DNP 830 Data Collection and Level of Measurement
DNP 830 Data Collection and Level of MeasurementDNP 830 Data Collection and Level of
MeasurementThe 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.Project
Topic: For immediate postpartum mothers 19 years and older, will the implementation
ofeducation and immediate support provider-driven mother-newborn uninterrupted skin-
to-skin contact within the first 24 hours after birth compared to current practice, facilitate
breastfeeding initiation rates?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 MeasurementDoctoral learners
are required to use APA style for their writing assignments.This assignment uses a rubric.
Review the rubric prior to beginning the assignment to become familiar with the
expectations for successful completion.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 2,000-2,250 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.ORDER NOW FOR
CUSTOMIZED, PLAGIARISM-FREE PAPERSPAIRED SAMPLE T-TEST: Identify the variables
Baseline Weight and Intervention Weight. Using the Analysis menu in SPSS, go to Compare
Means, Go to the Paired Sample t-test. Add the Baseline Weight and Intervention Weight 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 Intervention Groups and
Patient Weight. Go to the Analysis Menu, go to Compare Means, Go to Independent
Samples tT-test. Add Intervention Groups to the Grouping Factor. Define the groups
according to codings in the variable view (1=Intervention, 2 =Baseline). Add Patient Weight
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 DNP 830 Data Collection and Level of MeasurementCHI-SQUARE
(Independent): Identify the variables Baseline Readmission and Intervention Readmission.
Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add Baseline
Readmission to the row and Intervention Readmission 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 ?2 (df) =value, p =value. Report the p level out three
digits.MCNEMAR (Paired): Identify the variables Baseline Compliance and Intervention
Compliance. Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add
Baseline Compliance to the row and Intervention Compliance 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 Intervention Groups and Patient Satisfaction. Using the Analysis
Menu, go to Non-parametric Statistics, go to LegacyDialogs, go to 2 Independent samples.
Add Intervention Groups to the Grouping Variable and Patient Satisfaction 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 Baseline Weight and Intervention Weight. Go to
the Analysis Menu, go to Non-parametric Statistics, go to LegacyDialogs, go to 2 Related
samples. Add the Baseline Weight and Intervention Weight 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.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 ?2(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), ?2(1) = 1.75, p = .26.DNP 830 Data
Collection and Level of Measurement