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DNP 830: Data Collection and Level of Measurement

DNP 830: Data Collection and Level of Measurement

DNP 830: Data Collection and Level of Measurement

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.

Project Topic: For immediate postpartum mothers 19 years and older, will the implementation of education 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?

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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
  • Doctoral 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.

  • PAIRED 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
  • CHI-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 ꭓ2 (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 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.