According to Bryman (2008, p. 25), a sample has advantages than a complete count in that;
Different sampling techniques exist and they are divided into two categories namely formal and informal (non probability sampling). The formal sampling techniques operate on a known probability of choosing and they include; simple random sampling, stratified sampling, cluster sampling, systematic sampling and multistage sampling. Simple random sampling is a technique where all the elements are said to have the same chance of being selected. The probability of selecting any elements in the population is equal (Green and Salkind 2008, p. 57). This technique is applied in cases where all the elements in that certain population have same traits (characteristics). Such populations are said to be homogenous.
Another formal sampling technique is stratified sampling and is applied in cases where the population is made up of elements of different traits. In this technique, the population is sub-divided into non-overlapping sub-groups called strata (each is a stratum) each made up of elements with the same traits. Once this has been done, a simple random sample is selected from each stratum and then combined for final analysis. This technique is advantageous in that it eliminates biasness which is present if simple random sampling is used and also leads to higher precision. Cluster sampling on the other hand is a probability sampling technique in which the population is divided into clusters and then the researcher selects randomly the clusters to be included in the final analysis. It is mostly used in cases where getting the entire population for the research study is impossible or where the study population is concentrated in regions e.g. schools, churches, counties etc. The only disadvantage of this technique is less precision than even simple random sampling and stratified sampling.
Systematic sampling is the selecting of sample