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INTRODUCTION TO SURVEY SAMPLINGPowerPoint Presentation

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

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

Why sample instead of taking a census?

- Less expensive
- Less time-consuming
- More accurate
- Samples can lead to statistical inference about the entire population

- 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

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

- Population of adults (18+) in Champaign County
- UIUC faculty, staff, students
- Youth age 5 to 18 in Champaign County

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

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

- Simple random samples
- Systematic samples
- Stratified samples
- Cluster
- Multi-stage

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

- 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

- 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

- 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

- 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

- 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 no = CI2 * (pq)
- Sample size not dependent on population size.

Sample size: Other issues

- Finite Population Correction
n = no/(1 + no/N)

- Design effects
- Analysis of subgroups
- Increase size to accommodate nonresponse
- Cost

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 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

- Sampling addresses from a near universal listing of residential mail delivery locations (Michael Link)
- Post-office Delivery Sequence Files (DSF)

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

- Incomplete in rural areas (although improving with 9-1-1 address conversion)
- Difficulties with “multidrop” addresses

Before taking questions…

- Slides available at www.srl.uic.edu; click on “Seminar Series”
- Next seminar: Introduction to Web Surveys, Wednesday, March 2
- Evaluation

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