Introduction
Current nursing and medical studies demonstrate researchers’ obvious reliance on statistical methods for conclusion formulation. Non-parametric statistical analysis methods are instrumental in processing and analyzing quantitative data sets with non-normal continuous data, whereas parametric tests are more accurate in terms of prediction and applicable to typical distribution patterns. This paper seeks to discuss the uses of non-parametric tests in the assigned articles and explore the issue of test selection with reference to mental health nursing research.
Article Analysis
In their research, Fisher et al. (2010) explore the applications of conjoint analysis to explaining the characteristics of nurses’ decision-making in emergency care settings. The article’s purpose and goals involve increasing the number of available analytical instruments by testing the feasibility of clinical simulation for the analysis of nursing decision-making processes taking place after the admission of patients with intellectual disabilities (ID). Fisher et al. (2010) use Fisher’s exact tests and Chi-square to establish links between nurses’ characteristics and decision-making patterns. The tests’ application results in the identification of no individual-level predictors of displaying certain patterns. The reasons for parametric methods’ inappropriateness may include the study’s orientation at generalization and small sample size (less than 30 participants). The work’s strengths are represented by having enough participants for a feasibility study and the ethical appropriateness of clinical simulation use, whereas weaknesses include the findings’ limited transferability, non-randomized sampling, and possible divergences from real-life clinical scenarios. Regarding the contributions to nursing EBP, it is reasonable to think that the study could inspire large-scale experiments, thus leading to the elimination of biases and ineffective steps in caring for emergency patients with ID.
The project by Tjia et al. (2010) pursues the purpose of establishing guidelines for the prevention of adverse drug-related events during high-risk medication use and determining the optimal frequency of laboratory testing. The use of Cuzick’s test for trends across ordered groups enables the team to test for the presence of links between the frequency of prescribing a certain drug and the completion of recommended drug testing procedures for this pharmaceutical product. Due to this non-parametric test, Tjia et al. (2010) reveal a significant positive relationship between the frequency of prescription and the likelihood of appropriate drug testing. Parametric tests would not be a preferable option due to trend identification as the key purpose of these research endeavors, datasets’ unknown variation, and the presence of ordinal data. The strong points are sample size (the resulting recommendations cover over 60 lab tests and more than 30 drug groups) and statistical procedures’ clarity. The limitations might include the results’ limited applicability to differing clinical settings and the inability to account for patients’ medication adherence. Their findings could support EBP and patient safety by promoting innovative drug use monitoring procedures.
Recurring Statistical Analysis Methods in the Previously Reviewed Literature
The previous work related to the literature review portion of the research proposal can shed light on the frequency of statistical methods’ use in the mental healthcare field and the studies of music therapy in schizophrenia. For a more informed answer based on multiple cases, it is reasonable to analyze two sources of level I evidence by Geretsegger et al. (2017) and Jia et al. (2020). From article summary tables and other single randomized experiments used in the previously submitted proposal, it can be assumed that the independent samples t-test, which is a popular parametric approach to analysis, finds the most extensive use in experimental studies peculiar to mental healthcare (Geretsegger et al., 2017; Jia et al., 2020). In contrast, well-known non-parametric statistical methods are not mentioned as frequently as the two-sample t-test.
Mental Health Nursing and Statistical Analysis Methods
The independent samples t-test is very popular in RCTs peculiar to the effects of music therapy (MT) interventions on the health of adult patients with schizophrenia. The reasons why other types of tests are less common might relate to the nature of experiments with MT. Although the positive effects of MT on diverse domains of mental health well-being are known, modern researchers continue to experiment with diverse MT program configurations and traditional music (Geretsegger et al., 2017; Jia et al., 2020). Every new MT program aimed at schizophrenic patients is basically regarded as a unique and relatively unexplored intervention with an unknown degree of effectiveness, and comparing each course of action with standard care for schizophrenia is essential to prove its potential practical value. Single RCTs typically test only one program, and the implementation of a two-group design with comparison (standard care) and intervention (routine care and MT programs) groups acts as the easiest way to demonstrate effectiveness. The t-test for two independent groups is the most suitable type of test for this research design, which explains its popularity compared to tests for three or more groups.
Conclusion
To sum up, non-parametric tests serve numerous purposes, including facilitating analytical activities in studies with small sample sizes and ordinal data. In research endeavors focused on mental health settings and adjunctive non-pharmaceutical treatments for schizophrenia, the two-sample t-test seems to be particularly useful. This tendency might stem from the peculiarities of the RCT design when it comes to studies that test new developments in MT interventions and their effectiveness compared to standard care.
References
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.
Geretsegger, M., Mössler, K. A., Bieleninik, Ł., Chen, X. J., Heldal, T. O., & Gold, C. (2017). Music therapy for people with schizophrenia and schizophrenia-like disorders. Cochrane Database of Systematic Reviews, (5), 1-85.
Jia, R., Liang, D., Yu, J., Lu, G., Wang, Z., Wu, Z., Huang, H., & Chen, C. (2020). The effectiveness of adjunct music therapy for patients with schizophrenia: A meta-analysis. Psychiatry Research, 293, 1-10.
Tjia, J., Field, T. S., Garber, L. D., Donovan, J. L., Kanaan, A. O., Raebel, M. A., Zhao, Y., Fuller, J. C., Gagne, S. J., Fischer, S. H., & 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.