# Spearman correlation coefficient

**Topics:**Spearman's rank correlation coefficient, Correlation and dependence, Pearson product-moment correlation coefficient

**Pages:**6 (950 words)

**Published:**May 14, 2014

Coyne and Messina Articles, Part 3 Spearman Coefficient Review

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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 correlation results when X and Y are related by any monotonic function, can be contrasted with the Pearson correlation, giving a perfect value only when X and Y are related by a linear function. The other reason being, exact sampling distributions can be obtained without requiring knowledge of the joint probability distribution of X and Y (Sheskin, 2003). The Spearman correlation coefficient is based on the assumption that both the predictor and response variables have numeric values, this assumption, however, the Spearman correlation coefficient can be used to analyze variables that are markedly skewed. The Spearman correlation coefficient operates on the null hypothesis that the ranks of one variable does not vary the same with the ranks of the other variable, meaning that, an increase in the ranks of one variable will most likely not produce an increase in the ranks of the other variable (Sheskin, 2003). The Spearman Correlation Coefficient formed the basis of analysis in finding out the Relationship between Patient Satisfaction and Inpatient Admissions across Teaching and Nonteaching Hospitals by Messina and Coyne. The research ‘The relationship between patient satisfaction and inpatient admission across teaching and nonteaching hospital’ was based on two main questions. The first was to determine, the nature of the relationship that existed between patient satisfaction and inpatient admissions in acute care hospitals. The Second one was to establish if the relationship between patient satisfaction and inpatient admissions differ between teaching hospitals and nonteaching hospitals. To answer these questions, the study focused on two variables, which were patient satisfaction and admissions. The study was to provide Heath Executives with information to help them have a better understanding of the relationship between patient satisfaction and admission levels as there was an increase in patient expectations in the health sector. The Spearman coefficient correlation was used to analyze relationships between the independent variable, which was patient satisfaction, which was determined using patients’ satisfaction mean score, and the dependent variable which was admissions, admissions were measured using income (Lee & John 2013). The use of Spearman coefficient correlation enabled the researchers to answer the research questions as they were able to establish that, there exist a positive correlation between patient satisfaction and admission volumes in learning hospitals, whereas, there was a negative correlation between patient satisfaction and admission volumes in non-learning hospitals. The combined learning and non-learning study, a negative, statistically significant, correlation was observed between patient satisfaction and admission volumes. Admission volumes were found to partly affect the financial performance of both learning and non learning hospitals, while patient satisfaction determined the number of people both learning and non-learning hospitals received (Wager, Lee& Glaser. 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...

References: David J.S. (2003). Handbook of Parametric and Nonparametric Statistical Procedures. Florida: CRC Press.

Arpad K, Arpad K & Yulan L.(2008). Computational Intelligence in Medical Informatics. New York: Spinger.

Karen W, Frances Lee & John G. (2013). Health Care Information Systems: A Practical Approach for Health Care Management. New Jersey: John Wiley & Sons.

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