Identification of Relevant Data Collection and Analysis
1. Discuss the reliability and validity statistics of this instrument-SET-M
2. Explicate how the data will be analyzed to clarify the parameters of decreasing heart failure readmission.
3. Discuss possible bias inherent in the instrument
Data collection and analysis is important as it give the research information about the effects that the project had on the correct situation at the medical center. Collection of data should be done meticulously with reduced amount of errors. Data analysis depends on data collection and in situations where data collected is inaccurate, the research tend not to achieve it goal. 
Data collection is essential to the institution’s need-based assessment analysis to be successful. According to Jones & Roussle (2016), the uniformity and feasibility of doing a study are revealed in light of the accuracy of collecting relevant data. Using statistical instruments to analyze, it reveals the trend and impact that the teach-back method has concerning decreasing heart failure readmissions. Additionally, comparing data obtained during the project’s implementation and before the implementation reveals the effect the project has made to the situation for better or worse as compared to before, thus measuring its effectiveness and efficiency (Almkuist, 2017). The DNP student used the SET-M tool first composed in 2006 and revised in 2015 to compare the changes that the teach-back method had on readmission rates by comparing it with the baseline (the current data on readmission rates). The DNP student also used visuals like bar graphs and line graphs to analyze the number of cases recorded. It is important to note that the analysis will take into consideration changes before and after the analysis. Also, the percentage of patients assuming recover will be recorded during the period and the percentage compared with the pre-project