Composites are a statistical technique used to combine multiple variables into a single score. They are important in relationship studies because they allow researchers to analyze the overall effect of multiple variables on a relationship, rather than just looking at each variable individually.
R1 and R2 are calculated by first standardizing the individual variables and then taking the average of the standardized values. R1 is the average of the values for one group, while R2 is the average of the values for the other group. These values are then compared to determine the strength of the relationship between the two groups.
Yes, composites can be used for non-linear relationships. However, it is important to note that the strength of the relationship may not be accurately represented by R1 and R2, as these values are based on linear associations.
One limitation of composites is that they require a large sample size in order to accurately represent the relationship between variables. Additionally, composites can only be used for continuous variables, so they may not be suitable for all types of relationships.
R1 and R2 values can range from -1 to +1, with higher values indicating a stronger relationship. They can also be compared to each other to determine which group has a stronger association with the relationship being studied.