Foundations of Psychological Testing : A Practical Approach (9781506396385) by Miller Leslie A.; Lovler Robert L. & Robert L. Lovler

Author:Miller, Leslie A.; Lovler, Robert L. & Robert L. Lovler
Language: eng
Format: epub
Publisher: Lightning Source Inc
Published: 2019-01-22T14:49:32.553425+00:00

Source: Fink, A. (2003). The survey handbook (2nd ed.). Thousand Oaks, CA: Sage. Copyright © 2003 by SAGE Publications. Reprinted by permission of SAGE Publications, Inc.

Probability sampling is a type of sampling that uses statistics to ensure that a sample is representative of a population. Simple random sampling, stratified random sampling, and cluster sampling are examples of probability sampling methods.

With simple random sampling, every member of a population has an equal chance of being chosen as a member of the sample. To select a random sample, many people use a table of random numbers. Using this technique, a researcher assigns consecutive numbers to each individual in the population. Then, using a table of random numbers (found in the appendices of many statistics books), the researcher reads the numbers in any direction. When he or she reaches a number that matches one of the assigned numbers, the individual corresponding to that number becomes a member of the sample. Of course, researchers could also write the names of individuals on pieces of paper, throw them into a hat, and select individuals to be included in their sample by randomly pulling pieces of paper out of the hat!

Because each member of a population has an equal chance of being selected, we often presume that a simple random sample will be representative of the characteristics of a population. Unfortunately, simple random sampling does not ensure that the sample will include adequate proportions of individuals with certain characteristics. For example, if a particular population is 75% female and 25% male, simple random sampling will not guarantee the same proportion of female to male respondents in the sample.

A variation of simple random sampling is systematic sampling, in which every nth (e.g., every fifth) person in a population is chosen as a member of the sample. To sample systematically, the researcher assigns consecutive numbers to each individual in the population and then selects every nth person to become a member of the sample. This technique has the same weakness as random sampling; it might not have the same proportion of individuals as the population, and if the list is arranged alphabetically, not everyone will have an equal chance of being chosen.

Unlike simple random sampling, with stratified random sampling, a population is divided into subgroups or strata (e.g., gender, age, socioeconomic status). A random sample is selected from each stratum. The strata should be based on some evidence that they are related to the issue or problem the survey addresses. For example, if you are interested in exploring how high school seniors feel about the value of the SAT for predicting college success, your population would include all high school seniors. You may wish to stratify your sample by gender because SAT score seems to be a better predictor for female students. This becomes especially important when one of the survey objectives is to make inferences about subgroups (e.g., male vs. female students) in the population. If there is not a sufficient number of respondents in some of

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