Topic 2: population and sampling distributions
The strategy used to select a sample from a population will depend on various factors, such as the research question, the nature of the population, available resources, and time constraints. Generally, there are two main approaches to sampling: probability sampling and non-probability sampling.
Probability sampling involves selecting a sample that is representative of the population and is based on the principles of random selection. There are several types of probability sampling, including simple random sampling, stratified random sampling, and cluster sampling. In simple random sampling, each member of the population has an equal chance of being selected for the sample. In stratified random sampling, the population is divided into strata or subgroups, and then a random sample is selected from each stratum. In cluster sampling, the population is divided into clusters or groups, and then a random sample of clusters is selected, and all members within the selected clusters are included in the sample.
Non-probability sampling, on the other hand, does not involve random selection and may not be representative of the population. Non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling. Convenience sampling involves selecting individuals who are most easily accessible. Purposive sampling involves selecting individuals based on specific criteria related to the research question. Snowball sampling involves recruiting participants through referrals from other participants.
Ultimately, the choice of sampling strategy will depend on the specific research question, resources available, and the nature of the population being studied. It’s essential to carefully consider the pros and cons of each sampling method before selecting one for a study to ensure that the resulting sample is appropriate and can provide accurate and reliable results.