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Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1)

Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1). Neumann, pp. 86-93. HOW AND WHY DO SAMPLES WORK?. A proper, representative sample lets you study features of the sample and produce highly accurate generalizations about the entire population

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Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1)

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  1. Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1) Neumann, pp. 86-93.

  2. HOW AND WHY DO SAMPLES WORK? • A proper, representative sample lets you study features of the sample and produce highly accurate generalizations about the entire population • The most representative samples use random selection • The random process allows us to build on mathematical theories about probability • Due to their use of random selection, probability samples are also called random samples

  3. Sample, population, random sample • sample: a small collection of units taken from a larger collection • population: a larger collection of units from which a sample is drawn • random sample: a sample drawn in which a random process is used to select units from a population

  4. Sampling in qualitative vs quantitative research • Qual. & quant. researchers both use sampling, but qualitative researchers have different goals than to get a representative sample of a large population, so they rarely use random sampling • Instead, they actually want to learn how a small collection of cases, units, or activities, can illuminate key features of an area of social life • Use sampling less to represent a population than to highlight informative cases, events, or actions • Goal is to clarify and deepen understanding based on what's learned from highlighted cases

  5. FOCUSING ON A SPECIFIC GROUP: 4 TYPES OF NONRANDOM SAMPLES • Random samples are best to get an accurate representation of a population, but they are difficult to conduct • Researchers who cannot draw random samples use nonprobability sampling techniques, e.g., • Convenience sampling • Quota sampling • Purposive or judgmental sampling • Snowball sampling

  6. Convenience Sampling • convenience sampling: a nonrandom sample in which you use a nonsytematic selection method that often produces samples very unlike the population • Also called accidental or haphazard sampling, it’s cheap and fast, but of limited use • With caution, can be used for the preliminary phase of an exploratory study

  7. Quota sampling • quota sampling: nonrandom sample in which you use any means to fill preset categories that are characteristics of the population • Not as accurate as a random sample, much easier and faster • Identify several categories of people or units that reflect aspects of diversity in population you believe to be important (gender, age, etc.) • Decide how many units to get for each category, i.e., what the quota will be • After setting categories and # of units in each category, select units by any method

  8. Purposive or Judgmental Sampling • purposive sampling: a nonrandom sample in which you use many diverse means to select units that fit very specific characteristics • It’s like convenience sampling for a highly targeted, narrowly defined population • Can be used in two types of situations: • to select especially informative cases • to select cases from a specific but hard-to-reach population

  9. Snowball Sampling • snowball sampling: a nonrandom sample in which selection is based on connections in a preexisiting network • Also called network, chain-referral or reputational sampling, it’s a special technique in which goal is to capture an already existing network • It is a multistage technique • The crucial feature is that each person or case has a connection with the others

  10. Networks for which researchers used snowball sampling • Scientists around world investigating same issue • The elites of a medium-sized city who consult with one another • Drug dealers and suppliers in a distribution network • People on a college campus who have had sexual relations with one another

  11. COMING TO CONCLUSIONS ABOUT LARGE POPULATIONS • sampling element: a case or unit of analysis of the population that can be selected for a sample • can be a person, a group, an organization, a written document or symbolic message, or a social action or event (e.g., an arrest, a protest event, divorce, a kiss)

  12. 3 terms with similar meanings are often confused, but they’re related by degree of specificity (from less to more) • universe: the broad group to whom you wish to generalize your theoretical results • e.g., all people in FL • population: a collection of elements from which you draw a sample • e.g., all adults in the Miami metro area • target population: the specific population that you used • e.g., all adults who had a permanent address in Dade country, FL in Sept 2007, and who spoke English, Spanish, or Haitian Creole

  13. Once you have a target population… • …you must create a list of all its sampling elements, your sampling frame • sampling frame: a specific list of sampling elements in the target population • population parameter: any characteristic of the entire population that you estimate from a sample • sampling ratio: the ratio of the sample size to the size of the target population

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