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bmgt 311 marketing research chris lovett fall 2014

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- 1. BMGT 311: Chapter 10 Determining the Size of a Sample
- 2. Learning Objectives • To understand the eight axioms underlying sample size determination with a probability sample • To know how to compute sample size using the conﬁdence interval approach • To become aware of practical considerations in sample size determination • To be able to describe diﬀerent methods used to decide sample size, including knowing whether a particular method is ﬂawed
- 3. Key Points • Many managers falsely believe that sample size and sample representativeness are related, but they are not. • A sample size decision is usually a compromise between what is theoretically perfect and what is practically feasible. • Many practitioners have a large sample bias, which is the false belief that sample size determines a sample’s representativeness.
- 4. Important Points about Sampling • Sampling method (not sample size) is related to representativeness. • Only a probability sample (random sample) is truly representative of a population. • Sample size determines accuracy of ﬁndings. • The only perfect accurate sample is a census - which is for the most part, not positive in Marketing Research
- 5. Sample Accuracy • Sample accuracy: refers to how close a random sample’s statistic is to the true population’s value it represents
- 6. Page 239
- 7. Two Types of Error • Non sampling error: pertains to all sources of error other than sample selection method and sample size • Sampling error: involves sample selection and sample size
- 8. Sample Size and Accuracy • Which is of these is more accurate? • A large probability sample or • A small probability sample? • The larger a probability sample is, the more accurate it is (less sample error).
- 9. Page 240
- 10. Page 241
- 11. Sample Error • Variability refers to how similar or dissimilar responses are to a given question.
- 12. Sample Error
- 13. The Conﬁdence Interval Method of • Conﬁdence interval approach: applies the concepts of accuracy, variability, and conﬁdence interval to create a “correct” sample size • The conﬁdence interval approach is based upon the normal curve distribution. • We can use the normal distribution because of the Central Limit Theorem.
- 14. Central Limit Theorem • Since 95% of samples drawn from a population will fall within + or – 1.96 × sample error (this logic is based upon our understanding of the normal curve), we can make the following statement . . . • If we conducted our study over and over, 1,000 times, we would expect our result to fall within a known range. Based upon this, we say that we are 95% conﬁdent that the true population value falls within this range.
- 15. Example - Page 243
- 16. Figuring out the Sample Error - Module 1 Handout • n Values: • n = 1,000 • n = 500 • n = 100 • n = 50 • p and q = 50 • Conﬁdence Interval = 95% or 1.96
- 17. Figuring out the Sample Error - Module 1 Handout • n Values: • n = 1,000 Sample Error _____ • n = 500 Sample Error _____ • n = 100 Sample Error _____ • n = 50 Sample Error _____ • p and q = 50 • Conﬁdence Interval = 95% or 1.96
- 18. Sample Size Formula • Need to know • Variability: p × q • Acceptable margin of sample error: e • Level of conﬁdence: z
- 19. Standard Sample Size Formula
- 20. Example: Estimating a Sample Size • What is the required sample size? • Five years ago, a survey showed that 42% of consumers were aware of the company’s brand (Consumers were either “aware” or “not aware.”) • After an intense ad campaign, management wants to conduct another survey and they want to be 95% conﬁdent that the survey estimate will be within ±5% of the true percentage of “aware” consumers in the population. • What is n?
- 21. Example: Estimating a Sample Size • Five years ago, a survey showed that 42% of consumers were aware of the company’s brand (Consumers were either “aware” or “not aware.”) • After an intense ad campaign, management wants to conduct another survey and they want to be 95% conﬁdent that the survey estimate will be within ±5% of the true percentage of “aware” consumers in the population. • Z=1.96 (95% conﬁdence) • p=42 • q=100-p=58 • e=5 • What is n?
- 22. Example: Estimating a Sample Size • Five years ago, a survey showed that 42% of consumers were aware of the company’s brand (Consumers were either “aware” or “not aware.”) • After an intense ad campaign, management wants to conduct another survey and they want to be 95% conﬁdent that the survey estimate will be within ±5% of the true percentage of “aware” consumers in the population. • Z=1.96 (95% conﬁdence) • p=42 • q=100-p=58 • e=5 • What is n? • n = 374
- 23. Chapter 10 Handout Module 2, Figure out n
- 24. First Step = Figure out z and q Situation Conﬁdence Level Value of z p q (100-p) Allowable Error 1 95% 1.96 65 35 3.5 2 99% 2.58 65 35 3.5 3 95% 1.96 60 40 5 4 99% 2.58 60 40 5 5 95% 1.96 50 50 4
- 25. Example 1: Page 247
- 26. Example 2: Page 247
- 27. Practical Considerations • How to estimate variability (p times q) in the population? • Expect the worst cast (p = 50; q = 50) • Estimate variability • Previous studies? • Conduct a pilot study?
- 28. Practical Considerations • How to determine the amount of acceptable sample error. • Researchers should work with managers to make this decision. How much error is the manager willing to tolerate? • See page 251 for practical example • Researchers should work with managers to take cost into consideration in this decision.
- 29. Practical Considerations • How to decide on the level of conﬁdence to use. • Researchers typically use 95% or 99%. • Most clients would not accept a conﬁdence interval below 95% as a representative of the overall population
- 30. Other Methods of Sample Size Determination • Arbitrary “percentage rule of thumb” • Conventional sample size • Statistical analysis approach requirements • Cost basis
- 31. Sampling from Small Populations • With small populations, use the ﬁnite population multiplier to determine small size.
- 32. Example: Page 255
- 33. Example: Page 255
- 34. More Practice for Test Questions • Page 258 - Question #13 - Crest Toothpaste Sample Size • Page 247 - Sample Size Calculations Practice • Make sure you practice and know all of the equations discussed in class • Sample Size Margin of Error • Sample Size Formula • Small Population Formula

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