1. DATA COLLECTION AND SAMPLING MKT525

2. DATA COLLECITON 4 Telephone 4 Mail 4 Panels 4 Personal Interviews 4 Internet

3. Criteria : evaluate data collection methods 4 Versatility 4 Cost 4 Time 4 Sample control 4 Quantity of data from a respondent 4 Quality of data

4. Non-Response Errors

5. Reducing Non-Response:Phone 4 At least 3 call backs 4 At least 5 rings 4 Explicitly mention topic 4 Short interview 4 Prior notification of call 4 Monetary incentive 4 Foot-in-door technique

6. Reducing Non-Response: mail 4 Prior notification 4 First class postage 4 Monetary incentive 4 Promise anonymity 4 Follow up contacts

7. Sampling:Some Definitions 4 Sampling = a compromise 4 Population 4 Sampling unit 4 Sampling frame

8. Steps in Drawing a Sample 4 Define population 4 Identify sampling frame 4 Select sampling procedure 4 Determine sample size 4 Select sample elements 4 Collect data

9. Selecting Sampling Procedure 4 Cost 4 Time 4 Risk you will take of being wrong 4 Precision needed 4 Responsibility for choosing sample 4 Non-probability sample –convenience –judgement –quota

10. Probability Sampling 4 Why? –Sample itself of no interest; interested in what we can infer about the population 4 Mean 4 Variance, sum of squares, standard deviation 4 Parameter 4 Statistic

11. Sampling Distribution 4 Frequency distribution of the means of all possible samples of size n from population of size N. 4 Want sample statistic to be an unbiased estimate of population parameter. 4 Central Limit Theorem describes characteristics of the sampling distribution: If many simple random samples of size n are drawn from the parent population, then when n is large, the sample mean will be approximately normally distributed with sample mean=population mean and variance = sample variance/n.’

12. Sampling Distribution: Key Concepts 4 Mean of sampling distribution ~ population mean 4 Variance of sampling distribution = standard error of the mean and depends on: -size of sample -variance of population 4 Distribution is a normal curve-if samples large enough. 4 Regardless of shape of distribution of values in population, shape of SAMPLING DISTRIBUTION approximates a normal curve.

13. Sampling Distribution: Why Care? 4 Larger sample - smaller standard error - more precise estimate of population parameter 4 When sampling distribution known, can generalize from statistics on a sample to entire population. 4 Sampling distribution can only be described for a probability sample; not for a non-probability sample.

14. How to Estimate a Parameter from a Statistic 4 Z = sample mean - population mean standard error of mean 4 Population mean = sample mean+/- (z)(std. Error) 4 Confidence interval = +/- (z)(std. Error of mean)

15. Probability Sampling Procedures 4 Simple random sample 4 Stratified sample 4 Cluster sample –Area sampling 4 Systematic sample

16. Sample Size: Issues 4 Homogeneity of population 4 How precise you want estimate to be 4 Size of error you are willing to accept:how much risk are you willing to take that the estimate of the parameter is wrong? 4 Also: –cost –time constraints –type of data analysis planned –study objectives

17. To Estimate Sample Size 4 Need estimate of Std. Deviation of population (S) 4 Need magnitude of error willing to accept (E) 4 Need confidence level - risk willing to accept (Z) n = Square of : ZS E