INTRODUCTION
TO
SURVEY SAMPLING
February 23, 2011
Karen Foote Retzer
Survey Research Laboratory
University of Illinois at Chicago
www.srl.uic.edu
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Census or sample?
Census:
• Gathering information about every
individual in a population
Sample:
• Selection of a small subset of a
population
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Why sample instead of taking a census?
• Less expensive
• Less time-consuming
• More accurate
• Samples can lead to statistical inference
about the entire population
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Probability Sample
• Generalize to the entire population
• Unbiased results
• Known, non-zero probability of selection
Non-probability Sample
• Exploratory research
• Convenience
• Probability of selection is unknown
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Target population
Definition: The population to which we
want to generalize our
findings.
• Unit of analysis: Individual/Household/City
• Geography: State of Illinois/Champaign
County/City of Urbana
• Age/Gender
• Other variables
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Examples of target populations
• Population of adults (18+) in Champaign
County
• UIUC faculty, staff, students
• Youth age 5 to 18 in Champaign County
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Sampling frame
• A complete list of all units, at the first
stage of sampling, from which a sample
is drawn
• For example,
 Lists of addresses
 Phone numbers in specific area codes
 Maps of geographic areas
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Sampling frames
Example 1:
• Population: Adults (18+) in Champaign County
• Possible Frame: list of phone numbers, list of block
maps, list of addresses
Example 2:
• Population: Females age 40–60 in Chicago
• Possible Frame: list of phone numbers, list of block maps
Example 3:
• Population: Youth age 5 to 18 in Cook County
• Possible Frame: List of schools
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Sample designs for probability samples
• Simple random samples
• Systematic samples
• Stratified samples
• Cluster
• Multi-stage
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Simple random sampling
• Definition: Every element has the same
probability of selection and every combination
of elements has the same probability of
selection.
• Probability of selection: n/N,
where n = sample size; N = population size
• Use Random Number tables, software
packages to generate random numbers
• Most precision estimates assume SRS
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Systematic sampling
• Definition: Every element has the same probability of
selection, but not every combination can be selected.
• Use when drawing SRS is difficult
 List of elements is long & not computerized
• Procedure





Determine population size N and sample size n
Calculate sampling interval (N/n)
Pick random start between 1 & sampling interval
Take every ith case
Problem of periodicity
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Stratified sampling: Proportionate
• To ensure sample resembles some aspect of
population
• Population is divided into subgroups (strata)
 Students by year in school
 Faculty by gender
• Simple Random Sample (with same probability
of selection) taken from each stratum.
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Stratified sampling: Disproportionate
• Major use is comparison of subgroups
• Population is divided into subgroups (strata)
 Compare girls & boys who play Little League
 Compare seniors & freshmen who live in dorms
• Probability of selection needs to be higher for
smaller stratum (girls & seniors) to be able to
compare subgroups.
• Post-stratification weights
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Cluster sampling
• Typically used in face-to-face surveys
• Population divided into clusters
 Schools (earlier example)
 Blocks
• Reasons for cluster sampling
 Reduction in cost
 No satisfactory sampling frame available
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Determining sample size: SRS
• Need to consider
 Precision
 Variation in subject of interest
• Formula
 Sample size
no = CI2 * (pq)
Precision
 For example:
no = 1.962 * (.5 * .5)
.052
• Sample size not dependent on population size.
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Sample size: Other issues
• Finite Population Correction
n = no/(1 + no/N)
• Design effects
• Analysis of subgroups
• Increase size to accommodate
nonresponse
• Cost
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Changes in Field of Survey
Research
From Random Digit Dial to
Address Based Sampling
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Cell Phones
• 24.5% of US Households are cell phone
only (Blumberg & Luke, 2010)
• Cell phone only households:
•
•
•
•
Unrelated adults
Non-white
Young (<=29)
Lower SES
• RDD sample frames tend not to include
cell phones and can lead to bias
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Cell Phones, cont
• Cell phone frames harder to target
geographically than landline frame
• Frame overlap with RDD
• Public Opinion Quarterly, 2007 Special
Issue, Vol. 71, Num. 5
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Address Based Sampling
• Sampling addresses from a near
universal listing of residential mail
delivery locations (Michael Link)
• Post-office Delivery Sequence Files
(DSF)
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Address Based Sampling
Advantages
• Coverage of target population is very
high
• Can be matched to name (~85%) and
listed telephone numbers (~65%)
• Includes non-telephone households and
cell-only households
• More efficient than traditional blocklisting
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Address Based Sampling
Disadvantages
• Incomplete in rural areas (although
improving with 9-1-1 address
conversion)
• Difficulties with “multidrop” addresses
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Before taking questions…
• Slides available at www.srl.uic.edu; click
on “Seminar Series”
• Next seminar: Introduction to Web
Surveys, Wednesday, March 2
• Evaluation
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