With examples explain the following research techniques: simple random sampling, systematic sampling cluster sampling and snowball sampling.
Sampling is that part of statistical technique concerned with the selection of an unbiased or random subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for the purposes of making predictions based on statistical inference. In research methods, a number of these techniques are used for the sake of gathering data and these include: simple random sampling, systematic sampling, cluster sampling, convenience sampling and snowball sampling. This writing focuses on, using examples, explaining each of these mentioned techniques, but, as a point cutting across these different techniques, it can be noted that sampling is an important aspect of data collection and it is carried out so as to ensure that the cost is lower, data collection is faster, and since the data set is smaller it is possible to ensure homogeneity and to improve the accuracy and quality of the data.
Simple random sampling
Simple random sampling is a technique that gives all units in the population an equal opportunity of being selected by using a method that will select units completely at random (Krathwohl, 1998). Thus, everyone or object in the entire target population has an equal chance or probability of being selected. This minimises bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. Simple random sampling methods that are commonly used in evaluation for selecting units include using a basic lottery system or drawing numbers/names from a hat. Both methods are very effective if you have a small or moderate sized population. For example in the national lottery, if the...