Sample Design

1
Sample Design
(Click icon for audio)
2
(No Transcript)
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
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

• Probability sample
• Known, nonzero probability for every element
• Non-probability sample
• Probability of selecting any particular member is
  unknown

13
(No Transcript)
14
Conditions Favoring Non-probability vs.
Probability Samples
15
Different Sampling Techniques
16
Non-probability Samples

• Convenience
• Judgment
• Quota
• Snowball

17
(No Transcript)
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
20
(No Transcript)
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
31
(No Transcript)
32
(No Transcript)
33
(No Transcript)
34
(No Transcript)
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

37
(No Transcript)
38
(No Transcript)
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

50
(No Transcript)
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