Currently, the project dedicated to CAUTI reduction is in progress, and most patient data are not available. However, the staff data (demographics and pre-and post-training scores in CAUTI prevention proficiency) have been analyzed. The process consisted of collecting the data, inserting them in Excel and Minitab files, checking them, using descriptive statistics, determining the appropriate inferential test, and interpreting the output based on whether it supported the hypothesis or not.
The demographic information has been summarized with percentages and illustrated with graphs; no inferential tests were required for it. The proficiency scores were analyzed for statistically significant differences. The project needed to determine the effect of the independent variable, and it involved one group, for which pre-and post-training data were presented. The analysis of the data showed that they were not normally distributed and that the differences between the two datasets were symmetrically distributed. Based on this information, Wilcoxon signed ranks test was the correct choice (Looney & Hagan, 2015; Polit & Beck, 2017). Minitab run all the score-related tests, and the demographics were summarized with the help of Excel graphs. The results (p<0.05) showed that the independent variable (in this case, training) affected the participants’ performance scores.
Looney, W. S., & Hagan, L. J. (2015). Analysis of biomarker data: A practical guide. Hoboken, NJ: John Wiley & Sons.
Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott, Williams & Wilkins.