Chapter11ws sampling
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This document discusses sampling fundamentals and different sampling methods. It begins by defining key terms like population, sample, census, and parameter/statistic. It then explains that the goal of sampling is to select a sample that is as representative of the population as possible in order to make accurate inferences. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It also briefly outlines non-probability sampling techniques and their uses and benefits.
Chapter11ws sampling
- 1. Sampling Fundamentals 1Sampling 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 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 type Group size 10 Percent directly proportional stratified sample size Brand-loyal 400 40 Variety-seeking 200 20 Total 600 60 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 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