Discuss the concept of sampling and list reasons for sampling.

2. Learning Objectives • Discuss the concept of sampling and list reasons for sampling. • Identify and explain the different roles that sampling plays in the overall information research process. • Demonstrate the basic terminology used in sampling decisions. • Understand the concept of error in the context of sampling. McGraw-Hill/Irwin

3. Learning Objectives • Discuss and calculate sampling distributions, standard errors, and confidence intervals and how they are used in assessing the accuracy of a sample. • Discuss the factors that must be considered when determined sample size. • Discuss the methods of calculating appropriate sample sizes. McGraw-Hill/Irwin

4. Value of Sampling in Information Research Discuss the concept of sampling and list reasons for sampling • Sampling • Selection of a small number of elements from a larger defined target group—information gathered will allow judgments to be made about the larger group • Census • Includes data about every member of the defined target population • Sampling • Used when it is impossible to conduct a census of the population McGraw-Hill/Irwin

5. Value of Sampling in Information Research Identify and explain the different roles that sampling plays in the overall information research process • Role of Sampling • Identifying, developing, and understanding new marketing constructs that need to be investigated • Plays an indirect role in the design of the questionnaire • Enables the researchers to make decisions using limited information McGraw-Hill/Irwin

6. Value of Sampling in Information Research Discuss the concept of sampling and list reasons for sampling • Concept of Sampling • Making the right decision in the selection of items (i.e., people, products or services) • Feeling confident that data from the sample can be transformed into accurate information about the target population McGraw-Hill/Irwin

7. Overview: The Basics of Sampling Theory Discuss the concept of sampling and list reasons for sampling • Basic Sampling Terminology • Population • Defined target population • Element • Must be unique • Must be countable • Target population • Identify correctly • Sampling Units • Sampling Frames McGraw-Hill/Irwin

8. Overview: The Basics of Sampling Theory Discuss the concept of sampling and list reasons for sampling • Main Factors Underlying Sampling Theory • Sampling Discussions • Logic Behind this Perspective • Important Assumption • Probability distribution • Sampling distribution McGraw-Hill/Irwin

9. Discuss the concept of sampling and list reasons for sampling Exhibit 9.2 McGraw-Hill/Irwin

10. Discuss the concept of sampling and list reasons for sampling Exhibit 9.3 McGraw-Hill/Irwin

11. Overview: The Basics of Sampling Theory Discuss the concept of sampling and list reasons for sampling • Central Limit Theorem • for almost all target populations the sampling distribution of the sample mean or the percentage value derived from a simple random sample will be approximately normally distributed, provided that the sample size is sufficiently large ( i.e., when n is ≥ 30) McGraw-Hill/Irwin

12. Overview: The Basics of Sampling Theory Discuss the concept of sampling and list reasons for sampling • With an understanding the basics of the central limit theorem, the researcher can: • Draw representative samples from any target population • Obtain sample statistics from a random sample that serve as accurate estimate of the target population’s parameters • Draw one random sample instead of many, reducing the costs of data collection • Test more accurately the reliability and validity of constructs and scale measurements • Statistically analyze data and transform them into meaningful into meaningful information about the target population. McGraw-Hill/Irwin

13. Overview: The Basics of Sampling Theory Understand the concept of error in the context of sampling • Types of Errors • Classified as being either sampling or non-sampling • Random sampling errors • Sampling Error • Any type of bias that is attributable to mistakes in either drawing a sample or determining sample size • Central Limit Theorem—sampling error can be reduced by increasing the size of the sample McGraw-Hill/Irwin

14. Discuss the concept of sampling and list reasons for sampling Exhibit 9.4 McGraw-Hill/Irwin

15. Overview: The Basics of Sampling Theory Understand the concept of error in the context of sampling • Nonsampling Error • A bias that occurs in a research study regardless of whether a sample or census is used • Population frame error • Measurement error • Response error • Errors in gathering and recording data McGraw-Hill/Irwin

16. Overview: The Basics of Sampling Theory Discuss and calculate sampling distributions, standard errors, and confidence intervals • Statistical Precision • Critical Level of Error • General Precision • Precise Precision • Estimated standard Error • Measure of the sampling error and an indication of how far the sample result lies from the actual target population McGraw-Hill/Irwin

17. Estimating Standard Error-General Precision • Standard error of the sample mean=Estimated standard deviation of the sample mean divided by the square root of the Sample size • Standard error of the sample percentage value = square root of [(the % of the sample possessing the characteristic times the % of the sample NOT possessing the characteristic) divided by the Sample size] McGraw-Hill/Irwin

18. Estimating Standard Error-General Precision • A) Calculate the Standard error for the sample mean if 900 people were interviewed with a estimated sample deviation of 12.5 • The sample mean was 36 • B) Calculate the Standard error of sample percentage if 65% of the sample of 489 people have VCR’s q=(100-p) • The sample proportion was • A) = +- .406 • B) = +- 2.16% • We can then use estimated standard error to construct a confidence interval McGraw-Hill/Irwin

19. Confidence Interval • Confidence Interval • Statistical range of values within which the true value of the defined target population parameter is expected to be _ - Confidence Intervals range from almost zero to almost 100 percent, but the most commonly used confidence levels are the 90, 95, and 99 percent levels McGraw-Hill/Irwin

20. Confidence Interval for population mean and proportions • Critical Z Value for 90% is 1.65, 95% is 1.96, 99% is 2.58 • Calculate the Confidence interval for the population mean if the sample mean is 25 and the Standard error of the sample mean is 2 with a 90% confidence level • Answer = 25 +- 3.3 • Calculate the Confidence interval for a population proportion if the sample proportion is 75% and the Estimated standard error of the sample proportion is 5% with a confidence level of 95% • 75% +- 9.8% McGraw-Hill/Irwin

21. Probability Sampling and Sample Sizes • Determining Sample Size • 3 Factors in Determining Sample Sizes • Variability of the population characteristic under investigation • Standard deviation • Level of confidence desired in the estimate • Degree of precision desired in estimating the population characteristic McGraw-Hill/Irwin

22. Probability Sampling and Sample Sizes Discuss the methods of calculating appropriate sample size • When estimating a population mean n = (Z2B,CL)(σ2/e2) • When estimates of a population proportion are of concern n = (Z2B,CL)([P x Q]/e2) Estimate the sample size having a 95% confidence level, a estimate population standard deviation of 5 and a 3% tolerance level of error McGraw-Hill/Irwin

23. Probability Sampling and Sample Sizes Discuss the methods of calculating appropriate sample size • There is a direct relationship between the desired CL (90% 95%, 99%) and the require sample size • CL are directly associated with corresponding critical z-values • The higher the level of confidence required the larger the sample size • Acceptable tolerance level of error—amount of precision desired (2%, 5%, or 10%) McGraw-Hill/Irwin

24. Probability Sampling and Sample Sizes Discuss the methods of calculating appropriate sample size • Sample Size • Not a product of the population size, it is not a direct factor in determining sample size • Finite Correction Factor • Should be used if the sample size is greater than 5% of the population _ FCF = √N-n/N-1 McGraw-Hill/Irwin

25. Probability Sampling and Sample Sizes Discuss the methods of calculating appropriate sample size • Determine if the sample size is more than 5% of the population by taking the calculated sample size and dividing it by the known defined target population size • If it is, then calculate the appropriate finite correction factor and multiply the originally calculated sample size by it to adjust the required sample size • If the target population size is ≤500 should consider doing a census McGraw-Hill/Irwin

26. Sample Sizes Versus Usable Observations for Data Analysis Discuss the methods of calculating appropriate sample size • Sample Size • Researchers can estimate the number of sampling units that must be surveyed • Not all initial responses are usable • Inactive mailing addresses • Telephone number no longer in service • Incomplete responses • Factors to consider in drawing a sample • Reachable rate RR • Who is qualified to be included in the survey Overall Incidence Rate OIR • Expected completion rate ECR McGraw-Hill/Irwin

27. Calculating the Number of Contacts • Calculate the number of contacts you require if need a sample 1500 students but only 90% answer the phone and you have determine that 20% of students are taking marketing and do not qualify. Finally you estimate that only 90% will answer all the questions in the survey. • Number of Contacts is 2315 McGraw-Hill/Irwin

28. Summary • Value of Sampling in Marketing Research • Overview: The Basics of Sampling Theory • Probability Sampling and Sample Sizes • Nonprobability Sampling and Sample Size • Sample Sizes versus Usable Observations McGraw-Hill/Irwin