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The Logic of Sampling

The Logic of Sampling. Nonprobability Sampling. Convenience Sampling Purposive or Judgmental Sampling Snowball Sampling Quota Sampling. The Logic of Probability Sampling. A sample must reflect the same degree of variation as the actual population.

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The Logic of Sampling

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  1. The Logic of Sampling

  2. Nonprobability Sampling • Convenience Sampling • Purposive or Judgmental Sampling • Snowball Sampling • Quota Sampling

  3. The Logic of Probability Sampling • A sample must reflect the same degree of variation as the actual population. • Nonprobability samples are riddled with potential bias. • To ensure representativeness all members of the population must have an equal chance of being selected (EPSEM sample) • Uses probability theory to assess the degree of representativeness of the sample

  4. Element Population Study Population Sampling Unit Sampling Frame Observation Unit Variable Parameter Statistic Sampling Error Confidence Levels and Confidence Intervals Sampling Concepts

  5. Probability Sampling Theory • Random selection is key • Guards against conscious or unconscious bias • Enables the prediction of population parameters and estimates of error • The standard error indicates the degree to which the sample statistics are distributed around the population parameter. • The standard error will decrease as the sample size increases. • The confidence interval indicates range of values in which the population parameter is estimated to lie. • The confidence level estimates the probability that the population parameter falls within that range of values (the confidence interval)

  6. Types of Probability Sampling Designs • Simple Random Sampling • Systematic Sampling • Sampling Interval = population size/sample size • Sampling Ratio = sample size/population size • Stratified Sampling • Multistage Cluster Sampling • Probability Proportionate to Size Sampling

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