This document provides instructions for a DNP student to complete a statistical analysis assignment using SPSS. It outlines running various statistical tests, including paired t-tests, independent t-tests, chi-square tests, Mann-Whitney U tests, and Wilcoxon tests on mock patient data. It also provides guidelines for reporting the results of the statistical analyses and completing a 1000-1250 word data analysis paper discussing the findings. Upon completion, the student must include a practice hours statement verifying the number of practice immersion hours earned for the assignment.

1. 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:

2. 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.
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."
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.
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,

3. 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

4. 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.
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 I
nterventionGroups
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 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

6. p
level. Report as (Z
=
value,
p =
value). Report the
p
level out three digits.
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 non-
parametric tests.
Description of the reported results of the statistical tests above.
Summary of the conclusive result of the data analyses.
Outputs from the statistical analysis provided as an Appendix to
the paper.
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

7. 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

8. = .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

9. 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.