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### Chapter 7

The Logic Of Sampling

Chapter Outline

- Introduction
- A Brief History of Sampling
- Nonprobability Sampling
- The Theory and Logic of Probability Sampling

Chapter Outline

- Populations and Sampling Frames
- Types of Sampling Designs
- Multistage Cluster Sampling
- Probability Sampling in Review

Political Polls and Survey Sampling

- In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating the votes of 100 million people. (Polling is amazingly consistent)
To gather this information, they interviewed fewer than 2,000 people.

Presidential Elections

- 1936 Dewey vs. Roosevelt—what happened? (Poor?)
- Gallup—used Quota Sampling—while previously effective—what happened?

NON-PROBABLILTY SAMPLING

- NON-PROBABILITY SAMPLING—
When samples are selected using pragmatic means to obtain people. We use these because they work—not because they are accurate—just useful.

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.

Types of Nonprobability Sampling

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

Types of Nonprobability Sampling

- Purposive or judgmental sampling
- Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. (Use judgement—which ones is most representative!)
- Used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors

Types of Nonprobability Sampling

- Snowball sampling
- Appropriate when members of a population are difficult to locate.
- Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.

Types of Nonprobability Sampling

- Quota sampling
- Begin with a matrix of the 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 population.
- Data should provide a representation of the total population.

Selecting Informants

- Informants—a member of the group who can talk directly about the group per se. (Well-versed member of group.)
- Informants usually are representative of the group.
- Respondents—respond about themselves

Probability Sampling

- Used when researchers want precise, statistical descriptions of large populations.
- A sample of individuals from a population must contain the same variations that exist in the population. (OVERALL GOAL—richest possible data/representativeness)

Probability Sampling

- Every member of the population—must have an equal chance of being selected.
- Bias in sampling means those selected are not representative of the characteristics of the population.

Variation

- See Chart—p.193—the population of 100 folks varies by race and gender, among other things unnamed.
- We are trying to explain this variety.
- Show standard deviation—how it measures variety.

Advantages of Probability Sampling

- Representativeness (more than quota)
- Allows us to estimate probability of sample error.

Population Definition

- Population—the theoretically specified aggregation of study elements.
- Study population—the aggregate of elements from which the sample is actually drawn. (Someone left off list)

Ten Cases

- Parameter—the summary description of a given variable. (μ=mean of population)
(σ=st.dev. of population)

Explain the Theoretical Sampling Distribution (see page 200)

The Normal Curve

- Has a mean of 0 and a standard deviation of 1.
- Unimodal
- Symmetrical
- Areas under curve (probability)
(STOP HERE FOR TODAY)

Populations and Sampling Frames

- Findings based on a sample represent the aggregation of elements that compose the sampling frame.
- Sampling frames do not always include all the elements their names imply. (PROBLEMS WITH LISTS)
- All elements must have equal representation in the frame.

Types of Sampling Designs

- Simple random sampling (SRS)
- Systematic sampling
- Stratified sampling
- Cluster sampling/Multi-stage cluster

Simple Random Sampling

- Feasible only with the simplest sampling frame.
- Not the most accurate method available.

Systematic Sampling

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

Stratified Sampling

- Rather than selecting sample for population at large, researcher draws from homogenous subsets of the population. (WHY—fear of sample bias)
- Results in a greater degree of representativeness by decreasing the probable sampling error.

Multistage Cluster Sampling

- Used when it's not possible or practical to create a list of all the elements that compose the target population.
- Involves repetition of two basic steps: listing and sampling.
- Highly efficient but less accurate.

Probability Proportionate to Size (PPS) Sampling

- Sophisticated form of cluster sampling.
- Used in many large scale survey sampling projects.

Probability Sampling

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

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