Categories of Sampling Techniques n Statistical ( Probability ) Sampling: –Simple Random Sampling –Stratified Random Sampling –Cluster Random Sampling.

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Categories of Sampling Techniques n Statistical ( Probability ) Sampling: –Simple Random Sampling –Stratified Random Sampling –Cluster Random Sampling –Systematic Sampling n Non-Statistical (Non-Probability) Sampling –Judgment Sampling –Convenience Sampling

Simple Random Sampling Simple Random Sampling is a method of selecting n units out of a population of N such that every one of the N C n distinct samples has an equal chance of being drawn. P(any sample of n from a population of N) is equal to the reciprocal of N C n.

Stratified Random Samples In stratified sampling, the population of N units is first divided into sub-populations of N 1, N 2, … N L units, respectively. These subpopulations (strata) are non-overlapping, and together comprise the whole of the population, so that N 1 +N 2 +…+N L = N When the strata have been determined, a sample is drawn from each stratum. The sample sizes within the strata are denoted by n 1, n 2, … n L

Cluster Sampling Cluster sampling is a method by which the population is divided into groups, or clusters, and a sample of clusters is taken to represent the population. Clusters should be representative of the entire population. The objective is to form groups of clusters that are small images of the target population.

Systematic Sampling For systematic sampling, elements of the population are categorized in some way (such as alphabetically or numerically) and a random starting point is selected. Then every N th item of the categorized population is included until the sample size n is satisfied.