Marketing Research: An Applied Orientation: Global Edition, 6/E

1. Chapter Eleven
Sampling:
Design and Procedures

2. 11-2
Chapter Outline
1) Overview
2) Sample or Census
3) The Sampling Design Process
i. Define the Target Population
ii. Determine the Sampling Frame
iii. Select a Sampling Technique
iv. Determine the Sample Size
v. Execute the Sampling Process

3. 11-3
Chapter Outline
4) A Classification of Sampling Techniques
i. Nonprobability Sampling Techniques
a. Convenience Sampling
b. Judgmental Sampling
c. Quota Sampling
d. Snowball Sampling
ii. Probability Sampling Techniques
a. Simple Random Sampling
b. Systematic Sampling
c. Stratified Sampling
d. Cluster Sampling
e. Other Probability Sampling Techniques

4. 11-4
Chapter Outline
5. Choosing Nonprobability versus Probability
Sampling
6. Uses of Nonprobability versus Probability Sampling
7. International Marketing Research
8. Ethics in Marketing Research
9. Internet and Computer Applications
10. Focus On Burke
11. Summary
12. Key Terms and Concepts

5. 11-5
Sample vs. Census
Table 11.1
Condit ions Favor ing t he Use of
Type of St udy Sam ple Census
1. Budget Sm all Large
2. Tim e available Short Long
3. Populat ion size Large Sm all
4. Variance in t he charact erist ic Sm all Large
5. Cost of sam pling errors Low High
6. Cost of nonsam pling errors High Low
7. Nat ure of m easurem ent Dest ruct ive Nondest ruct ive
8. At t ent ion t o individual cases Yes No

6. 11-6
The Sampling Design Process
Fig. 11.1
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

7. 11-7
Define the Target Population
The target population is the collection of elements or
objects that possess the information sought by the
researcher and about which inferences are to be
made. The target population should be defined in
terms of elements, sampling units, extent, and time.
 An element is the object about which or from
which the information is desired, e.g., the
respondent.
 A sampling unit is an element, or a unit
containing the element, that is available for
selection at some stage of the sampling process.
 Extent refers to the geographical boundaries.
 Time is the time period under consideration.

8. 11-8
Define the Target Population
Important qualitative factors in determining the
sample size
 the importance of the decision
 the nature of the research
 the number of variables
 the nature of the analysis
 sample sizes used in similar studies
 incidence rates
 completion rates
 resource constraints

9. Sample Sizes Used in Marketing
11-9
Research Studies
Table 11.2
Type of St udy Minim um Size Typical Range
Problem ident if icat ion r esear ch 500 1,000- 2,500
( e.g. m ar ket pot ent ial)
Problem -solving r esear ch ( e.g. 200 300- 500
pricing)
Product t est s 200 300- 500
Test m ar ket ing st udies 200 300- 500
TV, radio, or prin t adver t ising ( per 150 200- 300
com m ercial or ad t est ed)
Test -m arket audit s 10 st or es 10- 20 st ores
Focus gr oups 2 gr oups 4- 12 gr oups

10. Classification of Sampling 11-10
Techniques
Fig. 11.2
Sampling Techniques
Nonprobability Probability
Sampling Techniques Sampling Techniques
Convenience Judgmental Quota Snowball
Sampling Sampling Sampling Sampling
Simple Systematic Stratified Cluster Other Sampling
Random Sampling Sampling Sampling Techniques
Sampling

11. 11-11
Convenience Sampling
Convenience sampling attempts to obtain a
sample of convenient elements. Often, respondents
are selected because they happen to be in the right
place at the right time.
 use of students, and members of social
organizations
 mall intercept interviews without qualifying the
respondents
 department stores using charge account lists
 “people on the street” interviews

12. 11-12
Judgmental Sampling
Judgmental sampling is a form of convenience
sampling in which the population elements are
selected based on the judgment of the researcher.
 test markets
 purchase engineers selected in industrial
marketing research
 bellwether precincts selected in voting behavior
research
 expert witnesses used in court

13. 11-13
Quota Sampling
Quota sampling may be viewed as two-stage restricted judgmental
sampling.
 The first stage consists of developing control categories, or quotas,
of population elements.
 In the second stage, sample elements are selected based on
convenience or judgment.
Population Sample
composition composition
Control
Characteristic Percentage Percentage Number
Sex
Male 48 48 480
Female 52 52 520
____ ____ ____
100 100 1000

14. 11-14
Snowball Sampling
In snowball sampling, an initial group of
respondents is selected, usually at random.
 After being interviewed, these respondents are
asked to identify others who belong to the target
population of interest.
 Subsequent respondents are selected based on
the referrals.

15. 11-15
Simple Random Sampling
 Each element in the population has a known and
equal probability of selection.
 Each possible sample of a given size (n) has a known
and equal probability of being the sample actually
selected.
 This implies that every element is selected
independently of every other element.

16. 11-16
Systematic Sampling
 The sample is chosen by selecting a random starting point and
then picking every ith element in succession from the sampling
frame.
 The sampling interval, i, is determined by dividing the population
size N by the sample size n and rounding to the nearest integer.
 When the ordering of the elements is related to the
characteristic of interest, systematic sampling increases the
representativeness of the sample.
 If the ordering of the elements produces a cyclical pattern,
systematic sampling may decrease the representativeness of
the sample.
For example, there are 100,000 elements in the population and
a sample of 1,000 is desired. In this case the sampling interval,
i, is 100. A random number between 1 and 100 is selected. If,
for example, this number is 23, the sample consists of elements
23, 123, 223, 323, 423, 523, and so on.

17. 11-17
Stratified Sampling
 A two-step process in which the population is
partitioned into subpopulations, or strata.
 The strata should be mutually exclusive and
collectively exhaustive in that every population
element should be assigned to one and only one
stratum and no population elements should be
omitted.
 Next, elements are selected from each stratum by a
random procedure, usually SRS.
 A major objective of stratified sampling is to increase
precision without increasing cost.

18. 11-18
Stratified Sampling
 The elements within a stratum should be as homogeneous as
possible, but the elements in different strata should be as
heterogeneous as possible.
 The stratification variables should also be closely related to the
characteristic of interest.
 Finally, the variables should decrease the cost of the
stratification process by being easy to measure and apply.
 In proportionate stratified sampling, the size of the sample
drawn from each stratum is proportionate to the relative size of
that stratum in the total population.
 In disproportionate stratified sampling, the size of the sample
from each stratum is proportionate to the relative size of that
stratum and to the standard deviation of the distribution of the
characteristic of interest among all the elements in that stratum.

19. 11-19
Cluster Sampling
 The target population is first divided into mutually exclusive and
collectively exhaustive subpopulations, or clusters.
 Then a random sample of clusters is selected, based on a
probability sampling technique such as SRS.
 For each selected cluster, either all the elements are included in
the sample (one-stage) or a sample of elements is drawn
probabilistically (two-stage).
 Elements within a cluster should be as heterogeneous as
possible, but clusters themselves should be as homogeneous
as possible. Ideally, each cluster should be a small-scale
representation of the population.
 In probability proportionate to size sampling, the clusters
are sampled with probability proportional to size. In the second
stage, the probability of selecting a sampling unit in a selected
cluster varies inversely with the size of the cluster.

20. 11-20
Types of Cluster Sampling
Fig. 11.3 Cluster Sampling
One-Stage Two-Stage Multistage
Sampling Sampling Sampling
Simple Cluster Probability
Sampling Proportionate
to Size Sampling

21. Strengths and Weaknesses of
11-21
Basic Sampling Techniques
Table 11.3
Technique Strengths Weaknesses
Nonprobability Sampling Least expensive, least Selection bias, sample not
Convenience sampling time-consuming, most representative, not recommended for
convenient descriptive or causal research
Judgmental sampling Low cost, convenient, Does not allow generalization,
not time-consuming subjective
Quota sampling Sample can be controlled Selection bias, no assurance of
for certain characteristics representativeness
Snowball sampling Can estimate rare Time-consuming
characteristics
Probability sampling Easily understood, Difficult to construct sampling
Simple random sampling results projectable frame, expensive, lower precision,
(SRS) no assurance of representativeness.
Systematic sampling Can increase Can decrease representativeness
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Stratified sampling Include all important Difficult to select relevant
subpopulations, stratification variables, not feasible to
precision stratify on many variables, expensive
Cluster sampling Easy to implement, cost Imprecise, difficult to compute and
effective interpret results

22. 11-22
Procedures for Drawing Probability Samples
Fig. 11.4
Simple Random
Sampling
1. Select a suitable sampling frame
2. Each element is assigned a number from 1 to N
(pop. size)
3. Generate n (sample size) different random numbers
between 1 and N
4. The numbers generated denote the elements that
should be included in the sample

23. Procedures for Drawing
11-23
Probability Samples
Fig. 11.4 cont. Systematic
Sampling
1. Select a suitable sampling frame
2. Each element is assigned a number from 1 to N (pop. size)
3. Determine the sampling interval i:i=N/n. If i is a fraction,
round to the nearest integer
4. Select a random number, r, between 1 and i, as explained in
simple random sampling
5. The elements with the following numbers will comprise the
systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i

24. Procedures for Drawing
11-24
Probability Samples
Fig. 11.4 cont. Stratified
Sampling
1. Select a suitable frame
2. Select the stratification variable(s) and the number of strata, H
3. Divide the entire population into H strata. Based on the
classification variable, each element of the population is assigned
to one of the H strata
4. In each stratum, number the elements from 1 to Nh (the pop.
size of stratum h)
5. Determine the sample size of each stratum, nh, based on
proportionate or disproportionate stratified sampling, where
H
nh = n
h=1
6. In each stratum, select a simple random sample of size nh

25. Procedures for Drawing
11-25
Probability Samples Cluster
Fig. 11.4 cont. Sampling
1. Assign a number from 1 to N to each element in the population
2. Divide the population into C clusters of which c will be included in
the sample
3. Calculate the sampling interval i, i=N/c (round to nearest integer)
4. Select a random number r between 1 and i, as explained in simple
random sampling
5. Identify elements with the following numbers:
r,r+i,r+2i,... r+(c-1)i
6. Select the clusters that contain the identified elements
7. Select sampling units within each selected cluster based on SRS
or systematic sampling
8. Remove clusters exceeding sampling interval i. Calculate new
population size N*, number of clusters to be selected C*= C-1,
and new sampling interval i*.

26. 11-26
Procedures for Drawing Probability Samples
Fig. 11.4 cont.
Cluster
Sampling
Repeat the process until each of the remaining
clusters has a population less than the
sampling interval. If b clusters have been
selected with certainty, select the remaining
c-b clusters according to steps 1 through 7.
The fraction of units to be sampled with
certainty is the overall sampling fraction = n/N.
Thus, for clusters selected with certainty, we
would select ns=(n/N)(N1+N2+...+Nb) units. The
units selected from clusters selected under
PPS sampling will therefore be n*=n- ns.

27. Choosing Nonprobability vs.
11-27
Probability Sampling
Table 11.4 cont.
Condit ions Favoring t he Use of
Fact or s Nonprobabilit y Probabilit y
sam pling sam pling
Nat ur e of resear ch Explorat ory Conclusive
Relat ive m agnit ude of sam pling Nonsam pling Sam pling
and nonsam pling errors errors ar e errors are
larger larger
Variabilit y in t he populat ion Hom ogeneous Het er ogeneous
( low ) ( high)
St at ist ical considerat ions Unf avorable Favorable
Operat ional considerat ions Favorable Unfavorable

28. 11-28
Tennis' Systematic Sampling Returns a Smash
Tennis magazine conducted a mail survey of its subscribers
to gain a better understanding of its market. Systematic
sampling was employed to select a sample of 1,472
subscribers from the publication's domestic circulation list. If
we assume that the subscriber list had 1,472,000 names, the
sampling interval would be 1,000 (1,472,000/1,472). A
number from 1 to 1,000 was drawn at random. Beginning
with that number, every 1,000th subscriber was selected.
A brand-new dollar bill was included with the questionnaire
as an incentive to respondents. An alert postcard was
mailed one week before the survey. A second, follow-up,
questionnaire was sent to the whole sample ten days after
the initial questionnaire. There were 76 post office returns,
so the net effective mailing was 1,396. Six weeks after the
first mailing, 778 completed questionnaires were returned,
yielding a response rate of 56%.