Generally speaking it is the coefficient that produces a ratio between variables of 1:1. If the variables are of a dependent/independent framework, I find that Chronbach's or Pearson's produces the most accurate (desirable) results.
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The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.
A correlation coefficient represents the strength and direction of a linear relationship between two variables. A correlation coefficient close to zero indicates a weak relationship between the variables, where changes in one variable do not consistently predict changes in the other. However, it is important to note that a correlation coefficient of zero does not necessarily mean there is no relationship between the variables, as non-linear relationships may exist.
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.
The weakest correlation coefficient is 0, which means there is absolutely no relationship between the two variables you are correlating.
The strength of the linear relationship between two quantitative variables is measured by the correlation coefficient. The correlation coefficient, denoted by "r," ranges from -1 to 1. A value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. The closer the absolute value of the correlation coefficient is to 1, the stronger the linear relationship between the variables.