Hospital-acquired pressure ulcers (HAPUs) affect many individuals, meaning that it is not a surprise that many scholarly articles address this topic to identify the practical ways of how to manage and prevent the problem. Researchers apply various methods and designs to investigate the issue comprehensively, which results in the fact that they have different data analysis approaches. Thus, the given paper will comment on how data analysis is performed in quantitative, qualitative, and mixed-method articles related to the selected topic.
Types and Applicability of Statistical Tests
Since quantitative research relies on numerical data, such articles should draw specific attention to the use of statistical tests. A randomized controlled trial by Pickham et al. (2016) offers a wide variety of tests. The study is “a single-site, open-label, two-arm” trial with 1,812 individuals to determine the effectiveness of 2-hourly turning (Pickham et al., 2016, p. 1). For example, a t-test is used to identify the differences in turning compliance between control and treatment groups. This statistical value can show whether the difference between two or more means is significant. A chi-square test is useful to analyze the participants’ pressure ulcer rates. This test can show whether there is a related variable that determines the presence of HAPUs. Finally, Pickham et al. (2016) use a Kruskal-Wallis rank test to identify a connection between HAPUs and being admitted by medical or surgical service. These statistical methods denote that the researchers invest much effort in dealing with the numerical data.
When it comes to qualitative research, such articles do not draw much attention to statistical data. A scoping review by Jocelyn Chew et al. (2017) is a suitable example of this claim because the authors wanted to find current evidence on turning frequencies’ effectiveness. Even though the research piece focuses on statistical data from ten articles, the authors did not take any action to apply statistical tests. The studies’ conclusions manifest themselves in the summary of the chosen works to demonstrate that eight articles failed to identify that some turning frequencies were more effective than others.
Furthermore, it is worth mentioning that mixed-method studies have the features of both qualitative and quantitative research. Thus, Martin et al. (2017) conducted a research piece that included a repeat observational study, pre- and post-test analysis, and qualitative interviews to identify how medical professionals evaluate regular turning. That is why the researchers used chi-square analysis to compare pre-test and post-test HAPU incidence. Thus, the given test was chosen because it is suitable to identify a significant correlation between the proposed intervention and HAPU incidence. Regarding the healthcare professionals’ thoughts, focus group and individual interviews were performed to identify significant themes.
Differences between Parametric and Nonparametric Tests
When it comes to statistical tests, it is worth mentioning that they can be parametric and nonparametric. The difference between the two groups refers to the fact that parametric tests rely on distributions in data, while nonparametric ones do not focus on distributions. It is so because a parametric approach considers the mean values and “makes assumptions about the parameters of the population distribution” (Rana et al., 2016, p. 95). Simultaneously, also known as distribution-free tests, nonparametric ones are applied to nominal data or when necessary to identify ratios for the data that do not have a normal distribution (Rana et al., 2016). Thus, it is possible to conclude that parametric tests are applied to generate assumptions about the population distribution when the population variances are equal, while a nonparametric approach does not focus on any assumptions.
As for the selected articles, they have presented both parametric and nonparametric tests. Regarding the article by Martin et al. (2017), it used a nonparametric chi-square test because the authors did not want to make assumptions regarding the general population. In addition to chi-square and Kruskal-Wallis tests, Pickham et al. (2016) used parametric statistics, including a t-test. This fact denotes that the researchers dealt with the data with normal distribution and could generate assumptions regarding the major population.
Reliability and Validity
Reliability and validity are of significance for every study because the two show whether it is possible to trust the findings. Scholars can utilize different approaches to ensures that their articles are valid and reliable. Firstly, Pickham et al. (2016) achieve this goal by conducting a randomized controlled trial. It is so because this design has internal validity and reliability since the methods minimize the possibility that a chance can influence the results.
Secondly, a specific design is not the only option to improve study quality. For example, Jocelyn Chew et al. (2017) demonstrate that data collection approaches can also cope with the task. It refers to the fact that “to ensure reliability, three authors separately reviewed the literature” (p. 227). It means that the authors took some effort to make sure that the data were collected in an impartial and unbiased manner. It is one of the most suitable approaches to ensure the validity and reliability of scoping reviews.
Thirdly, it is also possible to rely on the reliability of specific research instruments. The study by Martin et al. (2017) relied on a particular knowledge assessment tool because “psychometric assessments deemed it as reliable and valid” (p. 475). In addition to that, the researchers used the modified Braden Scale that had high predictive validity and interrater reliability (Martin et al., 2017). These facts demonstrate that it is possible to use various means to make a study more valid and reliable.
Summary
In conclusion, I can admit that the chosen studies can be beneficial within the context of my practice for a few reasons. On the one hand, the articles have demonstrated that it is possible to use different designs and methodologies to investigate the same issue. Irrespective of the specific approach, however, it is essential to focus on statistical data because they are significant when it comes to generating conclusions. On the other hand, the studies have shown that it is necessary to draw attention to validity and reliability. Data collection approaches, design, and the impact of research instruments help cope with the task. Thus, I can say that the studies will make my practice more professionals and science-based.
References
Jocelyn Chew, H.-S., Thiara, E., Lopez, V., & Shorey, S. (2017). Turning frequency in adult bedridden patients to prevent hospital-acquired pressure ulcer: A scoping review. International Wound Journal, 15(2), 225-236. Web.
Martin, D., Albensi, L., Haute, S. V., Froese, M., Montgomery, M., Lam, M., Gierys, K., Lajeunesse, R., Guse, L., & Basova, N. (2017). Healthy skin wins: A glowing pressure ulcer prevention program that can guide evidence-based practice. Worldview on Evidence-Based Nursing, 14(6), 473-483. Web.
Pickham, D., Ballew, B., Ebong, K., Shinn, J., Lough, M. E., & Mayer, B. (2016). Evaluating optimal patient-turning procedures for reducing hospital-acquired pressure ulcers (LS-HAPU): Study protocol for a randomized controlled trial. Trials, 17(190), 1-8. Web.
Rana, R. K., Singhal, R., & Dua, P. (2016). Deciphering the dilemma of parametric and nonparametric tests. Journal of the Practice of Cardiovascular Sciences, 2(2), 95-98. Web.