There are several types of data relationships that need to be known and understood by researchers at the time of the analysis and interpretation of data that has been processed. There data relationship type that can be explained as follows.
1. symmetrical relationship
If a variable is related to other variables, but the variable is not caused by the first variable, the relationship is called a symmetrical relationship. Symmetrical relationships occur when both variables are the result of a similar factor. Example: a researcher analyzing the two variables, namely the growing of international branded consumer goods and increasing number of vehicles crowded the streets of the capital. The both variable is caused by the same factors, namely an increase in per capita income of members of the community.
2. asymmetrical relationship
When a variable is related to other variables, but the relationship is not reciprocated then this relationship is called asymmetric. For example, the relationship between hard work with success, creativity with productivity, and work ethic with efficiency.
If a variable is related to other variables, and each other reciprocally influence each other (two-way relationship), this relationship is known as reciprocity. In other words, the X variable affects Y variable, and vice versa. Example: the low level of education will affect the welfare of a person. This also applies vice versa, well-being also affect the level of education.
After learning data relationship, there is no harm in re-examination of the overall study results. It is important to check and ensure compliance is scientifically true.