1. Sampling

2. The Logic of Sampling Virtually ALL social research entails “sampling,” including approaches that don’t engage human subjects. “Probability” versus “nonprobability” sampling are both, or CAN both, be “scientific” but have to be done with care.

3. Nonprobability Sampling Approaches Nonprobability sampling is sampling in which the likelihood of selection of any member of the population is unknown and/or unknowable. Four types: – Convenience or Haphazard – Quota – Purposive/Judgmental/Ideographic – Snowball/Network/Chain Referral/Reputational

4. Probability Sampling Approaches Probability Sampling usually starts with a sampling frame (though RDD changes this). There are good and bad examples of sampling frames, and techniques for targeting special populations. Four types of Probability Samples: – Random – Systematic – Stratified – Cluster

5. Sample Size: Four Considerations The degree of accuracy required: Larger samples are more accurate. The amount of diversity in the population: More diversity requires larger samples. The number of different variable examined in the study: More complexity requires larger samples. The size of the population: Smaller populations require proportionally larger samples, e.g., in small populations (under 1000) a sample of 30% may be required, but in very large populations (over 10 million) a sample size of.025% may be sufficient.