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Sample Design

Sample Design. (Click icon for audio). Photographic Example of How Sampling Works. Sampling Terminology. Population or universe Population element Census Sample. Population/Universe. Any complete group People Sales territories Stores Total group from which information is needed.

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Sample Design

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  1. Sample Design (Click icon for audio) Dr. Michael R. Hyman, NMSU

  2. Photographic Example of How Sampling Works

  3. Sampling Terminology • Population or universe • Population element • Census • Sample

  4. Population/Universe • Any complete group • People • Sales territories • Stores • Total group from which information is needed

  5. Census Investigation of all individual elements that make up a population

  6. Sample Subset of a larger population of interest

  7. Define the target population Select a sampling frame Determine if probability or non-probability sampling method will be chosen Stages in Selecting a Sample Plan procedure for selecting sampling units Determine sample size Select actual sampling units Conduct fieldwork

  8. Define Target Population • Look at research objectives • Relevant population • Operationally define • Consider alternatives and convenience

  9. Select Sampling Frame • List of elements from which sample may be drawn • Mailing and commercial lists can be problematic (more on this later)

  10. Sampling Units • Group selected for the sample • Can be persons, households, businesses, et cetera • Primary sampling units • Secondary sampling units

  11. Choose Probability or Non-probability Sample • Probability sample • Known, nonzero probability for every element • Non-probability sample • Probability of selecting any particular member is unknown

  12. Conditions Favoring Non-probability vs. Probability Samples

  13. Different Sampling Techniques

  14. Non-probability Samples • Convenience • Judgment • Quota • Snowball

  15. Convenience Sample • Also called haphazard or accidental sampling • Sampling procedure for obtaining people or units that are convenient to researchers

  16. Discrepancy between Implied and Ideal Populations in Convenience Sampling

  17. Judgment Sample • Also called purposive sampling • Experienced person selects sample based on his or her judgment about some appropriate characteristics required of sample members

  18. Discrepancy between Implied and Ideal Populations in Judgment Sampling

  19. Quota Sample • Various population subgroups are represented on pertinent sample characteristics to the extent desired by researchers • Do not confuse with stratified sampling (discussed later)

  20. Representative Quota Sample Requirements

  21. Snowball Sample • Initial respondents selected by probability methods • Additional respondents obtained from information provided by initial respondents

  22. Probability Samples • Simple random sample • Systematic sample • Stratified sample • Cluster sample

  23. Simple Random Sample Ensures each element in the population has an equal chance of selection

  24. Systematic Sample • A simple process • Every nth name from list will be drawn

  25. Stratified Sample • Probability sample • Sub-samples drawn within different strata • Each stratum more or less equal on some characteristic • Do not confuse with quota sample

  26. Drawing a Stratified Sample: Example

  27. Disproportionate Stratified Random Sampling Used by A.C. Nielsen

  28. Cluster Sample • Purpose: to sample economically while retaining characteristics of a probability sample • Primary sampling unit is not individual element in population • Instead, it is larger cluster of elements located in proximity to one another

  29. Examples of Populations and Clusters

  30. More Examples of Clusters

  31. Strengths and Weakness of Sampling Techniques

  32. Bases for Choosing a Sample Design • Degree of accuracy • Resources • Time • Advanced knowledge of population • National versus local • Need for statistical analysis

  33. After Sample Design is Selected • Determine sample size • Select actual sample units • Conduct fieldwork

  34. Sampling Error

  35. Types of Sampling Errors • Sampling frame error • Random sampling error • Non-response error

  36. Errors Associated with Sampling

  37. Random Sampling Error • Difference between sample results and result of a census conducted using identical procedures • Statistical fluctuation due to chance variations

  38. Key Aspects of Sample Frame Error

  39. Systematic Errors • Non-sampling errors • Unrepresentative sample results caused by flawed study design or imperfections in execution rather than chance

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