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Comment 1 Random sampling usually provides a sample that is representative of a population because each member of the 2023 Assignment
Random sampling usually provides a sample that is representative of a population because each member of the population is selected independently and has an equal chance, or probability, of being included in the study. (Grove, 2015, p. 37). It is important to conduct random sampling because it allows the study to be presented in the most unbiased form of data collection. Choosing factors at random and by chance provides a general understanding for what statistic is being collected. It limits and prejudice from the collectors, whether it is intentional or unintentional selections for the study. On the contrary, there are complications that may arise in random sampling. When sampling, there must be multiple trials in order to fully grasp a complete understanding that the statistics are accurate. Collecting one sample is only a snap shot of what is occurring at that one particular time. Multiple data collection trials should take place to guarantee the accuracy based on reoccurrence and the mean and mode of the elements in question. With random sampling, your data lacks a baseline to compare information because elements are nonspecific. To prevent from having an unsteady baseline for random sampling, some limitations must be put into play to ensure accuracy of the statistic. For example if you wanted to conduct a study about how often people wash their hands through out the day, maybe conduct the study among a certain occupation or hobby instead of generalizing the population.
Random sampling (also known as probability sampling) is to extract samples with equal, unbiased approach to include in the study. The goal of this method is to have subset of participants or samples that will most represent the target population. This is one of the most common methods used in statistics in conducting a quantitative research to decrease sampling error. Before random selection, a sampling frame (or list of all elements in the population from which sample is drawn) is established (Grove & Cipher, 2017). With this, all participants have same probability of being chosen.
The limitations or issues in using random sampling are the time and costs required gather full list of the population. It requires great effort (man power) to contact possible participants. Another problem is the size of the sample; it might be too small to represent the target population. An alternative to random sampling is to utilize systematic sampling. Systematic sampling is a method where a participant is picked based on random starting point, every “kth” position or interval. k is obtained by dividing the potential participants by desired sample size (Grove & Cipher, 2017). For example, in a population, there are 2000 possible nurses (samples) who can participate in the survey and desired sample size is 200 nurses. So, 2000 ÷ 200 = 10, which means, the 10th nurse on the list will be the participant. With this method, researcher must ensure that the original list was pre-ordered to avoid influencing the result.
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