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. A puzzle is a sample until it’s done
A puzzle is a sample until it’s done. The sample allows one to guess at the picture.

4. Why Sample? Pragmatic Reasons Accurate and Reliable Results
Budget & 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.
Sampling may be more accurate than a census.
Destruction of Test Units
Sampling reduces the costs of research in finite populations.

5. Table 12.1 Sample Versus Census
Sample vs. Census
Table 12.1 Sample Versus Census

6. Stages in Sample Selection

7. Identifying a Relevant Population
Defining the Target Population
What is the relevant population?
Whom do we want to talk to?
Population is operationally defined by specific & explicit tangible characteristics.
If you want to target “Women”, do you mean
All women still capable of bearing children or,
All women between the ages of 12 and 50?

8. Identifying a Sampling Frame
A list of elements from which a sample may be drawn
Also called the working population.
Sampling Frame Error occurs when certain sample elements
are not listed, or
are not accurately represented in a sampling frame.
Sampling services (list brokers)
Provide lists or databases of the names, addresses, phone numbers, & addresses of specific populations.
Reverse directory
A directory similar to a telephone directory except that listings are by city & street address or by phone number rather than alphabetical by last name.
Online Panels
Lists of respondents who have agreed to participate in marketing research via .
International Research
Availability of sampling frames varies dramatically around the world.

9. Sampling Units Sampling Unit Primary Sampling Unit (PSU)
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.

10. Random Sampling & Nonsampling Errors
Random Sampling Error
The difference between the sample result & 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.
Probability of such error increases as sample size decreases.
Systematic Sampling Error
Systematic (nonsampling) error results from nonsampling factors, primarily the nature of a study’s design & the correctness of execution.
It is not due to chance fluctuation.
Probability of such error increases as sample size increases.

11. Errors Associated with Sampling

12. 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

13. Nonprobability Sampling
Convenience Sampling
Obtaining people or units that are most conveniently available.
Judgment (Purposive) Sampling
Experienced individual selects 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.
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.

14. 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
Highly Subjective
Does not allow generalizations
Quota Sampling
Can control sample characteristics
Greatest probability of representative sample
Most likely not representative
Snowball Sampling
Can estimate rare characteristics
Time consuming

15. 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
Proportional vs. Disproportional Sampling
Cluster Sampling

16. Probability Sampling Simple Random Sampling Systematic 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.

17. Systematically Sampling from a List

18. Proportional vs. Disproportional Sampling
Proportional Stratified Sample
Number of sampling units drawn from each stratum is in proportion to population size of that stratum.
Disproportional Stratified Sample
Sample size for each stratum is allocated according to analytical considerations.

19. Disproportional Sampling: Hypothetical Example

20. Cluster Sampling
Economically efficient sampling technique in which primary sampling unit is not the individual element in the population but a large cluster of elements.
Clusters are selected randomly.

21. Examples of Clusters

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

23. Internet Samples Recruited Ad Hoc Samples Opt-in Lists
Potential subjects unaware they might be asked to participate in a study
Less expensive
Lower selection bias
Opt-in Lists
Potential subjects know they might be asked to participate in studies as they have previously agreed to receive such invitations.
More expensive
Greater chance of selection bias
“Much Better” response rates (????)

24. Sample Size Bigger Is Better — Right?
One study indicates that 60% of consumers believe that there is too much violence in video games, but another study suggests that 75% of parents do not believe it harms children.
Another shows that 40% of Nintendo owners are highly likely to buy a new game that has been concept tested.
How good are these descriptive statistics? Consider the sample!

25. Information Needed to Determine Sample Size
Variance (standard deviation)
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.
Get from pilot study or rule of thumb (managerial judgment)
Magnitude of error
How precise must the estimate be?
Managerial judgment or calculation
Confidence level
How much error will be tolerated?
Managerial judgment
Most commonly used standards are a 95% confidence level (Z score = 1.96), or 99% confidence level (Z score = 2.57).

26. Sample Size Formula for Questions Involving an Analysis of Means

27. 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.

28. Sample Size Formula - Example

29. 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.

30. Sample Size Formula - Example

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

32. Sample Size for an Analysis of Proportions

33. 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)

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