13342046.ppt

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