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A Brief Introduction to Sampling in Marketing Research: Techniques and Terminology

This chapter explores the fundamental concepts of sampling within the marketing research process. It discusses the importance of selecting a representative sample from a population to draw accurate inferences. Various sampling methods, including probability and non-probability sampling techniques, are outlined, highlighting when each should be used. Key terminologies, such as population, sample frame, and sampling unit, are clarified. By understanding these principles, researchers can enhance their data collection strategies and improve the reliability of their findings.

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A Brief Introduction to Sampling in Marketing Research: Techniques and Terminology

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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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