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.
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 Is the Sample large enough? Sampling Techniques Probability
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