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## APR 280 Week 5 Notes

by: Tricia Sylvia

11

0

4

# APR 280 Week 5 Notes APR 280

Tricia Sylvia
UA
GPA 3.7

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Week 5 notes for test 1
COURSE
Investigation and Insights
PROF.
Brandon K. Chicotsky
TYPE
Class Notes
PAGES
4
WORDS
CONCEPTS
public relations, Advertising, investigation and insights, APR, APR280
KARMA
25 ?

## Popular in Investigation and Insights

This 4 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 11 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  13   Thursday,  March  3,  2016   8:41  AM   Inherent  Error  in  Sampling     •   This  is  where  formulaic  statistics  comes  in     •   Practitioners/researchers   employ  simple  random  sampling  by  clearly  and  unambiguously   identifying  each  member  of  a  population  through  the  use  of  a  comprehensive  sampling  frame     Systematic  random  sampling     1.   Determine  final  number  of  interviews  needed  for  a  study,  or  total  sample  size   2.   Determine  sampling  interval   o   Divide  the  number  of  elements  in  the  sampling  frame  (total  population)  by  desired  total   sample  size   o   The  result  is  a  number  (n)   •   Generate  sample  by  selecting  every  nth  element  from  a  sampling  frame     •   First  sample  element  must  be  se lected  randomly     Periodicity   •   Bias  that  occurs  when  a  sampling  list  has  a  cyclical  repetition  of  some  population  characteristic   that  coincides  with  a  sampling  interval     Cluster  Sampling     •   Using  groups,  rather  than  individuals   •   Each  cluster  serves  as  a  sample   element     •   Geographical  designations  (one  neighborhood/one  university)   •   Used  when  an  exhaustive  list  of  elements  is  not  available     o   Ex:  tornado  disaster  relief         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  siz es  used  in  other   research  projects   •   WRONG   o   Make  thoughtful  decisions  based  on  individual  requirements  of  research  needs           Sample  Distribution     •   A  grouping  or  arrangement  of  a  characteristic  that  researchers  measure  for  each  sample  member   •   It  reflects  the  frequency  with  which  researchers  assign  sample  characteristics  to  each  point  on  a   measurement  scale     •   Common  in  survey  research.     •   What's  measured  might  include   o   Opinions     o   Attitudes   o   Behaviors     o   And  related  characteristics       Standard  Deviation     •   The  Standard  Deviation  is  a  measure  of  how  spread  out  numbers  are.  Its  symbol  is   σ  (the  Greek   letter  sigma)  The  formula  is  easy:  it  is  the  square  root  of  the  Variance.     Lesson  12   Tuesday,  March  1,  2016   8:14  AM   Parsimony:   •   If  something  is  parsimonious  it  is  simple-­‐-­‐easily  applicable  to  other  areas  of  research     Model:   •   Helps  explain  relationships  in  a  way  that  is  easy  to  classify     Sampling  Methods   •   Has  direct  relationship  with  generalizability     Probability  and  Non-­‐Probability  sampling  methods   o   Probability  is  more  reliable     o   Probability  means  everyone  has  the  same  chance  of  getting  selected   o   Non-­‐probability  is  not  as  random     Population     •   Constitutes  all  the  members  of  a  group  or  an  entire  collection  of  objects   •   In  PR  it  is  the  target  public     Parameters   •   A  characteristic  or  property  of  a  population,  which  are  found  or  determined  after  collecting  data   o   Ex:  if  a  random  probability  of  1000  students,  35%  own  Uggs,  you  can  say  35%  of  all  students   are  Ugg  owners   •   Assessed  after  data  is  collected       •   There  is  some  error,  but  small  enough  to  infer  characteristics  of  populations  with  a   high  degree  of   confidence   o   Sample  is  a  subset  of  a  population  or  universe   Representativeness   •   Would  the  size  of  the  sample   add  validity  if  its  not  representative  of  the  target  demographic     o   NO!  It's  not  a  part  of  the  target  demographic     Sample  frame   •   List  of  members  in  a  target  audience   Sample  unit   •   Individual  persons  from  this  list     Probability  Sampling     • Random  samples   •   Produce  results  that  are  highly  generalizable  to  a  population   Non  Probability  Sampling       •   Low  in  generalizability  and  external  validity  so  why  use  it?   o   Quick  and  easy  to  generate     o   Lower  cost     •   Precursor  research,  or  something  before  a  major  study  (pretest)   •   Lack  of  generalizability  is  a  serious  limitation     o   Not  selecting  randomly,  not  everyone  has  an  equal  chance  of  being  selected   1.   Convenience  sampling   •   Ex:  college  students     •   Why  is  sample  representation  limited?   o   Because  only  people  exposed  to  the  survey  will  hav e  access  to  it   •   E.g.  GQ  Magazine     2.   Quota  sampling   •   To  get  your  quota   3.   Dimensional  sampling   •   Making  sure  that  someone  from  a  demographic  is  represented   4.   Purposive  (judgmental)  sampling   •   A  subjective  judgment     •   Example:  only  picking  "cool"  people   •   Forces  diversification-­‐-­‐not  representative     5.   Volunteer  sampling   6.   Snowball  sampling     •   You  get  a  volunteer  and  ask  them  to  grab  someone  who  will  grab  someone  etc.     Stratified  sampling   •   Breaking  up  the  sample  into  smaller  groups  and  then  picking  people  from  each  strata   (group)   Simple  Random  Sampling   •   Most  scientifically  valid  methodological     Forms  of  simple  random  sampling     1.   Systematic  sampling       • Example:  picking  every  seven  people,  but  first  person  must  be  random     2.   Stratified  sampling     •  Break  into  groups,  select  equal  samples  from  e ach  group     3.   Cluster  sampling     •  Take  homogenous  groups,  break  them  into  groups  then  proportionally  draw  from  each   group     •   Why  simple  random  sampling   o   Helps  eliminate  sample  bias   o   Reduces  the  chance  of  subgroup  overrepresentation  or  underrepresentation     Inherent  Error  in  Sampling     •   This  is  where  formulaic  statistics  comes  in

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