Coyne and Messina Articles, Part 3 Spearman Coefficient Review

Student Name

Instructor’s Name

Institution

Date
Coyne and Messina Articles, Part 3 Spearman Coefficient Review
The Spearman Correlation Coefficient remains one of the most important nonparametric measures of statistical dependence between two variables. The Spearman Correlation Coefficient facilitates the assessment of two variables using a monotonic function. This representation is only possible if the variables are perfect monotones of each other and if there are no repeated data values. This enables one to obtain a perfect Spearman correlation of either +1 or -1. The Spearman correlation coefficient nonparametric because, a perfect Spearman …show more content…

2013).
.
The researchers in this study effectively applied the Spearman correlation coefficient in this study as they were dealing with a case with few observations, and so the correlation coefficient was used to effectively quantify the data as this is one of the attributes of Spearman correlation coefficient. By using the Spearman coefficient correlation, the researchers were able to record values that can only be determined from descriptive attributes that have varying intensities. Using the Spearman coefficient correlation enabled the researchers to make generalizations while exploring the two variables, admissions and patient satisfaction. This generalization is necessary as there were other factors that determined customer satisfaction such as the quality of communication, perception of service provider competence, quality of facilities, the hospital staff conduct, and perception satisfaction and patient costs (Wager, Lee& Glaser. 2013).
The advantages of using Spearman correlation coefficient to study the relationship between patient satisfaction and admissions were; The fact that the correlation coefficient can be used to assess relationships for both continuous sets of data and discreet sets of data, the correlation was effective as the researchers were able to show correlations even when the actual values of the variables were unknown. This is because Spearman