Sampling

1. Sampling

2. Sampling To do most research, one must have people to study. Sampling refers to selecting cases, or plain and simple, getting a group of people (or other elements) out of the population to study. Whenever we attempt to make statements about a set of people in general using a smaller group of people—generalizing—the data we use is from a sample. Sample vs. Census Census: “A complete count of an entire population” So why don’t we always do a census?

3. Sample vs. Population Sample Population

4. Sampling Types of Samples (You can sample almost anything): Case Studies Persons in Field Studies Contexts Observed Archival Data Experiment Participants Persons answering a Survey Depending on how the sample was generated, there are limits on how much we may generalize. Given limits on generalizability, the purpose of your research will help determine the type of sampling you do.

5. Sampling Sampling Techniques • Nonprobability: Sampling methods that do not let us know in advance the likelihood of selecting for the sample each element or case from a population vs. • Probability: Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample Knowing the chance of selection allows one to control sampling bias (under or overrepresentation of a population characteristic in a sample)

6. Sampling Sampling Techniques • Nonprobability (Very common in psychology, medicine, sociology) • Availability Sampling, convenience sampling—selection of cases based on what is easiest to get • Experiments • Exploratory and Qualitative research • Avoid this if you can • Quota Sampling—Knowing something about your target population, you select your availability sample to ensure that it looks similar to your population

7. Sampling Sampling Techniques • Nonprobability • Snowball Sampling—Respondent-driven sampling where initial respondents refer others to the researcher • Usually used with hard-to-discover populations • Bias introduced by structured nature of affiliation • Can be improved with incentives to subjects to recruit a certain number of new respondents • Purposive Sampling—targeting select people for a sample because of their unique position • Helps get understanding of systems or processes or information on a target population • Not representative of population in general

8. Sampling Sampling Techniques • Nonprobability • Nonprobability samples have limited generalizability—you can never be sure the sample “represents” the population • But, researcher can work to establish what the sample represents • Why use nonprobability samples? • Well-suited for exploratory and evaluation research • Nonprobability does not mean “intentional attempt to make sample nonrepresentative” • We cannot all be identified by sampling frames, sometimes making nonprobability sampling more accurate • More Efficiency • Social and social psychological “processes” can be effectively studied and described • No project is ever enough anyway, community of scholars can add information through other research—collections of projects can create a complete picture

9. Sampling Sampling Techniques • Probability Sampling: Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample Goal: A representative sample of a target population Probability sampling begins with a sampling frame, or a list of all elements or other units containing the elements in a population. E.g., Phone book, All Universities, Known Addresses, Subscribers to a magazine. If a sampling frame is incomplete (which they usually are) then the accuracy of the sample is compromised. The researcher has the burden of assessing the sampling error or bias.

10. Sampling Sampling Techniques • Probability • Simple Random Sampling—cases are identified strictly on the basis of chance. • Random number table to select from sampling frame • Random digit dialing • Equal probability of selection • Systematic Random Sampling—using a list, the first case is selected randomly, then subsequent cases are selected at equal intervals. • Typically the same as Simple Random Sampling • Be aware of periodicity

11. Sampling Sampling Techniques • Probability • Cluster Sampling—used when sampling frames of individuals are difficult to obtain, but clusters are identifiable. Randomly select clusters, then use the clusters’ sampling frames to select cases. • E.g., There is no national list of independent Baptists, but almost all independent Baptist churches can be identified. • Select down to smaller number of clusters, then do the difficult work of identifying elements (persons to participate) • Generally better to maximize the number of clusters and minimize number of cases from each cluster because clusters tend to be homogeneous • Often called “multistage sampling.” When one uses two or more successive sampling steps one is doing multistage sampling. • Each stage produces sampling error; more stages, more error

12. Sampling Sampling Techniques • Probability • Stratified Random Sampling—sampling frame is divided into strata of interest, cases are drawn from each stratum on the basis of chance. • Small subpopulations of interest may yield too few cases in simple random sampling. To compensate, the researcher draws samples from each subpopulation independently. • E.G., Latino population of Santa Clara County is around 25%. A random sample of 100 would produce 20 – 30 Latinos—too few to generalize to Santa Clara County Latinos. • Do independent sampling from each stratum.

13. Sampling Sampling Techniques • Probability • Stratified Random Sampling • Proportionate Stratified Sampling—select cases in a way that ensures the same proportion from each stratum in the sample as exists in the population. • Population: 4% black, 25% Latino, 27% Asian, 44% white • Sample of 1,000: 40 black, 250 Latino, 270 Asian, 440 white • Disproportionate Stratified Sampling—Proportion selected from each stratum is not the same as in the population. • Population: 4% black, 25% Latino, 27% Asian, 44% white • Sample of 1,000: 250 black, 250 Latino, 250 Asian, 250 white • Idea is to get a lot of cases in each stratum • When combining all cases into one sample, use weighted averages

14. Sampling Sampling Techniques • Probability • Just because a sample is random, that does not mean that it is representative or that the research is good. • Limited Sampling Frame • Think of presidential phone polls: • Who is at home? Type of person, day of polling, etc. • Who has a land line? • Problems of non-response—random non-response okay, but systematic non-response is biasing • Phone surveys typically do not report response rate. They are often below 30% • How were questions worded: Measurement error • Problems of misspecified models: Leads to not asking the right questions

15. Sampling Sampling Techniques • Probability • Is the Sample large enough? • Larger samples produce less sampling error • Too large is a waste of money • Big is good, but accurate and appropriate are better • Fraction of population sampled does not increase accuracy unless fraction is very large • The more heterogeneous the population, the larger the sample needed. • The more variables of interest, the larger the sample needed. • The weaker the effects, or the smaller the differences between groups, the larger the sample needed to see effects or differences between groups. • TO SUM: MORE COMPLEXITY REQUIRES LARGER SAMPLES