Simple Random Sampling Technique
The simple random sampling method is regarded as a fundamental sampling process where there is the selection of a group of subjects called a sample used for the study derived from a larger group which is the population. Each person is chosen to base on the chance, and every member of the more critical team has the equal opportunity of being included in the sampling process as every sample of a particular size having the same chance of selection (Levy & Lemeshow, 2013). This strategy has been examined and grouped as the purest and the most advanced probability sampling method. Also, it is appropriate for a larger group of sampling size where every member of the population will have the equal chance of being chosen.
The type of analytical data procedures appropriate in the simple random sampling is through the use of lottery method where each member of the targeted population is given a unique number that is different from other numbers (Levy & Lemeshow, 2013). The numbers are then dipped in a bowl, or a box then mixed thoroughly then the researcher picks the number tags where the persons having the numbers are then selected by the researcher as subjects of the study and influential in answering the research questions. Consequently, the use of computers is another method that can be adequately applied in the creation of hypothesis where the computer will do a random sampling of a more significant population. The lottery method is appropriate in that it avoids biases in the selection process where every individual has the equal opportunity of being picked. The use of a computer is essential for a larger group of people thus saving time and cost.
As observed by Suresh, K. P., & Chandrashekara, S. (2012), research questions are fundamental in the simple random sampling techniques in that they focus the analytical data procedure. The used research question was: highlight some of the steps for acquiring a simple random sample for the outcomes of asthma in U.S general hospitals. The measurement was the results of asthma in public hospitals. Some of the steps used the computer technique due to the larger population where a list was created of all the asthma hospitals in U.S and a sample frame generated. Step two was to assign a sequential number to every asthma hospital such as (1, 2, 3…n) which acted as a sampling frame and later out figured the sample size. Lastly, the computer applied both the sample, sample frame and the sample size to generate random numbers.
Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: methods and applications. John Wiley & Sons.
Suresh, K. P., & Chandrashekara, S. (2012). Sample size estimation and power analysis for clinical research studies. Journal of human reproductive sciences, 5(1), 7.