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APR 280 Week 6 Notes

by: Tricia Sylvia

APR 280 Week 6 Notes APR 280

Tricia Sylvia
GPA 3.7

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About this Document

Week 6 notes for test 2
Investigation and Insights
Brandon K. Chicotsky
Class Notes
public relations, Advertising, investigation and insights, APR, APR280
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This 7 page Class Notes was uploaded by Tricia Sylvia on Friday March 25, 2016. The Class Notes belongs to APR 280 at University of Alabama - Tuscaloosa taught by Brandon K. Chicotsky in Spring 2016. Since its upload, it has received 14 views. For similar materials see Investigation and Insights in Advertising at University of Alabama - Tuscaloosa.


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Date Created: 03/25/16
Lesson  14   Tuesday,  March  8,  2016   8:08  AM   What  is  research?   •   Trying  to  find  solutions     • Explanations     •   New  facts     Why  does  research  matter?   •   Save  time  and  money   •   Gain  insights     •   Insights  lead  to  action     o   You  know  what  to  do  on  behalf  of  your  client     Goals  of  Research     •   Describe     •   Diagnose     •   Predict   Sources  of  Secondary  Data   •   Internal  corporate  information     •   Gov't  agencies   •   Trade  and  industry  associations     •   Marketing  research  firms   •   Commercial  publications   •   News  media     o   Pros:  allows  for  specialized  searches  generally  easy  to  use/navigate   o   Cons:  often  no  raw  data.  Sometimes  need  licenses  to  access  which   may  be  cost  prohibitive         Databases   •   pew  Research  Center   o   Media     o   Us  politics     o   Social  trends   o   Religion     o   Internet  and  technology     o   Hispanics     o   Global     •   Lexus  nexus     o   News  media     o   Legal  pairings     •   Simmons  one  view     o   Contains  data  profiling   consumers  of  a  wide  range  of  products  including  both  demographic   data  and  media  preferences     Types  of  Research  methods  (field  research)   •   Test  marketing     o   Good  data  if  random  sampling     •   Surveys     o   Questionnaires     •   Postal     •   Face  to  face   o   Consumer  panels   o   Interviews   •   Telephone   •   Open  ended   •   Group  interviews   •   Observation   Validity       •   Face  validity  =  criterion     •   Critical  because  it  weighs  on  the  perception  of  your  client  that  you  are  doing  something  of  value     o   Internal  validity     •   Are  you  doing  what  you  set  out  to  study   o   External  validity     •   Can  other  people  replicate     •   Is  it  correct?   Reliability     •   The  consistency  or  repeatability  of  the  measurement  tools  and  the  results         Example:  your  friend  who  lies  all  the  time  is  reliable  in  that  they  will  like  but  they  will  not  be  valid  in   what  they  say         Generalization     •   Sample  represents  the  population         Population  (N)   Sample  (n)   Sample  frame   •    the  list  of  pop  elements  from  which  we  draw  the  sample     Sample  size   •   The  people  we  actually  sample     Bigger  sample  does  not  always  mean  better  results     In  stats,  we  have  confidence  intervals  and  margin  of  error  that  can  tell  us  how  the  sample   represents  the  population     Example:  confidence  interval  =  95%,  margin  of  error  (SE)  =  +/ -­‐3%    60%  of  people   Meaning  if  we  conducted  th is  survey  100  times,  the  percentage  who  gave  the   same  response  to  this  question  will  range  from  57% -­‐63%  95  out  of  the  100   times     Sampling  steps     1.   Determine  the  population     2.   Get  the  sampling  frame   3.   Draw  your  samples         Sampling  methods   •   Probability     o   Random  selection   o   Each  unit  has  a  non-­‐zero  chance  of  being  selected   •   Non  probability     o   Does  not  use  random  selection     Simple  random  sampling  is  ideal     Probability:  Systematic  random  sampling     1.   Obtain  list  of  entire  population  (N)   2.   Determine  sample  size  (n)   3.   Determine  sample  width  (k)   4.   Calculate  (k)  by  N/n       Probability:  Stratified  sampling     •   Population  is  divided  into  different  subgroups  based  on  demographic   •   Sample  is  picked  from  each  strata         Non-­‐Probability:  Convenient  sampling       Non-­‐  Probability:  Purposive  sampling     •   Samples  are  selected  based  on  specific,  preselected  criteria  or  profile   o   Example:  iPhone  users,  mothers,  people  who  have  brain  injury  etc.       How  is  purposive  sampling  similar  and  different  to  stratified  sampling?< -­‐-­‐  on  test       Non  probability:  snowball     •   Each  person  brings  3  people       Lesson  15   Thursday,  March  10,  2016   8:09  AM   Probability   •   Simple  random     o   Most  basic   o   Units  have  equal  chance  of  selection     •   Systematic  random   o   Units  are  ordered  and  every  nth  member  of  the  population  is  selected   o   First  n  is  random         Probability:   •   You  can  generalize  to  the  population     •   Pros   o   Less  prone  to  bias     o   Calculation  of  sampling  error  is  possible  form  which  one  can  determine  statistical   significance     •   Cons   o   Requires  a  list  of  all  elements   o   Time  consuming/costly     o   No  advantage  w  small  numbers   Non  probability   •   Pros   o   Quicker     o   Cheaper   o   Flexible     •   Cons   o   Less  generalizable         Surveys   •   With  focus  groups,  these  are  the  most  popular  form  of  market  research             Survey  Research  can  show  correlation  but  not  causation         •   Pitfalls  of  survey  research     o   You  must  ensure  a  large  number  of  the  selected  sample  will  reply     •   Responding  rate   •   Partial/full  question  completion     o   Are  people  telling  the  truth   •   Malingering     •   Social  desirability  bias   o   Wording  can  diminish  validity     •   Survey  design     o   Think  small-­‐-­‐most  surveys  will  be  taken  on  a  phone  screen     o   Make  it  clean     o   Clarity   o   Flow     •   Start  easy,  be  engaging     o   Relevance   o   Objectivity   •   Avoid  question  bias   •   Space  choices  evenly   •   Randomize  choices     o   Look  and  feel     o   Question  structure       • Nominal  questions   o   Answers  like  male/female     •   Ordinal     o   Ranking  things     •   What  makes  a  good  survey  question?   o   Simple   o   Specific   o   Individual  question  at  once   o   Exhaustive  (all  possible  answer  choices  are  given)   •   Closed  ended  questions     o   Dichotomous     •   Yes/no   o   Multiple  choice  with  an   option  for  other     •   Exhaustive  and  exclusive   o   Ranking  scales     •   Likert  scale   §   Agree-­‐disagree   •   Semantic  differential  scale     §   Happy-­‐sad     •   Open  ended  questions   o   Include  a  few   o   Ensures  that  people  pay  attention,  more  possibilities   o   Big  consideration  how  much  is  too  much     o   Is  the  range  of  answers  extremely  long??  Use  open  ended   o   Are  you  unsure  how  respondents  might  answer?  Use  open  ended     •   Tips   o   Go  over  objectives   •   Make  sure  your  questions  feed  into  them     o   Avoid  leading/loaded  questions   •   There  should  not  be  a  right  or  wrong  answer     §   Example:  how  would  you  rate  the  career  of   legendary  outfielder  joe  DiMaggio   o   Multiple  choice  questions  should  be  mutually  exclusive  and  exhaustive     o   Ask  DIRECT  questions     • Don’t  assume  respondents  will  understand   this  is  not  a  test   §   Example:  what  suggestions   do  you  have  for  improving  the  apple  watch     •   This  could  be  interpreted  in  many  ways;  feel,  look,  system   o   Don’t  use  unbalanced  scales   •   Example:  strongly  agree,  agree,  neither,  strongly  disagree     o   Avoid  double  barreled  questions   •   One  question,  one  answer  at  a  time   §   Example:  DON’T:  how  likely  are  you  to  go  to  dinner  AND  a  movie  this  weekend   o   Pretest  the  survey     Myths  about  sampling:       Myth:   •   Large  sample  sizes  are  best     •   WRONG   o   Representativeness  is  key   Myth:   •   Researches  should  sample  a   fixed  %  of  a  population  to  produce  an  acceptable  sample  size   •   WRONG   o   Probability  based  sampling  uses  mathematical  calculations  to  debunk  this     Myth:   •   Researches  should  bases  sample  sizes  on  industry  standards  or  "typical"  sample  sizes  used  in  other   research  projects   •   WRONG   o   Make  thoughtful  decisions  based  on  individual  requirements  of  research  needs           Sample  Distribution     •   Grouping  or  arrangement  of  a  characteristic  that  researches  measure  for  each  sample  member   •   It  reflects  the  frequency  with  which  research  assign  a  sample  characteristics  to  each  point  on  a   measurement  scale     •   Common  in  survey  research     Normal  Distribution   •   Bell  curve     •   Frequency  by  which  data  occurs         Conducting  a  "census"   •   Everyone  in  target  population  is  included     Standard  deviation   •   Standardized  measurement  of  dispersion  (or  variation)  around  a  mean     •   Simple  unit  of  measurement     •   Dispersion  of  possibilities  or  responses   •   Gives  researchers  a  basis  for  estimating  the  probability  of  normal  distribution  and  probability   based  sample  distribution  that  always  contains  some  error     Confidence  Level     •   Degree  of  certainty  researchers  can  have  when  they  draw  inferences  about  a  population  based  on   data  from  a  sample     •   How  confident  are  we  that  our  sample  is   representative  of  the  population  (+/-­‐)   •   95%  is  standard  rarely  is  a  lower  level  used     Confidence  interval     •   Range  or  margin  of  error  that  researchers  permit  when  making  inferences  from  a   population/sample   •   Usually  stated  as  a  positive  to  negative  range  (+/ -­‐3)   o   Margin  of  error  is  SIX     Variance       • How  something  is  dispersed  throughout  a  population     •   Variance  measure  most  used  in  research:  .5   o   This  represents  50%  of  a  population  belonging  to  a  category         Calculating  sample  size:   •   Variance  is  always  .5     •   Confidence  elvel  and  margin   of  error  turn  into  decimals  (+/ -­‐3=  .03)       Standard  Deviation     •   90%  confidence  level=  1.65   •   95%  =  1.96   •   99%  =  2.58           Read  the  final  part  of  chapter  six   •   Calculating  sample  size  strategic  management  austin  and  pinkleton      


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