1. Sampling Design

2. Sampling Terminology Sample Population or universe Population element
A subset, or some part, of a larger population
Population or 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. Why Sample?
Sampling
Cuts costs
Reduces labor requirements
Gathers vital information quickly
Most properly selected samples give sufficiently accurate results

4. Table 12.1 Sample Versus Census

5. Target Population A.k.a., the Relevant population Operationally define
All women still capable of bearing children vs.
All women between the ages of 12 and 50
Comic book reader?
Does this include children under 6 years of age who do not actually read the words?

6. A list of elements from which the sample may be drawn
Sampling Frame
A list of elements from which the sample may be drawn
A.K.A., the working population
Mailing lists - data base marketers
Sampling services or list brokers

7. Two Major Categories of Sampling
Probability sampling
Known, nonzero, & equal probability of selection for every population element
Nonprobability sampling
Probability of selecting any particular member is unknown

8. Nonprobability Sampling
Convenience
Judgment
Quota
Snowball

9. Convenience Sampling Also called haphazard or accidental sampling
The sampling procedure of obtaining the people or units that are most conveniently available

10. Judgment Sampling Also called purposive sampling
An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member

11. Quota Sampling
Ensures that the various subgroups in a population are represented on pertinent sample characteristics to the exact extent that the investigators desire
It should not be confused with stratified sampling.

12. Snowball Sampling A variety of procedures
Initial respondents are selected by probability methods
Additional respondents are obtained from information (or referrals) provided by the initial respondents

13. Comparing the Nonprobability Techniques
Strengths
Weaknesses
Convenience Sampling
Least expensive
Least time needed
Most convenient
Selection bias
Not representative
Judgmental Sampling
Low expense
Little time needed
Convenient
Subjective
Does not allow generalizations
Quota Sampling
Can control sample characteristics
Most likely not representative
Snowball Sampling
Can estimate rare characteristics
Time consuming

14. Figure 12.8 Probability Sampling Techniques
Most Commonly-Used
Probability Sampling Techniques
Figure Probability Sampling Techniques
Probability Sampling Techniques
Simple Random
Sampling
Systematic
Sampling
Stratified
Sampling

15. Simple Random Sampling
A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample

16. Systematic Sampling A simple process
Every nth name from the list will be drawn
Periodicity
Problem that occurs in systematic sampling when the original list has a systematic pattern (I.e., the original list is not random in character)

17. Stratified Sampling Probability sample
Subsamples are drawn within different strata using simple random sampling
Each stratum is more or less equal on some characteristic
Do not confuse with quota sample

18. Comparing the Probability Techniques
Strengths
Weaknesses
Simple Random Sampling
Easily understood
Can project results
Expensive
Difficult to construct sampling frame
No assurance of representativeness
Systematic Sampling
Easier to implement than SRS
Increased representativeness
Sampling frame not necessary
Can decrease representativeness
Stratified Sampling
Precision
Includes all important subpopulations
Selection of stratification variables difficult

19. What is the Appropriate Sample Design?
Degree of accuracy
Resources
Time
Advanced knowledge of the population
National versus local
Need for statistical analysis

20. Choosing Between Nonprobability & Probability Sampling
Factor
Nonprobability
Probability
Nature of Research
Exploratory
Conclusive
Relative Magnitude of Sampling & Nonsampling Errors
Nonsampling errors larger
Sampling errors larger
Population Variability
Homogeneous
(low variability)
Heterogeneous
(high variability)
Statistical Considerations
Unfavorable
Favorable
Operational Considerations

21. Internet Samples
Recruited Ad Hoc Samples
Opt-in Lists

22. Information Needed to Determine Sample Size
Variance (standard deviation)
Get from pilot study or rule of thumb (managerial judgment)
Magnitude of error
Managerial judgment or calculation
Confidence level
Managerial judgment

23. Sample Size Formula for Questions Involving Means

24. Sample Size Formula - Example
Suppose a survey researcher is studying expenditures on lipstick
Wishes to have a 95 percent confident level (Z) and
Range of error (E) of less than $2.00.
The estimate of the standard deviation is $29.00.

25. Sample Size Formula - Example

26. Sample Size Formula - Example
Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00 (rather than the original $2.00), sample size is reduced.

27. Sample Size Formula - Example

28. Calculating Sample Size
99% Confidence [ ]
1389 =
265 . 37 2 53 74 ú û ù ê ë é ) 29 )( 57 ( n
347
6325 18 4

29. Sample Size for a Proportion

30. E pq z n = Where: n = Number of items in samples2 E pq z n =
Where:
n = Number of items in samples
Z2 = The square of the confidence interval in standard error units.
p = Estimated proportion of success
q = (1-p) or estimated the proportion of failures
E2 = The square of the maximum allowance for error between the
true proportion and sample proportion or zsp squared.

31. Sample Size for a Proportion: Example
A researcher believes that a simple random sample will show that 60 percent of a population (p = .6) recognizes the name of an automobile dealership.
Note that 40% of the population would not recognize the dealership’s name (q = .4)
The researcher wants to estimate with 95% confidence (Z = 1.96) that the allowance for sampling error is not greater than 3.5 percentage points (E = 0.035)

32. Calculating Sample Size at the 95% Confidence Level
753 =
001225 .
922 ) 24
)(.
8416 3 (
035
( . 4 6 (. 96 1. n q p 2