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INTRODUCTION TO SURVEY SAMPLING

INTRODUCTION TO SURVEY SAMPLING. October 6, 2010 Linda Owens 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: Selection of a small subset of a population.

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INTRODUCTION TO SURVEY SAMPLING

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  1. INTRODUCTION TO SURVEY SAMPLING October 6, 2010 Linda Owens Survey Research Laboratory University of Illinois at Chicago www.srl.uic.edu

  2. Census or sample? • Census: • Gathering information about every individual in a population • Sample: • Selection of a small subset of a population

  3. Why sample instead of taking a census? • Less expensive • Less time-consuming • More accurate • Samples can lead to statistical inference about the entire population

  4. 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

  5. Target 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

  6. Examples of target populations • Population of adults (18+) in Cook County • UIC faculty, staff, students • Youth age 5 to 18 in Cook County

  7. Sampling 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

  8. Sampling frames • Example 1: • Population: Adults (18+) in Cook 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

  9. Sample designs for probability samples • Simple random samples • Systematic samples • Stratified samples • Cluster • Multi-stage

  10. 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

  11. 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

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. Sample size: Other issues • Finite Population Correction • n = no/(1 + no/N) • Design effects • Analysis of subgroups • Increase size to accommodate nonresponse • Cost

  17. Cell 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

  18. Cell 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

  19. Address 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)

  20. Address Based Sampling Advantages • 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

  21. Address Based Sampling Disadvantages • 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

  22. Before taking questions… • Slides available at www.srl.uic.edu; click on “Seminar Series” • Next seminar: Introduction to Web Surveys, Thursday, Oct. 14 • Evaluation

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