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