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Chapter 14

Chapter 14. Sampling. Learning Objectives. Understand . . . The two premises on which sampling theory is based. The accuracy and precision for measuring sample validity. The five questions that must be answered to develop a sampling plan. Learning Objectives. Understand . . .

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Chapter 14

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  1. Chapter 14 Sampling

  2. Learning Objectives Understand . . . • The two premises on which sampling theory is based. • The accuracy and precision for measuring sample validity. • The five questions that must be answered to develop a sampling plan.

  3. Learning Objectives Understand . . . • The two categories of sampling techniques and the variety of sampling techniques within each category. • The various sampling techniques and when each is used.

  4. The Nature of Sampling • Sampling • Population Element • Population • Census • Sampling frame

  5. Greater speed Why Sample? Availability of elements Lower cost Sampling provides Greater accuracy

  6. When Is a Census Appropriate? Feasible Necessary

  7. What Is a Valid Sample? Accurate Precise

  8. Sampling Design within the Research Process

  9. Types of Sampling Designs

  10. Steps in Sampling Design What is the target population? What are the parameters of interest? What is the sampling frame? What is the appropriate sampling method? What size sample is needed?

  11. Population variance Desired precision Number of subgroups Confidence level Small error range When to Use Larger Sample Sizes?

  12. Advantages Easy to implement with random dialing Disadvantages Requires list of population elements Time consuming Uses larger sample sizes Produces larger errors High cost Simple Random

  13. Advantages Simple to design Easier than simple random Easy to determine sampling distribution of mean or proportion Disadvantages Periodicity within population may skew sample and results Trends in list may bias results Moderate cost Systematic

  14. Advantages Control of sample size in strata Increased statistical efficiency Provides data to represent and analyze subgroups Enables use of different methods in strata Disadvantages Increased error will result if subgroups are selected at different rates Especially expensive if strata on population must be created High cost Stratified

  15. Advantages Provides an unbiased estimate of population parameters if properly done Economically more efficient than simple random Lowest cost per sample Easy to do without list Disadvantages Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous Moderate cost Cluster

  16. Stratified Population divided into few subgroups Homogeneity within subgroups Heterogeneity between subgroups Choice of elements from within each subgroup Cluster Population divided into many subgroups Heterogeneity within subgroups Homogeneity between subgroups Random choice of subgroups Stratified and Cluster Sampling

  17. Area Sampling

  18. Advantages May reduce costs if first stage results in enough data to stratify or cluster the population Disadvantages Increased costs if discriminately used Double Sampling

  19. Time Nonprobability Samples No need to generalize Feasibility Limited objectives Cost

  20. Nonprobability Sampling Methods Convenience Judgment Quota Snowball

  21. Area sampling Census Cluster sampling Convenience sampling Disproportionate stratified sampling Double sampling Judgment sampling Multiphase sampling Nonprobability sampling Population Population element Population parameters Population proportion of incidence Probability sampling Key Terms

  22. Proportionate stratified sampling Quota sampling Sample statistics Sampling Sampling error Sampling frame Sequential sampling Simple random sample Skip interval Snowball sampling Stratified random sampling Systematic sampling Systematic variance Key Terms

  23. Appendix 14a Determining Sample Size

  24. Random Samples

  25. Increasing Precision

  26. Confidence Levels & the Normal Curve

  27. Standard Errors

  28. Central Limit Theorem

  29. Estimates of Dining Visits

  30. Calculating Sample Size for Questions involving Means Precision Confidence level Size of interval estimate Population Dispersion Need for FPA

  31. Metro U Sample Size for Means

  32. Proxies of the Population Dispersion • Previous research on the topic • Pilot test or pretest • Rule-of-thumb calculation • 1/6 of the range

  33. Metro U Sample Size for Proportions

  34. Central limit theorem Confidence interval Confidence level Interval estimate Point estimate Proportion Appendix 15a: Key Terms

  35. Addendum: Keynote CloseUp

  36. Keynote Experiment

  37. Keynote Experiment (cont.)

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