The Use of Spearman Rank Correlation Coefficient by Messina and Coyne Term Paper

The Use of Spearman Rank Correlation Coefficient by Messina and Coyne - Term Paper Example From this research it is clear that in statistics the Spearman’s rank correlation coefficient (SRCC), also known as the Spearman’s rho, is greatly used. This provision derives its name from Charles Spearman and may be denoted by symbols rs or P. Maturi and Abdelfattah clearly explain that as a non-parametric measure used to gauge statistical independence that may exist between two variables, the Spearman’s rho is used to assess the most appropriate way of describing two variables through the use of a monotonic function. The Spearman’s rho is used mostly when both independent and dependent variables are ordinal, or when one of the variables is a continuous one, and the other an ordinal numeric. Conversely, Spearman’s rho may be used when the variables being measured are both continuous. In the article by Messina, Scott, Ganey, Zipp and Mathis it is clear that the use of the Spearman’s rho is very plausible. This is because patient satisfaction is not only the independent variable in Messina’s research analysis but also a continuous variable. At the same time, patient admission across teaching and nonteaching hospitals acts as both a dependent and continuous variable. Thus, in this work by Messina et al. the Spearman’s rho is used to study the relations that exist between the dependent variable and independent variable, and these variables are being represented by the volume that has been measured by admissions and the patient contentment mean score respectively. The use of Spearman’s rho’s correlation analysis is seen in the fact that Messina and his companions carried out the analysis on a pooled sample of seven nonteaching and seven teaching hospitals. It is from this development that the differences between the subsamples in teaching and nonteaching hospitals are analyzed through the use of Mann-Whitney U-Test. From this juncture, it became possible to determine whether or not there is an e xplicit association between admissions and patient satisfaction mean score in respect to the two variables. As one reads the study, it becomes clear that the mean for all admissions in teaching and nonteaching hospitals is 19,111 within the time frame of 1999-2003. The range is from 4,513 to 70,465. The mean score for the aggregate patient satisfaction is 82.57 within the five-year timeframe. The minimum aggregate is 79.0 while the maximum is 86.18. The use of Kurtosis as a form of descriptive analysis indicates that the mean score for patient satisfaction was normally distributed. In a nutshell, the use of the Spearman’s rho indicates a strong negative correlation between hospital admission within a given sample and patient satisfaction (rs = -.287, P = .018). The import of all these results is that lower inpatient volumes (in both teaching and nonteaching hospitals) are compatible with or relatable to higher patient satisfaction mean scores. 2) Comments on the Variables Use d and Spearman Rank Correlation Coefficient It is a fact that the variables that have been used by Messina, Scott, Ganey, Zipp and Mathis (2009) are very appropriate and applicable to the determination of the Spearman’s rho. Spearman’s rho determines the statistical independence between two variables, and it is a fact that Messina and his group use two variables: patient satisfaction and inpatient admissions in teaching and nonteaching hospitals. Again, just as Maturi and Abdelfattah (2008) explain, the Spearman’s rho is applicable in an area where one variable is continuous and the other an ordinal numeric, or where both variables are continuous in nature. Messina’s et al. (2009) independent (patient satisfaction) and dependent (inpatient admissions in teaching and nonteaching hospitals) variables are both continuous. These characteristics make the variables used qualify for Spearman’s rho analysis, so that, in light of the specific requirements o f SRCC, there is