Download Sociological Objectives

Sampling
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?
Sample vs. Population
Sample
Population
Sampling
Types of Samples (You can sample almost anything):
Case Studies
Persons in Field Studies
Archival Data
Experiment Participants
Contexts Observed
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.
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)
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
Sampling
Sampling Techniques
 Nonprobability
 Snowball Sampling—Respondent-driven sampling
where initial respondents refer others to the
researcher
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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
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Helps get understanding of systems or processes or
information on a target population
Not representative of population in general
Sampling
Sampling Techniques
 Nonprobability
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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?
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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
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.
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
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.
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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
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.
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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.
Sampling
Sampling Techniques
 Probability
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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.
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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.
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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
Sampling
Sampling Techniques
 Probability
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Just because a sample is random, that does not mean that
it is representative or that the research is good.
 Limited Sampling Frame
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Think of presidential phone polls:
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Who is at home? Type of person, day of polling, etc.
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Who has a land line?
 Problems of non-response—random non-response okay,
but systematic non-response is biasing
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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
Sampling
Sampling Techniques
 Probability
 Is the Sample large enough?
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Larger samples produce less sampling error
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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