This document discusses key concepts in sampling design and procedures. It covers reasons for sampling such as pragmatic and cost reasons. It also discusses defining the target population and sampling frame. The document contrasts probability sampling techniques like simple random sampling, systematic sampling, and stratified sampling with non-probability techniques like convenience sampling and snowball sampling. It discusses factors to consider in determining sample size such as variance, desired confidence level and interval. Sample size formulas for estimating means and proportions are also provided.

1. viewords.wordpress.com
Sampling Design and Procedu
Lecture & Seminar

2. If you decide whether or not you want to see a
new movie or television program on the basis of
the “coming attractions” or television
commercial previews, are you using a sampling
technique? A scientific sampling technique?

3. Population
Sampl
e
Sampl
e
Sampl
e
Sampling Terminology

4. Why Sample?
Pragmatic Reasons
Accurate and Reliable Results
Destruction of Test Units
www.mktginc.com

5. Stages in the Selection of a Sample
16–5

6. bee-dsm.in
Practical Sampling Concepts

7. Practical Sampling Concepts
Defining the Target Population
• What is the relevant population?
• Whom do we want to talk to?
• Population is operationally defined by specific
and explicit tangible characteristics.
The Sampling Frame
• A list of elements from which a sample
may be drawn; also called working
population.
• Sampling Frame Error
• Occurs when certain sample elements are not
listed or are not accurately represented in a
sampling frame.

8. 16–8
Sampling Units
Sampling Unit
A single element or group of elements subject to
selection in the sample.
1. Primary Sampling Unit (PSU)
2. Secondary Sampling Unit
3. Tertiary Sampling Unit

9. 16–9
Random Sampling and Non-sampling
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.
Non-Sampling (Systematic Sampling) Error
• Systematic (nonsampling) error results from
nonsampling factors, primarily the nature of a
study’s design and the correctness of
execution.

10. 16–10
• Less than Perfectly Representative
Samples
– Random sampling errors and systematic
errors associated with the sampling
process may combine to yield a sample
that is less than perfectly representative
of the population.
Random Sampling and Nonsampling
Errors (cont’d)

11. Errors Associated with Sampling
16–11

12. http://bee-dsm.in/Tools_1.aspx
Sampling Technique

13. 16–13
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.
Probability versus Nonprobability
Sampling

14. 16–14
Nonprobability Sampling

15. http://lc.gcumedia.com/hlt362v/the-visual-learner/convenience-sampling.html
Nonprobability Sampling: Convenience Sampling
Obtaining those people or units that are most conveniently available

16. www.memrise.com
Nonprobability Sampling: Judgment (Purposive) Sampling
An experienced individual selects the sample based on personal
judgment about some appropriate characteristic of the sample member.

17. Nonprobability Sampling: Quota Sampling
Ensures that various subgroups of a population will be represented
on pertinent characteristics to the exact extent that the investigator
desires.
BUYERS RESPONDENT QUOTA
(SAMPLE SIZE= 200)
MEN 40% 80
WOMEN 60% 120

18. Nonprobability Sampling: Snowball Sampling
A sampling procedure in which initial respondents are selected by probability
methods and additional respondents are obtained from information provided by
the initial respondents.
slides.com

19. 16–19
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.

20. www.nedarc.org
Probability Sampling: Simple Random Sampling
Assures each element in the population of an equal chance
of being included in the sample

21. Probability Sampling : Systematic Sampling
A starting point is selected by a random process and then
every nth number on the list is selected
http://lc.gcumedia.com/hlt362v/the-visual-learner/systematic-sample.html

22. Probability Sampling: Stratified Sampling
Simple random subsamples that are more or less equal on
some characteristic are drawn from within each stratum of
the population
http://lc.gcumedia.com/hlt362v/the-visual-learner/stratified-sample.html

23. 16–23
• 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.
Proportional versus
Disproportional Sampling

24. Disproportional Sampling: Hypothetical
16–24

25. 16–25
Probability Sampling: 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.
http://lc.gcumedia.com/hlt362v/the-visual-learner/cluster-sampling.html

26. 16–26
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.

27. http://bee-dsm.in/Tools_1.aspx
Sampling Technique

28. 16–28
What is the Appropriate Sample
Design?
Degree of accuracy
Resources
Time
Advanced knowledge of the
population
National versus local project

29. 16–29
Internet Sampling is Unique
• Website Visitors
– Internet surveys use unrestricted
samples.
– May not be representative.
• Panel Samples
• Recruited Ad Hoc Samples

30. Determination of Sample
Size

31. Frequency Distribution of Deposits
17–31

32. Percentage Distribution of Deposits
17–32

33. Probability Distribution of Deposits
17–33

34. 17–34
Factors of Concern in Choosing
Sample Size
1. Variance (or Heterogeneity)
• A heterogeneous population has more
variance (a larger standard deviation) which
will require a larger sample.
• A homogeneous population has less variance
(a smaller standard deviation) which permits a
smaller sample.
2. Magnitude of Error (Confidence Interval)
• How precise must the estimate be?
3. Confidence Level
• How much error will be tolerated?

35. 17–35
• Sequential Sampling
– Conducting a pilot study to estimate the population parameters so
that another, larger sample of the appropriate sample size may be
drawn.
• Estimating sample size:
Estimating Sample Size for Questions
Involving Means

36. 17–36
Sample Size Example
Suppose a survey researcher, studying expenditures
on lipstick, wishes to have a 95 percent confident
level (Z) and a range of error (E) of less than $2.00.
The estimate of the standard deviation is $29.00.
What is the calculated sample size?

37. 17–37
Sample Size Example
Suppose, in the same example as the one before, the
range of error (E) is acceptable at $4.00. Sample size is
reduced.

38. Calculating Sample Size at the 99
Percent Confidence Level
17-38

39. Determining Sample Size for
Proportions
17-39

40. Determining Sample Size for
Proportions (cont’d)
17-40

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Calculating Example Sample
Size at the 95 Percent
Confidence Level
17-41

42. Low Dispersion versus High
Dispersion
17–42

43. LOs
1. Explain reasons for taking a sample rather than a
complete census
2. Describe the process of identifying a target
population and selecting a sampling frame
3. Compare random sampling and systematic
(nonsampling) errors
4. Identify the types of nonprobability sampling,
including their advantages and disadvantages
5. Discuss how to choose an appropriate sample
design