NR 439 Week 6 Graded Discussion Topic: Data Analysis and Results
Purpose
This week’s graded topics relate to the following Course Outcomes
... [Show More] (COs).
• CO 2: Apply research principles to the interpretation of the content of published research studies. (PO 4 & 8)
• CO 5: Recognize the role of research findings in evidence-based practice. (PO 7 & 8)
• The Assignments
Data analysis is key for discovering credible findings from implementing nursing studies. Discussion and conclusions can … made about the meaning of the findings from the data analysis.
Share what you learned about descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data; include something that you learned that was interesting to you and your thoughts on why data analysis is necessary for discovering credible findings for nursing.
Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice.
ANSWER
1. Discuss one of the four basic rules for understanding results in a research study.
One of the four basic rules for understanding results in a research study is to understand the purpose of the study (Chamberlain Week 6 Lesson). It is entirely important to recognize the purpose of the study to determine what the results mean towards the question being asked. Without understanding the question being asked or the focus guiding the research, the numerical data or ordinal data collected has no support or definition and can be quite confusing.
2. Compare clinical significance and statistical significance. Which one is more meaningful when considering applying evidence to your practice?
When nurses conduct a quantitative analysis of a study, it is important to determine if the results have clinical and statistical significance to the practice. According to Houser, clinical significance is “generally expected to reflect the extent to which an intervention can make a real difference in patients’ lives” (Houser, pg. 357). Statistical significance is based off of the probability (p) due to standard error. Depending on the amount of the probability calculated, Houser states “ If the pvalue is very small, then the probability that the results were due to error is very small, and the researcher can be very confident that the effects of the intervention are real” where as “ If the p value is very large (greater than 0.05 or 5%), then the probability that the results were due to error is very large, and the researcher cannot conclude that the intervention had an effect greater than would be expected from random variations” (Houser, pg. 357). The smaller the p value is, the better the statistical value of the results are and the strength of the data collected. However, the probability just explains the chances of getting similar results. When considering results to apply to evidence-based practice, the more meaningful significance would be clinical significance. Though statistical significance shows the strength in numbers, the ultimate idea of which I would include results to my general practice would be whether or not the results would make a better change towards those patients that I am taking care of.
3. Compare descriptive statistics and inferential statistics in research. Please give an example of each type that could be collected in a study that would be done on your nursing clinical issue you identified in previous weeks.
Descriptive statistics according to Houser are “concerned with accurately describing the characteristics of a sample or population” (Houser pg. 353). In relation to my clinical issue of reduction of non-action required alarms from telemetry monitors, an example of this in the article Stop the Noise: A Quality Improvement Project to Decrease Electrocardiographic Nuisance Alarms, would be when they explained in the results about each instance of change in their bundle for alarm reduction. The bundle included methods such as disposable electrodes, daily changes and changing of thresholds (reducing duplicate alarms, shutting off bigeminy and couplet alarms, and changing tachycardia and bradycardia thresholds (40 = brady; 150 = tachy). They described the results of changing the leads and making them disposable did not change the alarm rate of the monitor, however, the threshold change and the silencing of bigeminy and couplet alarms reduced the frequency from a mean of 3.58 per day per bed, to 3.29 per day per bed, and eventually to 3.05 per day per bed.
Inferential statistics are “used to determine if results found in a sample can be applied to a population—a condition necessary for confidently generalizing research as a basis for evidence for nursing practice” (Houser, pg. 353). In relation to my clinical issue of reduction of non-action required alarms from telemetry monitors, an example of this from the article Stop the Noise: A Quality Improvement Project to Decrease Electrocardiographic Nuisance Alarms, would be the inferring that with changing all monitors in the world to similar setting adjustments would provide the same suggested reduction of 80 to 90% of non-action required alarms when initiated. That same percentage may not be acquirable but the suggested resolution attempts show that there will be a positive change in the frequency of these alarms.
References:
Chamberlain College of Nursing. (2016). Reading Research Literature Results. Week 6 [Online lesson].
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed., p.291,356-357, 482). Sudbury, MA: Jones & Bartlett.
Sendelbach, S., Wahl, S., Anthony, A., & Shotts, P. (2015). Stop the Noise: A Quality Improvement Project to Decrease Electrocardiographic Nuisance Alarms. Critical Care Nurse, 35(4), 15-23. doi:10.4037/ccn2015858 [Show Less]