The Sampling Design

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.

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.

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.

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.

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

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.

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.

Types of Sampling Designs

• Probability

• Nonprobability

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?

Probability Sampling Designs

• Simple random sampling

• Systematic sampling

• Stratified sampling– Proportionate– Disproportionate

• Cluster sampling

• Double sampling

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

Nonprobability Sampling: Types

• Convenience Sampling

• Purposive Sampling– Judgment Sampling– Quota Sampling

• Snowball Sampling