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Sampling ADV 3500 Fall 2007 Chunsik Lee

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A sample is some part of a larger body specifically selected to represent the whole. Sampling is the process by which this part is chosen. Sample vs. Population

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Sample vs. Census Why do we take a sample rather than a complete census? For efficiency and generalization

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Sampling methods & procedures The sampling process: Define the population (clear & tangible characteristics) Determine sampling method Specify the sampling frame Determine sample size Select the sample

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Sampling methods & procedures Two types of sampling procedures Probability sampling We can specify the probability or likelihood that a given element of the population will be included in the sample. Non-probability sampling We cannot specify the likelihood that a given element from the population will be included in the sample.

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Characteristics of probability samples Always involves chance selection of the elements for inclusion in the sample. Each element will have a non-zero chance of selection. Only with a probability sample can we estimate the likelihood that a sample will represent the population. We can estimate the error associated with the sample.

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Characteristics of non- probability samples We have no assurance that every element of the population has a chance to be included. We do not have the ability to estimate the error associated with the sample drawn.

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Types of probability sampling Simple random sampling Systematic random sampling Stratified sampling

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Probability sampling Simple random sampling Every element in the population will have an equal chance of being selected. Tables of random number or computer generated random numbers are used.

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Probability sampling Systematic random sampling Initial starting point is selected randomly, then every n th number on the list is selected. Example: You wish to take a sample of 1,000 from a list consisting of 200,000 names. Using systematic selection, every 200th name from the list will be drawn. -- sampling interval = 200 -- 200,000/1,000 = 200

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Probability sampling Stratified sampling Break population into groups or strata and then take random sample within each group. Treat each stratum as a separate subpopulation for sampling purposes. Strata are homogeneous within and heterogeneous between (or maximally different from each other).

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Probability sampling Stratified sampling Proportionate stratified random sampling is done in proportion to the group’s representation in the population Disproportionate stratified random sampling is a means of weighting a group’s representation in a sample to accommodate broader research objectives

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Types of non-probability sampling Convenience sampling Judgment (Purposive) sampling Quota sampling Snowball sampling

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Non-probability sampling Convenience sampling Take what is available. Used in exploratory situations or non- generalization research (e.g., experimental research)

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Non-probability sampling Judgment (Purposive) sampling Choose people to achieve a specific analytical objective, typically to make certain that there are sufficient numbers of elements. But, doesn’t consider characteristics of the target population.

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Non-probability sampling Quota sampling Selected purposively in such a way that the characteristics of interest are “represented” in the sample in the same proportion as they are in the population.

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Non-probability sampling Snowball sampling Subsequent respondents are obtained through initial respondent referrals. Used to locate rare populations by referrals.

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