Generalizability and the Sampling Theory (Essay Sample)

Generalizability and the Sampling Theory
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Generalizability and the Sampling Theory
Sampling theory is a field of statistics that involves collecting, analyzing, and explaining data collected from random samples of the population being studied. The application of this statistical analysis method involves the appropriate selection of observations from the chosen universe or population that constitutes the random sample. It also involves using probability theory to analyze data from random samples and make conclusions based on the analysis. When implementing the probability theory, four different probabilistic ways can be used: stratified, cluster, systematic random, and simple random sampling.
For simple random sampling, all cases in the chosen universe/population are numbered. Once numbered, a random number generator selects different cases, giving each case a fair chance of being picked (Wu, 2021). For instance, in a population of 100 cows, all the cows can be numbered, beginning with 1 to 100. After that, 20 different numbers can be picked through the random number generator. In this case, all the 100 cows are given a fair chance of being selected, but only 20 can be picked randomly and have different numbers ranging from 1 to 100.
On the other hand, the systematic random sampling procedure involves allocating specific numbers to each case in the selected population. However, in this case, sample members from a bigger population are chosen based on a random starting point (Wu, 2021). This starting point is called the random start and has a set, periodic interval called the random sampling interval. The sampling interval is easily obtained by dividing the whole population size by the specified sample size. For example, from a population of 5000 people, a random group of 100 people can be selected by listing all potential participants and a starting point, or random start, selected. Here, every 50th person on the list starting from the random start is chosen since 5000 divided by 100 gives 50.
For the stratified sampling method, different random samples are chosen from various groups of the entire population. A random sample can be selected from single cases, while another random sample can be selected from the whole population. For cluster sampling analysis, simple random samples are selected from specific spatial areas of the main population (Wu, 2021). Thus, the researcher can randomly pick specific areas of the selected site and collect data from every case in all areas.
Generalizability and How It Can Be Applied in Health Research
Probability sample methods are used to generate resear