introduction to survey sampling

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INTRODUCTION INTRODUCTION TO TO

SURVEY SAMPLINGSURVEY SAMPLING

October 6, 2010

Linda OwensSurvey Research Laboratory

University of Illinois at Chicagowww.srl.uic.edu

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Survey Research Laboratory

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/Cook County/

Chicago• Age/Gender• Other variables

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Examples of target populationsExamples of target populations

• Population of adults (18+) in Cook County

• UIC faculty, staff, students• Youth age 5 to 18 in Cook 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 Phone numbers in specific area codes Maps of geographic areas

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Sampling framesSampling framesExample 1:• Population: Adults (18+) in Cook County• Possible Frame: list of phone numbers, list of

block maps, list of addressesExample 2:• Population: Females age 40–60 in Chicago• Possible Frame: list of phone numbers, list of

block mapsExample 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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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)• Poor

• RDD sample frames often do not 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• Cell phone surveys expensive and

have low rates of participation• Public Opinion Quarterly, 2007

Special Issue, Vol. 71, Num. 5

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Address Based SamplingAddress Based Sampling

• Subject of many papers at 2010 AAPOR

• 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

• Can be matched to name (85%) and listed telephone numbers (65%)

• Can be used for multiple modes of administration

• 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• Incomplete coverage for mail only or

telephone only administration• Best when used as part of multi-mode

administration

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Before taking questions…Before taking questions…

• Slides available at www.srl.uic.edu; click on “Seminar Series”

• Next seminar: Introduction to Web Surveys, Thursday, Oct. 14

• Evaluation

introduction to survey sampling

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