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SAMPLING

SAMPLING. EDUCATIONAL RESEARCH. WHY SAMPLE?. Not needed when can access entire population. Must sample when not feasible to access entire population. Sample Should Be Representative of the Larger Population. IMPORTANT TERMS. Sample --subset of people selected to study

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SAMPLING

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  1. SAMPLING EDUCATIONAL RESEARCH

  2. WHY SAMPLE? • Not needed when can access entire population. • Must sample when not feasible to access entire population.

  3. Sample Should Be Representative of the Larger Population...

  4. IMPORTANT TERMS • Sample--subset of people selected to study • Population--larger group from which sample comes to which we will infer

  5. STEPS IN SAMPLING • ID ideal/preferred population • Identify accessible population • Use Principle of Sampling

  6. Principle of Sampling • If sample is representative of accessible population, findings can be generalized to population • Generalizing from sample to population involves risks

  7. TYPES OF SAMPLING PROCEDURES • Probability • Nonprobability

  8. PROBABILITY SAMPLING • Participants drawn by chance (random) • Every member has known probability of being chosen (1:10; 1:100) • EPSEM--equal probability of selection method

  9. Probability Sampling: Types • Simple Random Sampling • Two-Stage Random Sampling • Stratified Sampling • Cluster Sampling • Systematic Sampling

  10. SIMPLE RANDOM SAMPLING • All have equal and independent chance of selection

  11. Performing Random Sampling • Define and list ALL population members • Assign each a # from 0 to ???? • Group columns of digits according to # needed • Two digits for #s up to 99 • Three digits for #s up to 999

  12. Performing Random Sampling • Arbitrarily select a # in the random number table • If selected # corresponds to # of member of identified population, member is in sample • go down # list • repeat above selection method until desired # of participants obtained

  13. Two-Stage Random Sampling • Useful in large populations • Select random clusters • Select random individuals from random clusters

  14. STRATIFIED SAMPLING • Information known about total population prior to sampling • Know population subgroups/strata • Distinguish all elements in population according to value on characteristic(s)

  15. Performing Stratified Sampling • Identify subgroups/strata • Select randomly a specific # of subjects from each stratum • Revisit random sampling

  16. Cluster Sampling • Unit chosen is NOT an individual, but a group of individuals naturally together • Constitute a cluster • Are alike with respect to characteristics relevant to the study

  17. Performing Cluster Sampling • Chosen at random from population of clusters • All members of clusters chosen must be included in sample • Using clusters as individuals, follow steps outlined in random sampling

  18. Examples of Cluster Sampling • Schools:clusters for sampling students • Blocks: clusters for sampling residents • Counties: clusters for sampling general populations • Businesses:clusters for sampling employees

  19. SYSTEMATIC SAMPLING • Convenient to draw a random sample when population elements are arranged sequentially

  20. Systematic Sampling • Variation of simple random sampling • Different: choices not independent • Yields simple random sample in most, but NOT all, sampling situations • If sequence varies in regular, periodic pattern, then will not have a random sample---rarely occur

  21. Performing Systematic Sampling • Determine size of sample • Divide sample # into population • Randomly select starting point • Select every nth subject • Need 5 people, have 45 in pop, select every 9th person once starting point chosen

  22. Nonprobability Sampling • Probability of selection of population elements is NOT known. • Participants NOT chosen by chance • Cannot estimate likelihood selection • More convenient and economical

  23. NONPROBABILITY SAMPLING TYPES • Accidental Purposive • Quota Snowball • Convenience • Cannot expect representative sample using nonprobability methods

  24. ACCIDENTAL SAMPLING • Haphazard, availability, or convenience • Interviewing/surveying first X number of individuals encountered • EXTREMELY weak, but fairly popular • Most psych research is accidental

  25. PURPOSIVE SAMPLING • Subjects judged to be representative are chosen from larger population • Doesn’t produce representative sample • Results may be misleading

  26. PURPOSIVE SAMPLING • Each sample selected for purpose, usually because of unique position of sample element • Used in national elections (referred to as bell-weather districts)

  27. QUOTA SAMPLING • Selection of typical cases from diverse strata of a population • Must know characteristics of entire population to set right quotas • Approximation of population with respect to selected characteristics

  28. SNOWBALL SAMPLING • Useful for hard to reach or identify, but interconnected populations • May consider when cannot think of another method • Generalizations are very tentative

  29. Performing Snowball Sampling • Identify one member of a population • Speak with member • Ask member to identify others • Speak with those members • Ask those members to identify more • and so on and on and on....

  30. Snowball Sampling Examples • drug dealers • prostitutes • practicing criminals • gang leaders • Alcoholics Anonymous members

  31. CONVENIENCE SAMPLING • When random is impossible • Individuals who are available • Likely to be biased • Not representative of any population • Should be avoided if possible • Should be replicated

  32. Sample Representativeness • Sampling Goal: representativeness • Larger the sample, more confidence we have in representativeness of sample • More homogeneous the population, the more confidence of representativeness

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