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Chapter 9

Chapter 9. Sampling. Sampling. Gathering information about a concept, phenomenon, event, or group Primary focus is a specific population Population: complete group from which information is gathered Sampling a population Identify sampling frame Choose type of sample

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Chapter 9

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  1. Chapter 9 Sampling

  2. Sampling Gathering information about a concept, phenomenon, event, or group Primary focus is a specific population Population: complete group from which information is gathered Sampling a population Identify sampling frame Choose type of sample Probability or nonprobability sample

  3. Probability Theory Over time, there is a statistical order in which things occur Representative samples Every number has the same chance of being chosen

  4. Probability Sampling (1 of 2) Obtain a representative sample Results can be applied to the whole population Every member must have an equal chance of being selected Types: simple random, stratified random, systematic, and cluster

  5. Probability Sampling (2 of 2) Simple random: selection of each member is independent from selection of any other member Stratified random: chosen from population divided into subgroups Strata based on specific characteristics Systematic: every nth item is included Cluster: randomly selected groups Multistage sample

  6. Nonprobability Sampling (1 of 2) Does not give all members the opportunity to be selected Representative if enough characteristics of the target population exist in the sample Types: purposive, quota, snowball, and convenience

  7. Nonprobability Sampling (2 of 2) Purposive: Selection based on knowledge of the topic, target populations, and accessibility Quota: based on researcher’s judgment for inclusion Snowball: begins with a person who provides names of other people Convenience: “in the right place at the right time”

  8. Sample Size Quality depends on size The larger the sample, the more likely it will reflect the population Sample size is the result of How accurate the sample must be Economic feasibility Availability of requisite variables Accessibility to target population

  9. Confidence Levels Confidence intervals: range of numbers Sample is an estimated reflection of the target population Confidence interval suggests accuracy of the estimate Smaller the confidence interval, the more accurate the sample Confidence level: probability that a population parameter will fall within the confidence interval

  10. Sample Size Selection Chart Error tolerance Confidence levels (percent) 95% 99% • 1 9604 16,587 • 2 2401 4147 • 3 1068 1843 • 4 601 1037 • 5 385 664 Source: Adapted from Cole, R. L.. Introduction to Political Science and Policy Research. St.Martin’s Press, 1996.

  11. Sampling Formula How large a sample is required to attain the best confidence level Formulas, tables, or computer statistical packages can determine sample size Commonly Used Sampling Formula Larger samples more likely to be representative Rely on probability theory Use knowledge of acceptable sample sizes Population size, confidence level, and sample error

  12. Summary Sample: sampling frame and type Probability Sample Random, stratified random, systematic, and cluster Nonprobability Sample Purposive, quota, snowball, and convenience Sample Size Confidence levels

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