1. What key terms are associated with sampling? The population are all the people that hold an interest to the study. The sample is a representative portion of people that hold an interest in the study. Perfect induction is being able to look at all the examples of the study possible to make a proper generalization. No error happens in this situation, but this situation is not practical. Imperfect induction is only being able to look at a sample, and this,of course, leaves room for error.

2. Why do we sample? Researchers sample because they cannot realistically reach everyone. Time and money are major limitations. But when sampling is done well, reaching everyone is not necessary.

3. What are the steps to sampling? First, the researcher identifies the target population. Second, the accessible population is determined. Third, a sample is selected. Fourth, the research is conducted. Fifth, the results are generalized back to the accessible population. Sixth, the results are inferred back to the target population.

4. What are the characteristics of sampling error? The sampling error is the difference between the sample and the population. Sampling error has been determined to follow the following patterns: (1) the average of all sampling errors is zero (if we could look at the whole population), (2) sampling error is an inverse function of the sample size (greater sample size means less error), (3) it is a direct function of variability, where variability involves number of choices (voting Democrat or Republican gives fewer choices than voting Democrat, Republican, or Independent), (4) sampling error is normally distributed around a mean of zero, and (5) the standard error of the mean can be calculated by dividing the standard deviation of the sample by the square root of the sample size.

5. What are the types of sampling? The two types of sampling are probability and nonprobability sampling.

Probability sampling is driven by some chance procedures. Chance (random) is how the researcher chooses the sample, and this is the best way. The types of probability sampling are: (1) simple sampling (means that everyone has a nonzero chance of being selected and that each person's selection is independent of the other selections), (2) stratified sampling (sampling size where the researcher recognizes the existence of subpopulations (male and female populations in a people population), (3) cluster sampling (randomly selecting intact groups such as dividing a city into 40 approximately equal groups and using 10 of them for the study to randomly select people from), and (4) systematic sampling means that the researcher selects by a formula, such as every 5th house or every 3rd person.

With nonprobability sampling, people are selected by design, not randomly. The quality of the sample is reduced to the researcher's judgment about their design. The reader can also invalidate the results if they disagree with the researcher's design. The types are: (1) accidental sampling (weakest approach) where sample is based on whoever's available, (2) purposive sampling where the population is chosen for a specific purpose, and (3) quota sampling (like stratified sampling) where the researcher chooses from a subpopulation rather than letting the choices be made randomly.

6. What differentiates each type of sampling from the others? The two types of sampling are probability and nonprobability sampling. Probability sampling is driven by some chance procedures. Chance (random) is how the researcher chooses the sample, and this is the best way. With nonprobability sampling, people are selected by design, not randomly. The quality of the sample is reduced to the researcher's judgment about their design. The reader can also invalidate the results if they disagree with the researcher's design.

7. How do we determine sample size? Sample size is usually determined by the statistics that are used and the larger the sample size the better.

				Tom of Bethany

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