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Schindler and Cooper Sampling PPT for Business
- 1. Chapter 14 Sampling McGraw-Hill/Irwin Copyright© 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
- 2. 14-2 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. 14-3 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. 14-4 Small Samples CanEnlighten “The proof of the pudding is in the eating. By a small sample we may judge of the whole piece.” Miguel de Cervantes Saavedra author
- 5. 14-5 PulsePoint: Research Revelation 80 Theaverage number of text messages sent per day by American teens.
- 6. 14-6 The Nature ofSampling •Population •Population Element •Census •Sample •Sampling frame
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- 8. 14-8 What Is aSufficiently Large Sample? “In recent Gallup ‘Poll on polls,’ . . . When asked about the scientific sampling foundation on which polls are based . . . most said that a survey of 1,500 – 2,000 respondents—a larger than average sample size for national polls—cannot represent the views of all Americans.” Frank Newport The Gallup Poll editor in chief The Gallup Organization
- 9. 14-9 When Is aCensus Appropriate? Necessary Feasible
- 10. 14-10 What Is aValid Sample? Accurate Precise
- 11. 14-11 Sampling Design within theResearch Process
- 12. 14-12 Types of SamplingDesigns Element Selection Probability Nonprobability •Unrestricted • Simple random • Convenience •Restricted • Complex random • Purposive • Systematic • Judgment •Cluster •Quota •Stratified •Snowball •Double
- 13. 14-13 Steps in SamplingDesign 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?
- 14. 14-14 When to UseLarger Sample? Desired precision Number of subgroups Confidence level Population variance Small error range
- 15. 14-15 Simple Random Advantages • Easyto implement with random dialing Disadvantages • Requires list of population elements • Time consuming • Larger sample needed • Produces larger errors • High cost
- 16. 14-16 Systematic Advantages • Simple todesign • 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
- 17. 14-17 Stratified Advantages • Control ofsample size in strata • Increased statistical efficiency • Provides data to represent and analyze subgroups • Enables use of different methods in strata Disadvantages • Increased error if subgroups are selected at different rates • Especially expensive if strata on population must be created • High cost
- 18. 14-18 Cluster Advantages • Provides anunbiased 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
- 19. 14-19 Stratified and ClusterSampling 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
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- 21. 14-21 Double Sampling Advantages • Mayreduce costs if first stage results in enough data to stratify or cluster the population Disadvantages • Increased costs if discriminately used
- 22. 14-22 Nonprobability Samples Cost Feasibility Time No needto generalize Limited objectives
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- 24. 14-24 Key Terms • Areasampling • 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
- 25. 14-25 Key Terms • Proportionatestratified 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
- 26. Appendix 14a Determining Sample Size McGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
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- 29. 14-29 Confidence Levels &the Normal Curve
- 30. 14-30 Standard Errors Standard Error (Zscore) % of Area Approximate Degree of Confidence 1.00 68.27 68% 1.65 90.10 90% 1.96 95.00 95% 3.00 99.73 99%
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- 32. 14-32 Estimates of DiningVisits Confidence Z score % of Area Interval Range (visits per month) 68% 1.00 68.27 9.48-10.52 90% 1.65 90.10 9.14-10.86 95% 1.96 95.00 8.98-11.02 99% 3.00 99.73 8.44-11.56
- 33. 14-33 Calculating Sample Sizefor Questions involving Means Precision Confidence level Size of interval estimate Population Dispersion Need for FPA
- 34. 14-34 Metro U SampleSize for Means Steps Information Desired confidence level 95% (z = 1.96) Size of the interval estimate .5 meals per month Expected range in population 0 to 30 meals Sample mean 10 Standard deviation 4.1 Need for finite population adjustment No Standard error of the mean .5/1.96 = .255 Sample size (4.1)2/ (.255)2 = 259
- 35. 14-35 Proxies of the PopulationDispersion • Previous research on the topic • Pilot test or pretest • Rule-of-thumb calculation – 1/6 of the range
- 36. 14-36 Metro U SampleSize for Proportions Steps Information Desired confidence level 95% (z = 1.96) Size of the interval estimate .10 (10%) Expected range in population 0 to 100% Sample proportion with given attribute 30% Sample dispersion Pq = .30(1-.30) = .21 Finite population adjustment No Standard error of the proportion .10/1.96 = .051 Sample size .21/ (.051)2 = 81
- 37. 14-37 Appendix 14a: KeyTerms • Central limit theorem • Confidence interval • Confidence level • Interval estimate • Point estimate • Proportion
- 38. Addendum: Keynote CloseUp McGraw-Hill/Irwin Copyright© 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
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- 41. Determining Sample Size Appendix 14a McGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
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- 44. 14-44 Metro U. DiningClub Study