# Probability Sampling

## Abstract

A sampling method has four main elements. First, defining the population of interest (the target population). Second, constructing a list of the units of the target population (the sampling frame). Usually, the units of behavioral studies are humans. Third, sampling of units. A distinction is made between probability and nonprobability sampling. Fourth, obtaining participation of selected units. Incorrect definitions of the target population, incorrect lists of units, and nonparticipation of selected units are systematic errors that bias the study results. Procedures are described to increase the participation rate of selected persons. Probability sampling methods select units from the target population by a random procedure. Sample statistics (e.g., means, variances, and correlations) are computed to estimate corresponding population parameters. The estimation is affected by random errors, but is based on sound statistical theory. The precision of the estimates depends on the sample size and the sampling method. Methods to determine the sample size that is needed for a prespecified precision are discussed. A simple random sample is obtained by randomly selecting units without replacement from the target population. A stratified random sample is obtained by dividing the target population into subpopulations and randomly selecting units without replacement from each of the subpopulations. In practice, it is often more convenient and less expensive to select groups of units (clusters) instead of individual units. A cluster sample is obtained by randomly selecting clusters without replacement. A stratified random sample often increases the estimation precision compared to a simple random sample of the same size, whereas a cluster sample decreases the precision.

## Keywords

Cluster sample Intraclass correlation Missingness Post hoc stratification Proportional allocation Simple random sample Stratified random sample## References

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