*DQ: Identify which statistical test you would use in conjunction with your selected research design from DQ 1 to evaluate the outcomes for your evidence-based project proposal and explain why you selected this test*

**NUR 590 Topic 4 DQ 2**

**NUR 590 Topic 4 DQ 2**

DQ Identify which statistical test you would use in conjunction with your selected research design from DQ 1 to evaluate the outcomes for your evidence-based project proposal and explain why you selected this test

Cognizant of the role that training plays when it comes to improving a nurse’s competencies in EBP and thus empowering them to contribute to the development of EBP, here are certain strategies that can be undertaken from both an organizational level, to the larger professional level. At the organizational level, the organization can organize for opportunities where their nurses can get trained on evidence based practice. On the greater professional levels, professional bodies such as the ANA and the ANCC have developed certification program for nurses. By including components of evidence based practice in the certification exams, this ensures that nurses will prepare and apprise themselves on EBP and thus, in order to earn the certification, they will have to be competent in EBP. Alternatively, the institutions can include a whole different certification for EBP, where nurses will specifically be trained on EBP, tested on the same and thus, their competency will be proven by their certification. This will ultimately improve their ability to participate in the development and implementation of EBP.

According to Parab & Bhalerao, “statistical tests are mathematical tools for analyzing quantitative data generated in a research study” (2010). There are a number or test that researchers can use which can also become overwhelming and cause confusion for the research, and that can lead to sabotaging and tainting their study. Selecting the statistical test helps the researcher understand what to look for in the study as well as help organize their data. Parab & Bhalerao (2010) stated that “Before selecting a statistical test, a researcher has to simply answer the following six questions, which will lead to correct choice of test:”.

- How many independent variables covary (vary in the same time period) with the dependent variable?
- At what level of measurement is the independent variable?
- What is the level of measurement of the dependent variable?
- Are the observations independent or dependent?
- Do the comparisons involve populations to populations, a sample to a population, or are two or more samples compared?

- Is the hypothesis being tested comparative or relationship?

Statistical testing is used explain the results of a study. The test that I would use would be the t-test. “A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features” (Investopedia, n.d.). I would used this test because of show the averages of nurses to patient ratios to help determine the correlation between low staffing and high staffing and whether each has a positive or negative effect on patient health outcomes.

Reference:

Investopedia. (n.d.). T-Test. Retrieved from https://www.investopedia.com/terms/t/t-test.asp

Parab, S. & Bhalerao, S. (2010). Choosing Statistical Test. International Journal of Ayurveda Research. 1(3): 187-191. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996580/

Statistical tests can be chosen based on independent factors and other project designs. My project seeks to identify if PPE education

increases informal (family) caregiver compliance to PPE usage. I’ve identified at least one confounding variable, which is the effects of PPE usage modeling by staff. Siebert et al. (2018) found staff modeling and teaching was a big part of compliance of visitors. However, there is a test which accounts for differences in participants.

I am choosing a mixed design ANOVA test due to the used of different participants in each group. A mixed design measures “change over time, differences between the groups, interaction of time and group effects” (Tappen, 2016). This will show the differences between groups (education versus no education) and within the groups themselves. It can be used to measure the change between before and after the intervention of education. It will help control for the different participants in each intervention group. It could measure the change from several different time points, as desired.

**Click here to ORDER an A++ paper from our MASTERS and DOCTORATE WRITERS: DQ: Identify which statistical test you would use in conjunction with your selected research design from DQ 1 to evaluate the outcomes for your evidence-based project proposal and explain why you selected this test**

**Click here to ORDER an A++ paper from our MASTERS and DOCTORATE WRITERS: DQ: Identify which statistical test you would use in conjunction with your selected research design from DQ 1 to evaluate the outcomes for your evidence-based project proposal and explain why you selected this test**

Seibert, G., Ewers, T., Barker, A. K., Slavick, A., Wright, M. O., Stevens, L., & Safdar, N. (2018). What do visitors know and how do they feel about contact precautions? *American Journal of Infection. *46(1): 115–117.

Tappen, R. (2016). Advanced Nursing Research. Jones & Bartlett.

It is important to choose the correct statistical test when conducting research, as research should maintain validity. To correctly perform the statistical analysis of quantitative data, two key points should be considered: One is to identify the type of experimental design correctly, and the other is to check whether data meets the preconditions of the parameter test (Liang & Wang, 2019. If these are not considered, it can cause misuse of data and can possibly conclude false conclusions. I believed that the Paired T- Test would best fit my project proposal. The Paired T-Test tests the difference between two variables with the same population. For example pre and post test scores. This would allow the comparison of performance before and after the completion of the organizational change implementation. Determining the amount of hands off time during cardiopulmonary resuscitation would be the first variable, while the data obtained for hands off time during CPR with implementation of continuous compressions during defibrillation would be the second variable. Comparing these two variables should produce the conclusion that continuous compressions during hands on defibrillation decreases hands off time during CPR and increase patient outcomes. Ultimately this test would determine the amount of “hands-off” during CPR comparing standard CPR and continuous compressions during defibrillation.

Liang, G., Fu, W., & Wang, K. (2019). Analysis of t-test misuses and SPSS operations in medical research papers. *Burns & trauma*, *7*.

Quantitative data come from measurements that yield data in numeric form ranging from binary to continuous numeric expressions(Polit, 2017).The statistical test that I would use is a paired T-test. This is a type of Parametric test which is applied when data is normally distributed not skewed (Najmi et al., 2021). The data should be normally distributed and quantitative. This is appropriate for my project which is a quantitative design. The paired T-test is used when one group serves as its own control group. It is used to compare the two means and is used in small samples(Najmi et al., 2021). My project is to reduce CAUTI rates by using patient/family engagement and empowerment. I would use a T-test to compare pre and post intervention CAUTI rates to determine if the intervention was successful in reducing incidence.

Najmi, A., Sadasivam, B., & Ray, A.(2021). How to choose and interpret a statistical test? An update for budding researchers. Journal of Family Medicine and Primary Care, 10(8), 2763-2767. https://doi-org.lopes.idm.oclc.org/10.4103/jfmpc.jfpc-433-21

Pilot, D.F.(2017). Data collection methods. J. Fitzpatrick(ed.), *Encyclopedia of nursing research* (4th ed.). Springer Publishing Company.

Using statistical data analysis can be used in many ways and be of benefit in not only the business world, but in the healthcare world as well, especially research. One would need to understand what statistical methods are and the principles behind them to be able to analyze or correctly interpret the data that comes from the results (Wienclaw, 2021). The statistical test that I would use in the evidence-based practice (EBP) project proposal to evaluate the outcomes would be that of regression analysis. This is considered a reliable method of being able to identify which variables have an impact on the outcome of the topic of interest. There are different types of regression analysis that can be used in which one type is linear regression.

Linear regression analysis would be the specific statistical test that I would use for my project. The reason why I selected this test was because I want to determine what impact my independent variables (education and no education) have on the outcome of my dependent variable (cervical cancer screening rates). Linear regression analysis statistical test is used to “estimate the association of greater than or equal to 1 independent (predictor) variables with a continuous dependent variable” (Schober & Vetter, 2021, p. 108). The kind of information that this test will provide about my outcomes on my project would be an estimate of the effect of my independent variables (education and no education) on my dependent variable (cervical cancer screening rates). This will help determine the impact of education versus no education on the increasing cervical cancer screening rates in my population of ethnic minority women. The use of linear regression analysis can also provide me with information on the interaction of my two independent variables and whether the effect of one depends on the value of the other (Schober & Vetter, 2021).

Reference:

Schober, P., & Vetter, T. R. (2021). Linear regression in medical research. *Anesthesia & Analgesia, 132*(1), 108-109. https://doi.org/10.1213/ANE.0000000000005206

Wienclaw, R. A. (2021). Statistics and data analysis. *Salem Press Encyclopedia. *https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ers&AN=89163982&site=eds-live&scope=site&custid=s8333196&groupid=main&profile=eds1

Predictive analytics moves beyond standard forecasting or estimating and is a form of data science (*What is predictive analytics? The PAW resource guide*, 2021). Prediction is at the center of big data, and the whole point of data is to learn from it to predict. Predictions drive and render organizational and operational decisions. Rather than solely providing insights, a predictive model generates a predictive score for each individual, which directly drives or informs decisions for that individual, e.g., whether to apply a specific medical treatment. Decision-makers can allocate budgets based on per-person predictions, assisting health leaders in the challenge of resource allocation (Giga, 2017). A predictive model supports early identification, allowing preventative interventions to begin earlier while possibly decreasing the need for invasive investigative procedures later in the continuum of care.

The mission of Predictive Analytics World (PAW) is to foster breakthroughs in the value-driven operationalization of established deep learning methods (*What is predictive analytics? The PAW resource guide*, 2021). Their mission aligns for this evidence-based practice (EBP) project, and to process the pilot implementation data before deciding whether the change is appropriate for adoption into practice and if the process should be hard-wired and integrated system-wide. Ultimately, producing better patient outcomes by helping to target and treat high-risk patients is the goal (Giga, 2017). Predictive analytics technology learns from the data to predict or infer an unknown, resulting in improved outcomes, lower costs, and higher patient satisfaction. The data will determine if the unknown, whether conducting routine sleep screening increases the discovery and treatment of obstructive sleep apnea (OSA). From this starting point, the data collection will build on evaluating the potential rewards against expenditures while providing high-value patient outcomes (Giga, 2017).

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In my first discussion post for this week, I identified that quantitative research designs would be better suited to my evidence-based project because of the need for subjective data as evidence to support my proposed intervention. As my project is centered around trends in patient vital signs, particularly blood pressure, in response to either intravenous fluid resuscitation or vasopressor administration, much of the data will be gathered from patient electronic medical records (Bertelsen et al., 2020). Knowing this, the independent variables that my project will examine are patient blood pressure and medication administration (either fluids or vasopressors); the patients’ need for flap revision surgery being the dependent variable. A chi-square test to determine whether each intervention correlates with increased rates of flap failure and need for revision surgery can be conducted. This would help determine whether negative outcomes from each intervention have any relation with each other, which might indicate complications other than hypotension necessitating flap revision arising in the studied population (Burkhard et al., 2021). In order to determine the overall effectiveness of the PICOT intervention, I would use a multiple linear regression test to examine the relationship between both groups of patients in the study, as this would allow the consideration of other factors that affect flap outcomes in correspondence with my studied intervention (Bertelsen et al., 2020).

Though, as statistical analysis is not my strong suit, I will be doing additional research to consider different methods of analyzing my data as well.

References

Bertelsen, C., Hur, K., Nurimba, M., Choi, J., Acevedo, J. R., Jackanich, A., Sinha, U. K., Kochhar, A., Kokot, N., & Swanson, M. (2020). Enhanced Recovery After Surgery-Based Perioperative Protocol for Head and Neck Free Flap Reconstruction. *OTO open*, 4(2), 2473974X20931037. https://doi-org.lopes.idm.oclc.org/10.1177/2473974X20931037

Burkhard, J. P., Pfister, J., Giger, R., Huber, M., Lädrach, C., Waser, M., Olariu, R., Engel, D., Löffel, L. M., Schaller, B., & Wuethrich, P. Y. (2021). Perioperative predictors of early surgical revision and flap-related complications after microvascular free tissue transfer in head and neck reconstructions: a retrospective observational series. *Clinical oral investigations*, *25*(9), 5541–5550. https://doi-org.lopes.idm.oclc.org/10.1007/s00784-021-03864-1

Melnyk, B. M., & Fineout-Overholt, E. (2019). *Evidence-based practice in nursing and healthcare: A guide to best practice* (4th ed.). Wolters Kluwer. ISBN-13: 9781496384539