Sampling Fundamentals 1

1. 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 • Sampling Error • Non-sampling Error (dealt with in chapter 4)

6. Sampling Process: Identify Population • Question: For a toy store in RH • 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 • Superset problem • Sampling frame is larger than the population • Intersection problem • A combination of the subset and superset problem

9. Problems with sampling frames

10. Sampling Process: Sampling Procedure Probability Sampling Nonprobability Sampling

11. Sampling Procedure -Simple Random Sampling -Systematic Sampling -Stratified Sampling -Cluster Sampling Probability Sampling Here’s the difference! Sampling Procedures -Convenience Sampling -Judgmental Sampling -Snowball Sampling -Quota Sampling Non-Probability Sampling 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. Directly Proportionate Stratified Sampling

17. 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 • 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:

18. Probability Sampling Techniques Cluster Sampling • Involves dividing population into clusters • Random sample of clusters is selected and all members of a cluster 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 • Math 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

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 • Snowball • Convenience • Quota