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Chapter Eleven

Chapter Eleven. Sampling Fundamentals 1. Sampling Fundamentals. Population Sample Census Parameter Statistic. The One and Only Goal in Sampling!!. Select a sample that is as representative as possible. So that an accurate inference about the population

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Chapter Eleven

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  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 -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 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

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