Correlation

The measurement of the association between two variables is referred to as correlation regardless of choosing or categorizing the dependent and independent variable(s), and the values of the coefficient of correlation lie amidst -1 and +1. A positive sign of coefficient indicates a directly proportional relationship amongst the variables, i.e. both variables move in the same direction, raise one the other is also increase and if the value of one variable will be decreased, so it will also decrease the second variable value. A correlation coefficient of 0 designates that there is no correlation between the variables. The negative sign of coefficient demonstrating a negative association amongst variables and moving in the opposite direction. For instance, if the value of one of the variable upsurges, the value of the second variable will decline. A decrease in the value of one variable leads to a rise in the value of the second variable. -1 indicates a strong negative relationship.

It's not enough to say that two variables show a positive or negative correlation. We want to be more specific about that relationship. That is, we want to be able to think about the relationship between two variables in a more quantitative fashion. For example, if two variables exhibit a positive correlation, how strong is that correlation? We're going to see that a positive correlation can have different strengths. Similarly, if two variables are negatively correlated, how strong is that correlation? Negative correlations also have varying degrees of strength.

We measure the degree of correlation with a value referred to as r, which is called the correlation coefficient. This variable r simply tells us how strong a certain relationship is. When we plot data on a scatterplot, there are many software packages, including Excel, which will calculate the value of r based on the data we have input. We don't need to know how to calculate r, but we do need to understand what it tells us.

The coefficient of correlation, r, is ranging from -1 to +1. When r = +1, it designates a perfect positive correlation amongst the two variables. When r = -1, it refers to a perfect negative correlation amongst the two variables. While r = 0, shows no correlation between the two variables. In reality, it's very rare to find r values of +1 or -1; rather, we see r values somewhere between these two extremes. Thus, a coefficient value less than 0.30 suggests weak, amongst 0.30-0.49 indicates moderate, while above 0.50 demonstrates a strong association.

How to perform Correlation in SPSS

Open SPSS, click Analyze, then Correlate, and then click Bivariate Correlation. A dialog Box will appear.