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When conducting research, it is necessary that the researcher not only know how to find the sources needed to answer the question that they have created but also how to analyze that information to understand which research design was used. Doing so will allow the researcher to provide the evidence needed to support or reject the question being asked. Quantitative research is the investigation of phenomena that lends themselves to precise measurement and quantification, often involving a controlled design (Polit & Beck, 2017). This discussion will look at two different quantitative studies and the qualities that make them so. 

Sleep Apnea Study Number One

 This study by Boulos et al.(2017) looks at the effectiveness of using home sleep apnea testing (HSAT) as a means of detecting obstructive sleep apnea (OSA)  in stroke or transient ischemic attack (TIA) inpatients and outpatients. OSA can negatively impact poststroke functional recovery and by using HSAT these patients can be screened and diagnosed for OSA sooner and improve their poststroke functional and motor recovery (Boulos et al., 2017).

The question being asked is therapeutic in nature. The design of the study is listed under the methods section as a single-center prospective observational study. An observational study means that the researchers do not intervene by manipulating the independent variable  (Polit & Beck, 2017). The independent variable within this study would be that all participants have had a stroke or TIA. Prospective designs are studies that begin with a presumed cause and look forward in time for its effect (Polit & Beck, 2017). Within this study, OSA was the presumed cause in a delay of functional and motor recovery for those patients who suffered a stroke or TIA. This design method was appropriate for the group being used. A control group would not have helped to validate the use of HSAT in stroke recovery since those within that group would not be suffering from the same effects. The use of t-tests, Wilcoxon rank sum-test, and multivariate logistic regression were used to analyze the data (Boulos et al., 2017). The results demonstrated that the use of HSAT in the poststroke or TIA population was effective at expediting the diagnosis and treatment of OSA (Boulos et al., 2017). 

Sleep Apnea Study Number Two

The second study is similar to the first in that it evaluated patients with acute ischemic stroke for the prevalence of sleep apnea and compared the functional outcomes of patients with and without sleep apnea at the 3rd month after an acute ischemic stroke (Nair et al., 2019). The type of question being asked is an etiology in that it looks to see if OSA is a risk factor for stroke. The design of the study is under the methodology section and is listed as a prospective observational study. This type of study is also known as a cohort design and as stated by Polit & Beck (2017), it is the strongest design for etiology questions when randomization is impossible. This method study is appropriate in that no manipulation was done to the independent variable (stroke). Randomization would not be appropriate for this particular study as the only treatment option would be the use of Continuous Positive Airway Pressure (CPAP) and the use of such treatment was not evaluated.

Questionnaires such as the sleep disordered Questionnaire, Berlin Questionnaire, and Epworth sleepiness scale were used to diagnose sleep apnea in the patients being evaluated and the results divided the group into those who had sleep apnea and those who did not. The two groups were then compared using Barthel scores at baseline and at 3 months. Using repeated measure of ANOVA, the results showed a significant difference with an improved functional gain in patients in the no sleep apnea group demonstrating that sleep apnea is associated with an increased risk of ischemic stroke and poor functional outcome (Nair et al., 2019).  

Conclusion

  For researchers, understanding which design method to use when creating a research study can be a great asset to promoting the change that they wish to bring about. Utilizing the wrong research design may diminish the quality of the results and may create doubt about the study overall. Understanding that quantitative studies aim to explain cause-and-effect relationships will help to guide the researcher to design their research to demonstrate causality and bring attention to the problem that they are determined to address (Polit & Beck, 2017).

 

References

Boulos, M. I., Elias, S., Wan, A., Im, J., Frankul, F., Atalla, M., … Murray, B. J. (2017). 

Unattended Hospital and Home Sleep Apnea Testing Following Cerebrovascular Events. Journal of Stroke & Cerebrovascular Diseases, 26(1), 143–149. https://doi-org.ezp.waldenulibrary.org/10.1016/j.jstrokecerebrovasdis.2016.09.001

Nair, R., Radhakrishnan, K., Chatterjee, A., Gorthi, S. P., & Prabhu, V. A. (2019). Sleep 

Apnea-Predictor of Functional Outcome in Acute Ischemic Stroke. Journal of Stroke & Cerebrovascular Diseases, 28(3), 807–814. https://doi-org.ezp.waldenulibrary.org/10.1016/j.jstrokecerebrovasdis.2018.11.030

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for 

nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.

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