1. Chapter Eleven Sampling Fundamentals 1

2. Sampling Fundamentals Population Sample Census Parameter Statistic

3. The One and Only Goal in Sampling!! Select a sample that is as representative as possible. So that an accurate inference about the population can be made – goal of marketing research

4. Sampling Fundamentals When Is Census Appropriate? When Is Sample Appropriate?

5. Error in Sampling Total Error –Difference between the true value (in the population) and the observed value (in the sample) of a variable Sampling Error –Error due to sampling (depends on how the sample is selected, and its size) Non-sampling Error (dealt with in chapter 4) –Measurement Error, Data Recording Error, Data Analysis Error, Non-response Error

6. Sampling Process: Identify Population Question: For a toy store in Charlotte (be as specific as possible) Question: For a small bookstore in RH specializing in romance novels

7. Sampling Process: Determine sampling frame List and contact information of population members used to obtain the sample from Example – to address a population of all advertising agencies in the US, the sampling frame would be the Standard Directory of Advertising Agencies Availability of lists is limited, lists may be obsolete and incomplete

8. Problems with sampling frames Subset problem –The sampling frame is smaller than the population –Another sampling frame needs to be tapped Superset problem –Sampling frame is larger than the population –A filter question needs to be posed Intersection problem –A combination of the subset and superset problem –Most serious of the three

9. Problems with sampling frames

10. Sampling Process: Sampling Procedure Probability Sampling Each member of the population stands an equal chance of getting into the sample Preferred due to greater representativeness Nonprobability Sampling Convenience sampling – some members stand a better chance of being sampled than others

11. Sampling Procedure Sampling Procedures Non-Probability Sampling Probability Sampling -Simple Random Sampling -Systematic Sampling -Stratified Sampling -Cluster Sampling -Convenience Sampling -Judgmental Sampling -Snowball Sampling -Quota Sampling Here’s the difference! Probability Sampling: Each subject has the same non-zero probability of getting into the sample!

12. Probability Sampling Techniques Simple Random Sampling Each population member has equal, non-zero probability of being selected Equivalent to choosing with replacement

13. Probability Sampling Techniques Accuracy – cost trade off Sampling Efficiency = Accuracy/Cost –Sampling efficiency can be increased by either reducing the cost, increasing the accuracy or doing both –This has led to modifying simple random sampling procedures

14. Probability Sampling Techniques Stratified Sampling The chosen sample is forced to contain units from each of the segments or strata of the population Sometimes groups (strata) are naturally present in the population Between-group differences on the variable of interest are high and within-group differences are low Then it makes better sense to do simple random sampling within each group and vary within-group sample size according to –Variation on variable of interest –Cost of generating the sample –Size of group in population Increases accuracy at a faster rate than cost

15. Stratified Sampling – what strata are naturally present

16. Consumer typeGroup size10 Percent directly proportional stratified sample size Brand-loyal40040 Variety-seeking20020 Total60060 Directly Proportionate Stratified Sampling

17. 600 consumers in the population: 200 are heavy drinkers 400 are light drinkers. If heavy drinkers opinions are valued more and a sample size of 60 is desired, a 10 percent inversely proportional stratified sampling is employed. Selection probabilities are computed as follows: Denominator Heavy Drinkers proportion and sample size Light drinkers proportion and sample size 600/200 + 600/400 = 3 + 1.5 = 4.5 3/ 4.5 = 0.667; 0.667 * 60 = 40 1.5 / 4.5 = 0.333; 0.333 * 60 = 20 Inversely Proportional Stratified Sampling

18. Probability Sampling Techniques Cluster Sampling Involves dividing population into subgroups Random sample of subgroups/clusters is selected and all members of subgroups are interviewed Advantages –Decreases cost at a faster rate than accuracy –Effective when sub-groups representative of the population can be identified

19. Cluster Sampling Geography knowledge of all middle school children in the US Attitudes to cell phones amongst all college students in the US Knowledge of credit amongst all freshman college students in the US Combine cluster and stratified sampling

20. A Comparison of Stratified and Cluster Sampling Stratified sampling Homogeneity within group Heterogeneity between groups All groups are included Random sampling in each group Sampling efficiency improved by increasing accuracy at a faster rate than cost Cluster sampling Homogeneity between groups Heterogeneity within groups Random selection of groups Census within the group Sampling efficiency improved by decreasing cost at a faster rate than accuracy.

21. Probability Sampling Techniques Systematic Sampling –Systematically spreads the sample through the entire list of population members –E.g. every tenth person in a phone book –Bias can be introduced when the members in the list are ordered according to some logic. E.g. listing women members first in a list at a dance club. –If the list is randomly ordered then systematic sampling results closely approximate simple random sampling –If the list is cyclically ordered then systematic sampling efficiency is lower than that of simple random sampling

22. Non-Probability Sampling Benefits –Driven by convenience –Costs may be less Common Uses –Exploratory research –Pre-testing questionnaires –Surveying homogeneous populations –Operational ease required

23. Non-Probability Sampling Techniques Judgmental –Selected according to ‘expert’ judgment Snowball –Each sample member is asked to recommend another –Used when populations are highly specialized / niched Convenience –‘ whosoever is convenient to find’ Quota –Judgment sampling with a stipulation that the sample include a minimum number from each specified sub-group