Introduction
This paper aims at displaying the skills that a student has gained in carrying out research. The paper examines the applicability of sampling in real research settings. The paper begins by indicating the selected population and the formulated research question/hypothesis. The paper then illustrates how the sample was selected (exclusion and inclusion criteria). The paper further points out how the sample represents the population under study and why it is not generalizable to the target population. Finally, the paper indicates how to determine whether the sample was large enough.
Hypothesis
A majority of Nurse Practitioners (NP) in the U.S venture into private practice.
Null hypothesis
Nurse Practitioner Graduates in the U.S work in both private and public after graduation.
Study objectives
The sampling procedure aimed at finding out whether a majority of Nurse Practitioners in the U.S venture into private practice or not after their graduation. The study also anticipated finding out why Nurse Practitioners join private practice. In addition, the paper sought to find out why some Nurse Practitioners work in both the private and public sectors.
Inclusion Criteria
The paper focused on Nurse Practitioners in the U.S (includes all states). Reports indicate that the U.S may be having an approximated 167, 857 Nurse Practitioners (Statehealthfacts, 2012). However, the study focused only on the 230 Nurse Practitioners from 43 states listed on the health-grade (2012) website. In addition, the study included those Nurse Practitioners whose contacts are listed on the website.
Exclusion Criteria
The study excluded Nurse Practitioners who do not appear on the health-grade (2012) website. Nurse Practitioners whose contacts were not listed on the health-grade (2012) website were also excluded. The website has 230 Nurse Practitioners from 43 states. However, only 225 had their contacts listed. Therefore, the sample size was put at 225 Nurse Practitioners. A randomizer tool found on Research-Randomizer (2012) website was used to eliminate biases during the selection of participants from different states.
Is the sample a true representation of the population?
The sample does represent the population because only Nurse Practitioners form the U.S participated in the study. The target population was Nurse Practitioners in the U.S. In addition, the respondents came from 47 out of the 50 states of the U.S.
Generalization of the sample provided to the whole population
The study results deduced from the sample provided will not be generalizable to the whole population. The sample size (n= 225) is extremely small when compared to the entire Nurse Practitioner population in the U.S. The study focused on the Nurse Practitioners whose contacts appear on the website. Questionnaires were sent to the participants via mail or interviewed on the phone. A large number of potential participants were left out because of the unavailability of their contact information. This introduced a substantial degree of biases.
Ways of determining whether the sample is large enough
Sample size plays a key role in the determination of the accuracy of results. Results of a given study should be able to represent the views of the target population. The sample size determines a researchers’ confidence in the results of the study they intend to conduct. Obviously, for a researcher to attain high confidence level, the sample size should be large; the larger the sample, the higher the confidence level. Researchers may use different approaches to determine whether a given sample is large enough to provide study results that are generalizable to the target population. Consider a situation where a researcher anticipates a confidence level of 95%; this would mean the margin of error would be 0-5%. In other words, if the researcher surveyed the entire target population, there would a 5% difference in the study results of the sample and target populations. This is called the margin of error (M. O). The margin of error tends to be wide in case the size of the sample was unusually small. Sample that is large enough produces a low margin of error. Thus, the researcher gains confidence in the study results. A low margin of error indicates better accuracy. Consequently, a high margin error indicates poor accuracy and lowers the confidence of the researcher in the study results. Therefore, to determine whether the sample was large enough (low margin of error), then the researcher can use the following options (Stat Trek, 2012).
Option 1: Margin of Error = Critical Value x Standard Deviation of Statistic
Option 2: Margin of Error= Critical value x Standard Error of Statistic.
For a researcher to determine the standard deviation of a sample statistic, the value of two or more parameters of the population should be known. However, it is not possible to compute some values of the population thus, making it difficult to calculate the standard deviation of the statistic. Statisticians recommend the use of standard error in the place of standard deviation should the researcher fail to compute the standard deviation of the statistic. Thus, option two becomes crucial in the absence of the standard deviation of the statistic (Stat Trek, 2012).
References
Health- grade (2012). Web.
StatTrek (2012). Web.
StateHealthFacts (2012). Web.
Research-Randomizer (2012). Web.