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SAMPLING

SAMPLING. Why sample? Practical consideration – limited budget, convenience, simplicity. Generalizability –representativeness, desire to establish the broadest possible generalizations. What happens when you don’t sample?

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SAMPLING

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  1. SAMPLING Why sample? • Practical consideration – limited budget, convenience, simplicity. • Generalizability –representativeness, desire to establish the broadest possible generalizations.

  2. What happens when you don’t sample? • Probability sample – everyone has a chance of being included in the sample.

  3. Populations • Defining population of interest • Target population – the population to which the researcher would like to generalize their results. • Sampling frame—operational definition of the population. Is the list of units of analysis from which you take your sample and to which you generalize.

  4. 2) Population parameter vs. Sample estimates: • Population parameter – pertains to the population. E.g. the average age of all people is a parameter. • Sample statistic – pertains to the sample – sample characteristics.

  5. Probability Sampling What is the difference between probability and non-probability sampling? • Probability sampling --- every member has an equal chance of being selected. Major advantage: reduces possibility of bias. • Non-probability – we don’t know the probability of selecting a unit into a particular sample.

  6. Types of Sampling Designs Simple Random Sample • Most basic sampling method. Systematic Random Sample • List everyone in population, then start with a randomly selected person, and take every Kth person. • Problem – affected by periodicity.

  7. Stratified Sampling • Ensures that key sub-populations are included in your sample. Rules on stratification • If differences on a dependent variable are large across age, sex, race, etc, then stratify • If differences are small, do not stratify

  8. Disproportionate Sampling • Appropriate whenever an important subpopulation is likely to be underrepresented in a simple random sample or stratified sample. • Suitable as long as the two samples are analyzed separately. • Have to weight if you combine the two samples. Weighting compensates for disproportionate sampling.

  9. Multistage Cluster Sampling • Used where there are no convenient list or sampling frames. • Minimizes travel time to scattered units of data collection

  10. Nonprobability Sampling Quota sampling • Decide on subpopulation of interest and on the proportions of those subpopulations in the final sample. • Quotas ensure that sample is representative of certain characteristics in proportion to their prevalence in population.

  11. Purpose Sampling • You decide the purpose you want informants to serve, and you go out to find some. Good reasons for use of purposive sampling • used in pilot studies • selection of few cases for intensive study • Studying critical cases-- key informants.

  12. Snowball Sampling • Locate one or more key individuals and ask them to name others who would be likely candidates for your research.

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