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 Census or sample?. Census: Gathering information about every individual in a population Sample:

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Introduction to survey sampling


February 23, 2011

Karen Foote Retzer

Survey Research Laboratory

University of Illinois at Chicago

Census or sample

Census or sample?


  • Gathering information about every individual in a population


  • Selection of a small subset of a population

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

Introduction to survey sampling

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

Target population

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

Examples of target populations

Examples of target populations

  • Population of adults (18+) in Champaign County

  • UIUC faculty, staff, students

  • Youth age 5 to 18 in Champaign County

Sampling frame

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

Sampling frames

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

Sample designs for probability samples

Sample designs for probability samples

  • Simple random samples

  • Systematic samples

  • Stratified samples

  • Cluster

  • Multi-stage

Simple random sampling

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

Systematic sampling

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

Stratified sampling proportionate

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.

Stratified sampling disproportionate

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

Cluster sampling

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

Determining sample size srs

Determining sample size: SRS

  • Need to consider

    • Precision

    • Variation in subject of interest

  • Formula

    • Sample size no = CI2 * (pq)


    • For example: no = 1.962 * (.5 * .5)


  • Sample size not dependent on population size.

Sample size other issues

Sample size: Other issues

  • Finite Population Correction

    n = no/(1 + no/N)

  • Design effects

  • Analysis of subgroups

  • Increase size to accommodate nonresponse

  • Cost

Changes in field of survey research

Changes in Field of Survey Research

From Random Digit Dial to

Address Based Sampling

Cell phones

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

Cell phones cont

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

Address based sampling

Address Based Sampling

  • Sampling addresses from a near universal listing of residential mail delivery locations (Michael Link)

  • Post-office Delivery Sequence Files (DSF)

Address based sampling advantages

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 block-listing

Address based sampling disadvantages

Address Based Sampling Disadvantages

  • Incomplete in rural areas (although improving with 9-1-1 address conversion)

  • Difficulties with “multidrop” addresses

Before taking questions

Before taking questions…

  • Slides available at; click on “Seminar Series”

  • Next seminar: Introduction to Web Surveys, Wednesday, March 2

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