Beyond the investigation of a research hypothesis, quantitative research projects often have an implicit goal of identifying knowledge from a sample which can be applied to an entire population. As a result, the sampling strategies employed are concerned with, and designed to allow for, generalization of the results to a wider population. These strategies rely upon random processes to increase such generalizability. Random here refers to the creation of conditions where each portion of a sample has an equal probably of being selected; hence the term ‘probability sampling’ (Neuman 2007, 148). Four types of probability sampling will be reviewed below, with the descriptions either excerpted from, or based upon, the work of Neuman (2007, 148-157).
Simple random sampling: After defining the population, and assigning a number to each individual, a researcher uses a random list of numbers to decide which individuals to include in the sample. Several software packages now have the ability to assist researchers in randomly selecting a sample.
Systematic sampling: After defining the population, and assigning a number to each individual, a researcher begins at a random starting point and selects individuals based on a predetermined interval (e.g. every fourth individual); similar to simple random sampling, but with an interval of selection (system) dictating selection of the sample. Not advisable when the population list is organized in a meaningful and/or purposeful way (i.e. spousal pairs), as the chosen interval may produce a skewed sample.
Stratified sampling: After defining the population, a researcher divides the population into two or more subpopulations, using one or more characteristics of the population as the basis for stratification. Once the population stratum(s) (subpopulation groups) have been created, the researcher then uses simple random sampling or systemic sampling to create the sample. Applicable when a population characteristic (i.e. sex, age) is thought to impact the phenomenon being studied. Assuming accurate knowledge of the population, stratums allow a researcher to ensure the sample mirrors the population on the basis of the characteristics chosen.
Cluster sampling: A method for drawing samples when one of two obstacles exists: (1) a good list of a dispersed population does not exist, and (2) the cost to reach individuals in a dispersed sample would be very high. Surveying college students will be used as an example. After defining the population as best they can, a researcher randomly selects a cluster of colleges from a list. Within this cluster of colleges, a researcher then randomly selects clusters of students and surveys them. Now, rather than potentially traveling to every college to survey one or two students, a researcher travels to a portion of colleges and surveys a larger, randomly selected sample of students at each.
Probability Sampling (Research Methods Knowledge Base)
Probability Sampling (Statistics Canada)