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S a mpling in R e s e a rc h

   By G lynd ore s Y. Bau tis talla
A fa mous s a mpling mis ta ke
A fa mous s a mpling mis ta ke

 That’s Truman




                  They only asked rich, white
                   people with telephones
                  who’d they vote for. Sadly,
                     they published their
                           mistake
E ven with proper sampling…beware!



  “…predicting behavior on the basis of
knowledge of attitude is a very hazardous
   venture.” Meaning, predicting social
behavior is often misguided. Keep that in
                  mind!
Wha t e xa c tly IS a
  “ s a mple” ?
Wha t e xa c tly IS a
         “ s a mple” ?

 A subset of the population, selected by
either “probability” or “non-probability”
   methods. If you have a “probability
sample” you simply know the likelihood
of any member of the population being
    included (not necessarily that it is
               “random.”
Type s of s a mple s
S imple R a ndom S a mple
1. G e t a lis t or “ s am p ling fram e ”
2. G e ne rate rand om nu m b e rs
3. S e le ct one p e rs on p e r rand om nu m b e r
S ys te ma tic R a ndom
               S a mple
• S e le ct a rand om nu m b e r, wh ich will b e
  known as k
• G e t a lis t of p e op le , or ob s e rve a flow of
  p e op le (e .g., p e d e s trians on a corne r)
• S e le ct e ve ry kth p e rs on
  – C are fu l th at th e re is no s ys te m atic rh yth m to
    th e flow or lis t of p e op le .
  – If e ve ry 4th p e rs on on th e lis t is , s ay, “ rich ”
    or “ s e nior” or s om e oth e r cons is te nt
    p atte rn, avoid th is m e th od
S tra tifie d R a ndom S a mple
1. S e p arate you r p op u lation into grou p s or
   “ s trata”
2. D o e ith e r a s im p le rand om s am p le or
   s ys te m atic rand om s am p le from th e re
  a. N ote you m u s t know e as ily wh at th e
     “ s trata” are b e fore atte m p ting th is
  b. If you r s am p ling fram e is s orte d b y, s ay,
     s ch ool d is trict, th e n you ’ re ab le to u s e th is
     m e th od
Multi-s ta ge C lus te r S a mple
1. G e t a lis t of “ clu s te rs ,” e .g., b ranch e s of
   a com p any
2. R and om ly s am p le clu s te rs from th at lis t
3. H ave a lis t of, s ay, 1 0 b ranch e s
4. R and om ly s am p le p e op le with in th os e
   b ranch e s
  a. Th is m e th od is com p le x and e xp e ns ive !
The C onve nie nc e S a mple

1. F ind s om e p e op le th at are e as y to find
The S nowba ll S a mple

1. F ind a fe w p e op le th at are re le vant to
   you r top ic.
2. As k th e m to re fe r you to m ore of th e m .
The Q uota S a mple

1. D e te rm ine wh at th e p op u lation looks
   like in te rm s of s p e cific qu alitie s .
2. C re ate “ qu otas ” b as e d on th os e
   qu alitie s .
3. S e le ct p e op le for e ach qu ota.
The The ore tic a l S a mple

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Sampling in research

  • 1. S a mpling in R e s e a rc h By G lynd ore s Y. Bau tis talla
  • 2. A fa mous s a mpling mis ta ke
  • 3. A fa mous s a mpling mis ta ke That’s Truman They only asked rich, white people with telephones who’d they vote for. Sadly, they published their mistake
  • 4. E ven with proper sampling…beware! “…predicting behavior on the basis of knowledge of attitude is a very hazardous venture.” Meaning, predicting social behavior is often misguided. Keep that in mind!
  • 5. Wha t e xa c tly IS a “ s a mple” ?
  • 6. Wha t e xa c tly IS a “ s a mple” ? A subset of the population, selected by either “probability” or “non-probability” methods. If you have a “probability sample” you simply know the likelihood of any member of the population being included (not necessarily that it is “random.”
  • 7. Type s of s a mple s
  • 8. S imple R a ndom S a mple 1. G e t a lis t or “ s am p ling fram e ” 2. G e ne rate rand om nu m b e rs 3. S e le ct one p e rs on p e r rand om nu m b e r
  • 9. S ys te ma tic R a ndom S a mple • S e le ct a rand om nu m b e r, wh ich will b e known as k • G e t a lis t of p e op le , or ob s e rve a flow of p e op le (e .g., p e d e s trians on a corne r) • S e le ct e ve ry kth p e rs on – C are fu l th at th e re is no s ys te m atic rh yth m to th e flow or lis t of p e op le . – If e ve ry 4th p e rs on on th e lis t is , s ay, “ rich ” or “ s e nior” or s om e oth e r cons is te nt p atte rn, avoid th is m e th od
  • 10. S tra tifie d R a ndom S a mple 1. S e p arate you r p op u lation into grou p s or “ s trata” 2. D o e ith e r a s im p le rand om s am p le or s ys te m atic rand om s am p le from th e re a. N ote you m u s t know e as ily wh at th e “ s trata” are b e fore atte m p ting th is b. If you r s am p ling fram e is s orte d b y, s ay, s ch ool d is trict, th e n you ’ re ab le to u s e th is m e th od
  • 11. Multi-s ta ge C lus te r S a mple 1. G e t a lis t of “ clu s te rs ,” e .g., b ranch e s of a com p any 2. R and om ly s am p le clu s te rs from th at lis t 3. H ave a lis t of, s ay, 1 0 b ranch e s 4. R and om ly s am p le p e op le with in th os e b ranch e s a. Th is m e th od is com p le x and e xp e ns ive !
  • 12. The C onve nie nc e S a mple 1. F ind s om e p e op le th at are e as y to find
  • 13. The S nowba ll S a mple 1. F ind a fe w p e op le th at are re le vant to you r top ic. 2. As k th e m to re fe r you to m ore of th e m .
  • 14. The Q uota S a mple 1. D e te rm ine wh at th e p op u lation looks like in te rm s of s p e cific qu alitie s . 2. C re ate “ qu otas ” b as e d on th os e qu alitie s . 3. S e le ct p e op le for e ach qu ota.
  • 15. The The ore tic a l S a mple