INTRODUCTION TO SURVEY SAMPLING

October 5, 2016

Karen Foote Retzer

www.srl.uic.edu

General information

� Please hold questions until the end of the presentation

� Slides available at www.srl.uic.edu/SEMINARS/semnotes.htm

� Please raise your hand so that I can see that you can hear me

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Outline

� Introduction

� Target Populations

� Sample Frames

� Sample Designs

� Determining Sample Sizes

� Modes of Data Collection

� Questions

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Introduction

Census:

� Gathering information about every individual in a population

Sample:

� Selection of a 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 vs. non-probability

� 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 in Champaign County

� Faculty, staff, or students at the University of Illinois

� 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

� blocks or census tracts in specified geographic areas

� members of professional organization

� schools

� cell phone numbers

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Target populations, sample frames, and

coverage

Example 1:

� Population:Adults in Champaign County, IL

� Frames: List of census blocks, list of addresses

Example 2:

� Population: Youth age 5 to 18 in Cook County

� Frame: List of schools

Example 3:

� Population: Adults age 18-34 in United States

� Frame: ??

Coverage: What part of the target population is not included in these sample frames?

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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 periodicity13

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 male & female engineering students

� Compare seniors & freshmen who live in dorms

� Probability of selection needs to be higher for smaller stratum (female engineering students & 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

� Students in schools

� Households on 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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Modes of data collection

� Face to face

� Phone

� Web

� Mail

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Target population/frame/mode

correspondence

� Mode needs to be consistent with information in sample frame

� Mode needs to be consistent with target population

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Cell phone and landline frames

� Decreasing proportion of households with landline

� Increasing proportion of US households are cell phone only

� Dual cell and landline

� Cell-only frames

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Cell phone and landline frames, cont.

� Landline frames can be targeted geographically

� Cell phone frames harder to target geographically

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Address-based sampling

� Sampling addresses from a near universal listing of residential mail delivery locations

� Post Office Delivery Sequence Files (DSF)

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Address-based sampling: advantages

� Coverage of households is very high

� Can be matched to name and listed landline telephone numbers

� Includes non-telephone households

� Helps to identify households for face-to-face designs

� Can get very specific in terms of geographic

coverage

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Address-based sampling: disadvantages

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

� Excludes those who do not have addresses

� Difficulties with “multidrop” addresses

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Thank you!

Future noontime webinars

� Advanced Web Surveys, Wednesday, October 12

� Conducting Focus Groups, Wednesday, October 19

� Preventing and Detecting Interviewer Falsification, Wednesday, October 26

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Evaluation

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Questions

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