Sampling in Marketing Research
1
Basics of sampling I


A sample is a
“part 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 some
elements, sampling is the way to test, since
tests destroy the element itself.
2
Basics of sampling II
Limitations of Sampling




Demands more rigid control
in undertaking sample
operation.
Minority and smallness in
number of sub-groups often
render study to be
suspected.
Accuracy level may be
affected when data is
subjected to weighing.
Sample results are good
approximations at best.
Sampling Process
Defining the
population
Specifying
Sample
Method
Developing
a sampling
Frame
Determining
Sample
Size
SELECTING THE SAMPLE
3
Sampling: Step 1
Defining the Universe


Universe or population is the
whole mass under study.
Sampling: Step 2
Establishing the Sampling
Frame

A sample frame is the list of all
elements in the population
(such as telephone directories,
electoral registers, club
membership etc.) from which
the samples are drawn.

A sample frame which does not
fully represent an intended
population will result in frame
error and affect the degree of
reliability of sample result.
How to define a universe:
» What constitutes the units of
analysis (HDB apartments)?
» What are the sampling units
(HDB apartments occupied in
the last three months)?
» What is the specific designation
of the units to be covered (HDB
in town area)?
» What time period does the data
refer to (December 31, 1995)
4
Step - 3
Determination of Sample Size

Sample size may be determined by using:
» Subjective methods (less sophisticated methods)
– The rule of thumb approach: eg. 5% of population
– Conventional approach: eg. Average of sample sizes of
similar other studies;
– Cost basis approach: The number that can be studied
with the available funds;
» Statistical formulae (more sophisticated methods)
– Confidence interval approach.
5
Conventional approach of Sample size determination using
Sample sizes used in different marketing research studies
TYPE OF STUDY
Identifying a problem (e.g.market
segmentation)
Problem-solving (e.g., promotion)
Product tests
Advertising (TV, Radio, or print Media
per commercial or ad tested)
Test marketing
Test market audits
Focus groups
MINIMUM
SIZE
TYPICAL
RANGE
500
200
200
1000-2500
300-500
300-500
150
200
10
stores/outlets
2 groups
200-300
300-500
10-20
stores/outlets
4-12 groups
6
Sample size determination using statistical formulae:
The confidence interval approach

To determine sample sizes using statistical formulae,
researchers use the confidence interval approach based on the
following factors:
» Desired level of data precision or accuracy;
» Amount of variability in the population (homogeneity);
» Level of confidence required in the estimates of population values.


Availability of resources such as money, manpower and time
may prompt the researcher to modify the computed sample
size.
Students are encouraged to consult any standard marketing
research textbook to have an understanding of these formulae.
7
Step 4:
Specifying the sampling method

Probability Sampling
» Every element in the target population or universe [sampling
frame] has equal probability of being chosen in the sample for
the survey being conducted.
» Scientific, operationally convenient and simple in theory.
» Results may be generalized.

Non-Probability Sampling
» Every element in the universe [sampling frame] does not have
equal probability of being chosen in the sample.
» Operationally convenient and simple in theory.
» Results may not be generalized.
8
Probability sampling
Four types of probability sampling

Appropriate for
homogeneous population
» Simple random sampling
– Requires the use of a random
number table.
» Systematic sampling
– Requires the sample frame
only,
– No random number table is
necessary

Appropriate for
heterogeneous population
» Stratified sampling
– Use of random number
table may be necessary
» Cluster sampling
– Use of random number
table may be necessary
9
Non-probability sampling

Four types of non-probability sampling
techniques
» Very simple types, based on subjective criteria
– Convenient sampling
– Judgmental sampling
» More systematic and formal
– Quota sampling
» Special type
– Snowball Sampling
10
Simple Random Sampling


Also called random
sampling
Simplest method of
probability
sampling
Need to use
Random
Number Table
1
2
3 4
5
6
7
1
2
3
4
5
37
50
99
70
18
75
91
14
72
46
10
56
23
01
06
6
7
8
9
10
65
83
58
54
56
76
76
90
74
81
11
12
13
14
15
34
02
43
92
67
99
26
04
56
42
8
49
41
50
00
49
98
52
21
33
47
66
82
01
25
32
03 86 34 80 98 44 22 22
98 11 57 96 27 10 27 16
03 25 79 07 80 54 55 41
19 16 23 58 03 78 47 43
58 08 75 29 63 66 89 09
45
35
12
77
22
83
34
15
88
35
53
47
15
15
97
86
01
03
02
74
23
36
68
55
30
51
08
56
67
80
34
95
07
67
92
11
25
84
11
73
33
70
20
15
40
60
60
98
78
07
95
13
57
21
20
51
82
42
02
59
78
84
46
54
15
76
99
34
51
46
45
02
61
78
09
26
64
44
76
75
45
00
01
76
64
06
92
25
51
43
21
27
36
22
26
22
95
00
11
20
38 22 32 85 26 37 00 62 27 74 46 02 61 59 81
87 59 38 18 30 95 38 36 78 23 20 19 65 48 50
45 73 80 02 61 31 10 06 72 39 02 00 47 06 98
06 86 88 77 86 59 57 66 13 82 33 97 21 31 61
60 84 18 68 48 85 00 00 48 35 48 57 63 38 84
03
32
93
96
05
9 10 11 12 13 14 15 16 17 18 19 20
53
52
36
43
26
72
11
65
14
63
06
87
10
11
57
78
38
71
22
86
28
49
83
74
48
14
01
93
17
51
11
How to Use Random Number Tables
________________________________________________
1. Assign a unique number to each population element in the
sampling frame. Start with serial number 1, or 01, or 001,
etc. upwards depending on the number of digits required.
2. Choose a random starting position.
3. Select serial numbers systematically across rows or down
columns.
4. Discard numbers that are not assigned to any population
element and ignore numbers that have already been
selected.
5. Repeat the selection process until the required number of
sample elements is selected.
12
How to Use a Table of Random Numbers to Select a Sample
Your marketing research lecturer wants to randomly select 20 students from
your class of 100 students. Here is how he can do it using a random number table.
Step 1: Assign all the 100 members of the population a unique number.You may
identify each element by assigning a two-digit number. Assign 01 to the first name
on the list, and 00 to the last name. If this is done, then the task of selecting the
sample will be easier as you would be able to use a 2-digit random number table.
NAME
Adam, Tan
………………
Carrol, Chan
……………….
Jerry Lewis
……………….
Lim Chin Nam
……………….
Singh, Arun
NUMBER
01
08
…
18
…
26
…
30
NAME
Tan Teck Wah
……………………
Tay Thiam Soon
………………..
Teo Tai Meng
………………….
……………………
Yeo Teck Lan
Zailani bt Samat
NUMBER
42
…
61
…
87
…
…
99
00
13
How to use random number table to select a random sample
Step 2: Select any starting point in the Random Number Table and find the first number that
corresponds to a number on the list of your population. In the example below, # 08 has been
chosen as the starting point and the first student chosen is Carol Chan.
Starting point:
move right to the end
of the row, then down
to the next row row;
move left to the end,
then down to the next
row, and so on.
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 80
66 06 57 47 17 34
31 06 01 08 05 45
Step 3: Move to the next number, 42 and select the person corresponding to that number into
the sample. #87 – Tan Teck Wah
Step 4: Continue to the next number that qualifies and select that person into the sample.
# 26 -- Jerry Lewis, followed by #89, #53 and #19
Step 5: After you have selected the student # 19, go to the next line and choose #90. Continue
in the same manner until the full sample is selected. If you encounter a number selected
earlier (e.g., 90, 06 in this example) simply skip over it and choose the next number.
14
Systematic sampling
 Very similar to simple random sampling with one exception.
 In systematic sampling only one random number is needed throughout the
entire sampling process.
 To use systematic sampling, a researcher needs:
[i] a sampling frame of the population; and is needed.
[ii] a skip interval calculated as follows:
Skip interval = population list size
Sample size
 Names are selected using the skip interval.
 If a researcher were to select a sample of 1000 people using the local telephone
directory containing 215,000 listings as the sampling frame, skip interval is
[215,000/1000], or 215. The researcher can select every 215th name of the entire
directory [sampling frame], and select his sample.
15
Example: How to Take a Systematic Sample
Step 1: Select a listing of the population, say the City Telephone Directory, from which to
sample. Remember that the list will have an acceptable level of sample frame error.
Step 2: Compute the skip interval by dividing the number of entries in the directory by the
desired sample size.
Example: 250,000 names in the phone book, desired a sample size of 2500,
So skip interval = every 100th name
Step 3: Using random number(s), determine a starting position for sampling the list.
Example: Select: Random number for page number. (page 01)
Select: Random number of column on that page. (col. 03)
Select: Random number for name position in that column (#38, say, A..Mahadeva)
Step 4: Apply the skip interval to determine which names on the list will be in the sample.
Example: A. Mahadeva (Skip 100 names), new name chosen is A Rahman b Ahmad.
Step 5: Consider the list as “circular”; that is, the first name on the list is now the initial name
you selected, and the last name is now the name just prior to the initially selected one.
Example: When you come to the end of the phone book names (Zs), just continue on
through the beginning (As).
16
Stratified sampling I
A three-stage process:




Step 1- Divide the population into
homogeneous, mutually exclusive
and collectively exhaustive subgroups
or strata using some stratification
variable;
Step 2- Select an independent simple
random sample from each stratum.
Step 3- Form the final sample by
consolidating all sample elements
chosen in step 2.
May yield smaller standard errors of
estimators than does the simple random
sampling. Thus precision can be gained
with smaller sample sizes.
Stratified samples can be:


Proportionate: involving the
selection of sample elements
from each stratum, such that
the ratio of sample elements
from each stratum to the
sample size equals that of the
population elements within
each stratum to the total
number of population
elements.
Disproportionate: the sample
is disproportionate when the
above mentioned ratio is
unequal.
17
Selection of a proportionate Stratified Sample
To select a proportionate stratified sample of 20 members of the Island Video Club which has
100 members belonging to three language based groups of viewers i.e., English (E), Mandarin
(M) and Others (X).
Step 1: Identify each member from the membership list by his or her respective language groups
00 (E )
01 (E )
02 ( X )
03 (E )
04 (E )
05 (E )
06 (M)
07 (M)
08 (E )
09 (E )
10 (M)
11 (E )
12 ( X )
13 (M)
14 (E )
15 (M)
16 (E )
17 ( X )
18 ( X )
19 (M)
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
(M)
(X)
(E )
(X)
(E )
(M)
(E )
(M)
(X)
(E )
(E )
(E )
(E )
(M)
(E )
(M)
(E )
(E )
(X)
(X)
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
(E )
(X)
(X)
(E )
(M)
(E )
(X)
(M)
(E )
(E )
(E )
(M)
(X)
(M)
(E )
(E )
(M)
(E )
(M)
(M)
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
(X)
(M)
(M)
(E )
(E )
(X)
(M)
(E )
(M)
(E )
(E )
(E )
(M)
(E )
(X)
(E )
(E )
(M)
(M)
(E )
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
(M)
(E )
(E )
(M)
(X)
(E )
(E )
(M)
(X)
(E )
(X)
(E )
(M)
(E )
(E )
(X)
(E )
(E )
(M)
(E )
18
Selection of a proportionate stratified sample II
Step 2: Sub-divide the club members into three homogeneous sub-groups or strata by the
language groups: English, Mandarin and others.
EnglishLanguage
Stratum
00 22 40 64 82
01 24 43 67 85
03 26 45 69 86
04 29 48 70 89
05 30 49 71 91
08 31 50 73 93
09 32 54 75 94
11 34 55 76 96
14 36 57 79 97
16 37 63 81 99
Mandarin Language
Stratum
06 35 66
07 44 68
10 47 72
13 51 77
15 53 78
19 56 80
20 58 83
25 59 87
27 61 92
33 62 98
Other Language
Stratum
.
02 42
12 46
17 52
18 60
21 65
23 74
28 84
38 88
39 90
41 95
1. Calculate the overall sampling fraction, f, in the following manner:
f = n = 20 = 1 =
N 100
5
0.2
where n = sample size and N = population size
19
Selection of a proportionate stratified sample III
 Determine the number of sample elements (n1) to be selected from the English
language stratum. In this example, n1 = 50 x f = 50 x 0.2 =10. By using a simple
random sampling method [using a random number table] members whose numbers
are 01, 03, 16, 30, 43, 48, 50, 54, 55, 75, are selected.
 Next, determine the number of sample elements (n2) from the Mandarin language
stratum. In this example, n2 = 30 x f = 30 X 0.2 = 6. By using a simple random
sampling method as before, members having numbers 10,15, 27, 51, 59, 87 are
selected from the Mandarin language stratum.
 In the same manner, the number of sample elements (n3) from the ‘Other language’
stratum is calculated. In this example, n3 = 20 x f = 20 X 0.2 = 4. For this stratum,
members whose numbers are 17, 18, 28, 38 are selected’
 These three different sets of numbers are now aggregated to obtain the ultimate
stratified sample as shown below.
S = (01, 03, 10, 15, 16, 17, 18, 27, 28, 30, 38, 43, 48, 50, 51, 54, 55, 59, 75, 87)
20
Cluster sampling




Is a type of sampling in which clusters or groups of
elements are sampled at the same time.
Such a procedure is economic, and it retains the
characteristics of probability sampling.
A two-step-process:
» Step 1- Defined population is divided into number of mutually
exclusive and collectively exhaustive subgroups or clusters;
» Step 2- Select an independent simple random sample of clusters.
One special type of cluster sampling is called area sampling, where
pieces of geographical areas are selected.
21
Example : One-stage and two-stage Cluster sampling
Consider the same Island Video Club example involving 100 club members:
 Step 1: Sub-divide the club members into 5 clusters, each cluster containing 20 members.
Cluster
No.
1
2
3
4
5
English
00, 22, 40, 64, 82
01, 24, 43, 67, 85
03, 26, 45, 69, 86
04, 29, 48, 70, 89
05, 30, 49, 71, 91
08, 31, 50, 73, 93
09, 32, 54, 75, 94
11, 34, 55, 76, 96
14, 36, 57, 79, 97
16, 37, 63, 81, 99
Mandarin
06, 35, 66
07, 44, 68
10, 47, 72
13, 51, 77
15, 53, 78
19, 56, 80
20, 58, 83
25, 59, 87
27, 61, 92
33, 62, 98
Others
02, 42
12, 46
17, 52
18, 60
21, 65
23, 74
28, 84
38, 88
39, 90
41, 95
 Step 2: Select one of the 5 clusters. If cluster 4 is selected, then all its elements (i.e. Club
Members with numbers 09, 11, 32, 34, 54, 55, 75, 76, 94, 96, 20, 25, 58, 59, 83, 87, 28, 38, 84,
88) are selected.
 Step 3: If a two-stage cluster sampling is desired, the researcher may randomly select 4 members
from each of the five clusters. In this case, the sample will be different from that shown in step 2
above.
22
Stratified Sampling vs Cluster Sampling
Stratified Sampling
Cluster Sampling
1. The target population is sub-divided
1. The target population is subinto a few subgroups or strata, each
divided into a large number of
containing a large number of elements.
sub-population or clusters, each
containing a few elements.
2. Within each stratum, the elements are 2. Within each cluster, the elements
homogeneous. However, high degree of
are heterogeneous. Between
heterogeneity exists between strata.
clusters, there is a high degree of
homogeneity.
3. A sample element is selected each time. 3. A cluster is selected each time.
4. Less sampling error.
4. More prone to sampling error.
5. Objective is to increase precision.
5. Objective is to increase sampling
efficiency by decreasing cost.
23
AREA SAMPLING
 A common form of cluster sampling where clusters consist of geographic areas, such as
districts, housing blocks or townships. Area sampling could be one-stage, two-stage, or
multi-stage.
How to Take an Area Sample Using Subdivisions
Your company wants to conduct a survey on the expected patronage of its new outlet in a new
housing estate. The company wants to use area sampling to select the sample households to be
interviewed. The sample may be drawn in the manner outlined below.
___________________________________________________________________________________
Step 1: Determine the geographic area to be surveyed, and identify its subdivisions. Each
subdivision cluster should be highly similar to all others. For example, choose ten housing
blocks within 2 kilometers of the proposed site [say, Model Town ] for your new retail outlet;
assign each a number.
Step 2: Decide on the use of one-step or two-step cluster sampling. Assume that you decide to
use a two-stage cluster sampling.
Step 3: Using random numbers, select the housing blocks to be sampled. Here, you select 4
blocks randomly, say numbers #102, #104, #106, and #108.
Step 4: Using some probability method of sample selection, select the households in each of the
chosen housing block to be included in the sample. Identify a random starting point (say,
apartment no. 103), instruct field workers to drop off the survey at every fifth house
(systematic sampling).
24
Non-probability samples

Convenience sampling
» Drawn at the convenience of the researcher. Common in exploratory research.
Does not lead to any conclusion.

Judgmental sampling
» Sampling based on some judgment, gut-feelings or experience of the researcher.
Common in commercial marketing research projects. If inference drawing is not
necessary, these samples are quite useful.

Quota sampling
» An extension of judgmental sampling. It is something like a two-stage judgmental
sampling. Quite difficult to draw.

Snowball sampling
» Used in studies involving respondents who are rare to find. To start with, the
researcher compiles a short list of sample units from various sources. Each of
these respondents are contacted to provide names of other probable respondents.
25
Quota Sampling
 To select a quota sample comprising 3000 persons in country X using three control
characteristics: sex, age and level of education.
 Here, the three control characteristics are considered independently of one another.
In order to calculate the desired number of sample elements possessing the various
attributes of the specified control characteristics, the distribution pattern of the
general population in country X in terms of each control characteristics is examined.
Control
Characteristics
Population
Distribution
Sample Elements
Gender: ....
.................
Male......................
Female ..................
50.7%
49.3%
Male
Female
3000 x 50.7% = 1521
3000 x 49.3% = 1479
Age: .........
.................
.................
20-29 years ...........
30-39 years ...........
40 years & over ....
13.4%
53.3%
33.3%
20-29 years
30-39 years
40 years & over
3000 x 13.4% = 402
3000 x 52.3% = 1569
3000 x 34.3% = 1029
.
Religion: ..
Christianity ...........
76.4%
Christianity
3000 x 76.4% = 2292
.................
Islam .....................
14.8%
Islam
3000 x 14.8% = 444
.................
Hinduism ..............
6.6%
Hinduism
3000 x 6.6% = 198
.................
Others ...................
2.2%
Others
3000 x 2.2% = 66
_________________________________________________________________________________
_
26
Sampling vs non-sampling errors
Sampling Error [SE]
Non-sampling Error [NSE]
Very small sample Size
Larger sample size
Still larger sample
Complete census
27
Choosing probability vs. non-probability sampling
Probability
sampling
Evaluation Criteria
Conclusive
Nature of research
Exploratory
Relative magnitude
sampling vs.
non-sampling error
Larger non-sampling
Larger sampling
errors
Non-probability
sampling
error
High
[Heterogeneous]
Population variability
Low
[Homogeneous]
Favorable
Statistical Considerations
Unfavorable
High
Sophistication Needed
Low
Relatively Longer
High
Time
Budget Needed
Relatively shorter
Low
28