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Sampling

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.

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Sampling

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  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.

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