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.

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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., … & 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.

Clinical decision is of utmost importance to provide quality health outcomes. Based on this premise, the two articles establish decision-making procedures and practice guidelines relevant for clinical practice as illustrated below.

This first study assesses the feasibility of decision-making process by the nurses stationed at the emergency department of a care facility for clients with intellectual disability (Fisher, Orkin & Frazer, 2010). In order to draw clinical evidence on a factor in decision making, this article used nonparametric tests comprising of Fisher’s exact tests and chi-square. The study relied upon conjoint analysis to reflect on the decision-making patterns among nurses in the emergency department. The results of this study provided quality outcomes by indicating that nurses depended on the functional status of patients, future health status and family input to make decisions on healthcare delivery for their clients. Even though parametric tests are utilized to test for study group means, their effectiveness remain debatable in this study. For instance, the use of t-test and ANOVA require that data for the research to undergo normal distribution. Since data from this article was not distributed, it meant that skewing of non-normal distribution was to be taken into account to yield the result (Gibbons & Chakraborti, 2011). Therefore, it implies that the approach is based on assumption and as such is subject to error when used for this article.

Despite providing results on the clinical decision, this study had strengths and weakness. The article used conjoint analysis techniques to design a workable mathematic 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. Considerably, findings and recommendations in the first study provide the need for aligning clinical decisions as per the patients in the emergency department to improve quality of care (Fisher, Orkin & Frazer, 2010).

The article purposes to develop guidelines required to monitor the dispensing of high-risk medications while at the same time establish the prevalence of laboratory testing of these medications. T-test and Likert-type scale were used to develop guidelines for the use of high-risk drugs and to monitor the frequency of dispensing (Tjia et al., 2010). 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 (Gibbons & Chakraborti, 2011). However, the use of ANOVA and t-test requires studies that have a broad distribution of sample sizes which was contrary in this article. Besides, t-test are used to analyze randomized trials hence not applicable for this second study.

In this reading, the study design selected captured a multispecialty population and therefore provided a reflection of clinical practice in the United States of America (Tjia et al., 2010). However, use of the Likert-type scale could subject the study outcomes to errors due to a lack of consensus on the questions administered to participants. Apparently, this article provide guidelines for safe administration of high-risk medications to establish an evidence-based practice in a healthcare setting.

Statistical Analysis Most Frequently used in Research Literature

In the entire coursework, I find nonparametric tests commonly used to analyze data. Specifically, chi-square dominates most of the literature review in clinical research. It is evident that the use of this test is effective in analyzing nominal data. Besides, the technique is accurate as it is usually compared with observed frequencies obtained from null hypothesis. Other nonparametric tests such as the Wilcoxon matched-pairs test, Mann-Whitney U and Kruskal-Wallis test are not readily used because they measure rank-ordered data which is not the case in many clinical research where outliers can complicate the outcomes (Gibbons & Chakraborti, 2011). Moreover, chi-square tests are not readily affected by outliers.

Reference

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