This chapter is from the book
This chapter is from the book
This chapter is from the book
1.4 Sampling Concepts
Sampling
CONCEPT The process by which members of a population are selected for a sample.
EXAMPLES Choosing every fifth voter who leaves a polling place to interview, drawing playing cards randomly from a deck, polling every tenth visitor who views a certain Web site today.
INTERPRETATION The method by which sampling occurs, the identification of all items in a population, and the techniques used to select individual observations all affect sampling.
Probability Sampling
CONCEPT A sampling process that takes into consideration the chance of occurrence of each item being selected. Probability sampling increases your chances that the sample will be representative of the population.
EXAMPLES The registered voters selected to participate in a recent survey concerning their intention to vote in the next election, the patients selected to fill out a patient-satisfaction questionnaire, 100 boxes of cereal selected from a factory's production line.
INTERPRETATION You should use probability sampling whenever possible, because only this type of sampling allows you to apply inferential statistical methods to the data you collect. In contrast, you should use nonprobability sampling, in which the chance of occurrence of each item being selected is not known, to obtain rough approximations of results at low cost or for small-scale, initial, or pilot studies that will later be followed up by a more rigorous analysis. Surveys and polls that invite the public to call in or answer questions on a Web page are examples of nonprobability sampling.
Simple Random Sampling
CONCEPT The probability sampling process in which every individual or item from a population has the same chance of selection as every other individual or item. Every possible sample of a certain size has the same chance of being selected as every other sample that has that size.
EXAMPLES Selecting a playing card from a shuffled deck, generating a number by throwing a pair of perfect dice, or using a statistical device such as a table of random numbers.
INTERPRETATION Simple random sampling forms the basis for other random sampling techniques. The word random in the phrase random sampling may confuse you if you think that random implies the unexpected or the unanticipated, as the word often does in everyday usage (as in random acts of kindness). However, in statistics, random implies no repeating patterns—that is, in a given sequence, a given pattern is equally likely (or unlikely) as another. From this sense of equal chance (and not unexpected or unanticipated) comes the term random sampling.
Frame
CONCEPT The list of all items in the population from which samples will be selected.
EXAMPLES Voter registration lists, municipal real estate records, customer or human resource databases, directories.
INTERPRETATION Frames influence the results of an analysis, and using two different frames can lead to different conclusions. You should always be careful to make sure your frame completely represents a population; otherwise any sample selected will be biased, and the results generated by analyses of that sample will be inaccurate.