Correlation Matrix and Partial Correlation: Explaining Relationships
One of the thrust areas in the management research is to find the ways and means to improve productivity. It is therefore important to know the variables that affect it. Once these variables are identified, an effective strategy may be adopted by prioriti
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Correlation Matrix and Partial Correlation: Explaining Relationships
Learning Objectives After completing this chapter, you should be able to do the following: • Learn the concept of linear correlation and partial correlation. • Explore the research situations in which partial correlation can be effectively used. • Understand the procedure in testing the significance of product moment correlation and partial correlation. • Develop the hypothesis to test the significance of correlation coefficient. • Formulate research problems where correlation matrix and partial correlation can be used to draw effective conclusion. • Learn the application of correlation matrix and partial correlation through case study discussed in this chapter. • Understand the procedure of using SPSS in computing correlation matrix and partial correlation. • Interpret the output of correlation matrix and partial correlation generated in SPSS.
Introduction One of the thrust areas in the management research is to find the ways and means to improve productivity. It is therefore important to know the variables that affect it. Once these variables are identified, an effective strategy may be adopted by prioritizing it to enhance the productivity in the organization. For instance, if a company needs to improve the sale of a product, then its first priority would be to ensure its quality and then to improve other variables like resources available to the marketing team, their incentive criteria, and dealer’s scheme. It is because of the fact that the product quality is the most important parameter in enhancing sale. J.P. Verma, Data Analysis in Management with SPSS Software, DOI 10.1007/978-81-322-0786-3_4, # Springer India 2013
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4 Correlation Matrix and Partial Correlation: Explaining Relationships
To find how strongly a given variable is associated with the performance of an employee, an index known as product moment correlation coefficient “r” may be computed. The product moment correlation coefficient is also known as correlation coefficient, and it measures only linear relation between two variables. When we have two variables that covary, there are two possibilities. First, the change in a thing is concomitant with the change in another, as the change in a child’s age covaries with his weight, that is, the older, the heavier. When higher magnitude on one variable occurs along with higher magnitude on another and the lower magnitudes on both also occur simultaneously, then the things vary together positively, and we denote this situation as positive correlation. In the second situation, two things vary inversely. In other words, the higher magnitudes of one variable go along with the lower magnitudes of the other and vice versa. This situation is denoted as negative correlation. The higher magnitude of correlation coefficient simply indicates that there is more likelihood that if the value of one variable increases, the value of other variable also increases or decreases. However, correlation coefficient does not reveal the real rela
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