1. Sampling: Design and Procedures
Chapter Eleven
Sampling: Design and Procedures
Copyright © 2010 Pearson Education, Inc.
11-1

2. Chapter Outline
1) Overview
2) The Sampling Design Process
Define the Target Population
Determine the Sampling Frame
Select a Sampling Technique
Determine the Sample Size
Execute the Sampling Process

3. Chapter Outline 4) A Classification of Sampling Techniques
Nonprobability Sampling Techniques
Convenience Sampling
Judgmental Sampling
Quota Sampling
Snowball Sampling
Probability Sampling Techniques
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling

5. Sampling Sampling is the process
of selecting a small number of elements
from a larger defined target group
of elements such that
the information gathered
from the small group will allow judgments
to be made about the larger groups

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. 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. Yasar University Student Satisfaction Survey
Element: Yasar University’s enrolled student
Sampling units: Faculties, institutes, vocational school
Extent: Izmir
Time 2017

9. Define the Target Population
Important qualitative factors in determining the sample size are:
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
response rates
resource constraints (time, budget, software, human resources…)

10. Sample Sizes Used in Marketing Research Studies
Table 11.2

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

12. Nonprobability sampling approach
Relies on personal judgement of the researcher
The researcher can arbitrarily or consciously decide what elements to include
They do not allow objective evaluations
They still may yield good estimates

13. 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

14. 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
Expert respondents

15. 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.
Control Population Sample Variable composition composition Sex Percentage Percentage Number Male Female ____ ____ ____

16. Snowball Sampling
In snowball sampling, an initial group of respondents is selected first
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.

17. 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

18. Systematic Random 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.

19. 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 7000 elements (students) in Yasar University (the population) and a sample of 350 is desired. In this case the sampling interval, i, is 20. A random number between 1 and 20 is selected. If, for example, this number is 3, the sample consists of elements 3, 23, 43, 63, 83, 103, and so on.

20. 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. (Undergraduate and graduate students)
Next, elements are selected from each stratum by a random procedure.
A major objective of stratified sampling is to increase precision without increasing cost.

21. Cluster Sampling Cluster sampling is a method of probability sampling
where the sampling units
are selected in groups
rather than individually

22. Cluster Sampling
The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters. (e.g. Different neighbourhoods of a city)
Then a random sample of clusters is selected, based on a probability sampling technique.
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).

23. Strengths and Weaknesses of Basic Sampling Techniques
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
Systematic sampling
Can increase
representativeness,
easier to implement than
SRS
Can decrease
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 11.4

24. Choosing Nonprobability Vs. Probability Sampling
Table 11.4

26. Next Week: Consultation and report submission for TA
A report will be submitted covering:
Research Design
Your research objective
Importance of the subject
Secondary Data Findings
Research Methods planned to be used
Qualitative section (explanations)
Quantitive section (explanations)
Target population-sampling issues
Analysis methods planned
Expected results of the research

27. Copyright © 2010 Pearson Education, Inc. publishing as Prentice Hall
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.
Copyright © 2010 Pearson Education, Inc. publishing as Prentice Hall