1. Determining How to Select a Sample

2. Basic Concepts in Sampling
Population: the entire group under study as defined by research objectives
Researchers define populations in specific terms such as “heads of households located in areas served by the company who are responsible for making the pest control decision.”
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3. Basic Concepts in Sampling
Sample: a subset of the population that should represent the entire group
Sample unit: the basic level of investigation (e.g. a household, a person, a company).
Census: an accounting of the complete population
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4. Basic Concepts in Sampling
Sampling error: any error in a survey that occurs because a sample is used
Caused by (1) the sample selection method; and (2) the size of the sample.
A sample frame: a master list of the entire population
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5. Basic Concepts in Sampling
Sample frame error: the degree to which the sample frame fails to account for all of the population…a telephone book listing does not contain unlisted numbers
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6. Reasons for Taking a Sample
Practical considerations such as cost and population size
Inability of researcher to analyze huge amounts of data generated by census
Samples can produce precise results
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7. Two Basic Sampling Methods
Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample
Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown
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8. Probability Sampling Methods
Simple random sampling
Systematic sampling
Cluster sampling
Stratified sampling
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9. Probability Sampling Methods
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10. Probability Sampling: Simple Random Sampling
Simple random sampling: the probability of being selected into the sample is “known” and equal for all members of the population
E.g., Blind Draw Method
Random Numbers Method (see MRI 12.1, p. 335)
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11. Probability Sampling: Simple Random Sampling
Advantage:
Known and equal chance of selection
Disadvantages:
Complete accounting of population needed
Cumbersome to provide unique designations to every population member
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12. Simple Random Sampling
Random digit dialing (RDD)
Used in telephone surveys to overcome the problems of unlisted and new telephone numbers.
Telephone numbers are generated randomly with the aid of a computer.
May result in a large number of calls to nonexisting telephone numbers.
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13. Simple Random Sampling
Plus-one dialing procedure
Numbers are selected from a telephone directory and a digit “1” is added to each number to determine which telephone number is then dialed.
Alternatively, the last digit can be substituted with a random number.
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14. Probability Sampling Systematic Sampling
Systematic sampling: way to select a random sample from a directory or list that is much more efficient than simple random sampling
Skip interval=population list size/sample size
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15. Probability Sampling Systematic Sampling
Advantages:
Approximate known and equal chance of selection…it is a probability sample plan
Efficiency…do not need to designate every population member
Less expensive…faster than SRS
Disadvantage:
Small loss in sampling precision (e.g. unlisted members, not up to date list).
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16. Probability Sampling Cluster Sampling
Cluster sampling: method in which the population is divided into groups, any of which can be considered a representative sample
Area sampling
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17. Cluster Sampling
In cluster sampling the population is divided into subgroups, called “clusters.”
Each cluster should represent the population.
Area sampling is a form of cluster sampling – the geographic area is divided into clusters.
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18. Cluster Sampling
One cluster may be selected to represent the entire area with the one-step area sample.
Select one area randomly and perform a census of its members.
Several clusters may be selected using the two-step area sample.
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19. A Two-Step Cluster Sample
A two-step cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneous.
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20. Probability Sampling Cluster Sampling
Advantage:
Economic efficiency…faster and less expensive than SRS
Disadvantage:
Cluster specification error…the more homogeneous the clusters, the more precise the sample results
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21. Stratified Sampling
When the researcher knows the answers to the research question are likely to vary by subgroups…
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22. Stratified Sampling
Research Question: “To what extent do you value your college degree?” Answers are on a five point scale: 1= “Not valued at all” and 5= “Very highly valued”
We would expect the answers to vary depending on classification. Freshmen are likely to value less than seniors. We would expect the mean scores to be higher as classification goes up.
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23. Stratified Sampling
Research Question: “To what extent do you value your college degree?”
We would also expect there to be more agreement (less variance) as classification goes up. That is, seniors should pretty much agree that there is value. Freshmen will have less agreement.
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24. Stratified Sampling
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25. Probability Sampling Stratified Sampling
Stratified sampling: method in which the population is separated into different strata and a sample is taken from each stratum
Proportionate stratified sample
Disproportionate stratified sample
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26. Probability Sampling Stratified Sampling
Advantage:
More accurate overall sample of skewed population…see next slide for WHY
Disadvantage:
More complex sampling plan requiring different sample size for each stratum
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27. Stratified Sampling
Why is stratified sampling more accurate when there are skewed populations?
The less variance in a group, the less sample size it takes to produce a precise answer.
Why? If 99% of the population (low variance) agreed on the choice of Brand A, it would be easy to make a precise estimate that the population preferred Brand A even with a small sample size.
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28. Stratified Sampling
But, if 33% chose Brand A, and 23% chose B, and so on (high variance) it would be difficult to make a precise estimate of the population’s preferred brand…it would take a larger sample size…
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29. Stratified Sampling
Stratified sampling allows the researcher to allocate more sample size to strata with much variance and less sample size to strata with less variance. Thus, for the same sample size, more precision is achieved.
This is normally accomplished by disproportionate sampling. Seniors would be sampled LESS than their proportionate share of the population and freshmen would be sampled more.
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30. Stratified Sampling
Note that we would expect this question to be answered differently depending on student classification. Not only are the means different, variance is less as classification goes up…Seniors tend to agree more than Freshmen!
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31. Nonprobability Sampling
With nonprobability sampling methods selection is not based on fairness, equity, or equal chance.
Convenience sampling
Judgment sampling
Referral sampling
Quota sampling
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32. Nonprobability Sampling
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33. Nonprobability Sampling
May not be representative but they are still used very often. Why?
Decision makers want fast, relatively inexpensive answers… nonprobability samples are faster and less costly than probability samples.
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34. Nonprobability Sampling
May not be representative but they are still used very often. Why?
Decision makers can make a decision based upon what 100 or 200 or 300 people say…they don’t feel they need a probability sample.
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35. Nonprobability Sampling
Convenience samples: samples drawn at the convenience of the interviewer
Error occurs in the form of members of the population who are infrequent or nonusers of that location
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36. Nonprobability Sampling
Judgment samples: samples that require a judgment or an “educated guess” as to who should represent the population
Subjectivity enters in here, and certain members will have a smaller chance of selection than others
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37. Nonprobability Sampling
Referral samples (snowball samples): samples which require respondents to provide the names of additional respondents
Members of the population who are less known, disliked, or whose opinions conflict with the respondent have a low probability of being selected
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38. Nonprobability Sampling
Quota samples: samples that use a specific quota of certain types of individuals to be interviewed
Often used to ensure that convenience samples will have desired proportion of different respondent classes
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39. Online Sampling Techniques
Random online intercept sampling: relies on a random selection of Web site visitors
Invitation online sampling: is when potential respondents are alerted that they may fill out a questionnaire that is hosted at a specific Web site
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40. Online Sampling Techniques
Online panel sampling: refers to consumer or other respondent panels that are set up by marketing research companies for the explicit purpose of conducting surveys with representative samples
Other online sampling approaches
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41. Developing a Sample Plan
Sample plan: definite sequence of steps that the researcher goes through in order to draw and ultimately arrive at the final sample
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42. Developing a Sample Plan
Define the relevant population.
Obtain a listing of the population.
The incidence rate is the percentage of people on a list who qualify as members of the population
Design the sample plan (size and method).
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43. Developing a Sample Plan
Draw the sample.
Substitution methods:
Drop-down substitution
Oversampling
Resampling
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44. Developing a Sample Plan
Validate the sample.
Sample validation is a process in which the researcher inspects some characteristic(s) of the sample to judge how well it represents the population.
Resample, if necessary.
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