Bivariate analysis is concerned with the relationships between pairs of variables (X, Y) in a data set. Bivariate analysis, explores the concept of association between two variables. Bivariate analysis is based on how two variables simultaneously change together, that is, the notion of co-variation.
Bivariate analysis is the simultaneous analysis of two variables. Bivariate analysis is usually undertaken to see if one variable is related to another variable. Multivariate analysis is the simultaneous analysis of three or more variables. It is frequently done to refine a bivariate analysis, taking into account the possible influence of a third variable on the original bivariate relationship. Multivariate analysis is also used to test the joint effects of two or more variables upon a dependent variable.
Using bivariate analysis we test hypotheses of
"association" and causality. Association refers to the extent to which it
becomes easier to know/predict a value for the Dependent variable if we know a case's
value on the independent variable.
Each dot represents a paired value from the sample, and the scattergram reveals a typical oval shape that is due to central tendency (Sprinthall 1990,200).
Bivariate analysis helps compare and control two or more related variables in situations where quality depends on the combine effect of these variables. This method is most useful when two different variables work together to affect the acceptability of a process or part thereof.
The bivariate analysis should be useful in supporting or failing to support the arguments of dependency or dependent development theorists that there is an association between dependency and underdevelopment - The question of dependency and economic development: a quantitative analysis By Brian R. Farmer.