1. Chapter 7 The Logic Of Sampling

4. 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 (sample). Generalizing from a sample to a larger population is called probability sampling and involves random selection.

5. Types of Sampling Purposive or judgmental sampling Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. Used when field researchers are interested in studying cases that don’t fit into regular patterns.

6. Types of 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.

7. Probability Sampling Precise statistical descriptions of large populations. A sample of individuals from a population must contain the same variations that exist in the population. Representativeness: Quality of a sample having the same distribution of characteristics as the population from which it was selected

9. EPSEM Equal probability of selection method. A sample design in which each member of a population has the same chance of being selected into the sample. Is that possible? Telephone Face-to-face

10. Parameter Summary description of a given variable in a population.

17. Sampling Error The degree of error to be expected of a given sample design.

18. Confidence Level The estimated probability that a population parameter lies within a given confidence interval. Thus, we might be 95% confident that between 35 and 45% of all voters favor Candidate A. Confidence interval - The range of values within which a population parameter is estimated to lie.

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

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

21. 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.

24. Cluster Sampling A multistage sampling in which natural groups are sampled initially with the members of each selected group being subsampled afterward.

25. Weighting Giving some cases more weight than others. Assigning different weights to cases that were selected into a sample with different probabilities of selection. In the simplest scenario, each case is given a weight equal to the inverse of its probability o selection. When all cases have the same chance of selection, no weighting is necessary.