Sampling and sampling design

This article gives an overview of the process and logic of sampling. The article begins by describing the basic building blocks of sampling theory. The most common sampling designs that can be used in social science research are discussed and is divided into two broad categories : Probability sampling which include simple random sampling, systematic sampling, stratified sampling, cluster sampling, multi-stage cluster sampling and probability proportionate to size (PPA) sampling, and Non-probability sampling which include accidental sampling, purposive sampling, quota sampling and referral sampling which can be divided into network and snowball sampling. The article also assesses various factors that determine the choice of a sample design, which include the stage of the research process, availability of resources and the data collection methods applied. It concludes with a discussion on selecting the right sample size.