  +  0.726  ENVIRONMENTALLY-­‐FRIENDLY+  0.677  HIGHER  EDUCATION  +  0.524  PARKS  AND  RECREATION+  0.471  EMPLOYMENT  

   

FACTOR  2:  Versatility  Developed  Downtown  Core,  Cultural  Amenities,  Nightlife,  Social  Diversity,  Public  Transportation  

 Versatility  =  0.728  DEVELOPED  DOWNTOWN  CORE  +  0.726  CULTURAL  AMENITIES  +  0.694  NIGHTLIFE  +  0.658  SOCIAL  

DIVERSITY  +  0.521  PUBLIC  TRANSPORTATION    

   

Ashley  Quintilone  

To  develop  a  perceptual  map  based  on  the  factors  Amenability  and  Versatility,  I  analyzed  the  means  for  each  city.  This  allows  me  to  identify  where  Fort  Worth,  Kansas  City  and  Atlanta  fall  on  the  perceptual  map.  Because  there  is  no  ideal  vector,  I  will  not  compute  an  additional  linear  regression.    

 The  Report  above  indicates  the  mean  factor-­‐scores  of  Fort  Worth,  Kansas  City  and  Atlanta  in  terms  of  Amenability  and  Versatility.  In  the  perceptual  map  below,  the  means  of  each  city  are  plotted.  By  putting  them  next  to  each  other,  it  becomes  much  more  evident  how  the  cities  are  ranked  on  these  factors  comparatively.  Although  no  ideal  vector  is  computed,  it  is  likely  that  quadrant  1  is  the  optimal  quadrant  based  off  previous  analysis.        

Perceptual  Map      

   

Low  Versatility  

High  Versatility  

Low  Amen

ability  

High  Am

enability  

(Amenability,  Versatility)  Fort  Worth  (  -­‐0.2154,  0.1297)  Kansas  City  (-­‐0.4276,  -­‐0.1344)    

Atlanta  (0.5934,  0.0169)  

Ashley  Quintilone  

Recommendations      

Fort  Worth,  Kansas  City  and  Atlanta  all  have  room  to  improve  their  versatility  and  amenability.  Fort  Worth  ranks  highest  in  versatility,  but  fall  short  to  Atlanta  in  amenabilities.  Catering  to  young  professional’s  perceptions  on  attributes  in  Amenability  should  become  the  focal  point  moving  forward.      Fort  Worth  needs  to  foster  stronger  young  professional  perceptions  on  educational  systems,  safety  and  being  environmentally-­‐friendly.  It  is  apparent  that  Fort  Worth  lacks  recognition  in  characteristics  pertaining  to  security.  Young  professionals  are  looking  for  stability  in  a  city.  Both  public  education  and  higher  education  in  Fort  Worth  are  extremely  competitive.  Promoting  this  and  capitalizing  on  it  will  develop  the  positive  recognizance  to  push  Fort  Worth  Amenability  up.  Developing  community-­‐outreach  programs  that  foster  commitment  to  public  safety  would  help  young  professionals  feel  more  at  ease.  Increasing  law  enforcement  presence,  and  keeping  hostile  crimes  out  of  the  press  will  develop  the  stronger  sense  of  security  needed  to  increase  Amenability  in  Fort  Worth.  Lastly,  environmentally-­‐friendliness  has  become  a  new  factor  of  importance  country  wide.  Subtle  implementations  such  as  recycling  cans,  or  encouraging  businesses  to  utilize  sustainable  resources,  perceptions  would  improve.      Fort  Worth’s  downtown,  cultural  amenities  and  nightlife  compete  well  against  Kansas  City  and  Atlanta.  Although  there  is  still  room  for  improvement,  I  recommend  staying  committed  to  bettering  Fort  Worth’s  sustainable  environment.  The  downtown  scene  will  continue  to  evolve,  but  cultural  and  social  amenities  should  be  continually  manipulated  to  cater  to  young  professionals  in  the  upcoming  years.     Focusing  on  increasing  Amenability  and  Versatility,  Fort  Worth  should  strive  to  make  a  clear  division  from  Kansas  City  and  Atlanta.  Doing  so  would  help  to  retain  the  young  professionals  already  in  Fort  Worth  and  procure  those  in  neighboring  cities.              

Ashley  Quintilone  

Q5  Cluster  Analysis      Factor  Analysis  –  Atlanta  Young  Professionals  Only    Research  Question  /  Objectives      The  objective  of  factor  analysis  is  to  reduce  data  for  variable  summarization.  Exploratory  factor  analysis  will  develop  the  twelve  city  attributes  in  Q14_1-­‐12  into  factors.  To  summarize  the  city  attributes  in  the  most  simplistic  way,  a  two-­‐factor  orthogonal  analysis  will  force  the  model  to  develop  only  two  factors,  allowing  the  opportunity  to  utilize  the  factors  for  a  cluster  analysis.  The  factor  analysis  will  simplify  and  abridge  the  data  for  Atlanta  respondents  specifically.    Analysis  Plan    Data  was  collected  from  171  young  professionals  in  Atlanta.  The  sample  size  is  sufficient  for  this  analysis.  APPENDIX  I    

The  young  professionals  are  asked  to  evaluate  how  important  twelve  different  city  characteristics  are  when  deciding  where  to  live.  The  characteristics  include:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.      Respondents  evaluate  the  variables  on  a  1-­‐5  scale:  (1)  Not  at  all  Important,  (2)  Very  Important,  (3)  Neither  Important  nor  Unimportant,  (4)  Very  Important  and  (5)  Extremely  important.  Variables  are  both  metric  and  interval.      Model  Assumptions    The  Bartlett’s  Test  of  Sphericity  determines  whether  the  correlation  among  variables  is  sufficient  to  proceed.  The  significance  is  less  than  0.05,  so  we  reject  the  null.  Correlation  is  present.  The  KMO-­‐MSA  test  determines  if  variable  in  the  dataset  can  be  predicted  from  one  another.  MSA  is  greater  than  0.5.  The  data  set  has  sufficient  correlation  and  no  multicollinearity.  All  assumptions  are  met.    

                         MSA  =  0.793                              0.793  >  0.5  

   

       

 

Ashley  Quintilone  

Assess  Model  Fit      The  Total  Variance  Explained  table  depicts  the  forced  two-­‐factors  computed.  The  factors  are  based  on  what  young  professionals  in  Atlanta  identified  as  important  city  attributes.  Using  the  VERIMAX  orthogonal  rotation  technique,  the  Eigenvalues  for  both  factors  are  greater  than  one.  The  factors  are  sufficient  for  summary.      

   Factor  1  explains  36.675%  of  variance,  while  Factor  2  explains  12.680%  of  variance.  Collectively,  the  two  factors  can  explain  49.355%  of  all  factor  variance.      Interpretation      Using  a  forced  two-­‐factor  orthogonal  technique,  two  factors  were  computed.  The  Rotated  Component  Matrix  table  is  utilized  to  find  which  variables  are  included  in  the  factors.  Any  variable  with  a  factor  loading  less  than  |0.4|  is  not  represented  in  that  factor.          The  factors  are  a  weighted  linear  combination  of  the  select  city  attributes.  The  orthogonal  rotation  ensures  that  each  factor  is  made  up  of  different  variables.  The  attributes  with  the  largest  weight,  in  terms  of  absolute  value,  are  the  biggest  contributor  to  the  factor.  The  following  factors  were  derived  from  The  Rotated  Component  Matrix.  APPENDIX  J    

FACTOR  1  =  Social  Grace  Social  Grace  =  0.759  DEVELOPED  DOWNTOWN  CORE  +  0.749  NIGHTLIFE  +  0.733  CULTURAL  AMENITIES  +  0.642  PUBLIC  

TRANSPORTATION  +  0.591  PARKS  AND  RECREATION  +  0.538  ENVIRONMENTALLY-­‐FRIENDLY  +  0.50  SOCIAL  DIVERSITY    

 FACTOR  2  =  Indispensables  

Indispensables  =  0.838  PUBLIC  EDUCATION  +  0.745  HIGHER  EDUCATION  +  0.540  EMPLOYMENT  +  0.512  SAFETY    

Ashley  Quintilone  

Both  factors  must  be  tested  for  reliability.  The  Cronbach’s  Alpha  measures  the  correlations  among  the  designated  variables,  determining  if  the  factor  is  producing  appropriate  summarization.  The  Cronbach’s  Alpha  should  be  greater  than  0.7  to  substantiate  the  integrity  of  the  factor.          

 Social  Grace  =  0.805  

0.81  >  0.7    

Indispensables  =  0.680  0.68  <  0.7  

 Social  Grace  shows  sufficient  reliability  but  Indispensables  falls  below  the  0.7  threshold.  Indispensables  has  a  low  reliability  statistic  in  terms  of  how  well  the  variables  are  being  represented.  That  being  said,  the  sample  size  is  large  enough  to  assume  representation  so  I  will  continue  on.      Part  A  Cluster  Analysis     Research  Question  /  Objectives      The  objective  of  cluster  analysis  is  to  divide  a  set  of  objects  into  2  or  more  groups  based  on  the  objects’  similarity  to  a  set  of  specified  characteristics,  the  factors.  Using  the  factors  Social  Grace  and  Indispensables,  a  cluster  analysis  will  segment  the  Atlanta  young  professionals  into  summary  attitudes  about  the  twelve  city  attributes  they  deem  important.  The  goal  of  this  analysis  is  to  determine  where  the  young  professionals  in  Atlanta  fall  within  the  clusters  based  on  their  factor  scores.  The  objects  within  the  segments  will  be  homogeneous,  but  different  across.  The  variables  are  distributed  to  the  mutually  exclusive  groups  based  on  similarities  to  the  set  of  city  attributes.    

Ashley  Quintilone  

Analysis  Plan    There  are  171  young  professionals  from  Atlanta  in  this  sample.  Because  the  goal  is  grouping,  power  is  not  necessary  for  this  analysis.      Variables  represent  the  characteristics  on  the  axis.  Variable  forms  are  relevant  to  how  similarity  among  the  observations  is  measured.  Similarity  is  assessed  as  distance  among  the  observations.  The  proximity  in  distance  will  assess  how  similar  two  objects  are.  The  variables  are  metric  so  similarity  will  be  assessed  through  Euclidean.      The  distance  among  observations  is  sensitive  to  scaling,  reiterating  why  observations  must  be  standardized  and  relevant  to  analysis.    A  two-­‐factor  orthogonal  technique  was  utilized  to  ensure  standardization  between  the  twelve  city  characteristics:  Public  Education,  Higher  Education,  Employment  Opportunities,  Safety,  Environmentally-­‐Friendly,  Public  Transportation,  Social  Diversity,  Developed  Downtown  Core,  Nightlife,  Cultural  Amenities,  Parks  and  Recreation,  and  Cost  of  Living.      Outliers  should  be  recognized,  being  aware  of  their  power  over  cluster  forming.  It  is  important  to  look  for  outlying  observations  on  specific  variables  and  characteristics.  Without  distinction,  they  can  influence  how  the  clusters  form  or  where  the  new  cluster  will  fall.      Model  Assumptions    Because  factor  analysis  was  run  to  derive  Social  Grace  and  Indispensables  with  this  dataset,  it  is  not  essential  to  check  for  multicollinearity,  normality  or  linearity.  To  preserve  the  latency  of  no  multicollinearity,  the  orthogonally  rotated  factor  scores  should  be  utilized  moving  forward.  The  model  should  be  representative  of  the  population.        Assess  Model  Fit      Cluster  analysis  uses  an  algorithm  when  forming  clusters.  If  executed  correctly,  clusters  should  be  compact,  mutually  exclusive  and  as  far  apart  as  possible.  To  do  so,  I  chose  to  utilize  Ward’s  agglomerative  method.    An  agglomerative  method  begins  with  each  seed  being  independent  of  one  another.  It  works  to  unify  them  progressively  until  one  large  cluster  is  left.      Cluster  analysis  can  be  run  both  hierarchically  or  non-­‐hierarchically.    For  the  purpose  of  seeking  the  most  accurate  results,  I  will  run  both.  Non-­‐hierarchal  assigns  objects  to  clusters  once  a  predetermined  number  of  clusters  is  specified.    All  objects  within  a  fixed  distance  are  united  into  one  cluster.  Objects  may  be  reassigned  to  different  cluster  as  new  seeds  are  added.  Each  seed  integration  leads  to  rinsing  and  repeating.  Hierarchical  is  a  must  faster  method,  but  is  not  competent  for  analyzing  large  samples.      I  will  be  utilizing  hierarchal  to  determine  how  many  clusters  would  be  most  beneficial  to  the  data  set.  I  will  then  use  non-­‐hierarchal  to  provide  more  depth  to  the  clusters  chosen.  The  goal  is  to  

Ashley  Quintilone  

determine  where  the  biggest  leap  occurs  on  the  Agglomeration  Schedule.  APPENDIX  K  Running  the  non-­‐hierarchal  will  determine  the  final  number  of  clusters.      The  factors  Social  Grace  and  Indispensables  serve  as  the  axis  for  plotting.  The  Final  Cluster  Centers  table  gives  the  midpoint  of  the  clusters.  It  also  reveals  how  many  young  professionals  in  Atlanta  fall  within  each  cluster.    

Cluster  Analysis  Map  

   

 

High  Social  G

race  Low  Social  G

race  

Indispensables  Surplus  

Indispensables  Deficiency  

PARTY  IN  THE  USA  

UMBRELLA  

SINGLE  LADIES  

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Part  B      Profiling  and  Segmentation        The  Cluster  Analysis  Map  depicts  the  market  segmentation  of  young  professionals  in  Atlanta.  The  clusters  are  created  based  on  recorded  importance  of  factors  Social  Grace  and  Indispensables.  The  cluster  centers  and  case  totals  determined  the  location  and  size  represented  on  the  map.      

“Party  in  the  USA”  Miley  Cyrus  (0.84697,  0.00478)  

 The  Party  in  the  USA  cluster  follows  the  wise  influence  of  Miley  Cyrus,  letting  the  “butterfly’s  fly  away”  for  any  city  that  exhibits  high  Social  Grace.  These  young  professionals  value  a  city  for  its  developed  downtown  core,  exciting  nightlife  and  diverse  amenity  opportunities.  This  group  is  invested  in  the  social  scene,  looking  to  live  anywhere  they  can  hang  out  and  mingle  with  likeminded  people.  This  group  is  less  influenced  by  a  cities  Indispensables.  Education  and  employment  opportunities  are  not  as  important  in  comparison  to  the  various  social  attributes.        

“Umbrella”  Rihanna  (-­‐0.70845,  0.55381)  

 As  Bad  Girl  RiRi  preaches,  the  essentials  are  all  you  need  to  be  happy.  This  segment  of  young  professionals  prefer  to  be  under  an  “umbrella”  of  security.  They  are  less  concerned  with  a  city’s  nightlife,  but  value  the  notion  of  stability.  These  young  professionals  are  influenced  predominately  by  Indispensables.  The  Umbrella  cluster  is  responsive  to  strong  education  systems,  prospective  employment  opportunities,  and  safety.        

“Single  Ladies”  Beyoncé  (-­‐0.4230,  -­‐1.79126)  

 The  Single  Ladies  cluster  of  young  professionals  embody  the  soul  and  spirit  of  our  Queen  B.  As  she  sings  in  her  Grammy  Award  Winning,  2010  Song  of  the  Year:  Single  Ladies,  “I’m  doing  my  own  little  thing.”  These  young  professionals  find  Social  Grace  and  Indispensables  irrelevant  and  influential  when  deciding  where  to  live.  Social,  educational  and  professional  attributes  do  not  work  for  or  against  cities,  inconspicuous  to  where  they  live  now.  The  motives  influencing  where  these  young  professionals  live  are  likely  unparalleled  to  the  twelve  attributes  used  to  develop  the  model.      

 

*Formal  apology  for  the  ear  worms*    

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Appendix      

a.   Logistic  Regression  Included  Cases  

b.   Logistic  Regression  Model  Summary    

 c.   CMAX  and  CPRO  Logistic  Regression    

   

CMAX=  MAX  (255/438,  183/438)  =0.42  OR  0.582  

=0.582    

CPRO=  (0.582)2  +  (1-­‐0.582)2  =0.513  

     

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d.   Factor  Analysis  Sample  Size                                                                                              

10  *  (number  of  variables)  =  ideal  sample  size  for  population  generalization  10    *  12  city  attributes  =  120  

 534  >  120  

   

e.   Factor  Analysis  Tests  for  Correlation    

   

f.   Scree  Plot  (PCA  Factors)    

   

The  Scree  Plot  indicates  a  plateau  forming  between  values  2  and  3.  With  assistance  from  the  Eigenvalues  test,  I  can  conclude  that  three  is  the  optimal  number  of  factors  for  maximized  summarization.        

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g.   Screen  Plot  (Forced  Two-­‐factor)    

                                                                                   This  Scree  Plot  indicates  a  forced  plateau  between  values  1  and  2.      

h.   Rotated  Component  Matrix  (Forced  Two-­‐factor)    

     

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i.   Atlanta-­‐  Factor  Analysis  Sample  Size    

10  *  (number  of  variables)  =  ideal  sample  size  for  pop.  generalization  10    *  12  city  attributes  =  120  

171  >  120    

j.   Atlanta-­‐  Rotated  Component  Matrix    

   

k.   Agglomeration  Schedule    

 

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  Not  at  all  Important  (1)  

Very  Unimportant  

(2)  

Neither  Important  nor  Unimportant  

(3)  

Very  Important  (4)  

Extremely  Important  (5)  

Public Education (1) m   m   m   m   m  

Higher Education (2) m   m   m   m   m  

Employment opportunities in a

wide range of fields (3)

m   m   m   m   m  

Safety (e.g., crime rate, street

lights) (4) m   m   m   m   m  

Environmentally-friendly (e.g., air

quality, recycling) (5)

m   m   m   m   m  

Public transportation (e.g., access,

availability) (6)

m   m   m   m   m  

Social diversity (e.g., ethnic,

socio-economic) (7)

m   m   m   m   m  

Q14 How important are the following city characteristics to you in deciding where to live?

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Developed Downtown Core

(8) m   m   m   m   m  

Nightlife (e.g., restaurants, clubs, live music) (9)

m   m   m   m   m  

Cultural Amenities (e.g.,

museums, symphony, ballet) (10)

m   m   m   m   m  

Parks and Recreation (11) m   m   m   m   m  

Cost of Living (12) m   m   m   m   m  

Other (13) m   m   m   m   m  

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Q16 How likely are you to:

 Very  

Unlikely  (1)  

Unlikely  (2)  

Somewhat  Unlikely  (3)  

Undecided  (4)  

Somewhat  Likely  (5)   Likely  (6)   Very  

Likely  (7)  

Stay in Fort Worth if

offered an opportunity in another

city (1)

m   m   m   m   m   m   m  

Recommend Fort Worth to friends

(2)

m   m   m   m   m   m   m  

Come back to Fort

Worth if you ever left

(3)

m   m   m   m   m   m   m  

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  Very  Bad  (1)   Bad  (2)   Neither  Good  nor  Bad  (3)   Good  (4)   Very  Good  (5)  

Public Education (1) m   m   m   m   m  

Higher Education (2) m   m   m   m   m  

Employment opportunities in a

wide range of fields (3)

m   m   m   m   m  

Safety (e.g., crime rate, street

lights) (4) m   m   m   m   m  

Environmentally-friendly (e.g., air

quality, recycling) (5)

m   m   m   m   m  

Public transportation (e.g., access,

availability) (6)

m   m   m   m   m  

Social diversity (e.g., ethnic,

socio-economic) (7)

m   m   m   m   m  

Developed Downtown Core

(8) m   m   m   m   m  

Q19 What is your perception of Fort Worth on these city characteristics?

Ashley  Quintilone  

Nightlife (e.g., restaurants, clubs, live music) (9)

m   m   m   m   m  

Cultural Amenities (e.g.,

museums, symphony, ballet) (10)

m   m   m   m   m  

Parks and Recreation (11) m   m   m   m   m  

Cost of Living (12) m   m   m   m   m  

Other (13) m   m   m   m   m