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

1. Ch. 4, Sampling: How to Select a Few to Represent the Many (Pt. 1) Neumann, pp. 86-105.

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

14. Why Use a Random Sample? • Random samples are most likely to produce a sample that truly represents the population • True random processes are: 1) purely mechanical or mathematical without human involvement 2) they allow us to calculate the probability of outcomes with great precision

15. All samples contain a margin of error • A random process makes it possible to estimate mathematically the degree of match between sample and population, or sampling error • sampling error: the degree to which a sample deviates from a population

16. Random samples all have 3 key features: 1) They are based on an accurate sampling frame or list of elements in the target population 2) They use a random selection process without subjective human decisions (e.g., a computer program, random number table) 3) They rarely use substitutions for sampling elements

17. Types of Random Samples • Simple Random Samples • Systematic Sampling • Stratified Sampling • Cluster Sampling

18. Simple Random Samples • In simple random sampling, you: • First develop an accurate sampling frame • Select elements from the frame based on a mathematically random selection procedure • Locate the exact selected elements to be in your sample

19. Over many separate samples, the true population parameter is the most frequent result • sampling distribution: a plot of many random samples, with a sample characteristic across the bottom and the number of samples indicated along the side • The sampling distribution shows the same bell-shaped pattern whether your sample size is 1000 or 100

20. Systematic Sampling • If you lack the tools to select a pure random sample, systematic sampling is a quasi-random method • systematic sampling: an approximation to random sampling in which you select one in a certain number of sample elements; the number is from the sampling interval • sampling interval: the size of the sample frame over the sample size, used in systematic sampling to select units

21. Stratified Sampling • Sometimes researchers want to include specific kinds of diversity in their sample, e.g., racial diversity • stratified sampling: a type of random sampling in which a random sample is drawn from multiple sampling frames, each for a part of the population • Because you control the relative size of each stratum rather then letting random processes control it, you can be sure your sample will be representative of strata. • In general stratified sampling results in a slightly m representative sample than simple random sampling

22. Selecting a Stratified Sample • Step 1: You divide population into subpopulations (strata) To use this method, you must have info about strata in population (i.e., the population parameter). • Step 2: You create multiple sampling frames, one for each subpopulation • Step 3: Next draw random samples, one from each sampling frame

23. Cluster sampling • In some situations where there is no good sampling frame, you can use multiple-stage sampling with clusters • A cluster is grouping of the elements in the final sample that you are interested in • cluster sampling: a multistage sampling method in which clusters are randomly sampled, and then a random sample of elements is taken from sampled clusters

24. THREE SPECIALIZED SAMPLING SITUATIONS • Random-Digit Dialing (RDD) • Within-Household Sampling • Sampling Hidden Populations

25. Random-Digit Dialing • random-digit dialing:computer based random sampling of telephone numbers

26. Within-Household Sampling • A household can be thought of as a cluster in which there can be multiple sampling elements or individuals • To ensure random selection, create selection rules, and follow them consistently

27. Sampling Hidden Populations • hidden population: a group that is very difficult to locate and may not want to be found and is therefore difficult to sample