Systematic sampling is a probabilistic sampling technique that is a little bit similar to random sampling with a few modifications. In this sampling technique, the y=target population is selected at random, then intervals set. The breaks are set at an equal distance giving equal chances of each population being picked. The suspension is attained by dividing the population size by the sample size desired by the researcher. For example, a researcher that targets to investigate how single parenthood affects the parents’ mental health in Florida would like to use systematic sampling with a sample size of 60 participants.
The population will be divided into 20 groups which will be done after identifying the confidence interval. After dividing the population into 20 groups which are chosen are random. The researcher then picks three participants from each of these groups at random. After gathering at lucky three participants, they are brought together. The sum of the selected participants will then form the sample size. Thus, the techniques help the researcher give each member of the population an equal chance of participating in the study. Because of the advantages of using this technique, bias levels can be reduced to the lowest possible levels, decreasing the likelihood of receiving incorrect data. However, dividing the population, especially a large one, is next to impossible and comes with various inconveniences.
Snowball sampling is a technique that uses the already recruited participants to look for others. For example, when requiring a sample size of sixty participants, the researcher will look for five or ten participants. The five participants will then be asked to recruit others that they believe in the same category. Though cheap and effective in a large population, the technique comes with certain shortcomings. For example, there are high chances of the first recruits being biased in the recruiting process.Order Now