***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme

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***seems large-  (mostly describing the data) assignment is to analyzing data (attached files) to put in paper ***

Part 1:

Using the data in the “Comparison Table of the Variable’s Level of Measurement” ( attached) display the dependent variables and the level of measurement in a comparison table. You will attach the comparison table as an appendix to your paper.

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



Mock Data: this has already been done!!!

  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 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
  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 Report ꭓ2 (df) =value, p =value. Report the p level out three digits.
  4. MCNEMAR (Paired): Identify the variables BaselineCompliance and 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 Using the Analysis Menu, go to Nonparametric 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 Nonparametric 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 Report as (Z =value, p =value). Report the p level out three digits.    Part 2 see below –

****Write a 1,000-1,250-word data analysis paper outlining the procedures used to analyze the parametric and nonparametric variables in the


mock data (above),


the statistics reported, and a conclusion of the results. 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 nonparametric tests.
  3. Description of the reported results of the statistical tests above.
  4. Summary of the conclusive results of the data analyses.


  5. Attach the SPSS outputs from the statistical analysis as an appendix to the paper.


  6. Attach the “Comparison Table of the Variable’s Level of Measurement” as an appendix to the paper


    .

Use the following guidelines to report the test results for your paper:

  • 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 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.
  • Nonsignificant 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.

***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Appendix Table 1 Characteristics and Examples of Variables Level of Measurement Definition of Variable Example of Variable from SPSS Database Nominal Ordinal Interval Ratio Note. Add notes here = (Provide any reference, 2020). Table 2 Types of Inferential Statistical Tests Performed According to the Level of the Measurement of the Outcome Variable Level of Measurement Type of Comparison Recommended Statistical Test Nominal Independent Groups Paired Groups Ordinal Independent Groups Paired Groups Interval Independent Groups Paired Groups Ratio Independent Groups Paired Groups Note. Add notes here = (Provide any reference, 2020). © 2022. Grand Canyon University. All Rights Reserved.
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Paired Samples Test Paired Differences t Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Baseline Weight -This Column would contain the values for the baseline measure – Intervention Weight-This Column would contain the values for the intervention measure 39.16667 29.85838 5.45137 28.01736 50.31597 7.185 Paired Samples Test df Significance One-Sided p Two-Sided p Pair 1 Baseline Weight -This Column would contain the values for the baseline measure – Intervention Weight-This Column would contain the values for the intervention measure 29 <.001 <.001
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Independent Samples Test Levene’s Test for Equality of Variances t-test for Equality of Means F Sig. t df Significance One-Sided p Patient Weight in Pounds Equal variances assumed .019 .890 .084 28 .467 Equal variances not assumed .084 27.991 .467 Independent Samples Test t-test for Equality of Means Significance Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Two-Sided p Lower Patient Weight in Pounds Equal variances assumed .934 1.66667 19.84063 -38.97503 Equal variances not assumed .934 1.66667 19.84063 -38.97563 Independent Samples Test t-test for Equality of Means 95% Confidence Interval of the Difference Upper Patient Weight in Pounds Equal variances assumed 42.30836 Equal variances not assumed 42.30897
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Baseline Readmission Rate 0 = No 1 = Yes * Intervention Readmission Rate 0 = No 1 = Yes 30 100.0% 0 0.0% 30 100.0%
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Chi-Square Tests Value df Asymptotic Significance (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square 6.982a 1 .008 Continuity Correctionb 4.870 1 .027 Likelihood Ratio 9.562 1 .002 Fisher’s Exact Test .010 .010 Linear-by-Linear Association 6.749 1 .009 N of Valid Cases 30 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.03. b. Computed only for a 2×2 table
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Directional Measures Value Nominal by Interval Eta Baseline Readmission Rate 0 = No 1 = Yes Dependent .482 Intervention Readmission Rate 0 = No 1 = Yes Dependent .482
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Baseline Non-Compliance 0 = No 1 = Yes * Intervention Non-Compliance 0 = No 1 = Yes 30 100.0% 0 0.0% 30 100.0%
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Chi-Square Tests Value df Asymptotic Significance (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square 1.639a 1 .201 Continuity Correctionb .293 1 .588 Likelihood Ratio 2.381 1 .123 Fisher’s Exact Test .492 .313 Linear-by-Linear Association 1.584 1 .208 McNemar Test .007c N of Valid Cases 30 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is .87. b. Computed only for a 2×2 table c. Binomial distribution used.
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Directional Measures Value Nominal by Interval Eta Baseline Non-Compliance 0 = No 1 = Yes Dependent .234 Intervention Non-Compliance 0 = No 1 = Yes Dependent .234
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Paired Samples Test Paired Differences t Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Baseline Weight -This Column would contain the values for the baseline measure – Intervention Weight-This Column would contain the values for the intervention measure 39.16667 29.85838 5.45137 28.01736 50.31597 7.185 Paired Samples Test df Significance One-Sided p Two-Sided p Pair 1 Baseline Weight -This Column would contain the values for the baseline measure – Intervention Weight-This Column would contain the values for the intervention measure 29 <.001 <.001
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Independent Samples Test Levene’s Test for Equality of Variances t-test for Equality of Means F Sig. t df Significance One-Sided p Patient Weight in Pounds Equal variances assumed .019 .890 .084 28 .467 Equal variances not assumed .084 27.991 .467 Independent Samples Test t-test for Equality of Means Significance Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Two-Sided p Lower Patient Weight in Pounds Equal variances assumed .934 1.66667 19.84063 -38.97503 Equal variances not assumed .934 1.66667 19.84063 -38.97563 Independent Samples Test t-test for Equality of Means 95% Confidence Interval of the Difference Upper Patient Weight in Pounds Equal variances assumed 42.30836 Equal variances not assumed 42.30897
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Baseline Readmission Rate 0 = No 1 = Yes * Intervention Readmission Rate 0 = No 1 = Yes 30 100.0% 0 0.0% 30 100.0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square 6.982a 1 .008 Continuity Correctionb 4.870 1 .027 Likelihood Ratio 9.562 1 .002 Fisher’s Exact Test .010 .010 Linear-by-Linear Association 6.749 1 .009 N of Valid Cases 30 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.03. b. Computed only for a 2×2 table Directional Measures Value Nominal by Interval Eta Baseline Readmission Rate 0 = No 1 = Yes Dependent .482 Intervention Readmission Rate 0 = No 1 = Yes Dependent .482
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Baseline Non-Compliance 0 = No 1 = Yes * Intervention Non-Compliance 0 = No 1 = Yes 30 100.0% 0 0.0% 30 100.0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square 1.639a 1 .201 Continuity Correctionb .293 1 .588 Likelihood Ratio 2.381 1 .123 Fisher’s Exact Test .492 .313 Linear-by-Linear Association 1.584 1 .208 McNemar Test .007c N of Valid Cases 30 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is .87. b. Computed only for a 2×2 table c. Binomial distribution used. Directional Measures Value Nominal by Interval Eta Baseline Non-Compliance 0 = No 1 = Yes Dependent .234 Intervention Non-Compliance 0 = No 1 = Yes Dependent .234
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Ranks Intervention Groups – Baseline & Intervention N Mean Rank Sum of Ranks Patient Satisfaction 0 = Not satisfied, 1 = Satisfied, 2 = Very Satisfied Intervention Group 15 12.20 183.00 Baseline Group 15 18.80 282.00 Total 30 Test Statisticsa Patient Satisfaction 0 = Not satisfied, 1 = Satisfied, 2 = Very Satisfied Mann-Whitney U 63.000 Wilcoxon W 183.000 Z -2.110 Asymp. Sig. (2-tailed) .035 Exact Sig. [2*(1-tailed Sig.)] .041b a. Grouping Variable: Intervention Groups – Baseline & Intervention b. Not corrected for ties.
***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper *** Part 1: Using the data in the “Comparison Table of the Variable’s Level of Measureme
Ranks N Mean Rank Sum of Ranks Intervention Weight-This Column would contain the values for the intervention measure – Baseline Weight -This Column would contain the values for the baseline measure Negative Ranks 22a 11.50 253.00 Positive Ranks 0b .00 .00 Ties 8c Total 30 a. Intervention Weight-This Column would contain the values for the intervention measure < Baseline Weight -This Column would contain the values for the baseline measure b. Intervention Weight-This Column would contain the values for the intervention measure > Baseline Weight -This Column would contain the values for the baseline measure c. Intervention Weight-This Column would contain the values for the intervention measure = Baseline Weight -This Column would contain the values for the baseline measure Test Statisticsa Intervention Weight-This Column would contain the values for the intervention measure – Baseline Weight -This Column would contain the values for the baseline measure Z -4.307b Asymp. Sig. (2-tailed) <.001 a. Wilcoxon Signed Ranks Test b. Based on positive ranks.

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