sampling plans

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  1. population
    –The aggregate of cases in which a researcher is interested
  2. sampling
    –Selection of a portion of the population (a sample) to represent the entire population
  3. ™Eligibility criteria
    • –The characteristics that define the population
    • - inclusion criteria 
    • - exclusion criteria
  4. strata
    –Subpopulations of a population (e.g., male/female)
  5. ™Target population
    –The entire population of interest
  6. ™Accessible population
    • –The portion of the target population that is
    • accessible to the researcher, from which a sample is drawn
  7. ™Representative sample
    –A sample whose key characteristics closely approximate those of the population—a sampling goal in quantitative research
  8. Sampling Goal for Quantitative Research
    is to obtain a sample that represents the population
  9. representative sample is more easily achieved with
    • Probability sampling
    • –Homogeneous populations
    • Larger samples
  10. Sampling Problems for Quantitative Research
    • sampling error
    • sampling bias
  11. sampling bias
    • –The systematic over- or under-representation of segments of the population on key variables
    • when the sample is not representative
  12. ™Sampling error
    –Differences between sample values and population values
  13. sampling designs
    • probability sampling
    • non-probability sampling
  14. ™Probability sampling
    –Involves random selection of elements: each element has an equal, independent chance of being selected
  15. ™Nonprobability sampling
    –Does not involve selection of elements at random

    ™Not all people have an equal chance of being selected
  16. Non-Probability Sampling include ?
    ™Convenience sampling

    ™Snowball (network) sampling

    ™Quota sampling

    ™Purposive sampling
  17. Convenience Sampling
    a form of non probability sampling 

    use of the most convenient available people

    most widely used approach by quantitative researchers

    most vulnerable to sampling bias - may not represent the overall population
  18. snowball sampling
    a form of non probability sampling 

    referrals from other people already in sample

    used to identify people with distinctive characteristics 

    used by both quantitative and qualitative researchers
  19. Quota Sampling
    a form of non probability sampling 

    ™Convenience sampling within specified strata of the population

    –Goal is to Enhance the representativeness of sample

    –Infrequently used, despite being a fairly easy method of enhancing representativeness
  20. Consecutive Sampling
    a form of non probability sampling 

    ™Involves taking all of the people from an accessible population who meet the eligibility criteria over a specific time interval, or for a specified sample size

    • –A strong nonprobability approach for “rolling enrollment” type accessible populations
    • –
    • Risk of bias low unless there are seasonal or temporal fluctuations
  21. Purposive Sampling
    a form of non probability sampling 

    ™Sample members are hand-picked by researcher to achieve certain goals

    –Used more often by qualitative than quantitative researchers

    –Can be used in quantitative studies to select experts or to achieve other goals
  22. Types of Probability Sampling
    ™Simple random sampling

    ™Stratified random sampling

    ™Cluster (multistage) sampling

    ™Systematic sampling
  23. Simple Random Sampling
    form of probability sampling™

    Uses a sampling frame – a list of all population elements

    ™Involves random selection of elements from the sampling frame

    –Not to be confused with random assignment to groups in experiments

    –Cumbersome; not used in large, national surveys
  24. Stratified Random Sampling
    type of probability sampling 

    • ™Population is first divided into strata, then random
    • selection is done from the stratified sampling frames

    • ™Enhances representativeness
    • - Can sample proportionately or disproportionately from the strata
  25. Cluster (Multistage) Sampling
    type of probability sampling 

    ™Successive random sampling of units from larger to smaller units (e.g., states, then zip codes, then households)

    –Widely used in national surveys

    • –Larger sampling error than in simple random sampling,
    • but more efficient
  26. Sample Size
    ™The number of study participants in the final sample

    –Sample size adequacy is a key determinant of sample quality in quantitative research.

    • –Sample size needs can and should be estimated
    • through power analysis.
  27. Sampling for Qualitative Research
    ™Selection of sample members guided by desire for information-rich sources

    ™“Representativeness” not a key issue

    ™Random selection not considered productive
  28. Sampling Methods for Qualitative Reserach
  29. Sampling Methods for Qualitative Reserach
    ™Convenience (volunteer) sampling

    ™Snowball sampling

    ™Purposive sampling

    ™Theoretical sampling
  30. Theoretical Sampling
    Preferred sampling method in grounded theory research

    Involves selecting sample members who best facilitate and contribute to development of the emerging theory
  31. Sample size for Qualitative Research
    ™No explicit, formal criteria

    ™Sample size determined by informational needs

    ™Decisions to stop sampling guided by data saturation

    ™Data quality can affect sample size.
  32. Sampling for Ethnography
    Mingling with many members of the culture—a “big net” approach

    Informal conversations with 25 to 50 informants

    Multiple interviews with smaller number of key informants
  33. Sampling for Phenomenology
    Relies on very small samples (often 10 or fewer)

    Participants must have experienced phenomenon of interest
  34. Sampling for Grounded Theory
    ™Typically involves samples of 20 to 40 people

    ™Selection of participants who can best contribute to emerging theory (usually theoretical sampling)
Card Set
sampling plans
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