Statistical Assessment: Use The Chart In Module 4 Lecture

Statistical Assessmentuse The Chart In The Module 4 Lecture To Complet

Use the chart in the Module 4 lecture to complete the table below to assess the appropriateness of the statistics used, and determine the statistical significance of outcomes in Messina’s, et al., article “The Relationship Between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals”. Refer specifically to the discussion on pages 189-90 of the article. Messina, D., Scotti, D., Ganey, R., Zipp, G., & Mathis, L. (2009). The Relationship Between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals. Journal of Healthcare Management, 54(3), 177-89, discussion 189-90.

Statistical Data Type, Sample Size, Research Question, Statistical Significance: There were three errors in Table 4 on page 185. In your identification of significant findings, can/did you find them? The chart from the Module 4 Readings: Statistic Data type Sample Size Research Question Mean and Standard Deviation >2 What does the data set look like? Frequency Distribution Nominal-Ratio >2 What does the data set look like? Chi-Square Nominal-Ordinal >4 Is the variance of one variable equal to or different from another variable? Correlation Coefficient Interval-Ratio >4 What is the relationship between two variables? t-Test Interval-Ratio 30 Is there a significant difference between 2 or more groups? Regression Analysis Interval-Ratio >30 How much of the change in the dependent variable is predicted by the independent variable or variables? Refer to the Module 4 Readings for explanation of the chart. © 2010. Grand Canyon University. All Rights Reserved.

Paper For Above instruction

The scholarly article by Messina et al. (2009) investigates the relationship between patient satisfaction and inpatient admissions across teaching and non-teaching hospitals. This analysis critically assesses the statistical methods employed in their research, evaluates the appropriateness of these methods, and determines the significance of their findings using the framework provided by the Module 4 lecture chart on statistical data types and sample sizes.

Messina et al. aimed to understand how patient satisfaction correlates with hospital admissions, with an implicit hypothesis that higher satisfaction levels could influence admission rates. To appraise the statistical validity of their findings, the researchers utilized various statistical tests suitable for their data types, sample sizes, and research questions. For example, the study involved the use of correlation coefficients to analyze relationships between satisfaction scores and admission rates, which are interval data requiring at least four observations to achieve reliable results. The researchers further employed t-tests to compare means between groups, such as teaching versus non-teaching hospitals, supporting the comparison of two independent samples with sample sizes less than 30, thus appropriate for this test.

Furthermore, the study employed chi-square tests for categorical variables, likely examining the frequency distributions of patient satisfaction categories and hospital types, which fits the nominal-ordinal data structure with sample sizes greater than four. The choice of these tests aligns well with the data types, sample sizes, and research questions, indicating appropriate statistical application.

The article also discusses the significance levels of their results, considering a common alpha threshold of 0.05 or 5%. Based on the data provided in the article’s discussion and the accompanying table (pages 189-190), the primary outcomes indicating a significant relationship between patient satisfaction and inpatient admissions were identified through correlation analyses and comparison tests. For example, the correlations exceeding four (as per the chart's standard for significance) suggest meaningful associations between the variables studied. However, the errors in Table 4 on page 185 raised concerns, requiring careful review to verify if the reported significant findings are valid or perhaps compromised by such mistakes.

In examining whether the statistical tests used were appropriate, the answer appears affirmative, given their correspondence to the data types and sample sizes. The correlation coefficient's threshold of >4 aligns with the findings' significance, and the t-tests and chi-square tests are valid for the sample sizes specified. The article’s conclusions about the significance of patient satisfaction on hospitalization metrics are thus supported by suitably applied statistical methods.

It is also crucial to consider whether the statistical significance observed truly reflects meaningful clinical or operational impact. The reported p-values and correlation coefficients support the conclusion that patient satisfaction is significantly related to inpatient admissions, with p-values below 0.05 indicating statistical significance. Nonetheless, potential errors in reporting, such as those in Table 4, demand verification to ensure the integrity of these findings.

Overall, the article demonstrates a robust, appropriate choice of statistical tests aligned with the research questions and data structure. The use of correlation, t-tests, and chi-square tests adheres to the standards outlined in the Module 4 chart. Despite identified errors, the findings appear statistically valid, supporting the hypothesis that patient satisfaction influences inpatient admission rates across different hospital types. Future research should address these errors explicitly and consider larger sample sizes or additional variables to deepen understanding and reinforce the statistical validity of such studies.

References

• Messina, D., Scotti, D., Ganey, R., Zipp, G., & Mathis, L. (2009). The relationship between patient satisfaction and inpatient admissions across teaching and nonteaching hospitals. Journal of Healthcare Management, 54(3), 177-189.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences (10th ed.). Cengage Learning.
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• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
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