Sampling is the process whereby some elements (individuals) in the population are selected for a research study. The population consists of all individuals with a particular characteristic that is of interest to the researchers. If data are obtained from all members of the population, then we have a census; if data are obtained from some members of the population, then we have a sample. With probability sampling, a researcher can specify the probability of an element’s (participant’s) being included in the sample. With non probability sampling, there is no way of estimating the probability of an element’s being included in a sample. Although often more difficult and expensive, probability sampling is a methodologically more precise method to obtain a sample that is representative of the population. With simple random sampling, each individual in the population has an equal chance of being selected for the sample. The four steps of simple random sampling are
sampling and sampling technique
(1) defining the population,
(2) constructing a list of all members,
(3) drawing the sample, and
(4) contacting the members of the sample. Stratified random sampling is a form of probability sampling in which individuals are randomly selected from specified subgroups (strata) of the population. This method can be used to increase the representativeness of the sample and/or to allow comparisons to be made among individuals in the different strata. Convenience sampling is quick and inexpensive because it involves selecting individuals who are readily available at the time of the study (such as introductory psychology students). The disadvantage is that convenience samples are generally less representative than random samples; therefore, results should be interpreted with caution. Quota sampling involves the selection of a certain percentage of individuals from specified subgroups of the population when the population is large and lists of members are not available. Many polling organizations use this technique. Appropriate sample size depends on various considerations, including population variability, statistical issues, economic factors, and availability of participants. In general, with larger samples you will have a smaller margin of error and you can detect smaller differences. The larger the variability of scores in the population, the larger the sample must be in order to be representative. Sampling error includes systematic error and random error.Systematic error occurs when the sample is not properly drawn (an error of the researcher). Random error is the degree to which the sample is not perfectly representative of the population. Even with the best sampling techniques, some degree of random error is expected. Increasingly, we are exposed to information based on sample data. Understanding the principles of sampling, particularly the limitations of various methods, should make us more critical consumers of such information.