The relevance and validity of data used as evidence depend strongly on the research method used by the research team. Both the research design and the sampling method have a significant influence on the applicability and reliability of the results. The following paper describes the most common research design related to the research question and outlines the implications of the convenience sampling technique.
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The most appropriate research design employed for the research on hospital readmissions is a quantitative randomized trial. Such research design has several advantages in terms of data collection and analysis that ensures the reliability and validity of the results. First, the issue of readmission has numerous clinical, social, and economic implications that necessitate evidence-based solutions in order to provide the required degree of efficiency (Fontanarosa and McNutt 398). Therefore, the qualitative approach, which traditionally focuses on the perceptions of the issue by the stakeholders, is insufficient for determining a workable solution. On the other hand, a quantitative approach allows the researchers to focus on patient outcomes and conclusively quantify the results (as opposed to obtaining a detailed insight of the issue), thus providing them with the evidence sufficient for designing an intervention (Lagoe et al. 622). Another important aspect of the quantitative approach is the possibility of processing large amounts of data without compromising the quality of the result.
Considering the widespread nature of the issue as well as the availability of the documentation on its progression, it would be reasonable to utilize the data in the most productive way (Leppin et al. 1097). Third, the randomization component of the identified design ensures the applicability of the results for a large population. The problem of hospital readmissions is relevant in numerous settings, which requires the results of the research to be applicable to environments with similar characteristics (Fong 139). From this perspective, randomization provides the possibility to ensure that the chosen sample is representative of the target population to the degree that allows applying the findings across the settings. It is also worth pointing out that the statistical analysis (a common data analysis tool in the randomized trials) offers several solutions for ensuring the validity of the findings, such as determining the statistical significance of each metric.
Another issue that needs to be acknowledged during the process of applying the research findings is the implications of specific sampling techniques. The popularity of the convenience sampling technique has led to its widespread adoption and use in the academic literature, mainly due to its relative ease and low resource constraints. However, the said ease also introduces the possibility of bias in the data gathering process (Etikan et al. 2). The easiest example is the data gathered from a specific organization where the combination of factors renders the results useless for the majority of the hospitals. The second issue that should be taken into account is the possibility of the sampling error as a result of the biased data gathering process (Landers and Behrend 147). Namely, it is possible to imagine a situation where the data gathered using a convenience sample produces inaccurate results when processed. Since the possibilities of spotting the inaccuracies beyond the analysis phase are limited, it is necessary to identify the source of the error early in the course of research or account for it in the criteria of a systematic review. Finally, it should be pointed out that the sample obtained via the method in question will likely not be representative of the entire population.
As can be seen from the information above, the randomized trial design provides relatively reliable data on hospital readmissions. However, its validity can be undermined by the use of convenience sample. Therefore, its implications should be acknowledged during the search for evidence relevant to the research question at hand.
Etikan, Ilker, et al. “Comparison of Convenience Sampling and Purposive Sampling.” American Journal of Theoretical and Applied Statistics, vol. 5, no. 1, 2016, pp. 1-4.
Fong, Zhi Ven, et al. “Understanding Hospital Readmissions after Pancreaticoduodenectomy: Can We Prevent Them?” Journal of Gastrointestinal Surgery, vol. 18, no. 1, 2014, pp. 137-145.
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Fontanarosa, Phil B., and Robert A. McNutt. “Revisiting Hospital Readmissions.” JAMA, vol. 309, no. 4, 2013, pp. 398-400.
Lagoe, Ronald, et al. “Quantitative Tools for Addressing Hospital Readmissions.” BMC Research Notes, vol. 5, no. 1, 2012, pp. 620-628.
Landers, Richard N., and Tara S. Behrend. “An Inconvenient Truth: Arbitrary Distinctions between Organizational, Mechanical Turk, and Other Convenience Samples.” Industrial and Organizational Psychology, vol. 8, no. 2, 2015, pp. 142-164.
Leppin, Aaron L., et al. “Preventing 30-day Hospital Readmissions: a Systematic Review and Meta-Analysis of Randomized Trials.” JAMA Internal Medicine, vol. 174, no. 7, 2014, pp. 1095-1107.