Sampling

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.

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

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

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.