1. Sample Design
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Dr. Michael R. Hyman, NMSU

3. Photographic Example of How Sampling Works

4. Sampling Terminology Population or universe Population element Census
Sample

5. Population/Universe Any complete group People Sales territories Stores
Total group from which information is needed

6. Census
Investigation of all individual elements that make up a population

7. Sample
Subset of a larger population of interest

8. Stages in Selecting a Sample Define the target population
Select a sampling frame
Determine if probability or non-probability
sampling method will be chosen
Stages in
Selecting
a Sample
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork

9. Define Target Population
Look at research objectives
Relevant population
Operationally define
Consider alternatives and convenience

10. Select Sampling Frame List of elements from which sample may be drawn
Mailing and commercial lists can be problematic (more on this later)

11. Sampling Units Group selected for the sample
Can be persons, households, businesses, et cetera
Primary sampling units
Secondary sampling units

12. Choose Probability or Non-probability Sample
Known, nonzero probability for every element
Non-probability sample
Probability of selecting any particular member is unknown

14. Conditions Favoring Non-probability vs. Probability Samples

15. Different Sampling Techniques

16. Non-probability Samples
Convenience
Judgment
Quota
Snowball

18. Convenience Sample Also called haphazard or accidental sampling
Sampling procedure for obtaining people or units that are convenient to researchers

19. Discrepancy between Implied and Ideal Populations in Convenience Sampling

21. Judgment Sample Also called purposive sampling
Experienced person selects sample based on his or her judgment about some appropriate characteristics required of sample members

22. Discrepancy between Implied and Ideal Populations in Judgment Sampling

23. Quota Sample
Various population subgroups are represented on pertinent sample characteristics to the extent desired by researchers
Do not confuse with stratified sampling (discussed later)

24. Representative Quota Sample Requirements

25. Snowball Sample Initial respondents selected by probability methods
Additional respondents obtained from information provided by initial respondents

26. Probability Samples Simple random sample Systematic sample
Stratified sample
Cluster sample

27. Simple Random Sample
Ensures each element in the population has an equal chance of selection

28. Systematic Sample A simple process
Every nth name from list will be drawn

29. Stratified Sample Probability sample
Sub-samples drawn within different strata
Each stratum more or less equal on some characteristic
Do not confuse with quota sample

30. Drawing a Stratified Sample: Example

35. Disproportionate Stratified Random Sampling Used by A.C. Nielsen

36. Cluster Sample
Purpose: to sample economically while retaining characteristics of a probability sample
Primary sampling unit is not individual element in population
Instead, it is larger cluster of elements located in proximity to one another

39. Examples of Populations and Clusters

40. More Examples of Clusters

41. Strengths and Weakness of Sampling Techniques

42. Bases for Choosing a Sample Design
Degree of accuracy
Resources
Time
Advanced knowledge of population
National versus local
Need for statistical analysis

43. After Sample Design is Selected
Determine sample size
Select actual sample units
Conduct fieldwork

44. Sampling Error

45. Types of Sampling Errors
Sampling frame error
Random sampling error
Non-response error

46. Errors Associated with Sampling

47. Random Sampling Error
Difference between sample results and result of a census conducted using identical procedures
Statistical fluctuation due to chance variations

48. Key Aspects of Sample Frame Error

49. Systematic Errors Non-sampling errors
Unrepresentative sample results caused by flawed study design or imperfections in execution rather than chance

51. Example Mailing Lists

52. More Mailing List Examples

53. Problems with Lists Representativeness Omissions and duplications
Recency

54. Directories and Telephone Interviewing
Directories not current
Demographics and socioeconomics of voluntary non-list members differ from list members
Solution
Random digit dialing
Add ‘1’ to listed number

55. Weighting Samples

56. Weighting a Sample

57. Internet Samples

58. Internet Sampling is Unique
Internet surveys allow researchers to rapidly reach a large sample
Survey should be kept open long enough so all sample units can participate

59. Advantages and Disadvantages
Internet samples may be representative of target populations
e.g., visitors to a Web site
Hard to reach subjects may participate
Major disadvantage
Lack of PC ownership & Internet access among certain population segments

60. Web Site Visitors Unrestricted samples are clearly convenience samples
Randomly selecting visitors
Questionnaire request randomly "pops up"
Over-representing more frequent visitors

61. Panel Samples

62. Panel Samples Typically yield high response rates
Members may be compensated for time with sweepstake or small cash incentive
Database on members
Demographic and other information from previous questionnaires
Select quota samples based on product ownership, demographics, lifestyle, or other characteristics

63. Recap Basic sampling terminology Stages in selecting a sample
From target population definition to drawing the sample
Non-probability vs. probability samples
Types and appropriate usage
Sampling error
Internet and panel samples