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2.2: Sampling methods (pp. 17 – 20)

2.2: Sampling methods (pp. 17 – 20). Probability sampling : methods that can specify the probability that a given sample will be selected. Randomization : a technique for insuring that any member of a population has an equal chance of appearing in a sample.

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2.2: Sampling methods (pp. 17 – 20)

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  1. 2.2: Sampling methods (pp. 17 – 20) • Probability sampling:methods that can specify the probability that a given sample will be selected. • Randomization: a technique for insuring that any member of a population has an equal chance of appearing in a sample. • With randomization, sample statistics will on average have the same values as the population parameters. • Simple random sample: each possible sample of a given size has the same likelihood of being selected.

  2. How to select a simple random sample • 1. list all the subjects in a population • 2. assign a number to each subject • 3. pick numbers from a list of random numbers • 4. put the corresponding subjects in the sample. Cost and feasibility can be problems, especially if the population is large. OK, for people in households or students in classes.

  3. Non-probability sampling (pp. 20 – 21) • Non-probability sampling methods cannot specify the probability that a given sample will be selected. • Example: snowball sampling methods (Edin and Lein) • Why use such methods? • They are often inexpensive • They can provide information about groups that are difficult to sample or require great trust or will get lengthy unstructured interviews. • Some social variables and their relationships are universal, which makes sampling method irrelevant! • This is assumed for many psychology studies and medical studies.

  4. Common research designs (pp. 21 – 22) • Experimental design • Subjects are randomly assigned to treatments (=variables) by the researcher • Causal inferences are stronger • Random sampling from the population less important • Usually laboratory (exc. Moving to Opportunity, MTO) • Observational design (e.g., surveys) • Subjects are not randomly assigned to variables • Random sampling is important. • Selection bias • Causal inferences are compromised.

  5. Natural Experiments Observational studies (esp. surveys) where respondents’ values on a causal variable are plausibly random. Examples: • Military draft lottery • Births in last half of year • Indian panchayats headed by women • Parity 3 birth after same sex or opposite sex

  6. 2.3: Sampling and non-sampling variability (pp. 22 – 24) We ideally like sample statistics to be as close as possible to population parameters, but several factors can cause variability: • Sampling error: the difference between a sample statistic and its population parameter. • Random sampling allows us to estimate the typical size of the sampling error. • Non-sampling error: comes from other sources, can be systematically biased, and is difficult to estimate. • Examples of nonsampling error include undercoverage, nonresponse, question wording (e.g., response bias), question order.

  7. 2.4: probability sampling methods (pp. 25 – 28) • Systematic random sample: • Stratified random sample: • Cluster sampling: • Multistage sampling:

  8. 2.4: probability sampling methods (pp. 25 – 28) • Systematic random sample: • pick a random case from the first k cases of a sample; select every kth case after that one • Stratified random sample: • Cluster sampling: • Multistage sampling:

  9. 2.4: probability sampling methods (pp. 25 – 28) • Systematic random sample: • pick a random case from the first k cases of a sample; select every kth case after that one • Stratified random sample: • divide a population into groups, then select a simple random sample from each stratum • Cluster sampling: • Multistage sampling:

  10. 2.4: probability sampling methods (pp. 25 – 28) • Systematic random sample: • pick a random case from the first k cases of a sample; select every kth case after that one • Stratified random sample: • divide a population into groups, then select a simple random sample from each stratum • Cluster sampling: • divide the population into groups called clusters or primary sampling units (PSUs); take a random sample of the clusters • Multistage sampling:

  11. 2.4: probability sampling methods (pp. 25 – 28) • Systematic random sample: • pick a random case from the first k cases of a sample; select every kth case after that one • Stratified random sample: • divide a population into groups, then select a simple random sample from each stratum • Cluster sampling: • divide the population into groups called clusters or primary sampling units (PSUs); take a random sample of the clusters • Multistage sampling: • several levels of nested clusters, often including both stratified and cluster sampling techniques

  12. Examples of sampling in typical surveys • National Longitudinal Survey of Youth (NLSY) • 12,686 men and women ages 14-22 in 1979. • includes a multistage sample designed to be nationally representative. • includes oversamples of hispanic women and men, black nonhispanic women and men, poor white women and men, plus military subsamples, along with sampling weights. • A reinterview every two years loses some respondents (nonrandomly) to attrition. • Current Population Survey: http://www.census.gov/prod/2000pubs/tp63.pdf, section 14, especially Table 14-5 for DEFF

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