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Exploring Marketing Research

Exploring Marketing Research. Chapter 16: Sampling - A Brief Introduction . Sampling. Sampling - the process of selecting a sufficient number of elements from the population so that, by studying the sample, we can infer the characteristics of the population.

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Exploring Marketing Research

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  1. Exploring MarketingResearch Chapter 16: Sampling - A Brief Introduction

  2. Sampling • Sampling - the process of selecting a sufficient number of elements from the population so that, by studying the sample, we can infer the characteristics of the population. • Population characteristics are referred to astheparametersof the population and they are represented bysample statistics.

  3. Why Sample? • Pragmatic Reasons • Budget and time constraints • Limited access to total population • Accurate and Reliable Results • Samples can yield reasonably accurate information • Strong similarities in population elements makes sampling possible • Sampling may be more accurate than a census • Destruction of Test Units • Sampling reduces the costs of research in finite populations.

  4. Sampling Terminology • Population or universe - Any complete group: (people, sales territories, stores, etc.) • Population element - An individual member of a population • Sample - A subset of a larger population • Sample Frame - A list of elements from which the sample may be drawn • Sampling Unit - A single element or group of elements subject to selection in the sample

  5. Flowchart of the Marketing Research Process

  6. Learning Objectives • Know the steps in the sampling process. • Know the elements that make up a sampling plan.

  7. Stages in the Selectionof a Sample

  8. Learning Objective • Understand the difference between probability and non-probability samples and why each would be used.

  9. Probability versus Nonprobability Sampling • Probability Sampling • A sampling technique in which every member of the population has a known, nonzero probability of selection. • Sampling error is the amount of error that results due to the fact that no sample is a perfect representation of the population from which it is drawn. It is a function of sample size. • Only with a probability sample can we have confidence in the inferences we make about a population using sample data. • Nonprobability Sampling • A sampling technique in which units of the sample are selected on the basis of personal judgment or convenience; the probability of any particular member of the population being chosen is unknown.

  10. Stages in the Selectionof a Sample

  11. A Photographic Example of How Sampling Works

  12. Learning Objective • Be able to recognize an example of sampling frame error.

  13. Errors Associated with Sampling

  14. Learning Objective • Be able to recognize examples of the different types of probability and non-probability samples (i.e., simple random, stratified, systematic, quota, etc.), when each would be used and their advantages and disadvantages.

  15. Probability Sampling • Simple Random Sampling • Assures each element in the population of an equal chance of being included in the sample. • Systematic Sampling • A starting point is selected by a random process and then every nth number on the list is selected. • Stratified Sampling • Simple random subsamples that are more or less equal on some characteristic are drawn from within each stratum of the population.

  16. Proportional versus Disproportional Sampling • Proportional Stratified Sample • The number of sampling units drawn from each stratum is in proportion to the population size of that stratum. • Disproportional Stratified Sample • The sample size for each stratum is allocated according to analytical considerations.

  17. EXHIBIT 16.5Disproportional Sampling: Hypothetical Example

  18. Cluster Sampling • Cluster Sampling • An economically efficient sampling technique in which the primary sampling unit is not the individual element in the population but a large cluster of elements; clusters are selected randomly.

  19. EXHIBIT 16.6Examples of Clusters

  20. Nonprobability Sampling • Convenience Sampling • Obtaining those people or units that are most conveniently available • Judgment (Purposive) Sampling • An experienced individual selects the sample based on personal judgment about some appropriate characteristic of the sample member. • Quota Sampling • Ensures that various subgroups of a population will be represented on pertinent characteristics to the exact extent that the investigator desires.

  21. Learning Objective • Understand the factors that should be considered when choosing a sampling method.

  22. Degree of Accuracy Adaptation Appropriate Sample Design Resources Knowledge of Population Time What Is the Appropriate Sample Design?

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