sampling uki
TRANSCRIPT
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K E T I N R E E A R H T U D Y
Sampling in Marketing Research
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K E T I N R E E A R H T U D Y
Basics of sampling I
A sample is apart of a whole
to show what the
rest is like.
Sampling helps to
determine the
corresponding
value of the
population and
plays a vital role in
marketing
research.
Samples offer many benefits: Save costs:Less expensive to study the
sample than the population.
Save time:Less time needed to study the
sample than the population .
Accuracy:Since sampling is done with
care and studies are conducted by skilled
and qualified interviewers, the results are
expected to be accurate.
Destructive nature of elements:For someelements, sampling is the way to test, since
tests destroy the element itself.
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K E T I N R E E A R H T U D YBasics of sampling II
imitations of Sampling Deman!s more rigi! control
in un!ertaking sample
operation.
Minority an! smallness in
num"er of su"#groups oftenren!er stu!y to "e
suspecte!.
Accuracy level may "e
affecte! when !ata is
su"$ecte! to weighing.
Sample results are goo!
appro%imations at "est.
Sampling &rocess
Defining the
population
Developing
a sampling
Frame
Determining
Sample
Size
Specifying
Sample
Method
SELECTIN T!E S"M#LE
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K E T I N R E E A R H T U D Y
Sampling' Step (Defining the )niverse
)niverse or population is thewhole mass un!er stu!y.
ow to define a universe: *hat constitutes the units of
analysis +,DB apartments-
*hat are the sampling units
+,DB apartments occupie! in
the last three months-
*hat is the specific !esignation
of the units to "e covere! +,DB
in town area- *hat time perio! !oes the !ata
refer to +Decem"er /(0 (112-
Sampling' Step 34sta"lishing the Sampling
5rame
! sample frame is the list of all
elements in the population
"such as telephone directories,
electoral registers, club
membership etc.# from whichthe samples are drawn.
A sample frame which !oes not
fully represent an inten!e!
population will result inframe
error an! affect the !egree ofrelia"ility of sample result.
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K E T I N R E E A R H T U D YStep # /
Determination of Sample Si6e
Sample si6e may "e !etermine! "y using'
Su"$ective metho!s +less sophisticated methods-
7he rule of thum" approach' eg. 28 of population
9onventional approach' eg. Average of sample si6es ofsimilar other stu!ies:
9ost "asis approach' 7he num"er that can "e stu!ie!
with the availa"le fun!s:
Statistical formulae +more sophisticated methods-
9onfi!ence interval approach.
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K E T I N R E E A R H T U D Y
9onventional approach of Sample si6e !etermination using
Sample si6es use! in !ifferent marketing research stu!ies
7;&4 4
7;&I9A
RA=?4
I!entifying a pro"lem +e.g.market
segmentation- 2@@ (@@@#32@@
&ro"lem#solving +e.g.0 promotion- 3@@ /@@#2@@
&ro!uct tests 3@@ /@@#2@@
A!vertising +70 Ra!io0 or print Me!ia
per commercial or a! teste!- (2@ 3@@#/@@
7est marketing 3@@ /@@#2@@
7est market au!its (@storesoutlets
(@#3@storesoutlets
5ocus groups 3 groups C#(3 groups
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K E T I N R E E A R H T U D YSample si6e !etermination using statistical formulae'
$he confidence interval approach
7o !etermine sample si6es using statistical formulae0
researchers use the confi!ence interval approach "ase! on the
following factors'
%esired level of data precision or accuracy&
!mount of variability in the population "homogeneity#& Level of confidence required in the estimates of population values.
Availa"ility of resources such as money0 manpower an! time
may prompt the researcher to mo!ify the compute! sample
si6e. Students are encouraged to consult any standard marketing
research textbook to have an understanding of these formulae.
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K E T I N R E E A R H T U D Y
Step 4:
Specifying the sampling method
&ro"a"ility Sampling
4very element in the target population or universe sampling
frameE has eFual pro"a"ility of "eing chosen in the sample for
the survey "eing con!ucte!.
Scientific0 operationally convenient an! simple in theory. Results may "e generali6e!.
=on#&ro"a"ility Sampling
4very element in the universe sampling frameE !oes not have
eFual pro"a"ility of "eing chosen in the sample.
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K E T I N R E E A R H T U D Y
&ro"a"ility sampling
!ppropriate for
homogeneous population
Simple random sampling
'equires the use of a random
number table.
Systematic sampling
'equires the sample frame
only,
(o random number table isnecessary
!ppropriate for
heterogeneous population
Stratified sampling
)se of random number
table may be necessary
*luster sampling
)se of random number
table may be necessary
Four types of probability sampling
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K E T I N R E E A R H T U D Y
=on#pro"a"ility sampling
Four types of non+probability samplingtechniques
ery simple types, based on sub-ective criteria*onvenient samplingudgmental sampling
/ore systematic and formal0uota sampling
Special typeSnowball Sampling
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K E T I N R E E A R H T U D Y
Simple Ran!om Sampling
Also calle! ran!om
sampling
Simplest metho! of
pro"a"ility
sampling
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 37 75 10 49 98 66 03 86 34 80 98 44 22 22 45 83 53 86 23 51
2 50 91 56 41 52 82 98 11 57 96 27 10 27 16 35 34 47 01 36 08
3 99 14 23 50 21 01 03 25 79 07 80 54 55 41 12 15 15 03 68 56
4 70 72 01 00 33 25 19 16 23 58 03 78 47 43 77 88 15 02 55 67
5 18 46 06 49 47 32 58 08 75 29 63 66 89 09 22 35 97 74 30 80
6 65 76 34 11 33 60 95 03 53 72 06 78 28 14 51 78 76 45 26 45
7 83 76 95 25 70 60 13 32 52 11 87 38 49 01 82 84 99 02 64 00
8 58 90 07 84 20 98 57 93 36 65 10 71 83 93 42 46 34 61 44 01
9 54 74 67 11 15 78 21 96 43 14 11 22 74 17 02 54 51 78 76 76
10 56 81 92 73 40 07 20 05 26 63 57 86 48 51 59 15 46 09 75 64
11 34 99 06 21 22 38 22 32 85 26 37 00 62 27 74 46 02 61 59 81
12 02 26 92 27 95 87 59 38 18 30 95 38 36 78 23 20 19 65 48 50
13 43 04 25 36 00 45 73 80 02 61 31 10 06 72 39 02 00 47 06 98
14 92 56 51 22 11 06 86 88 77 86 59 57 66 13 82 33 97 21 31 61
15 67 42 43 26 20 60 84 18 68 48 85 00 00 48 35 48 57 63 38 84
(eed to use
Ran!om
=um"er 7a"le
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y
!o$ to %se &andom Num'er Ta'les
(((((((((((((((((((((((((((((((((((((((((((((((1. Assign a unique number to each population element in the
sampling frame. tart !ith serial number 1" or 01" or 001"
etc. up!ar#s #epen#ing on the number of #igits require#.
2. $hoose a ran#om starting position.
3. elect serial numbers s%stematicall% across ro!s or #o!n columns.
4. &iscar# numbers that are not assigne# to an% population
element an# ignore numbers that ha'e alrea#% been
selecte#.
5. (epeat the selection process until the require# number of sample elements is selecte#.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y
,ow to )se a 7a"le of Ran!om =um"ers to Select a Sample
1our marketing research lecturer wants to randomly select 23 students from
your class of 433 students. ere is how he can do it using a random number table.Step (: Assign all the 100 meme!s of the pop"lation a "ni#"e n"me!.$o" may
identify each element y assigning a t%o&digit n"me!. Assign 01 to the fi!st name
on the list' and 00 to the last name. (f this is done' then the tas) of selecting the
sample %ill e easie! as yo" %o"ld e ale to "se a 2&digit !andom n"me! tale.
=AM4 =)MB4R =AM4 =)MB4R
Adam' *an 01 *an *ec) +ah 42
,,,,,, ,,,,,,,, ,-a!!ol' -han 08 *ay *hiam Soon 61
,,,,,,. , ,,,,,,.. ,e!!y /e%is 18 *eo *ai eng 87
,,,,,,. , ,,,,,,,. ,
/im -hin am 26 ,,,,,,,, ,,,,,,,. , $eo *ec) /an 99
Singh' A!"n 30 ailani t Samat 00
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y
Step 3' Select any sta!ting point in the andom "me! *ale and find the fi!st n"me! that
co!!esponds to a n"me! on the list of yo"! pop"lation. (n the eample elo%' 08 has een
chosen as the sta!ting point and the fi!st st"dent chosen is -a!ol -han.
10 09 73 25 33 76
37 54 20 48 05 64
08 42 26 89 53 19
90 01 90 25 29 09
12 80 79 99 70 8066 06 57 47 17 34
31 06 01 08 05 45
Step /' oe to the net n"me!' 42 and select the pe!son co!!esponding to that n"me! into
the sample. 87 *an *ec) +ah
Step C' -ontin"e to the net n"me! that #"alifies and select that pe!son into the sample.
26 && e!!y /e%is' follo%ed y 89' 53 and 19Step 2' Afte! yo" hae selected the st"dent 19' go to the net line and choose 90. -ontin"e
in the same manne! "ntil the f"ll sample is selected. (f yo" enco"nte! a n"me! selected
ea!lie! e.g.' 90' 06 in this eample simply s)ip oe! it and choose the net n"me!.
Starting point:move right to the endof the row, then downto the next row row;move left to the end,then down to the next
row, and so on.
,ow to use ran!om num"er ta"le to select a ran!om sample
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y
Systematic sampling
ery similar to simple ran!om sampling with one e%ception.
In systematic sampling only one ran!om num"er is nee!e! throughout the
entire sampling process.
7o use systematic sampling0 a researcher nee!s'
iE a sampling frame of the population: an! is nee!e!.
iiE a skip interval calculate! as follows'
Skip interval G population list si5e
Sample si5e
=ames are selecte! using the skip interval.
6f a researcher were to select a sample of 4333 people using the local telephone
directory containing 247,333 listings as the sampling frame, skip interval is
8247,33394333, or 247. $he researcher can select every 247thname of the entire
directory 8samplingframe, and select his sample.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y;xample: ow to $ake a Systematic Sample
Step (' Select a listing of the population0 say the 9ity 7elephone Directory0 from which to
sample. Remem"er that the list will have an accepta"le level of sample frame error.
Step 3' 9ompute the skip interval "y !ivi!ing the num"er of entries in the !irectory "y the!esire! sample si6e.
;xample: 273,333 names in the phone book, desired a sample si5e of 2733,
So skip interval < every 433th
name
Step /' )sing ran!om num"er+s-0 !etermine a starting position for sampling the list.
;xample: Select: 'andom number for page number. "page 34#
Select: 'andom number of column on that page. "col. 3=#
Select: 'andom number for name position in that column ">=?, say, !../ahadeva#
Step C' Apply the skip interval to !etermine which names on the list will "e in the sample.
;xample: !. /ahadeva "Skip 433 names#, new name chosen is ! 'ahman b !hmad.
Step 2' 9onsi!er the list as circular: that is0 the first name on the list is now the initial name
you selecte!0 an! the last name is now the name $ust prior to the initially selecte! one.
;xample: @hen you come to the end of the phone book names "As#, -ust continue on
through the beginning "!s#.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D YSt!atified sampling (
A three#stage process'
Step (# Divi!e the population into
homogeneous0 mutually e%clusive
an! collectively e%haustive
su"groups or strata using some
stratification varia"le:
Step 3# Select an in!epen!ent simpleran!om sample from each stratum.
Step /# 5orm the final sample "y
consoli!ating all sample elements
chosen in step 3.
May yiel! smaller stan!ar! errors ofestimators than !oes the simple ran!om
sampling. 7hus precision can "e gaine!
with smaller sample si6es.
Stratifie! samples can "e'
&roportionate'involving the
selection of sample elements
from each stratum0 such that
the ratio of sample elements
from each stratum to the
sample si6e eFuals that of thepopulation elements within
each stratum to the total
num"er of population
elements.
Disproportionate'the sample
is !isproportionate when the
a"ove mentione! ratio is
uneFual.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y7o select a proportionate stratifie! sample of 3@ mem"ers of the Islan! i!eo 9lu" which has
(@@ mem"ers "elonging to three language "ase! groups of viewers i.e.0 4nglish +4-0 Man!arin
+M- an!
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K E T I N R E E A R H T U D Y
Step 3' Su"#!ivi!e the clu" mem"ers into three homogeneous su"#groups or strata "y the
language groups' 4nglish0 Man!arin an! others.
nglish/ang"age anda!in /ang"age ;the! /ang"age St!at"m St!at"m St!at"m .
00 22 40 64 82 06 35 66 02 42
01 24 43 67 85 07 44 68 12 46
03 26 45 69 86 10 47 72 17 52
04 29 48 70 89 13 51 77 18 60
05 30 49 71 91 15 53 78 21 65
08 31 50 73 93 19 56 80 23 7409 32 54 75 94 20 58 83 28 84
11 34 55 76 96 25 59 87 38 88
14 36 57 79 97 27 61 92 39 90
16 37 63 81 99 33 62 98 41 95
(.9alculate the overall sampling fraction0 f0 in the following manner'
f
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K E T I N R E E A R H T U D Y
Determine the num"er of sample elements +n(- to "e selecte! from the 4nglish
language stratum. In this e%ample0 n(G 2@ % f G 2@ % @.3 G(@. By using a simpleran!om sampling metho! using a ran!om num"er ta"leE mem"ers whose num"ers
are @(0 @/0 (0 /@0 C/0 CJ0 2@0 2C0 220 K20 are selecte!.
=e%t0 !etermine the num"er of sample elements +n3- from the Man!arin language
stratum. In this e%ample0 n3G /@ % f G /@ H @.3 G . By using a simple ran!om
sampling metho! as "efore0 mem"ers having num"ers (@0(20 3K0 2(0 210 JK areselecte! from the Man!arin language stratum.
In the same manner0 the num"er of sample elements +n/- from the L
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K E T I N R E E A R H T U D Y
9luster sampling
Is a type of sampling in which clusters or groups of
elements are sample! at the same time.
Such a proce!ure is economic0 an! it retains the
characteristics of pro"a"ility sampling.
A two#step#process' Step (# Define! population is !ivi!e! into num"er of mutually
e%clusive an! collectively e%haustive su"groups or clusters:
Step 3# Select an in!epen!ent simple ran!om sample of clusters.
Bne special type of cluster sampling is called area sampling, where
pieces of geographical areas are selected.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y;xample : Bne+stage and two+stage *luster sampling
9onsi!er the same Islan! i!eo 9lu" e%ample involving (@@ clu" mem"ers'
Step (' Su"#!ivi!e the clu" mem"ers into 2 clusters0 each cluster containing 3@ mem"ers.
$luster
)o. *nglish +an#arin ,thers
1 00" 22" 40" 64" 82 06" 35" 66 02" 42
01" 24" 43" 67" 85 07" 44" 68 12" 46
2 03" 26" 45" 69" 86 10" 47" 72 17" 52
04" 29" 48" 70" 89 13" 51" 77 18" 60
3 05" 30" 49" 71" 91 15" 53" 78 21" 65
08" 31" 50" 73" 93 19" 56" 80 23" 744 09" 32" 54" 75" 94 20" 58" 83 28" 84
11" 34" 55" 76" 96 25" 59" 87 38" 88
5 14" 36" 57" 79" 97 27" 61" 92 39" 90
16" 37" 63" 81" 99 33" 62" 98 41" 95
Step 2: Select one of the 5 cl"ste!s. (f cl"ste! 4 is selected' then all its elements i.e. -l"
eme!s %ith n"me!s 09' 11' 32' 34' 54' 55' 75' 76' 94' 96' 20' 25' 58' 59' 83' 87' 28' 38' 84'
88 a!e selected.
Step 3: (f a t%o&stage cl"ste! sampling is desi!ed' the !esea!che! may !andomly select 4 meme!s
f!om each of the fie cl"ste!s. (n this case' the sample %ill e diffe!ent f!om that sho%n in step 2
aoe.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D YStratifie! Sampling vs 9luster Sampling
Stratifie! Sampling 9luster Sampling
(.7he target population is su"#!ivi!e!
into a few su"groups or strata0 each
containing a large num"er of elements.
(.7he target population is su"#
!ivi!e! into a large num"er of
su"#population or clusters0 each
containing a few elements.
3.*ithin each stratum0 the elements are
homogeneous. ,owever0 high !egree ofheterogeneity e%ists "etween strata.
3.*ithin each cluster0 the elements
are heterogeneous. Betweenclusters0 there is a high !egree of
homogeneity.
/.A sample element is selecte! each time. /.A cluster is selecte! each time.
C.ess sampling error. C.More prone to sampling error.
2.
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K E T I N R E E A R H T U D YAR4A SAM&I=?
! common form of cluster sampling where clusters consist of geographic areas, such as
districts, housing blocks or townships. !rea sampling could be one+stage, two+stage, or
multi+stage.
ow to $ake an !rea Sample )sing Subdivisions
;our company wants to con!uct a survey on the e%pecte! patronage of its new outlet in a new
housing estate. 7he company wants to use area sampling to select the sample househol!s to "e
interviewe!. 7he sample may "e !rawn in the manner outline! "elow.
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
Step (' Determine the geographic area to "e surveye!0 an! i!entify its su"!ivisions. 4ach
su"!ivision cluster shoul! "e highly similar to all others. 5or e%ample0 choose ten housing
"locks within 3 kilometers of the propose! site say0 Mo!el 7own E for your new retail outlet:
assign each a num"er.
Step 3' Deci!e on the use of one#step or two#step cluster sampling. Assume that you !eci!e to
use a two#stage cluster sampling.
Step /' )sing ran!om num"ers0 select the housing "locks to "e sample!. ,ere0 you select C
"locks ran!omly0 say num"ers O(@30 O(@C0 O(@0 an! O(@J.Step C' )sing some pro"a"ility metho! of sample selection0 select the househol!s in each of the
chosen housing "lock to "e inclu!e! in the sample. I!entify a ran!om starting point +say0
apartment no. (@/-0 instruct fiel! workers to !rop off the survey at every fifth house
+systematic sampling-.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y
on&p!oaility samples
9onvenience sampling %rawn at the convenience of the researcher. *ommon in exploratory research.
%oes not lead to any conclusion.
Pu!gmental sampling
Sampling based on some -udgment, gut+feelings or experience of the researcher.
*ommon in commercial marketing research pro-ects. 6f inference drawing is not
necessary, these samples are quite useful.
Quota sampling !n extension of -udgmental sampling. 6t is something like a two+stage -udgmental
sampling. 0uite difficult to draw.
Snow"all sampling
)sed in studies involving respondents who are rare to find. $o start with, theresearcher compiles a short list of sample units from various sources. ;ach of
these respondents are contacted to provide names of other probable respondents.
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y0uota Sampling
7o select a Fuota sample comprising /@@@ persons in country H using three control
characteristics' se%0 age an! level of e!ucation.
,ere0 the three control characteristics are consi!ere! in!epen!ently of one another.
In or!er to calculate the !esire! num"er of sample elements possessing the various
attri"utes of the specifie! control characteristics0 the !istri"ution pattern of the
general population in country H in terms of each control characteristics is e%amine!.
9ontrol
9haracteristics &opulation Distri"ution Sample 4lements .
>ende!: .... ale...................... 50.7? ale 3000 50.7? < 1521
................. @emale .................. 49.3? @emale 3000 49.3? < 1479
Age: ......... 20&29 yea!s ........... 13.4? 20&29 yea!s 3000 13.4? < 402
................. 30&39 yea!s ........... 53.3? 30&39 yea!s 3000 52.3? < 1569
................. 40 yea!s oe! .... 33.3? 40 yea!s oe! 3000 34.3? < 1029
eligion: .. -h!istianity ........... 76.4? -h!istianity 3000 76.4? < 2292
................. (slam ..................... 14.8? (slam 3000 14.8? < 444
................. Bind"ism .............. 6.6? Bind"ism 3000 6.6? < 198
................. ;the!s ................... 2.2? ;the!s 3000 2.2? < 66
----------------------------------------------------------------------------------
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D YSampling vs non#sampling errors
Sampling 4rror S4E =on#sampling 4rror =S4E
)ery small sampleSize
Larger sample size
Still larger sample
Complete census
K E T I N R E E A R H T U D Y
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K E T I N R E E A R H T U D Y-hoosing p!oaility s. non&p!oaility sampling
&ro"a"ility ;valuation *riteria =on#pro"a"ilitysampling sampling
9onclusive (ature of research 4%ploratory
arger sampling 'elative magnitude arger non#sampling
errors sampling vs. error
non+sampling error
,igh Copulation variability ow
,eterogeneousE ,omogeneousE
5avora"le Statistical *onsiderations )nfavora"le
,igh Sophistication (eeded ow
Relatively onger $ime Relatively shorter
,igh Dudget (eeded ow