Sampling Fundamentals

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Sampling Fundamentals
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Basic Concepts

• Population the entire group under study (or of
  interest)
• Exercise Define population for a study seeking
  to assess SUU student attitudes towards a)
  program quality and delivery, b) program content,
  and c) social environment.
• Sample subset of the population
• Used to represent the population
• Sample unit (elements) basic unit investigated
  (choose sampling units/elements when sampling)
• Individuals, households, etc.
• Census data collected from EVERYONE in population

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Basic Concepts (continued)

• AGAIN total error sampling error nonsampling
  error
• Sampling error error due to taking a sample
  (/-zs)
• Nonsampling error everything else (measurement,
  data analysis, etc.)
• Sample frame list from which the sample is
  selected
• Sample frame error Popn members not in frame,
  and members in frame not in popn of interest

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Reasons for Sampling

• Cost
• Too much information to handle
• Sampling can be more accurate
• Nonsampling errors can overwhelm reduction in
  sampling errors
• Sampling work behaviors example
• Census Bureau
• Time problem

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Developing a sampling plana

• 1. Define the population of interest.
• 2. Choose a data-collection method (mail,
  telephone, Internet, intercept, etc.).
• 3. Identify a sampling frame.
• 4. Select sampling method
• 5. Determine sample size.
• 6. Develop operational procedures for selecting
  sampling elements/units.
• 7. Execute the operational sampling plan.

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PROBABILITY SAMPLING METHODS

• Each member of population has a known
  probability of being selected
• Simple Random Sampling Each member has an equal
  probability of being selected
• Blind Draw Method
• Table of Random Numbers
• Useful for small samples, when Random Digit
  Dialing (or 1) is appropriate, and computerized
  lists

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PROBABILITY METHODS (Contd)

• Stratified Sampling Population is segmented
  (stratified), and then samples are chosen from
  each strata using some other method
• Can be more efficient (smaller sampling error)
• Homogeneous within, heterogeneous without
• Useful when interested in different strata (e.g.,
  small numbers, etc)
• Disproportionate versus proportionate

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PROBABILITY METHODS (Contd)

• Cluster Sampling Population is divided into
  groups, or clusters, and then clusters are
  randomly chosen.
• Homogenous without, heterogeneous within
• Every unit in cluster examined, OR
• A Random (or systematic) sample is taken from
  chosen cluster (2-stage or 2-step approach)
• Careful with the probabilities!

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PROBABILITY METHODS (Contd)

• Systematic Sampling Randomly choosing a starting
  point and then choosing every nth member.
• Example Need 52 data points (daily sales) for a
  year
• Skip interval 365/527.01
• Randomly choose 1 day out of first 7, then choose
  every 7th one after that.
• Variation Choose every nth visitor

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NONPROBABILITY SAMPLING METHODS

• Probability of selection not known, and hence
  representativeness cannot be assessed
• Technically, confidence intervals, H0 tests, etc.
  not appropriate
• Convenience Samples
• Shopping mall intercepts, classes asked to fill
  out questionnaires, etc.
• Judgment Samples Someone puts together what is
  believed to be a relatively representative sample
• Ex. Test markets

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Nonprobability Sampling (Contd)

• Referral (or Snowball) Samples
• Quota Samples
• EXAMPLE Choose sampling units so their
  representation equals their frequency in the
  popn (e.g., 52 females, 48 males)

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Identifying the Target Population
Reconciling the Population, Sampling Frame
Differences
Determining the Sampling Frame
Selecting a Sampling Frame
Probability Sampling
Non-Probability Sampling
The Sampling Process
Determining the Relevant Sample Size
Execute Sampling
Data Collection From Respondents
Handling the Non-Response Problem
Information for Decision-Making
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Nonresponse Bias

• Reason for nonresponse
• Refusal
• Lack of ability to respond
• Not at home
• Inaccessible
• Handling nonresponse
• Improve research design
• Call-backs
• Estimate effects
• Sample nonrespondents trends