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Sampling

Sampling. Census and Sample (defined). A census is based on every member of the population of interest in a research project A sample is a subset of the population. Characteristics of a Sample. Representative of the larger population Can be more efficient in terms of cost, time

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Sampling

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  1. Sampling

  2. Census and Sample (defined) • A census is based on every member of the population of interest in a research project • A sample is a subset of the population

  3. Characteristics of a Sample • Representative of the larger population • Can be more efficient in terms of cost, time • Generalizable results • Can reflect animate or inanimate populations

  4. The Sampling Frame • The list of elements from which a sample may be drawn • Defines the criteria on which elements will be selected • But, does not ensure that some elements will not be excluded or accurately represented • Sample frame error

  5. Sampling Unit • A single element or group of elements subject to selection in a sample • Ex. An 18 to 24 year old male with senior academic classification • Ex. Grocery retailers that gross $30,000 in revenues monthly

  6. Sampling Methods Probability sampling • Elements each have a known, calculable non-zero probability of inclusion • The probability of inclusion is predictable across elements

  7. Sampling Methods Non-probability sample • Sampling does not ensure a representative range of elements found in the larger population

  8. Forms of Probability Sampling • Simple random sampling • Each member of the population has a known, equal chance of being selected • Allows comparable estimates without surveying the entire population

  9. Forms of Probability Sampling • Systematic random sampling • Sampling occurs based on a skip interval system where every nth member is selected from the population • Each element at the skip level is selected and interviewed

  10. Systematic Random Sampling • Directory of Physicians in the Gainesville, FL area

  11. Forms of Probability Sampling • Stratified random sampling • Sampling based on applying weights to population stratas • Proportionate vs. disproportionate stratums • Appropriate when the population is non-homogenous or has wide variations

  12. Stratified Random Sampling Proportionate to their representation in the population 65% 23% 12%

  13. Stratified Random Sampling Disproportionate to their representation in the population 33% 34% 33%

  14. Cluster Sampling • Segmenting the population to sample based on geography • Postal codes, electoral constituencies, states, regions

  15. Multi-stage Sampling • Two-step process • Select a primary sample based on a pre-specified sampling method • 35 – 45 YO Women • Then, selecting a secondary sub-sample from within the larger sample group • 35 – 45 YO Women who actively invest in the stock market

  16. Forms of Non-probability Sampling • Convenience sampling • Participants are selected based on convenience and accessibility • Quick, uncomplicated, low in cost • Useful for exploratory research or quick info

  17. Forms of Non-probability Sampling • Judgment sampling • Participants are selected based on an expert’s judgment of the characteristics of a representative sample • Example: the “typical” customer

  18. Forms of Non-probability Sampling • Quota sampling • Attempts to ensure demographic characteristics of interest are represented in the sample proportionately to their representation in the population • Sample based on population percentiles

  19. Quota Sampling 65% 12% 23%

  20. Forms of Non-probability Sampling • Snowball sampling • Initial respondents are selected by probability sampling techniques • Additional respondents are obtained by referral from initial respondents

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