Download - Lecture 7 Sampling
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Sampling for Surveys
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Surveys
What is a survey?
A process of presenting a standard series of questions to asample of persons.
The survey is the most widely used technique in criminologybecause it is best suited for looking at the complex social world.To capture that world accurately, we have to measure itin situ.
That means taking information from selected people,from where they are usually found.
Measures of many phenomena of interest are taken. Thepurpose is to accurately reflect the beliefs, attitudes, andbehaviors of the sample in order to generalize accurateinformation to a target population.
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Surveys
Survey research typically uses sampling rather than takingcensus.Sampling vs. Taking a Census Sampling: selecting cases (elements)or locating people (or other units of
analysis)from a target population in order to study the population.
Taking a Census: using all cases in an entire target (all elements)in order tostudy the population
So why dont we always take a census?
ASampleis a: Noun: the group from whom data are (or were) gathered, and
Verb: to select cases that represent a populationnot a musical term here
There are multiple ways to sample, but the goal is for thesample to maximally represent the target population
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Sample vs. Population
Population
Sample
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Sampling
Types of Samples (Multiple units of analysis can be sampled):
Cases Persons in Field Studies Situations
Archival Data Experiment Participants
Persons answering a Survey
Depending on how the sample was generated, there are limitsto how much findings can be generalized from it.
One aims for broad generalizability, but type of sampling isalso determined by the:
complexities of the target population, and
researchersresources
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Sampling
Sampling Techniques
Nonprobability: Sampling methods that do not let usknow in advance the likelihood of selecting for the
sample each element or case from a populationvs.
Probability: Sampling methods that allow us to knowin advance how likely it is that any element of apopulation will be selected for the sample
Knowing the chance of selection allows one to controlsampling bias (under or overrepresentation of apopulation characteristic in a sample)
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Nonprobability Sampling Nonprobability
(Very common in psychology, medicine, sociology)
1. Availability Sampling, convenience sampling
Selection of cases based on what is easiest to do Experiments
Exploratory and Qualitative research
Avoid this if you can
2. Quota SamplingAspects of target population are known. Selectsavailability sample ensuring that it reflects knownaspects of population
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Nonprobability Sampling
3. Snowball SamplingRespondent-driven sampling, initial respondents referothers 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
4. Purposive SamplingTargets 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
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Nonprobability Sampling
Critiques
Limited generalizabilityone cannot judge representativeness.
Researchers should estimate who the sample represents . . . The
sample at least represents populations that are similar to it.
Why use nonprobability samples? Nonprobability does not mean,intentional attempt to get a sample that is not representative:
1. Well-suited for exploratory and evaluation research2. Sampling frames (lists from which samples are drawn) are at
times inadequate or nonexistent
3. Quick, efficient4. Can be effectively used to study and describe social and socialpsychological processes
5. Any research is limited, but not having research is worse.6. Across samples, repeatedly finding the same results supports
generalizability.
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Probability Sampling
Sampling Techniques
Probability Sampling: Sampling method reveals in advance thelikelihood that any one element will be selected for the sample
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 amagazine.
If a sampling frame is incomplete (which they usually are) then the accuracyof the sample is compromised. The researcher has the burden of assessing
the sampling error or bias.
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Probability Sampling
1. 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
2. Systematic Random Sampling
First case selected randomly from list, subsequent
cases are selected at equal intervals. Typically the same as Simple Random Sampling
Be aware of periodicity
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Probability Sampling
3. Cluster Sampling
Use when sampling frame is difficult to obtain, butclusters are identifiable.
Randomly select clusters, then use obtainablesampling frames within the clusters to selectcases.
Example: There is no national list of independent Baptists,but almost all independent Baptist churches can beidentified. Members can be selected from membership lists.
Because clusters are generally homogeneous(e.g., all white churches) it is better to maximizethe number of clusters and minimize numberof cases from each cluster
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Probability Sampling
Multistage cluster sampling
Selecting clusters in two or more hierarchicalstages(e.g., selecting states, then selecting churches,then members)
Keep stages to a minimum because each stageproduces sampling error; more stages, more error
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Probability Sampling
4. Stratified Random Sampling
Sampling frame divided into strata, cases drawn fromeach stratum randomly.
Small subpopulations of interest may yield too fewcases in simple random sampling. Tocompensate, the researcher draws samples fromeach subpopulation independently.
Example: Latino population of Santa Clara County isaround 25%. A random sample of 100 wouldproduce 20 30 Latinostoo few to generalize toSanta Clara County Latinos.
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Probability Sampling
4. Stratified Random Sampling
Proportionate Stratified SamplingEnsuring that population proportions are reflected inproportions of each stratum of sample.
Population: 4% black, 25% Latino, 27% Asian, 44% white Sample of 1,000: 40 black, 250 Latino, 270 Asian, 440 white
Disproportionate Stratified SamplingPopulation proportions are NOT reflected inproportions of each stratum of sample. 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
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2010 GSS Sampling
Full probability sample of US householdseachhousehold has an equal chance of beingselected
Used stratified area probability sampling At the household level, 1 adult is selected at random
(Kish Table)
Sampling frame Most cases came from a list of addresses from USPS (over 2/3)
Remaining cases from NORC-generated lists of households
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2010 GSS SamplingStages used in four population area types, ending with random adult
1.Big MSAs (city), have USPS address42% of population1. Primary sampling unit: tract (1 -2K Housing units)168 selected
2. Housing Unit Selected from USPS List
2.Intermediate MSAs or counties, have USPS address30% of population
1. Primary sampling unit: MSA or part of county30 selected2. Secondary sampling unit: tract120 selected
3. Housing unit Selected from USPS List
3.Rural counties and Intermediate areas (2) without adequate USPS address list25% of population
1. Primary sampling unit: County, all or part25 selected
2. Secondary sampling unit: Segment (constructed to contain 300 Housing Units)100 selected
3. Housing unit from NORC-listed master
4.Big MSAs (city), without adequate USPS address list3% of population1. Primary sampling unit: Segment12 selected
2. Housing unit from NORC-listed master
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2010 GSS Sampling
Source: http://www.fcsm.gov/03papers/keynotespeaker.pdf, January 12, 2012
Stratum 3 =NORC list used
http://www.fcsm.gov/03papers/keynotespeaker.pdfhttp://www.fcsm.gov/03papers/keynotespeaker.pdf -
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2010 GSS Sampling
What are the implications of the General SocialSurveys sampling???
The GSS is an adults-only survey of persons in
households. Therefore, it underrepresents: 18 24 year-olds (many not living in households
military, college, roaming)
65 and over (many not living in householdsvacations, RVs, assisted living)
Persons who live in large households (only oneperson per household is interviewed)
Homeless, criminal, and some poor (not in officialhouseholds, in shelters, on streets, in apartments)
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Probability Sampling
Critiques Just being random does not ensure that a sample 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-responserandom 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 theright questions
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Probability Sampling
Critiques
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 accuracyunless fraction is very large
Larger samples are needed when:
The population is more heterogeneous.
There are more variables of interest. The weaker the effects, or the smaller the differences
between groups,
TO SUM: MORE COMPLEXITY REQUIRES LARGER SAMPLES