Bivariate correlation is one of the methods used in SPSS research.

This analysis involves comparing two variables at time to test if they are positively, negatively related or no relation between them.

A correlation coefficient has a value ranging from -1 to 1. Coefficient value near one indicates strong positive correlation while coefficient near zero indicates weak positive or no correlation. Coefficient value near negative one indicates strong negative correlation while coefficient near zero indicates weak negative or no correlation.

But the most efficient way to truly tell the correlation is by looking at significant value. For continuous data use Pearson Correlation coefficient and for ranked data either use Kendall’s taub or Spearman’s Correlation coefficient.

While using bivariate correlation you can use options in the bivariate dialogue to display sum of squares and cross-products of the variables under consideration. In this case sum of squares represent the variance and the sum of cross-product represent the covariance which is important to SPSS researchers.

Scatter plot is another method of bivariate statistics used in SPSS research to screen data.
Here you plot the independent variable on the x-axis against the dependent variable on the y-axis. Fit in the line of best fit, if the line upward sloping the there is positive relationship and if the line downward sloping then there is a negative relationship else if there is horizontal line there is no relationship. The more the plotted points are near the line of best fit the stronger the relationship and vice versa.

This bivariate statistics is use in SPSS research as a diagnostic test in linear regression analysis to determine the y-intercept and gradient when dealing with the independent variable and one dependent.

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Bivariate correlation is one of the methods used in SPSS research.

This analysis involves comparing two variables at time to test if they are positively, negatively related or no relation between them.

A correlation coefficient has a value ranging from -1 to 1. Coefficient value near one indicates strong positive correlation while coefficient near zero indicates weak positive or no correlation. Coefficient value near negative one indicates strong negative correlation while coefficient near zero indicates weak negative or no correlation.

But the most efficient way to truly tell the correlation is by looking at significant value. For continuous data use Pearson Correlation coefficient and for ranked data either use Kendall’s taub or Spearman’s Correlation coefficient.

While using bivariate correlation you can use options in the bivariate dialogue to display sum of squares and cross-products of the variables under consideration. In this case sum of squares represent the variance and the sum of cross-product represent the covariance which is important to SPSS researchers.

Scatter plot is another method of bivariate statistics used in SPSS research to screen data.

Here you plot the independent variable on the x-axis against the dependent variable on the y-axis. Fit in the line of best fit, if the line upward sloping the there is positive relationship and if the line downward sloping then there is a negative relationship else if there is horizontal line there is no relationship. The more the plotted points are near the line of best fit the stronger the relationship and vice versa.

This bivariate statistics is use in SPSS research as a diagnostic test in linear regression analysis to determine the y-intercept and gradient when dealing with the independent variable and one dependent.