1. Chapter 7 The Logic Of Sampling The 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

2. Two Types of Sampling Methods 1. Probability 2. Nonprobability

3. Four Types of Nonprobability Sampling Reliance on available subjects Purposive or judgmental sampling Snowball sampling Quota sampling

4. Advantages of Probability Sampling Provides precise statistical descriptions of large populations. Nonprobability sampling cannot guarantee that the sample observed is representative of the whole population.

5. 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.

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

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

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

9. 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.

10. 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.

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

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