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Sampling Fundamentals

Sampling Fundamentals. 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.

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Sampling Fundamentals

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  1. Sampling Fundamentals

  2. 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

  3. 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: Pop’n members not in frame, and members in frame not in pop’n of interest

  4. 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

  5. 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.

  6. 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

  7. PROBABILITY METHODS (Cont’d) • 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

  8. PROBABILITY METHODS (Cont’d) • 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!

  9. PROBABILITY METHODS (Cont’d) • 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 = 365/52=7.01 • Randomly choose 1 day out of first 7, then choose every 7th one after that. • Variation: Choose every nth visitor

  10. 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

  11. Nonprobability Sampling (Cont’d) • Referral (or Snowball) Samples • Quota Samples • EXAMPLE: Choose sampling units so their representation equals their frequency in the pop’n (e.g., 52% females, 48% males)

  12. 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

  13. 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

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