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Sampling Methods

Sampling Methods. Dr. Farzin Madjidi Pepperdine University Graduate School of Education and Psychology. Sampling Methods. Why take samples? Terminology: Universe Population Sample Subjects/Analysis Units Sampling Frame. 2. Sampling Methods. Probability Sampling

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Sampling Methods

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  1. Sampling Methods Dr. Farzin Madjidi Pepperdine University Graduate School of Education and Psychology

  2. Sampling Methods Why take samples? Terminology: • Universe • Population • Sample • Subjects/Analysis Units • Sampling Frame 2

  3. Sampling Methods • Probability Sampling • Used to generate “random/non-biased) samples required for conducting inferential analyses • Non-probability Sampling • Used mostly in qualitative analysis when the inferences are not sought 3

  4. Methods of Sampling • Probability Sampling • Simple Random • Complex or Systematic • Stratified (proportional/disproportional) • Cluster 4

  5. Probability Sampling All subjects have the same non-zero chance of being selected • Simple random Sampling • Sample selected by a lottery • Best chance of eliminating selection bias • Advantages and disadvantages 5

  6. Probability Sampling Complex/Systematic Sampling • Start with the sampling frame • Random start • Decide on a selection strategy • Ex: population of 10,000 • Need a sample of 200 • 10,000/200=50; Select every 50th name • Advantages and Disadvantages 6

  7. Probability Sampling Stratified Sampling • Stratify the sampling frame by some requirement into subgroups • Randomly survey within every strata(proportional/disproportional) • Ex: • First stratify by age, within each age subgroup, you may stratify by sex and within in each resulting subgroup, conduct random sampling 7

  8. Probability Sampling Cluster Sampling • Generally used when there is not an exhaustive list of all sample elements • Randomly select large clusters of your subjects (single/multi-staged) • Within each cluster, randomly select subjects 8

  9. Probability Sampling • Sample Size • Uniformity/homogeneity of population • Size of population • Degree of confidence sought • Maximum tolerable error • Require formula and/or tables 9

  10. Non-probability Sampling • Homogeneous • Look for uniformity within/among groups or subjects • Maximum Variation • Look for for a variety of subjects that identify important common patterns • Theoretical • Look for subjects/behaviors that exemplify theoretical constructs 10

  11. Non-probability Sampling • Typical Case • Look for a case that represents the norms • Critical Case • Look for a case that uniquely represent the key issues • Intensity • Look for cases that are information rich, but not extreme 11

  12. Non-probability Sampling • Politically Important Cases • Look for the cases that attract attention to desired issues • Extreme (Deviant) Cases • Look for highly unusual manifestation of the desired issues • Confirming/Disconfirming Cases • Elaborate on initial analysis, seek exceptions or variations 12

  13. Non-probabilitySampling • Snowball or Chain • Ask from one participant for others, who know others, … • Opportunistic • Look for unique opportunities consistent with your interest • Combination • Look for multiple cases, samples or subjects 13

  14. Non-probability Sampling • Convenience • Look for subjects, cases, or samples that are readily available • Criterion • Look for cases that meet a pre-described set of criteria 14

  15. Non-probability Sampling • Random purposeful • With large purposeful samples, randomly select a subgroup • Stratified Purposeful • Use stratification to sample subgroups within a large purposeful sample 15

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