Directions: 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, 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 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 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. 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. MANN WHITNEY U: Identify the variables InterventionGroups and PatientSatisfaction. Using the Analysis Menu, go to Non-parametric Statistics, go to LegacyDialogs, go to 2 I.

1. Directions:
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, 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 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

2. 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 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.
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.
WILCOXON Z: Identify the variables BaselineWeight and

3. 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:
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 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.

4. 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.