The Logic Of Sampling

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Chapter 7

• The Logic Of Sampling

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Chapter Outline

• A Brief 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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Political Polls and Survey Sampling

• One of the most visible uses of survey sampling
  is political polling that is then tested by
  election results.
• In the 2000 Presidential election, pollsters came
  within a couple of percentage points of
  estimating the votes of 100 million people.
• To gather this information, they interviewed
  fewer than 2,000 people.

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Election Eve Polls - Voting for U.S.Presidential
Candidates, 2000
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Observation and Sampling

• Polls and other forms of social research, rest on
  observations.
• The task of researchers is to select the key
  aspects to observe, or sampling.
• Generalizing from a sample to a larger population
  is called probability sampling and involves
  random selection.

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Nonprobability Sampling

• 1. Reliance on available subjects
• Only justified if less risky sampling methods are
  not possible.
• Researchers must exercise great caution in
  generalizing from their data when this method is
  used.

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Nonprobability Sampling

• 2. Purposive or judgmental sampling
• Selecting a sample on the basis of knowledge of
  a population, its elements, and the purpose of
  the study.
• Often used when field researchers are interested
  in studying cases that dont fit into regular
  patterns of attitudes and behaviors

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Nonprobability Sampling

• 3. Snowball sampling
• Appropriate when members of a population are
  difficult to locate (homeless, migrant workers,
  undocumented immigrants).
• Researcher collects data on members she can
  locate, then asks those individuals to help
  locate other members of that population.

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Nonprobability Sampling

• 4. Quota sampling
• Begins with a matrix of the target population.
• Data is collected from people with the
  characteristics of a given cell.
• Each group is assigned a weight appropriate to
  their portion of the total population.
• When the elements are properly weighted, the data
  should provide a representation of the total
  population.

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Probability Sampling

• Used when researchers want precise, statistical
  descriptions of large populations.
• In order to provide useful descriptions of the
  total population, a sample of individuals from a
  population must contain the same variations that
  exist in the 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.