This document discusses various sampling designs and procedures. It begins by explaining why sampling is used instead of a census, such as budget/time constraints and limited access. It then defines key sampling terminology like sample, population, and element. The document outlines the stages of sampling, including selecting primary and secondary sampling units. It distinguishes between probability and non-probability sampling techniques, such as simple random, stratified, cluster, and multistage area sampling. The goal of these techniques is to obtain a representative sample of a larger population.

1. 1
Sampling Designs
and Sampling
Procedures
(Based on: W.G Zikmund, B.J Babin, J.C Carr and
M. Griffin, Business Research Methods, 8th
Edition, U.S, South-Western Cengage
Learning, 2008)
• Key points
– Why Sampling
– Stage of sampling
– Concepts
– Probability and non-
probability sampling

2. 2
Sampling Terminology
• Sample
– A subset, or some part, of a larger population.
• Population (universe)
– Any complete group of entities that share
some common set of characteristics.
• Population Element
– An individual member of a population.
• Census
– An investigation of all the individual elements
that make up a population.

3. 3
Why Sample?
• Pragmatic Reasons
– Budget and time constraints.
– Limited access to total population.
• Accurate and Reliable Results
– Samples can yield reasonably accurate
information.
– Strong similarities in population elements makes
sampling possible.
• Destruction of Test Units
– Sampling reduces the costs of research in finite
populations.

4. 4
Stages in
the
Selection
of a Sample

5. 6
Sampling Units
• Sampling Unit
– A single element or group of elements subject
to selection in the sample.
– Primary Sampling Unit (PSU)
• A unit selected in the first stage of sampling.
– Secondary Sampling Unit
• A unit selected in the second stage of sampling.
– Tertiary Sampling Unit
• A unit selected in the third stage of sampling.

6. 7
Random Sampling and
Nonsampling Errors
• Random Sampling Error
– The difference between the sample result and the result of
a census conducted using identical procedures.
– A statistical fluctuation that occurs because of chance
variations in the elements selected for a sample.
• Systematic Sampling Error
– Systematic (nonsampling) error results from nonsampling
factors, primarily the nature of a study’s design and the
correctness of execution.
• It is not due to chance fluctuation.

7. 8
Errors Associated with Sampling

8. 9
Probability versus Nonprobability
Sampling
• Probability Sampling
– A sampling technique in which every member
of the population has a known, nonzero
probability of selection.
• Nonprobability Sampling
– A sampling technique in which units of the
sample are selected on the basis of personal
judgment or convenience.
– The probability of any particular member of
the population being chosen is unknown.

9. 10
Nonprobability Sampling
• Types
– Convenience (accidental) Sampling
• Obtaining those people or units that are most
conveniently available.
– Judgment (Purposive) Sampling
• An experienced individual selects the sample
based on personal judgment about some
appropriate characteristic of the sample member.
– Quota Sampling
• Ensures that various subgroups of a population will
be represented on pertinent characteristics to the
exact extent that the investigator desires.

10. 11
Nonprobability Sampling
(cont’d)
• Possible Sources Of Bias
– Respondents chosen because they were:
• Similar to interviewer
• Easily found
• Willing to be interviewed
• Advantages of Quota Sampling
– Speed of data collection
– Lower costs
– Convenience

11. 12
Probability Sampling
• Simple Random Sampling
– Assures each element in the population of an
equal chance of being included in the sample.
• Systematic Sampling
– A starting point is selected by a random
process and then every nth number on the list
is selected.
• Stratified Sampling
– Simple random subsamples that are more or
less equal on some characteristic are drawn
from within each stratum of the population.

12. 13
Proportional versus Disproportional
Sampling
• Proportional Stratified Sample
– The number of sampling units drawn from
each stratum is in proportion to the population
size of that stratum.
• Disproportional Stratified Sample
– The sample size for each stratum is allocated
according to analytical considerations.

13. 14
Disproportional Sampling: Hypothetical Example

14. 15
Cluster Sampling
• An economically efficient sampling
technique in which the primary sampling
unit is not the individual element in the
population but a large cluster of elements.
• Clusters are selected randomly.

15. 16
Examples of Clusters

16. 17
Multistage Area Sampling
• Multistage Area Sampling
– Involves using a combination of two or more
probability sampling techniques.
• Typically, geographic areas are randomly selected
in progressively smaller (lower-population) units.
• Researchers may take as many steps as
necessary to achieve a representative sample.
• Progressively smaller geographic areas are
chosen until a single housing unit is selected for
interviewing.

17. 18
Geographic Hierarchy Inside Urbanized Areas