Sampling Uki

8/21/2019 Sampling Uki

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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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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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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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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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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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&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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=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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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



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!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#.



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,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



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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



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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.



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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#.



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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.



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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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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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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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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.



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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.



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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-.



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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.



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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

----------------------------------------------------------------------------------



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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



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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

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Sampling Uki