Science, Tech, Math › Math Correlation Analysis in Research Comparing the Relationships Between Variables of Sociological Data Share Flipboard Email Print Pew Research Center Math Statistics Statistics Tutorials Formulas Probability & Games Descriptive Statistics Inferential Statistics Applications Of Statistics Math Tutorials Geometry Arithmetic Pre Algebra & Algebra Exponential Decay Functions Worksheets By Grade Resources View More By Ashley Crossman Updated July 15, 2019 Correlation is a term that refers to the strength of a relationship between two variables where a strong, or high, correlation means that two or more variables have a strong relationship with each other while a weak or low correlation means that the variables are hardly related. Correlation analysis is the process of studying the strength of that relationship with available statistical data. Sociologists can use statistical software like SPSS to determine whether a relationship between two variables is present, and how strong it might be, and the statistical process will produce a correlation coefficient that tells you this information. The most widely used type of correlation coefficient is the Pearson r. This analysis assumes that the two variables being analyzed are measured on at least interval scales, meaning they are measured on a range of increasing value. The coefficient is calculated by taking the covariance of the two variables and dividing it by the product of their standard deviations. Understanding the Strength of Correlation Analysis Correlation coefficients can range from -1.00 to +1.00 where a value of -1.00 represents a perfect negative correlation, which means that as the value of one variable increases, the other decreases while a value of +1.00 represents a perfect positive relationship, meaning that as one variable increases in value, so does the other. Values like these signal a perfectly linear relationship between the two variables, so that if you plot the results on a graph it would make a straight line, but a value of 0.00 means that there is no relationship between the variables being tested and would be graphed as separate lines entirely. Take for example the case of the relationship between education and income, which is demonstrated in the accompanying image. This shows that the more education one has, the more money they will earn in their job. Put another way, these data show that education and income are correlated and that there is a strong positive correlation between the two—as education rises, so too does income, and the same kind of correlation relationship is found between education and wealth as well. The Utility of Statistical Correlation Analyses Statistical analyses like these are useful because they can show us how different trends or patterns within society might be connected, like unemployment and crime, for example; and they can shed light on how experiences and social characteristics shape what happens in a person's life. Correlation analysis lets us say with confidence that a relationship does or does not exist between two different patterns or variables, which allows us to predict the probability of an outcome among the population studied. A recent study of marriage and education found a strong negative correlation between the level of education and the divorce rate. Data from the National Survey of Family Growth show that as education level increases among women, the divorce rate for first marriages decreases. It's important to keep in mind, though, that correlation is not the same as causation, so while there exists a strong correlation between education and divorce rate, that does not necessarily mean the decrease in divorce among women is caused by the amount of education received.