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Nonprobability Sampling Designs

This article discusses the major issues with nonprobability sampling designs, such as the likelihood of misrepresenting the population and the difficulty in detecting this misrepresentation. It also explores different types of nonprobability samples, including accidental, haphazard, convenience, modal instance, purposive, expert, quota, snowball, and heterogeneity sampling. Each type is explained in detail, highlighting their advantages and potential problems. This comprehensive guide will help researchers select the appropriate nonprobability sampling method for their study.

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Nonprobability Sampling Designs

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  1. Nonprobability Sampling Designs

  2. Major Issues • Likely to misrepresent the population • May be difficult or impossible to detect this misrepresentation

  3. Types of Nonprobability Samples • Accidental, haphazard, convenience • Modal instance • Purposive • Expert • Quota • Snowball • Heterogeneity sampling

  4. Accidental, Haphazard or Convenience Sampling • “Man on the street” • College psychology majors • Available or accessible clients • Volunteer samples • Problem: No evidence for representativeness

  5. Modal Instance Sampling • Sample for the typical case • Will it play in Peoria? • Typical voter? • Problem: May not represent the modal group proportionately

  6. Purposive Sampling • Might sample several pre-defined groups (e.g., the shopping mall survey that attempts to identify relevant market segments) • Deliberately sampling an extreme group • Problem: Proportionality • Problem: Need theory to correctly sample an extreme group

  7. Expert Sampling • Have a panel of experts make a judgment about the representativeness of your sample. • Advantage: At least you can say that expert judgment supports the sampling. • Problem: The “experts” may be wrong.

  8. Quota Sampling • Select people nonrandomly according to some quotas • Proportional quota sampling • Nonproportional quota sampling

  9. Proportional Quota Sampling • Objective: Represent major characteristics of population by sampling a proportional amount of each. For example, if you know the population has 40% women and 60% men, you want your sample to meet that quota. • Problem: How do you pick the characteristics? How do you know their proportion in population?

  10. Nonproportional Quota Sampling • Making sure you have enough units from each target group of interest (even if not proportional). • As with stratified random sampling, you might do this to assure that you have good representation of smaller population groups.

  11. Snowball Sampling • One person recommends another, who recommends another, who recommends another, etc. • Good way to identify hard-to-reach populations, for example, homeless persons

  12. Heterogeneity Sampling • Make sure you include all sectors -- at least several of everything -- don't worry about proportions (like in quota sampling). • Use when one or more people are a good proxy for the group, for instance, when brainstorming issues across stakeholder groups.

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