Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented? The importance of Random sampling is that it gives a sense of equality. Each person has the same probability of being chosen as their neighbor. This sampling is trying to represent the whole population. Since it is unlikely that the research could get to everyone in the population the sampling must occurring in an accessible population, which is represented as the entire population. “Without random sampling strategies, the researcher, who has a vested interest in the study, will tend (consciously or unconsciously) to select subjects whose conditions or behaviors are consistent with the study hypotheses,” (Burns, N. & Grove, S. (2011). Through obtaining a random sampling “researchers leave the selection to chance, thereby increasing the validity of their studies,” (Burns, N. & Grove, S. (2011).

Reference:

Burns, N. & Grove, S. (2011). Understanding nursing research (5th ed.). Maryland Heights, MO: Elsevier Saunders.

Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate. Sampling is a sub collection of subjects in a population, for a specific study. There were five techniques discussed in the “visual learner: statistics” four were probability techniques and one was nonprobability. 1.Simple random sampling is a selection in which each test subject has the same probability of being chosen as the next. Such as choosing a randomly out of a hat names for a drawing.

Each are “randomly selected from a sampling frame,” (Burns, 2011). 2.Stratified sampling is used when “a researcher knows some of the variables in the population that are critical for achieving representativeness,” (Burns, 2011). Such as categorizing groups by characteristics they have then choosing a member from each of those groups to make another group. If you had COPD patients in one group, Asthma patients, ARDS patients, and patients with emphysema. Then selecting one member out of each of these groups and placing them into a team. The researcher can use a smaller sample size to achieve the same degree of representativeness,” (Burns, 2011). 3.Cluster sampling “a researcher develops a sampling frame that includes a list of all the states, cities, institutions, or organizations with which elements of the identified population can be linked,” (Burns, 2011).

Such as if we were to select a hospital and review the different units of where the respiratory therapists worked, NICU, PICU, ER, ICU, IMC, and then choosing one cluster such as ER. All of those subjects in ER will be used in the research. 4.Systematic sampling “is used when an ordered list of all members of the population is available,” (Burns, 2011) then choosing every Kth element. Such as choosing a starting point in a classroom and every 5th student will be chosen for the study. The formula is k = population size ÷ by the desired sample size.

“It provides a random but not equal chance for inclusion,” (Burns, 2011). 5.Convenience sampling is obtaining data which is easiest to the researcher. This nonprobability sampling is an “approach decreases a sample’s representativeness of a population,” (Burns, 2011). This sampling is used quite often in the health care such as, when reports are to be performed in how many patients contracted a hospital acquired infection in the month of March at all hospitals in the valley. By choosing which hospitals they want to sample and not all the hospitals will become inaccurate and bias information.

Reference:

Burns, N. & Grove, S. (2011). Understanding nursing research (5th ed.). Maryland Heights, MO: Elsevier Saunders.