Sampling Design and Procedures

Chapter Ten
Sampling:
Design and Procedures

11-9
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

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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

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Sampling
Sampling;
• The process of selecting a small number of elements
from a larger defined target group of elements the
information gathered from the small group will allow
judgments to be made about the larger groups

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Population
Sample
Sampling

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Sample
The Sample

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Census
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List of Units
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Census
Sample or Census

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Sample vs. Census
Conditions Favoring the Use of
Type of Study Sample Census
1. Budget Small Large
2. Time available Short Long
3. Population size Large Small
4. Variance in the characteristic Small Large
8. Attention to individual cases Yes No

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• Budget and time constraints (in case of large populations)
• High degree of accuracy and reliability (if sample is
representative of population)
• Sampling may sometimes produce more accurate results
than taking a census as in the latter
• There are more risks for making interviewer and other
errors due to the high volume of persons contacted and
the number of census takers
• Some of whom may not be well-trained
Reasons for Sampling

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Who is the target group
for the study?
This is called the study
population
Who in the target group
should be surveyed?
This is called the sample.
How many people should
be surveyed?
This is called the sample
size.
How should the people to
be surveyed by selected?
This is called the sampling
method.
The Sampling

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Acknowledgments to Uma
Sekaran

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• A sample is a subset of a larger
population of objects individuals,
households, businesses,
organizations and so forth.
• Sampling enables researchers to
make estimates of some unknown
characteristics of the population in
question
• A finite group is called population
whereas a non-finite (infinite) group is
called universe
• A census is a investigation of all the
individual elements of a Population
Population
Sample
Sampling

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Classification of Sampling Techniques
Sampling Techniques
Non-probability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple Random
Sampling

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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

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Define the Target population
Select a Sampling Frame
Determine if a probability
or non-probability sampling
method will be chosen
Plan procedure for
selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork1
2
3
4
5
6
7
The Sampling Process

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Defining the Target Population
• The target population is that complete group whose
relevant characteristics are to be determined through the
sampling and a target population may be, for example,
• All faculty members in the PRESTON University Islamabad,
• All housewives in Islamabad
• All pre-college students in Rawalpindi
• All medical doctors in Pakistan
• The target group should be clearly defined if possible, for
example, do all pre-college students include only primary and
secondary students or also students in other specialized
educational institutions?

11-28
Define the Target Population
The target population;
 The target population should be defined in terms of
elements, sampling units 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.
• Time is the time period under consideration.

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• A list of population elements (people, companies, houses,
cities, etc.) from which units to be sampled can be selected.
• Examples;
• A student telephone directory (for the student population),
• The list of companies on the stock exchange,
• The directory of medical doctors and specialists,
• The yellow pages (for businesses)
• Difficult to get an accurate list.
• Sample frame error occurs when certain elements of the
population are accidentally omitted or not included in list
• Information relating to sampling frames can be obtained
from commercial organizations
The Sampling Frame

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List of
Units
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Sample
The Sampling Frame

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• The sampling unit is a single element – or group of
elements – subject to selection in a sample.
• Examples:
 Every student at PRESTON whose first name begins
with the letter “F”
 All child passengers under 18 years of age who are
traveling in a train from destination X to destination Y
 All jeweler shops in sectors F-6, F-7 and F-8 in
Islamabad
The Sampling Units

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Classification of Sampling Techniques
Sampling Techniques
Non-probability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple Random
Sampling

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Sampling
Probability sampling;
1. Simple Random Sampling;
2. Systematic Sampling;
3. Stratified Sampling;
4. Cluster Sampling;

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Simple Random Sampling
Simple random sampling;
• SRS is a method of probability sampling in which
every unit has an equal nonzero chance of being
selected

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Systematic Random Sampling
Systematic random sampling;
• A method of probability sampling in which the
defined target population is ordered the sample is
selected according to position using a skip interval

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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, 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.

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Systematic Sampling
 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, that 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.

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Stratified Random Sampling
Stratified random sampling;
• A method of probability sampling in which the
population is divided into different subgroups and
samples are selected from each subgroup.

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Stratified Sampling
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.

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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.
 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.

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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).
 In probability proportionate to size sampling, the clusters
are sampled with probability proportional to size.

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

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Random Sampling Error ;–
• The “difference between the sample result and the result of
a census conducted using identical procedures” and is the
result of chance variation in the selection of sampling units
• If samples are selected properly (for e.g. through the technique of
randomization), the sample is usually supposed to be a good
approximation of the population and thus capable of
delivering an accurate result
• Usually, the random sampling error arising from statistical
fluctuation is small, but sometimes the margin of error can
be significant
The Sampling Errors (1)

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Systematic (Non-Sampling) Errors;
• These errors result from factors such as an
• Improper research design that causes response error or
• Or errors committed in the execution of the research
• Errors in recording responses
• Non-responses from individuals who were not contacted
or who refused to participate
• Both Random sampling errors and systematic (non-
sampling) errors reduce the representativeness of a
sample and consequently the value of the information
which is derived by business researchers from it
The Sampling Errors (2)

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Total Population
Sampling Frame Error
Random Sampling Error
Sampling Frame
Planned
Sample
Non-Response Error
Respondents
(actual
sample)
The Sampling Errors

Yes
Choose one of the
PROBABILITY sampling
designs.
If purpose of study
mainly is for:
No
Generalizability
.
Assessing
differential
parameters in
subgroups of
population.
Collecting
information
in a localized
area.
Gathering more
information
from a subset of
the sample
To obtain quick,
even if unreliable
information
To obtain
information relevant
to and available only
with certain groups
Choose one of the
NONPROBABILITY
sampling designs.
If purpose of study
mainly is:
Choose
cluster
sampling
if not
enough $.
Choose
systematic
sampling.
Choose
simple
random
sampling.
All subgroups have
equal number of
elements?
Yes No
Choose
disproportionate
stratified random
sampling
Choose
proportionate
stratified random
sampling.
Choose
convenience
sampling.
Looking for
information that
only a few “experts”
can provide?
Need responses of
special interest
minority groups?
Choose
judgment
sampling.
Choose
quota
sampling.
Choose area
sampling.
Choose
double
sampling.
Is REPRESENTATIVENESS
of sample critical for the study?
Diagram 11.3
Choice Points in Sampling Design.

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Acknowledgments to Uma
Sekaran

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Sampling
Non-probability sampling;
1. Convenience sampling;
2. Judgmental sampling;
3. Quota sampling;
4. Snowball sampling;

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Convenience Sampling
Convenience sampling;
• Attempts to obtain a sample of convenient elements.
• 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

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Judgmental Sampling
Judgmental sampling;
 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
 Expert witnesses used in court

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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

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Snowball Sampling
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

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2. For smaller samples (N ‹ 100), there is
little point in sampling. Survey the
entire population.
1. The larger the population size, the
smaller the percentage of the
population required to get a
representative sample
Rules of thumb for determining the
sample size...

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4. If the population size is around 1500,
20% should be sampled.
3. If the population size is around 500
(give or take 100), 50% should be
sampled.
5. Beyond a certain point (N = 5000),
the population size is almost
irrelevant and a sample size of 400
may be adequate.
Rules of thumb for determining the
sample size...

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

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