INTRODUCTION TO SURVEY SAMPLING

October 8, 2014

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/Fall14Seminars.htm

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

2

Outline

� Introduction

� Target Populations

� Sample Frames

� Sample Designs

� Determining Sample Sizes

� Modes of Data Collection

� Questions

3

Introduction

Census:

� Gathering information about every individual in a population

Sample:

� Selection of a small subset of a population

4

Why sample instead of taking a census?

� Less expensive

� Less time-consuming

� More accurate

� Samples can lead to statistical inference about the entire population

5

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

6

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

7

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

8

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

� landline phone numbers in specific area codes

� blocks or census tracts in specified geographic areas

� members of professional organization

� schools

� cell phone numbers

9

Target populations, sample frames, and

coverage

Example 1:

� Population:Adults in Champaign County, IL

� Frames: List of landline numbers, 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?

10

Sample designs for probability samples

� Simple random samples

� Systematic samples

� Stratified samples

� Cluster

� Multi-stage

11

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

12

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.

14

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

15

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

16

Determining sample size: SRS

� Need to consider

� Precision

� Variation in subject of interest

� Formula � Sample size no = CI

2 * (pq)Precision

� For example: no = 1.962 * (.5 * .5)

.052

� Sample size not dependent on population size.

17

Sample size: Other issues

� Finite Population Correction

n = no/(1 + no/N)

� Design effects

� Analysis of subgroups

� Increase size to accommodate nonresponse

� Cost

18

Modes of data collection

� Face to face

� Phone

� Web

� Mail

19

Target population/frame/mode

correspondence

� Mode needs to be consistent with information in sample frame

� Mode needs to be consistent with target population

20

Cell phone and landline frames

� Increasing proportion of US households are cell phone only

� Cell phone only households tend to be• Unrelated adults

• Hispanic adults

• Younger

• Lower SES

� Landline sample frames can lead to bias

21

Cell phone and landline frames, cont.

� Cell phone frames harder to target geographically than landline frames

� Survey researchers are combining landline and cell phone frames

22

Address-based sampling

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

� Post Office Delivery Sequence Files (DSF)

23

Address-based sampling: advantages

� Coverage of households is very high

� Can be matched to name and listed telephone numbers

� Includes non-telephone households

� More efficient than traditional block-listing

24

Address-based sampling: disadvantages

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

� Difficulties with “multidrop” addresses

25

Thank you!

Future noontime webinars

�Introduction to Web Surveys, Wednesday, October 15

�Introduction to Questionnaire Design, Wednesday, October 22

�Introduction to Survey Data Analysis: Addressing Survey Design and Data Quality, Wednesday, October 29

26

Evaluation

27

Questions

28