Statistical Analyses in Nursing

 Statistical Analyses in Nursing

Despite providing results on the clinical decision and high-risk drug dispensing techniques, certain strengths and weakness characterized the studies. The first article used conjoint analysis techniques to design a workable mathematics model required for clinical decision-making process for nurses in the emergency department (Fisher, Orkin & Frazer, 2010). However, the technique involving proxy decision-making for this study is complex considering the premise that it does not uniformly address the responses of all nurses. As such, the study could be subject to speculation hence casting doubt on the accuracy of information obtained from the first study. In the article by Tjia et al. (2010), the selected study design captured a multispecialty population and therefore provided a reflection of clinical practice in the United States of America. However, utilization of the Likert-type scale could subject the study outcomes to errors due to a lack of consensus on the questions administered to participants. Considerably, findings and recommendations in the work of Fisher, Orkin and Frazer (2010) provide the need for aligning clinical decisions as per the patients in the emergency department for purposes of improving the quality of care. Correspondingly, the other article offers guidelines for safe administration of high-risk medications to establish an evidence-based practice in a healthcare setting.

In the entire coursework, the present author discovers nonparametric tests as commonly applied to the processes of analyzing data. Specifically, chi-square dominates most of the literature review in clinical research. Evidently, the adoption of this test has demonstrated effectiveness in the analysis of nominal data. Furthermore, the technique has a high level of accuracy since it has received comparison with observed frequencies obtained from null hypotheses. Nevertheless,  the adoption of other nonparametric tests such as the Wilcoxon matched-pairs test, Mann-Whitney U and Kruskal-Wallis tests does not readily occur since they measure rank-ordered data. According to Gibbons and Chakraborti (2011), the application of the above-mentioned non-parametric tests in multifarious clinical studies does not normally occur since outliers have the capacity to obscure the outcomes. Moreover, the outliers have minimal impact on the chi-square tests.

Reference

Fisher, K., Orkin, F., & Frazer, C. (2010). Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study. Applied Nursing Research, 23(1), 30-35.

Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. In International encyclopedia of statistical science (pp. 977-979). Springer, Berlin, Heidelberg.

Tjia, J., Field, T. S., Garber, L. D., Donovan, J. L., Kanaan, A. O., Raebel, M. A., … &