Assignment: DNP 830 Benchmark What Are the Data Saying
Assignment: DNP 830 Benchmark What Are the Data Saying
Assignment: DNP 830 Benchmark What Are the Data Saying
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 by performing the appropriate test for the given project data below using SPSS and by reporting the analyzed results in written paper.
General Requirements:
Use the following information to ensure successful completion of the assignment:
 Use the “Comparison Table of the Variable’s Level of Measurement,” located in the DNP830A folder of the DNP PI Workspace, to complete the assignment.
 Review the “Working with Inferential Statistics” and “Working With Descriptive Statistics” tutorials, located in the DNP830A folder of the DNP PI Workspace, for assistance as needed.
 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. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
 Learners will submit this assignment using the assignment dropbox in the digital classroom. In addition, learners must upload this deliverable to the Learner Dissertation Page (LDP) in the DNP PI Workspace for later use.
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Directions:
Part 1:
Using the data in the “Comparison Table of the Variable’s Level of Measurement” 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.
After downloading the data set, run the appropriate statistics in SPSS based on the steps listed below.
Provide a conclusive result of the data analyses based on the guidelines below for statistical significance.
 PAIRED SAMPLE TTEST: Identify the variables BaselineWeight and InterventionWeight. Using the Analysis menu in SPSS, go to Compare Means, Go to the Paired Sample ttest. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the mean weights, standard deviations, tstatistic, degrees of freedom, and p level. Report as t(df)=value, p = value. Report the p level out three digits.
 INDEPENDENT SAMPLE TTEST: Identify the variables InterventionGroups and PatientWeight. Go to the Analysis Menu, go to Compare Means, Go to Independent Samples t Ttest. 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, tstatistic, degrees of freedom, and p level. Report t(df)=value, p = value. Report the p level out three digits
 CHISQUARE (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 ChiSquare. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the total events, the chisquare statistic, degrees of freedom, and p Report ê“2 (df) =value, p =value. Report the p level out three digits.
 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 ChiSquare and McNemars. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the events, the Chisquare, and the McNemar’s p level. Report (p =value). Report the p level out three digits.
 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.
 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, Zstatistic, and p Report as (Z =value, p =value). Report the p level out three digits.
Part 2
Write a 1,0001,250word data analysis paper outlining the procedures used to analyze the parametric and nonparametric variables in the mock data, the statistics reported, and a conclusion of the results. 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 nonparametric tests.
 Description of the reported results of the statistical tests above.
 Summary of the conclusive results of the data analyses.
 Attach the SPSS outputs from the statistical analysis as an appendix to the paper.
 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 plevel 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 ê“2(1) = 4.75, p = .06.
 Nonsignificant Trend: If the pvalue is over .10, report results revealed a nonsignificant trend in the predicted direction. Example: Results indicated a nonsignificant trend for the intervention group (14) over the baseline (12), ê“2(1) = 1.75, p = .26.
The results of the inferential analysis are used for decisionmaking 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 Immersion Hours:
It may be possible to earn portfolio practice immersion hours for this assignment. Enter the following after the References section of your paper:
Practice Immersion Hours Completion Statement DNP830A
I, (INSERT NAME), verify that I have completed and logged (NUMBER OF) clock minutes/hours in association with the goals and objectives for this assignment. I also have tracked said practice immersion hours in the Lopes Activity Tracker for verification purposes and will be sure that all approvals are in place from my faculty and practice immersion preceptor/mentor before the end of the course.
Benchmark Information:
This benchmark assignment assesses the following programmatic competencies:
DNP
3.1: Demonstrate the conceptual ability and technical skills to develop and execute an evaluation plan involving data extraction from practice information systems and databases.
Benchmark – What Are the Data Saying? – Rubric
Criterion  1. Unsatisfactory  2. Insufficient  3. Approaching  4. Acceptable  5. Target 

Comparison Table of Level of Measurement for Variables
Comparison Table of Level of Measurement for Variables 
0 points
A comparison table of level of measurement for the variables is omitted. 
9.6 points
A comparison table of level of measurement for the variables is incomplete or has numerous errors. 
10.56 points
A comparison table of level of measurement for the variables is included but there are three or four errors. The table is attached in an appendix to the paper. 
11.04 points
A comparison table of level of measurement for the variables is complete but has one or two minor errors. The table is attached in an appendix to the paper. 
12 points
A comparison table of level of measurement for the variables is complete and accurate. The table is attached in an appendix to the paper. 
Types of Statistical Tests Used and Why Selected
Types of Statistical Tests Used and Why Selected 
0 points
A discussion of the types of statistical tests used and why they have been selected is not included. 
9.6 points
The discussion of the types of statistical tests used and why they have been selected is incomplete. 
10.56 points
A general discussion of the types of statistical tests used and why they have been selected is presented. Some detail and supporting rationale are needed. 
11.04 points
A discussion of the types of statistical tests used and why they have been selected is presented and supported with adequate rationale. 
12 points
A detailed discussion of the types of statistical tests used and why they have been selected is thorough supported with strong rationale. 
Difference Between Parametric and NonParametric Tests
Difference Between Parametric and NonParametric Tests 
0 points
The discussion of the differences between parametric and nonparametric tests is not included. 
9.6 points
The discussion of the differences between parametric and nonparametric tests is incomplete. 
10.56 points
A summary of the differences between parametric and nonparametric tests is presented. More detail is needed for support or accuracy. 
11.04 points
A discussion of the differences between parametric and nonparametric tests is presented. Some detail is need for clarity. 
12 points
A detailed discussion of the differences between parametric and nonparametric tests is presented. The discussion is well supported. 
Reported Results of Selected Statistical Tests
Reported Results of Selected Statistical Tests 
0 points
The description of the reported results of the selected statistical tests is omitted. 
9.6 points
The description of the reported results of the selected statistical tests is incomplete. 
10.56 points
A summary of the reported results of the selected statistical tests is presented. Some detail is needed. There are some inaccuracies. 
11.04 points
An adequate description of the reported results of the selected statistical tests is presented. Some detail is need for clarity. 
12 points
A thorough description of the reported results of the selected statistical tests is clearly presented. The description is accurate and well supported. 
Conclusive Results of Data Analyses
Conclusive Results of Data Analyses 
0 points
The summary of the conclusive results of the data analysis is omitted. 
9.6 points
The summary of the conclusive results of the data analysis is incomplete. 
10.56 points
A summary of the conclusive results of the data analysis is presented but some aspects are unclear. Some detail is needed. There are some inaccuracies. 
11.04 points
An adequate summary of the conclusive results of the data analysis is presented. The summary is supported and welldeveloped. Some detail is need for clarity. 
12 points
A clear and accurate summary of the conclusive results of the data analysis is presented. The summary is supported and welldeveloped. 
SPSS Statistical Output (B) (C3.1)
Statistical output attached is in appendices of paper. (C3.1) 
0 points
Output from the statistical analysis is not included. 
19.2 points
Output from the statistical analysis is incomplete or incorrect. The SPSS output is included but is not attached in the appendices of the paper. 
21.12 points
NA 
22.08 points
NA 
24 points
The SPSS statistical analysis output is complete and correct. The SPSS output is attached in the appendices of the paper. 
Thesis, Position, or Purpose
Communicates reason for writing and demonstrates awareness of audience. 
0 points
The thesis, position, or purpose is not discernible. No awareness of the appropriate audience is evident. 
4.8 points
The thesis, position, or purpose is discernable in most aspects but is occasionally weak or unclear. There is limited awareness of the appropriate audience. 
5.28 points
The thesis, position, or purpose is adequately developed. An awareness of the appropriate audience is demonstrated. 
5.52 points
The thesis, position, or purpose is clearly communicated throughout and clearly directed to a specific audience. 
6 points
The thesis, position, or purpose is persuasively developed throughout and skillfully directed to a specific audience. 
Development, Structure, and Conclusion
Advances position or purpose throughout writing; conclusion aligns to and evolves from development. 
0 points
No advancement of the thesis, position, or purpose is evident. Connections between paragraphs are missing or inappropriate. No conclusion is offered. 
4.8 points
Limited advancement of thesis, position, or purpose is discernable. There are inconsistencies in organization or the relationship of ideas. Conclusion is simplistic and not fully aligned to the development of the purpose. 
5.28 points
The thesis, position, or purpose is advanced in most aspects. Ideas clearly build on each other. Conclusion aligns to the development of the purpose. 
5.52 points
The thesis, position, or purpose is logically advanced throughout. The progression of ideas is coherent and unified. A clear and plausible conclusion aligns to the development of the purpose. 
6 points
The thesis, position, or purpose is coherently and cohesively advanced throughout. The progression of ideas is coherent and unified. A convincing and unambiguous conclusion aligns to the development of the purpose. 
Evidence
Selects and integrates evidence to support and advance position/purpose; considers other perspectives. 
0 points
Evidence to support the thesis, position, or purpose is absent. The writing relies entirely on the perspective of the writer. 
4.8 points
Evidence is used but is insufficient or of limited relevance. Simplistic explanation or integration of other perspectives is present. 
5.28 points
Relevant evidence that includes other perspectives is used. 
5.52 points
Specific and appropriate evidence is included. Other perspectives are integrated. 
6 points
Comprehensive and compelling evidence is included. Multiple other perspectives are integrated effectively. 
Mechanics of Writing
Includes spelling, capitalization, punctuation, grammar, language use, sentence structure, etc. 
0 points
Errors in grammar or syntax are pervasive and impede meaning. Incorrect language choice or sentence structure errors are found throughout. 
7.68 points
Frequent and repetitive mechanical errors are present. Inconsistencies in language choice or sentence structure are recurrent. 
8.45 points
Occasional mechanical errors are present. Language choice is generally appropriate. Varied sentence structure is attempted. 
8.83 points
Few mechanical errors are present. Suitable language choice and sentence structure are used. 
9.6 points
No mechanical errors are present. Skilled control of language choice and sentence structure are used throughout. 
Format/Documentation
Uses appropriate style, such as APA, MLA, etc., for college, subject, and level; documents sources using citations, footnotes, references, bibliography, etc., appropriate to assignment and discipline. 
0 points
Appropriate format is not used. No documentation of sources is provided. 
6.72 points
Appropriate format is attempted, but some elements are missing. Frequent errors in documentation of sources are evident. 
7.39 points
Appropriate format and documentation are used, although there are some obvious errors. 
7.73 points
Appropriate format and documentation are used with only minor errors. 
8.4 points
No errors in formatting or documentation are present. Selectivity in the use of direct quotations and synthesis of sources is demonstrated. 
Assignment: DNP 830 Benchmark What Are the Data Saying Sample
Data analysis and interpretation form a critical part of research since it leads to the fulfillment of the project objectives. Through data interpretation, the researchers are capable of fully understanding the results in relation to the research questions and objectives since the raw data may not provide useful insights and meaning in connection to the project. The implication is that the data should carefully be interpreted following the laid down principles to get the full meaning. Upon collecting the raw data, the data is then taken through statistical analysis and then represented in graphs, charts, and percentages as a way of data visualization (Kim et al.,2020). As such, the purpose of this assignment is to carry run appropriate statistics in SPSS, provide the results for the analyzed data, and compose an analysis explaining the procedure used in the analysis of the nonparametric and parametric variables.
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The Statistical Tests Used
Independent Sample Ttest
This is a parametric test usually applied to explore if two groups or populations have a similar mean based on a particular variable. The independent Ttest is used when the data to be analyzed has a continuous dependent variable not related within the groups, a categorical independent variable of at least two groups, while the data should have a normal distribution and must be a random sample (Gerald, 2018). In addition, the data to be analyzed should possess an equal variance across the group with no outliers. Besides, every group must also have at least six study subjects, with each group having an equal number of study participants. As such, in the cases of the data provided, this test was chosen to help in exploring and determining the means between the two groups provided.
Paired Sample Ttest
This is a statistical test usually used in comparing means from the same data sample to find out if the compared means are significantly different. Therefore, paired sample Ttest is usually used in research designs such as control experiments, pretest and posttest, and experimental designs. It is particularly applied when a researcher needs to make comparisons between two points, measurements, conditions, or matched pair (Afifah et al.,2022). It is worth noting that the tests are not applicable in cases where there are unpaired samples with no normal distribution around the mean and have more than two units. This test has been chosen since there is a need to compare the weight at baseline and the intervention weight with the main focus of determining if there is a significant difference. As such, through the test, it will be possible to determine if there has been a change in weight.
McNemar
This is another test that has been applied to the provided data sample. The McNemar test is used as a way of checking the marginal homogeneity of two different dichotomous variables. As such, it is used for two groups having similar participants and when the data is paired. The data to be analyzed should have an independent variable with two related groups, and the groups to be considered should be mutually exclusive and with a random sample (Pembury Smith & Ruxton, 2020). Therefore, the McNemar test was used in this case for comparing the compliance of the baseline to that of the intervention for the research subjects’ data.
Chisquare
This statistical test is usually applied in determining how two variables are associated; hence it is also referred to as the chisquare test of association. While it is key to identifying associations between variables, it cannot be used to draw inferences. For the ChiSquare test to be used, the data under consideration should have two categorical variables at least two categories in every variable (Connelly, 2019). The subjects should also be of a large sample and unrelated. As such, this test was also chosen in this case since there was a need to explore the intervention readmission and baseline readmission associations.
Wilcoxon Z
Wilcoxon Z is a statistical test used to compare the means of related samples. As such, it is applied in the analysis of repeated measures without or with intervention. Therefore, this test can be used in cases where there are matched subjects without an intervention and another with an intervention (Kim et al.,2020). For it to be used appropriately, the pair should be from a random sample and also independent of other pairs. This test was chosen to help in comparing the mean ranks of the intervention weight pairs and those of the baseline weight.
Mann Whitney U
Mann Whitney U test is a statistical test used when comparing the difference between independent samples that do not possess normal distribution. For the test to be used, all the variables should be in ordinal or continuous scales. In most cases, this test is used in cases when one of the considered parameters does not allow the use of independent sample ttests (Kim et al.,2020). As such, the statistical test was used as there was a need to evaluate if there was a difference in satisfaction between the intervention and baseline data.
Parametric and NonParametric Tests
Parametric and nonparametric tests are both used in data tests and analysis. The parametric tests are tests that make the assumption that sample data or population has a normal distribution around the mean. Examples of these tests include paired ttest, 2sample test, 1sample ttest, and oneway ANOVA. On the other hand, the nonparametric tests do not consider such an assumption; hence using such an assumption during the analysis may lead to inappropriate interpretations (Orcan, 2020). Some of the nonparametric tests include the 1sample Wilcoxon test, MannWhitney Test, Signedrank test, and Kruskal Wallis test. The nonparametric test is used in cases when meaningful interpretations can be drawn from the median with data samples not appearing normal and small sample.
Summary of Results
Paired Sample Ttest
Analysis was performed on the sample data provided. While the baseline weight mean was found to be 217.5 lbs, with an SD of 53.40, that of the intervention was found to be 178.3 lb, with an SD of 44.88. The results from the SPSS output show that t=7.188, df=29 t(df)=2.05, with a 95% confidence interval. The pvalue is 0.000, and p<0.005. Therefore, the difference between the means was statistically significant.
Independent Sample Ttest
This test shows that the mean weight for the intervention group is 218.3 lb with an SD of 54.8, a pvalue of 0.934, and t (28) = 0,084 at a 95% confidence interval. The assumption made is that the variance is equal. The output t=0.084 is lower than 1.074, which is the critical value. Hence the result is insignificant. As such, the sampling variability affects the baseline weight and the intervention weight mean.
McNemar
The McNemar test shows that the frequency of events is 30 with a chisquare value of 1.639 and a pvalue of 0.007. The implication is that it is statistically significant since it is lower than 0.05. As such, there is a difference between the intervention and baseline compliance is significant.
Chisquare
The analysis using ChiSquare shows a chisquare value of 1.639 with 1 df and a pvalue of 0.008. This value is lower than 3.84, which is the critical value. As such, the difference between the intervention and baseline readmissions is significant.
Wilcoxon Z
The analysis using Wilcoxon Z shows a mean difference of 11.5, Z=4.307, and p=.000. The implication is that the mean ranking between baseline weight and intervention weight is statistically significant.
Mann Whitney U
The analysis using Mann Whitney U shows 18.8 and 12.2 as the baseline and intervention group mean, respectively. The MannWhitney test is 63.0 with a pvalue of 0.035, a value lower than 0.05. The implication is that the result is that there is a statistical difference. The mean level of satisfaction is also lower in the intervention group in comparison to the baseline.
Conclusion
The SPSS software was used in analyzing the data to give results that can be interpreted to enhance an understanding of the collected data. Various statistical tests were used to obtain the required interpretation. Specifically, they offer appropriate information regarding the efficacy of the used intervention. For example, there was a significant difference in weight when an intervention was used. The groups also displayed a variance in both satisfaction and readmission.
References
Afifah, S., Mudzakir, A., & Nandiyanto, A. B. D. (2022). How to calculate paired sample ttest using SPSS software: From stepbystep processing for users to the practical examples in the analysis of the effect of application antifire bamboo teaching materials on student learning outcomes. Indonesian Journal of Teaching in Science, 2(1), 8192. https://doi.org/10.17509/ijotis.v2i1.45895
Connelly, L. (2019). Chisquare test. Medsurg Nursing, 28(2), 127–127. https://www.proquest.com/openview/04d2ff080887f9111b68eb7490a9630a/1?pqorigsite=gscholar&cbl=30764
Gerald, B. (2018). A brief review of independent, dependent and one sample ttest. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 5054. Doi: 10.11648/j.ijamtp.20180402.13
Kim, M., Mallory, C., & Valerio, T. (2020). Statistics for evidencebased practice in nursing. Jones & Bartlett Publishers.
Orcan, F. (2020). Parametric or nonparametric: Skewness to test normality for mean comparison. International Journal of Assessment Tools in Education, 7(2), 255–265. https://doi.org/10.21449/ijate.656077
Pembury Smith, M. Q., & Ruxton, G. D. (2020). Effective use of the McNemar test. Behavioral Ecology and Sociobiology, 74, 1–9. Doi: 10.1007/s0026502002916y
Appendices
Paired Samples Statistics  
Mean  N  Std. Deviation  Std. Error Mean  
Pair 1  Baseline Weight This Column would contain the values for the baseline measure  217.5000  30  53.39750  9.74901 
Intervention WeightThis Column would contain the values for the intervention measure  178.3333  30  44.88171  8.19424 
Paired Samples Correlations  
N  Correlation  Significance  
OneSided p  TwoSided p  
Pair 1  Baseline Weight This Column would contain the values for the baseline measure & Intervention WeightThis Column would contain the values for the intervention measure  30  .829  <.001  <.001 
Paired Samples Test  
Paired Differences  t  df  Significance  
Mean  Std. Deviation  Std. Error Mean  95% Confidence Interval of the Difference  OneSided p  TwoSided p  
Lower  Upper  
Pair 1  Baseline Weight This Column would contain the values for the baseline measure – Intervention WeightThis Column would contain the values for the intervention measure  39.16667  29.85838  5.45137  28.01736  50.31597  7.185  29  <.001  <.001 
Paired Samples Effect Sizes  
Standardizer^{a}  Point Estimate  95% Confidence Interval  
Lower  Upper  
Pair 1  Baseline Weight This Column would contain the values for the baseline measure – Intervention WeightThis Column would contain the values for the intervention measure  Cohen’s d  29.85838  1.312  .815  1.796 
Hedges’ correction  30.65936  1.277  .793  1.750  
a. The denominator used in estimating the effect sizes.
Cohen’s d uses the sample standard deviation of the mean difference. Hedges’ correction uses the sample standard deviation of the mean difference plus a correction factor. 
Group Statistics  
Intervention Groups – Baseline & Intervention  N  Mean  Std. Deviation  Std. Error Mean  
Patient Weight in Pounds  Intervention Group  15  218.3333  53.84059  13.90158 
Baseline Group  15  216.6667  54.82657  14.15616 
Independent Samples Test  
Levene’s Test for Equality of Variances  ttest for Equality of Means  
F  Sig.  t  df  Significance  Mean Difference  Std. Error Difference  95% Confidence Interval of the Difference  
OneSided p  TwoSided p  Lower  Upper  
Patient Weight in Pounds  Equal variances assumed  .019  .890  .084  28  .467  .934  1.66667  19.84063  38.97503  42.30836 
Equal variances not assumed.  .084  27.991  .467  .934  1.66667  19.84063  38.97563  42.30897 
Independent Samples Effect Sizes  
Standardizer^{a}  Point Estimate  95% Confidence Interval  
Lower  Upper  
Patient Weight in Pounds  Cohen’s d  54.33582  .031  .685  .746 
Hedges’ correction  55.84749  .030  .667  .726  
Glass’s delta  54.82657  .030  .686  .746  
a. The denominator used in estimating the effect sizes.
Cohen’s d uses the pooled standard deviation. Hedges’ correction uses the pooled standard deviation plus a correction factor. Glass’s delta uses the sample standard deviation of the control group. 
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% 
ChiSquare Tests  
Value  df  Asymptotic Significance (2sided)  Exact Sig. (2sided)  Exact Sig. (1sided)  
Pearson ChiSquare  6.982^{a}  1  .008  
Continuity Correction^{b}  4.870  1  .027  
Likelihood Ratio  9.562  1  .002  
Fisher’s Exact Test  .010  .010  
LinearbyLinear Association  6.749  1  .009  
N of Valid Cases  30  
a. 2 cells (50.0%) have an 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 
Case Processing Summary  
Cases  
Valid  Missing  Total  
N  Percent  N  Percent  N  Percent  
Baseline NonCompliance 0 = No 1 = Yes * Intervention NonCompliance 0 = No 1 = Yes  30  100.0%  0  0.0%  30  100.0% 
ChiSquare Tests  
Value  df  Asymptotic Significance (2sided)  Exact Sig. (2sided)  Exact Sig. (1sided)  
Pearson ChiSquare  1.639^{a}  1  .201  
Continuity Correction^{b}  .293  1  .588  
Likelihood Ratio  2.381  1  .123  
Fisher’s Exact Test  .492  .313  
LinearbyLinear Association  1.584  1  .208  
McNemar Test  .007^{c}  
N of Valid Cases  30  
a. 2 cells (50.0%) have an 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 NonCompliance 0 = No 1 = Yes Dependent  .234 
Intervention NonCompliance 0 = No 1 = Yes Dependent  .234 
Test Statistics^{a}  
Patient Satisfaction 0 = Not satisfied, 1 = Satisfied, 2 = Very Satisfied  
MannWhitney U  63.000 
Wilcoxon W  183.000 
Z  2.110 
Asymp. Sig. (2tailed)  .035 
Exact Sig. [2*(1tailed Sig.)]  .041^{b} 
a. Grouping Variable: Intervention Groups – Baseline & Intervention  
b. Not corrected for ties. 
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 
Ranks  
N  Mean Rank  Sum of Ranks  
Intervention WeightThis Column would contain the values for the intervention measure – Baseline Weight This Column would contain the values for the baseline measure.  Negative Ranks  22^{a}  11.50  253.00 
Positive Ranks  0^{b}  .00  .00  
Ties  8^{c}  
Total  30  
a. Intervention WeightThis Column would contain the values for the intervention measure < Baseline Weight This Column would contain the values for the baseline measure  
b. Intervention WeightThis Column would contain the values for the intervention measure > Baseline Weight This Column would contain the values for the baseline measure  
c. Intervention WeightThis Column would contain the values for the intervention measure = Baseline Weight This Column would contain the values for the baseline measure 
Test Statistics^{a}  
Intervention WeightThis Column would contain the values for the intervention measure – Baseline Weight This Column would contain the values for the baseline measure  
Z  4.307^{b} 
Asymp. Sig. (2tailed)  <.001 
a. Wilcoxon Signed Ranks Test  
b. Based on positive ranks. 
Table 1
Level of Measurement  Definition of Variable  Example of Variable from SPSS Database 
Nominal  Categorical variable used in measuring frequencies, labeling, and classifying data.  Nominal 
Ordinal  Categorical. Used to rank orders of degree without a definite interval.  Ordinal 
Interval  Continuous. Intervals between measurements with no true zero.  Scale 
Ratio  Continuous. Intervals between measurements are consistent, highest levels of measurement with a true.  Scale 
Table 2
Level of Measurement  Type of Comparison  Recommended Statistical Test 
Nominal  Independent Groups
Paired Groups 
Chisquare test
McNemar test 
Ordinal

Independent Groups
Paired Groups 
MannWhitney U test
Wilcoxon Z test 
Interval 
Independent Groups
Paired Groups 
Independent Ttest
Paired Ttest 
Ratio 
Independent Groups
Paired Groups 
Independent Sample ttest
Paired Samples Ttest 