Chapter%207%20The%20Logic%20Of%20Sampling

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Chapter 7The Logic Of Sampling

• The History of Sampling
• Nonprobability Sampling
• The Theory and Logic of Probability Sampling
• Populations and Sampling Frames
• Types of Sampling Designs
• Multistage Cluster Sampling
• Probability Sampling in Review

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Two Types of Sampling Methods

1. Probability
2. Nonprobability

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Four Types of Nonprobability Sampling

• Reliance on available subjects
• Purposive or judgmental sampling
• Snowball sampling
• Quota sampling

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Advantages of Probability Sampling

• Provides precise statistical descriptions of
  large populations.
• Nonprobability sampling cannot guarantee that
  the sample observed is representative of the
  whole population.

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Populations and Sampling Frames

• Findings based on a sample only represent the
  aggregation of elements that compose the sampling
  frame.
• Sampling frames do not always include all the
  elements their names might imply.
• All elements must have equal representation in
  the frame.

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Types of Sampling Designs

• Simple random sampling (SRS)
• Systematic sampling
• Stratified sampling

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Simple Random Sampling

• Feasible only with the simplest sampling frame.
• Not the most accurate method available.

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Systematic Sampling

• Slightly more accurate than simple random
  sampling.
• Arrangement of elements in the list can result in
  a biased sample.

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Stratified Sampling

• Rather than selecting sample for population at
  large, researcher draws from homogenous subsets
  of the population.
• Results in a greater degree of representativeness
  by decreasing the probable sampling error.

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Multistage Cluster Sampling

• Used when it's impossible or impractical to
  compile an exhaustive list of the elements
  composing the target population.
• Involves repetition of two basic steps listing
  and sampling.
• Highly efficient but less accurate.

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Probability Proportionate to Size (PPS) Sampling

• Sophisticated form of cluster sampling.
• Used in many large scale survey sampling projects.

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Probability Sampling

• Most effective method for selection of study
  elements.
• Avoids researchers biases in element selection.
• Permits estimates of sampling error.