Correlational Analysis
... two variables may also be identified as a negative correlation, which can be just as important as a positive correlation. Two variables are negatively correlated when one goes up while the other goes down, and vise versa. For example, when self-esteem increases the rate of teenage pregnancy decreases. Using correlational statistics to test a hypothesis makes causation difficult to evaluate. In a causal analysis, a variable is defined as the independent or casual factor in a relationship, and the other variable defined as the dependent or the variable caused by the independent variable. Correlation simply means that the two factors occur at the same time. It does not say anything about their causal relationship (cause-effect relationship), which variable is the cause; which variable is the result. There are circumstances when one would not use correlational statistics. For example, if one’s hypothesis were excessive eating causes obesity, than one would test to see if excessive eating is indeed the direct cause to obesity. However, if someone wanted to test just the existence and strength of the relationship between the two variables than the hypothesis would now read: excessive eating is correlated to becoming obese. There are circumstances when a causal relationship cannot be found between two variables. For example, it is found that there is a relationship between the height and weight of an adult human, such that taller people tend to weigh more than shorter people. While this can be a proven correlative relationship, it is not a causal relationship. It would be a mistake to assume that increasing someone's weight would also increase his/her height. Both variables are determined by other causes, which are chiefly genetics and childhood nutrition. Those underlying causes are the ones that need to be identified and manipulated in any causal system. Using my Political Science Research paper that I wrote last fall, I would like to describe correlational analysis in more depth. My research question was: why do pregnancy rates, girls 15-19, vary from state to state? A correlation analysis was performed to determine the type of relationship between teen pregnancy rates and the percentage of children under 18 living in families headed by a single parent. Pearson Correlation was used to measure how linear the relationship is with values that range from 0 to 1. It also ...