Statistical Analyses in Nursing
Clinical decision is of utmost importance in the provision of quality health outcomes. Contingent upon this premise, the two articles establish decision-making procedures and practice guidelines relevant for clinical practice. The first study assesses the feasibility of decision-making processes by nurses stationed at the emergency department of a care facility (Fisher, Orkin & Frazer, 2010). On the other hand, the work of Tjia et al. (2010) purposes to develop guidelines required to monitor the dispensation of high-risk medications while at the same time establish the prevalence of existing laboratory testing concerning these medications. In order to draw clinical evidence on a factor in decision making, the article by Fisher, Orkin and Frazer (2010) employed the usage of nonparametric tests comprising Fisher’s exact tests and chi-square. The study relied on conjoint analysis to reflect upon the decision-making patterns. The results of this study provided quality outcomes by demonstrating that nurses depended on the functional status of patients, future health status, and family input to undertake decisions on healthcare delivery for their clients. The article by Tjia et al. (2010) utilized t-test and Likert-type scale to formulate guidelines for the utilization of high-risk drugs and to monitor the frequency of dispensing them. The non-parametric test was instrumental in developing medication dispensing guidelines in terms of drug classes, the frequency of medication, monitoring and laboratory testing for efficacy.
According to numerous empirical studies, parametric parameters receive useful application in the testing of study group means. Nevertheless, the effectiveness of the methodology remains debatable within the context of the present articles. For instance, the use of t-test and ANOVA requires normal distribution of the applicable data regarding the research. Since data from the two articles were not distributed, it became paramount for the authors to consider skewing of non-normal distribution to produce the results (Gibbons & Chakraborti, 2011). Therefore, the approach remains embedded on assumptions and as such it has a high vulnerability to error. However, the assertion receives higher applicability in the second article. Nonetheless, the application of ANOVA and t-test requires studies that have a broad distribution of sample sizes, a threshold that neither of the two articles met.
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.
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., … & Gurwitz, J. H. (2010). Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting. The American journal of managed care, 16(7), 489-496.