introduction to survey sampling
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INTRODUCTION TO SURVEY SAMPLING. February 23, 2011 Karen Foote Retzer Survey Research Laboratory University of Illinois at Chicago www.srl.uic.edu. Census or sample?. Census: Gathering information about every individual in a population Sample: - PowerPoint PPT PresentationTRANSCRIPT
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INTRODUCTION INTRODUCTION TO TO
SURVEY SAMPLINGSURVEY SAMPLING
February 23, 2011
Karen Foote Retzer
Survey Research LaboratoryUniversity of Illinois at Chicago
www.srl.uic.edu
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Census:• Gathering information about every
individual in a population
Sample: • Selection of a small subset of a
population
Census or sample?Census or sample?
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Why sample instead of taking a census? 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 populationTarget 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 populationsExamples 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 frameSampling 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 framesSampling 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 samplesSample designs for probability samples
• Simple random samples• Systematic samples• Stratified samples• Cluster • Multi-stage
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Simple random samplingSimple 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 samplingSystematic 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: ProportionateStratified 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: DisproportionateStratified 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 samplingCluster 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: SRSDetermining 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 issuesSample 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 Changes in Field of Survey ResearchResearch
From Random Digit Dial to Address Based Sampling
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Cell PhonesCell 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, contCell 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 SamplingAddress 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 Address Based Sampling AdvantagesAdvantages
• 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 block-listing
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Address Based Sampling Address Based Sampling DisadvantagesDisadvantages
• Incomplete in rural areas (although improving with 9-1-1 address conversion)
• Difficulties with “multidrop” addresses