1. The Sampling Design

2. Sampling Design Selection of Elements –The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population. Population Element –A population element is the individual participant or object on which the measurement is taken. –It is the unit of study; it may be a person or may be something else. –Examples: Each staff member questioned about an optimal promotional strategy is a population element. –Each advertising account analyzed is an element of an account population –Each ad is an element of a population of advertisements.

3. Sampling Design Population –A population is the total collection of elements about which we wish to make some inferences. –All office workers in the firm compose a population of interest; all 4,000 files define a population of interest.

4. Sampling Design Census –A census is a count of all the elements in a population; –If 4,000 files define the population, a census would obtain information from every one of them.

5. Sampling Design Sample Frame –The listing of all population elements from which the sample will be drawn is called the sample frame. –Ideally it is the same as the population but it often differs due to practical considerations of information availability.

6. What is a Good Sample? Sampling is acceptable only when it adequately reflects the population from which it is drawn; No sample is a perfect representation of its population The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represents. –In measurement terms, the sample must be valid. –Validity of a sample depends on two considerations: Accuracy and Precision

7. Accuracy Accurate: absence of bias –In a sample, some of the observations understate the value you are trying to estimate but their effect is, in general, balanced out by other observations that overstate the value. –The result is a reasonably good estimate of the population parameter, unless something causes one side to systematically outweigh the other. –The best way to ensure accuracy is through random probability sampling.

8. Precision Sample precision s concerned with the random fluctuations that occur as one draws the members of the sample. Precision as a form of error is distinct from the sample accuracy problem. Precision considers the issue of sample size: whether the sample is large enough to limit the effects of random error. Accuracy is concerned with the problem of systematic bias, regardless of sample size.

9. Types of Sampling Designs Probability Nonprobability

10. Steps in Sampling Design What is the relevant population? What are the parameters of interest? What is the sampling frame? What is the type of sample? What size sample is needed? How much will it cost?

11. Probability Sampling Designs Simple random sampling Systematic sampling Stratified sampling –Proportionate –Disproportionate Cluster sampling Double sampling

12. Nonprobability Sampling Reasons to use Procedure satisfactorily meets the sampling objectives Lower Cost Limited Time Not as much human error as selecting a completely random sample Total list population not available

13. Nonprobability Sampling: Types Convenience Sampling Purposive Sampling –Judgment Sampling –Quota Sampling Snowball Sampling