BASICS OF SAMPLING

Benefits of sampling; Money, time, accuracy

Steps in sampling & sampling versus non-sampling error (frame, sampling, non- response, & response errors)

Non-probability versus probability sampling

Whether each element in the sample has a known chance of being selected or not

Non-probability sampling procedures

Convenience sampling

Judgment sampling - researcher uses judgment to decide elements to sample

Quota sampling - researcher ensures that sample is similar to population on certain

characteristics

Probability sampling

Simple random sampling - Using sampling procedures so that each element has equal chance of being selected in the sample

Sampling distributions - Distribution of means of samples drawn from a population

The Central Limit Theorem -When sample size is large, sampling distribution is normal

Estimating confidence intervals

Stratified sampling - Divide sample into strata & pick simple random samples from

each strata

Estimating confidence intervals

Proportionate/Disproportionate stratified sampling

Cluster sampling

Cluster sampling - Divide population into subsets & randomly select a few subsets

Systematic sampling - picking every kth element after a random start

Area sampling

DETERMININGSAMPLE SIZE

Sample size for estimating means

Population variance known

Population variance unknown

Absolute precision

Relative precision

Sample size for estimating proportions

Absolute precision

Relative precision

NONSAMPLING ERROR

Non-coverage error -problems with frame

Non-response error

Response rate

Contact rate

Increasing response rate

Adjusting results for non-response

Field error

Office error

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