Write a 1,000-1,250-word data analysis paper outlining the procedures used to analyze the parametric and nonparametric variables in the mock data, the statistics reported, and a conclusion of the results.

Assignment Question

Use the following information to ensure successful completion of the assignment: Use the “Comparison Table of the Variable’s Level of Measurement,” located in the DNP-830A folder of the DNP PI Workspace, to complete the assignment. Review the “Working with Inferential Statistics” and “Working With Descriptive Statistics” tutorials, located in the DNP-830A folder of the DNP PI Workspace, for assistance as needed. Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.

Part 1:

Using the data in the “Comparison Table of the Variable’s Level of Measurement” display the dependent variables and the level of measurement in a comparison table. You will attach the comparison table as an appendix to your paper. After downloading the data set, run the appropriate statistics in SPSS based on the steps listed below. Provide a conclusive result of the data analyses based on the guidelines below for statistical significance. PAIRED SAMPLE T-TEST: Identify the variables BaselineWeight and InterventionWeight. Using the Analysis menu in SPSS, go to Compare Means, Go to the Paired Sample t-test. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report as t(df)=value, p = value. Report the p level out three digits. INDEPENDENT SAMPLE T-TEST: Identify the variables InterventionGroups and PatientWeight. Go to the Analysis Menu, go to Compare Means, Go to Independent Samples t T-test. Add InterventionGroups to the Grouping Factor. Define the groups according to codings in the variable view (1=Intervention, 2 =Baseline). Add PatientWeight to the test variable field. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report t(df)=value, p = value. Report the p level out three digits CHI-SQUARE (Independent): Identify the variables BaselineReadmission and InterventionReadmission. Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineReadmission to the row and InterventionReadmission to the column. Click the Statistics button and choose Chi-Square. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the total events, the chi-square statistic, degrees of freedom, and p Report ꭓ2 (df) =value, p =value. Report the p level out three digits. MCNEMAR (Paired): Identify the variables BaselineCompliance and Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineCompliance to the row and InterventionCompliance to the column. Click the Statistics button and choose Chi-Square and McNemars. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the events, the Chi-square, and the McNemar’s p level. Report (p =value). Report the p level out three digits. MANN WHITNEY U: Identify the variables InterventionGroups and Using the Analysis Menu, go to Nonparametric Statistics, go to Legacy Dialogs, go to 2 Independent samples. Add InterventionGroups to the Grouping Variable and PatientSatisfaction to the Test Variable. Check Mann Whitney U. Click OK. Report the Medians or Means, the Mann Whitney U statistic, and the p level. Report (U =value, p =value). Report the p level out three digits. WILCOXON Z: Identify the variables BaselineWeight and InterventionWeight. Go to the Analysis Menu, go to Nonparametric Statistics, go to LegacyDialogs, go to 2 Related samples. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the Mean or Median weights, standard deviations, Z-statistic, and p Report as (Z =value, p =value). Report the p level out three digits.

Part 2

Write a 1,000-1,250-word data analysis paper outlining the procedures used to analyze the parametric and nonparametric variables in the mock data, the statistics reported, and a conclusion of the results. Include the following in your paper: Discussion of the types of statistical tests used and why they have been chosen. Discussion of the differences between parametric and nonparametric tests. Description of the reported results of the statistical tests above. Summary of the conclusive results of the data analyses. Attach the SPSS outputs from the statistical analysis as an appendix to the paper. Attach the “Comparison Table of the Variable’s Level of Measurement” as an appendix to the paper.

Use the following guidelines to report the test results for your paper: Statistically Significant Difference: When reporting exact p values, state early in the data analysis and results section, the alpha level used for the significance criterion for all tests in the project. Example: An alpha or significance level of < .05 was used for all statistical tests in the project. Then if the p-level is less than this value identified, the result is considered statistically significant. A statistically significant difference was noted between the scores before compared to after the intervention t(24) = 2.37, p = .007. Marginally Significant Difference: If the results are found in the predicted direction but are not statistically significant, indicate that results were marginally Example: Scores indicated a marginally significant preference for the intervention group (M = 3.54, SD = 1.20) compared to the baseline (M= 3.10, SD = .90), t(24) = 1.37, p = .07. Or there was a marginal difference in readmissions before (15) compared to after (10) the intervention ꭓ2(1) = 4.75, p = .06. Nonsignificant Trend: If the p-value is over .10, report results revealed a non-significant trend in the predicted direction. Example: Results indicated a non-significant trend for the intervention group (14) over the baseline (12), ꭓ2(1) = 1.75, p = .26. The results of the inferential analysis are used for decision-making and not hypothesis testing. It is important to look at the real results and establish what criterion is necessary for further implementation of the project’s findings. These conclusions are a start.