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

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., 1) Convenience sampling 2) Quota sampling 3) Purposive or judgmental sampling 4) 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 1) Identify several categories of people or units that reflect aspects of diversity in population you believe to be important (gender, age, etc.) 2) Decide how many units to get for each category, i.e., what the quota will be 3) 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: 1) to select especially informative cases 2) 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