The DNP must have a basic understanding of statistical measurements and how they apply within the parameters of data management and analytics. In this assignment, you will demonstrate understanding of basic statistical tests and how to perform the appropriate test for the project using SPSS or other statistical programs.

General Requirements:

Use the following information to ensure successful completion of the assignment:

• Refer to “Setting Up My SPSS,” “SPSS Database,” and “Comparison Table of the Variable’s Level of Measurement,” located in the DNP 830 folder of the DC Network Practice Immersion workspace.
• Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
• This assignment uses a rubric. Review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
• You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance. 

Directions:

1. Set up your IBM SPSS account and run several statistical outputs based on the “SPSS Database” Use “Setting Up My SPSS” to set up your SPSS program on your computer or device. You may also use programs such as Laerd Statistics or Intellectus, if you subscribe to them.
2. The patient outcome or dependent variables and the level of measurement must be displayed in a comparison table which you will provide as an Appendix to the paper. Refer to the “Comparison Table of the Variable’s Level of Measurement.”
3. Submit a 1,000-1,250 word data analysis paper outlining the procedures used to analyze the parametric and non-parametric variables in the mock data, the statistics reported, and a conclusion of the results.

Provide a conclusive result of the data analyses based on the guidelines below for statistical significance.

1. 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.
2. INDEPENDENT SAMPLE T-TEST: Identify the variables InterventionGroups and PatientWeight. Go to the Analysis Menu, go to Compare Means, Go to Independent Samples tT-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
3. 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 level. Report ꭓ2 (df) =value, p =value. Report the p level out three digits.
4. MCNEMAR (Paired): Identify the variables BaselineCompliance and InterventionCompliance. 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.
5. MANN WHITNEY U: Identify the variables InterventionGroups and PatientSatisfaction. Using the Analysis Menu, go to Non-parametric Statistics, go to LegacyDialogs, 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.
6. WILCOXON Z: Identify the variables BaselineWeight and InterventionWeight. Go to the Analysis Menu, go to Non-parametric 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 level. Report as (Z =value, p =value). Report the p level out three digits.

Include the following in your paper:

1. Discussion of the types of statistical tests used and why they have been chosen.
2. Discussion of the differences between parametric and non-parametric tests.
3. Description of the reported results of the statistical tests above.
4. Summary of the conclusive result of the data analyses.
5. Outputs from the statistical analysis provided as an Appendix to the paper.
6. Comparison table of the variable’s level of measurement provided as an Appendix to the paper.

Use the following guidelines to report the test results:

• 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 significant. 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.
• Non-Significant 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.

Portfolio Practice Hours:

Practice immersion assignments are based on your current course objectives and is application-based learning using your real-world practice setting. These assignments earn practice immersion hours and are indicated in the syllabus by a Portfolio Practice Hours statement, which reminds you, the learner, to enter in a corresponding case log in Typhon. Actual practice immersion hours are entered, but the average hours associated with each practice immersion assignment is 10.

You are required to complete your assignment using a real-world application. Real-world application requires the use of evidence-based data, contemporary theories, and concepts presented in the course. The culmination of your assignment must present a viable application in a current practice setting. For more information on parameters for practice immersion hours, please refer to DNP resources in the DC Network.

To earn portfolio practice hours, enter the following after the references section of your paper:

Practice Hours Completion Statement DNP-830

I, (INSERT NAME), verify that I have completed (NUMBER OF) practice immersion hours in association with the goals and objectives for this assignment. I have also tracked said practice immersion hours in the Typhon Student Tracking System for verification purposes and will be sure that all approvals are in place from my faculty and practice mentor.

Case Studies, Chapter 21, Nursing Management of Labor and Birth at Risk

1. Laura is a 26-year-old G2P1 who had a cesarean delivery for fetal distress with her first pregnancy. Laura is now struggling with deciding between a repeat cesarean delivery or attempting a VBAC. (Learning Objective 9)

a. In order to ensure that Laura has the facts to assist her in her decision, explain the risks of a repeat cesarean deliver

b. Laura can’t understand why more women don’t want to try a VBAC delivery. Discuss the possible reasons for this.

c. Describe the management of care for a woman attempting a VBAC delivery.

Antepartum Case Studies 

As you provide answers to the questions in the following case studies below please refer to your Essentials of Maternity, Newborn, and Women’s Health Nursing textbook’s Chapter 19 and 20 to provide you with pertinent information in reference to these cases.

Each of the objectives listed in the following case studies are in reference to those found in the textbook chapters listed alongside the case studies heading.

Please use this document as your template and insert your responses after each question in a different color or font. 

Case Studies, Chapter 19, Nursing Management of Pregnancy at Risk: Pregnancy-Related Complications

1. Teresa is a 36-year-old primigravida who is expecting twins. She is 26 weeks pregnant. She stays after your “What to Expect with Twins” class to talk to you. Although Teresa is a nurse, she has many questions and concerns. Her twins are a result of years of trying to get pregnant and in vitro fertilization. She is nervous about whether she will have a vaginal delivery or a cesarean section. She is worried about having the babies prematurely. She wants you to tell her everything that could go wrong so she can be prepared. (Learning Objectives 1, 2, 4, and 5)

a. Why is Teresa’s pregnancy considered a high-risk pregnancy?

b. Discuss potential pregnancy-related complications for Teresa.

c. Discuss the potential risks to the babies.

2. Sarah is 19-year-old G1P0 at 36 weeks’ gestation. Sarah has been followed weekly in the clinic for mild–moderate preeclampsia. At her clinic appointment today, Sarah’s blood pressure reading was 188/104. She is admitted to the antepartum unit for management of her worsening preeclampsia. You perform her admission assessment and note that her reflexes are brisk, her heart rate is 94, she complains of having an intense headache, and is seeing spots before her eyes. You perform an abdominal assessment and note that she has significant epigastric tenderness. (Learning Objective 4)

1. Develop a plan of care for the woman      experiencing preeclampsia and/or eclampsia

Case Studies, Chapter 20, Nursing Management of the Pregnancy at Risk: Selected Health Conditions and Vulnerable Populations

1. Nikki is a 17-year-old G1P0 at 33 weeks’ gestation. Nikki comes to the clinic today for her prenatal visit and you notice that her hair and clothing have the distinct odor of marijuana and tobacco smoke. You directly ask Nikki if she has been taking any other drugs besides marijuana. Nikki is surprised by the question and denies it. When you explain that you can smell it in her hair and on her clothing, she starts to cry and says, “Please don’t take my baby away from me.” After reassurance, Nikki further admits to sporadic binge alcohol consumption and smoking “pot” and cigarettes. She relates that she also consumes four to five caffeinated beverages per day. (Learning Objectives 10 and 12)

1. Describe      how cigarette smoking, marijuana use, binge alcohol consumption, and      excessive caffeine intake can affect Nikki’s pregnancy.
2. What      possible affects are there to Nikki’s baby from the maternal ingestion of      these substances?
3. Describe      the nursing care you would provide for Nikki.