NURS 8201 Statistical Analysis in Nursing
NURS 8201 Statistical Analysis in Nursing
NURS 8201 Statistical Analysis in Nursing
Statistical Analysis in Nursing
Statistical analysis is the science of organizing, exploring, summarizing, and presenting large amounts of data to discover underlying patterns and trends. Data is best represented by analyzing it using appropriate and valid statistical tests to reveal the truth of the data. A procedure that sheds light on the hidden truth is known as statistical analysis. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into lifeless data. The results and inferences are precise only if proper statistical tests are used. There are three purposes for statistical analysis: to describe and summarize information, make predictions about occurrences, and identify associations, relationships, or differences between observed or measured variables (Rebekah & Ravindran, 2018). For this week’s discussion, I choose the article, Effects of a work-based critical reflection program for novice nurses. BMC Medical Education, 18(30), 1–6. doi:10.1186/s12909-018-1135-0 (Kim et al., 2018).
The Goals and Purpose of the Research Study
Nurses’ clinical judgments and decisions are required to contribute to the quality of healthcare systems because they are significant decision-makers within such systems. Because the primary goal of nursing education is to advance the practical application of theoretical knowledge, it is critical to provide learners with opportunities for experiential learning. In this regard, critical reflection allows students to develop self-awareness and doubt, gaining a comprehensive perspective on specific issues. In other words, critical reflection is an essential component of clinical nursing practice, and it has the potential to influence personal and professional development. So the goal and purpose of the study were to evaluate the effectiveness of a work-based critical reflection program to enhance novice nurses’ clinical critical thinking abilities, communication, competency, and job performance.
How are nonparametric tests used in the research study? What are the results of their use?
The study selected the nonparametric Mann-Whitney U test and Wilcoxon rank-sum test to evaluate differences in mean ranks and to assess the null hypothesis that the medians were equal across groups. The data from the demographic questionnaire were used to ensure homogeneity between the experimental and control groups in terms of their general characteristics. No statistically significant differences were found, indicating that the two groups were largely homogeneous.
Why are parametric methods (t-tests and ANOVA) inappropriate for the statistical analysis of the research study’s data? Be specific and provide examples.
According to the research study, the medians were equal across the groups; when comparing two independent samples when the outcomes are not normally distributed, and the samples are small, a nonparametric test is more appropriate than the parametric methods ( t-tests and ANOVA). In addition to this, the researchers also want to select the nonparametric test to evaluate differences in mean ranks and to assess the null hypothesis that the medians were equal across groups, so parametric analysis to test group means and nonparametric analysis to test group medians.
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What are the strengths and weaknesses of the research study (e.g., study design, sampling, and measurement)?
The researcher’s study topic is very relevant and important for today’s healthcare environment. The work-based critical reflection program positively affected clinical decisions through communication and clinical critical thinking ability, and it helped novice nurses adapt to their working environment with ease (Knight, 2015). The weakness of the study the sample size was limited to that required for statistical power.
How could the findings and recommendations of the research study contribute to evidence-based practice for nursing?
The purposes of nursing research conducting to generate new knowledge and evidence-based nursing practice (utilizing best evidence as to the basis of nursing practice) so the study finding help to evident that critical reflection program had a positive effect on clinical decisions through communication and clinical critical thinking ability and also help novice nurses to adapt to heir working environment with ease.
References
Kim, Y. H., Min, J., Kim, S. H., & Shin, S. (2018). Effects of a work-based critical reflection program for novice nurses. BMC Medical Education, 18(30), 1–6. doi:10.1186/s12909-018-1135-0
Knight s. (2015). Realizing the benefits of reflective practice. Nursing Times;111:23/2, 17-19
Rebekah G., & Ravindran V. (2018). Statistical Analysis in Nursing Research. Indian J Cont. Nsg Edn.Available from: https://www.ijcne.org/text.asp?2018/19/1/62/286497
The research statement that I chose the research topic discusses the factors that contribute to the prevention of evidence-based pressure ulcers. Evidence-based therapy for pressure ulcer management is not being implemented. Because the hospital structure is complicated, greater expertise is required to comprehend how to enhance nurse care in this field (Sving et al., 2014). In this research problem, the main focus would be looking at the relationships that would be between variables at various levels in the healthcare institutions environments that include the patients, hospitals, the units in the hospitals, and medical providers, in addition to the recordkeeping of threats analysis and evaluation of the skin within 24 hours of hospital admission by use of pressure-relieving mattresses and scheduled realigning in beds.
In researching the relationships between the use of different variables used in reducing pressure ulcers within the first day (24 hours) of admission, there are independent and dependent variables that will be used. The dependent variables in the research would include the recordkeeping of the threats analysis, skin evaluation in the first 24 hours of admission, the employment pressure-relieving mattresses, and scheduled realigning in beds (Sving et al., 2014). The dependent variables would be categorized as continuous data. The documentation of records and threats analysis is a constant measure that cannot be quantified but measured and recorded progressively.
The independent variables would include age, gender, the number of days in hospitalization, the threat score on the research day or rather data collection day, the type of healthcare, the staffing of nurses, and finally, the workload in the hospital. The independent variables would be categorized as ordinal data that I would employ the Braden scale or Likert scale to measure the scores (Robitzsch, 2020). The variables would be analyzed statistically by getting the standard deviation, interquartile range and testing the hypothetical level by determining the confidence interval (Taherdoost, 2016). The disadvantages of the analysis process are that the sampled participants represent the whole community, and testing small data would probably not give the truth of the prevention of pressure ulcers in healthcare institutions.
References
Robitzsch, A. (2020). Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.589965
Sving, E., Idvall, E., Högberg, H., &Gunningberg, L. (2014). Factors contributing to evidence-based pressure ulcer prevention. A cross-sectional study. International Journal of Nursing Studies, 51(5), 717–725. https://doi.org/10.1016/j.ijnurstu.2013.09.007
Taherdoost, H. (2016). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.3205035
I acknowledge your great efforts in describing statistical analysis on a research study that was conducted by Fisher et al., (2010) who published a descriptive and quantitative study that uses conjoint analysis and clinical simulations to understand the technique of decision making by ER nurses when they are dealing with patients with intellectual disabilities. Conjoint analysis is a statistical model which adopts various attributes that need participants to make trade-offs in decisions. A comprehensive evaluation of these tradeoffs discloses the important attributes in the study and predictive component which enables researchers to extrapolate future situations. In this research the variables entailed communication styles, variable strategies for patient care and ER environment. The conclusion of the study was that nurses tend to depend more on future health status, patient’s functional status and family inputs in making health care decisions. More so, the research used standard ethical research strategies that entailed confidentiality in results, randomized sampling and research methodologies that did not cause withholding care or placebo for any patient. In the research, I agree that parametric tests are irrelevant since there is no assumption on the distribution factors and the results might be regarded as skewed since the research used simulation that does not exhibit the actual clinical care with a patient.
Nonparametric tests adhere to researches where the assumptions used for stronger parametric tests are not met and where the sample sizes are small (Polit, 2010). The researchers used chi-square to make inferences on two variables in a category, contingency tables and exact tests done by Fisher, Orkin and Frazer (2010) which are nonparametric and appropriate for evaluation of small sample sizes. Therefore, there are various analytical methods and the choice of the best fit for a needed relationship in the research, so there are various requirements for a sample size depending with the analytical method and study design.
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: ANR, 23(1), 30–35. https://doi-org.ezp.waldenulibrary.org/10.1016/j.apnr.2008.03.004
Polit, D. (2010). Statistics and data analysis for nursing research (2nd ed.). Upper Saddle River, NJ: Pearson Education Inc.
Fisher, Orkin, and Frazer (2010) conducted a study to identify if the conjoint analysis was appropriate to study the decision-making processes of emergency department (ED) nurses caring for patients with intellectual disabilities (ID). Nonparametric tests were used to explain the decision-making patterns associated with ED nurses’ characteristics. The nonparametric test confirmed that conjoint analysis is appropriate for studying the decision-making process of ED nurses caring for patients with ID. Parametric tests are not relevant because no assumption has been made about the distribution of factors. The strength and weakness of this study was the design. The simulation was created concisely to allow each element to be statistically independent. The study was conducted using simulation, which does not perfectly mimic actual clinical care with a patient; therefore, results may be skewed. The small sample size limited the generalizability of the study. The findings of this study highlight information that proxy decision-makers for ID patients value. As patient advocates, nurses can better support patients and proxy decision-makers.
The purpose of Tjia et al. (2010) study was to develop recommended guidelines to monitor high-risk medications and assess the prevalence of laboratory testing for these medications. Nonparametric tests were used to identify if more frequently dispensed drugs had a higher laboratory completion rate. Nonparametric tests showed a statistically significant trend in more regularly administered drugs and increased laboratory completion rate (<.001). Parametric tests are not appropriate because no assumption has been made about the distribution of variables. The strengths of this study include the samples evaluated. The findings are generalizable. Weaknesses of the study were identified as being unable to confirm patient compliance to medications and the reason for laboratory orders limited to the relationship with drugs. The study contributes to evidence-based practice by identifying guidelines around laboratory testing related to high-risk medications to prevent patient harm or mortality.
A statistical analysis method that has recurred in the articles used within my literature review is the nonparametric Wilcoxon signed ranks test. Wilcoxon signed ranks tests are “used to test group differences in ordinal-level measures when there are two paired groups (Polit, 2010, p. 185).
I think the method of statistical analysis that is most frequently used in my practice is the t-test. T-tests are used to compare the means of two sets of data (Gray, Grove, & Sutherland, 2017). There is typically a control group vs. experimental group or a pretest and posttest in education. Other forms of statistical analysis are used less frequently in education as we are testing single interventions to determine their effectiveness.
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. https://doi org.ezp.waldenulibrary.org/10.1016/j.apnr.2008.03.004
Gray, J. R., Grove, S. K., & Sutherland, S. (2017). The practice of nursing research: appraisal,synthesis, and generation of evidence (8th ed.). St. Louis, MO: Elsevier.
Polit, D. F. (2010). Statistics and data analysis for nursing research (2nd ed.). Upper Saddle
River, NJ: Pearson Education.
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. (n.d.). Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting. American Journal of Managed Care, 16(7), 489–496.
I agree that this study employed nonparametric tests to explain decision-making among nurses working in the emergency department. The specific tests that the researchers used for this purpose comprised Fisher’s exact tests and chi-square. Usually, a chi-squared test helps “compare the distribution of a categorical variable in a sample” (Kim, 2017, p. 152). Fisher’s exact test allows researchers to analyze small samples and serves as a replacement for the approximation method (Kim, 2017). These two tests applied to this particular research, considering the type of data analysis and the study aim. I agree that parametric tests cannot work well, in this case, because the current study does not fulfill the normality assumption, implying that there is no normal distribution of the sample group means. The small sample size further makes it challenging to know the distribution for this study (Kim, 2017). As a result, using nonparametric testing is a better choice than parametric statistical analysis. I agree applying simulation might cause skewed results. On this note, the researchers should use actual emergency departments to explore this topic more accurately. Researchers should also use a larger sample in future studies to guarantee generalizability.
Similarly, the study about high-risk medications employed a nonparametric test to explore laboratory completion rates for dispensed drugs. This particular test helped discover a trend in the ordered groups. As noted, generalizability is a positive element of this study because analogous institutions can use this approach to promote patient safety when dispensing high-risk medications. However, it only took place in single group practice hosting multiple medical specialties (Tjia et al., 2010). This limitation implies that the findings apply solely to similar types of facilities. In addition, I agree that this study supports evidence-based practice. It adds insights into the laboratory monitoring for high-risk medications available in ambulatory settings. Thus, it will improve patient safety in these practice environments and promote better pharmacotherapy outcomes.
References
Kim, H. Y. (2017). Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact
test. Restorative Dentistry & Endodontics, 42(2), 152–155. https://doi.org/10.5395/rde.2017.42.2.152
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.