1. Chapter 7
The Logic Of Sampling

2. Chapter Outline A Brief History of Sampling Nonprobability Sampling
The Theory and Logic of Probability Sampling
Populations and Sampling Frames
Types of Sampling Designs
Multistage Cluster Sampling
Probability Sampling in Review

3. Political Polls and Survey Sampling
One of the most visible uses of survey sampling is political polling that is then tested by election results.
In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating the votes of 100 million people.
To gather this information, they interviewed fewer than 2,000 people.

4. Election Eve Polls - Voting for U.S.Presidential Candidates, 2000
Agency
Gore
Bush
Nader
Buchanan
11/6
IDB/CSM 47 49 4
CBS 48 1
CNN/USA Today] 46
Reuters/ MSNBC 5
Voter.com 45 51
11/7
Results 3

5. Observation and Sampling
Polls and other forms of social research, rest on observations.
The task of researchers is to select the key aspects to observe, or sampling.
Generalizing from a sample to a larger population is called probability sampling and involves random selection.

6. Nonprobability Sampling
1. Reliance on available subjects
Only justified if less risky sampling methods are not possible.
Researchers must exercise great caution in generalizing from their data when this method is used.

7. Nonprobability Sampling
2. Purposive or judgmental sampling
Selecting a sample on the basis of knowledge of a population, its elements, and the purpose of the study.
Often used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors

8. Nonprobability Sampling
3. Snowball sampling
Appropriate when members of a population are difficult to locate (homeless, migrant workers, undocumented immigrants).
Researcher collects data on members she can locate, then asks those individuals to help locate other members of that population.

9. Nonprobability Sampling
4. Quota sampling
Begins with a matrix of the target population.
Data is collected from people with the characteristics of a given cell.
Each group is assigned a weight appropriate to their portion of the total population.
When the elements are properly weighted, the data should provide a representation of the total population.

10. Probability Sampling
Used when researchers want precise, statistical descriptions of large populations.
In order to provide useful descriptions of the total population, a sample of individuals from a population must contain the same variations that exist in the population.

11. Populations and Sampling Frames
Findings based on a sample only represent the aggregation of elements that compose the sampling frame.
Sampling frames do not always include all the elements their names might imply.
All elements must have equal representation in the frame.

12. Types of Sampling Designs
Simple random sampling (SRS)
Systematic sampling
Stratified sampling

13. Simple Random Sampling
Feasible only with the simplest sampling frame.
Not the most accurate method available.

14. Systematic Sampling
Slightly more accurate than simple random sampling.
Arrangement of elements in the list can result in a biased sample.

15. Stratified Sampling
Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population.
Results in a greater degree of representativeness by decreasing the probable sampling error.

16. Multistage Cluster Sampling
Used when it's impossible or impractical to compile an exhaustive list of the elements composing the target population.
Involves repetition of two basic steps: listing and sampling.
Highly efficient but less accurate.

17. Probability Proportionate to Size (PPS) Sampling
Sophisticated form of cluster sampling.
Used in many large scale survey sampling projects.

18. Probability Sampling
Most effective method for selection of study elements.
Avoids researchers biases in element selection.
Permits estimates of sampling error.