Spearman correlation coefficient
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 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
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 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
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.