How do you measure the strength of correlation in social sciences?

1 Types of correlation

There are different types of correlation, depending on the nature and scale of the variables involved. The most common one is the Pearson correlation coefficient, which measures the linear relationship between two continuous variables, such as height and weight. Another type is the Spearman correlation coefficient, which measures the monotonic relationship between two ordinal or ranked variables, such as grades and satisfaction. A third type is the point-biserial correlation coefficient, which measures the relationship between a continuous variable and a binary variable, such as income and gender.

2 How to calculate correlation

To calculate the correlation coefficient between two variables, you need to have a sample of paired observations for each variable, and then apply a formula that depends on the type of correlation. For example, the Pearson correlation coefficient is calculated by dividing the covariance of the two variables by the product of their standard deviations. The covariance is a measure of how much the two variables vary together, and the standard deviations are measures of how much they vary individually. Alternatively, you can use a software tool, such as Excel, SPSS, or R, to compute the correlation coefficient for you.

3 How to interpret correlation

The correlation coefficient tells you how strong and in what direction the relationship between the two variables is. A positive correlation means that as one variable increases, the other also tends to increase. A negative correlation means that as one variable increases, the other tends to decrease. The closer the correlation coefficient is to 1 or -1, the stronger the relationship is. The closer it is to 0, the weaker the relationship is. However, there are some caveats to keep in mind when interpreting correlation.

4 Caveats of correlation

First, correlation does not imply causation. There may be other factors that influence the relationship between the two variables, such as confounding variables, reverse causality, or spurious correlations. For example, ice cream sales and shark attacks are positively correlated, but it does not mean that ice cream causes shark attacks. Rather, both are influenced by a third variable, which is the temperature. To establish causation, you need to conduct an experiment or use a causal inference method.

Second, correlation is sensitive to outliers and non-normality. Outliers are extreme values that deviate from the rest of the data, and non-normality means that the data does not follow a bell-shaped distribution. Both can affect the accuracy and validity of the correlation coefficient. For example, if you have a few very wealthy people in your sample, they can inflate the correlation between income and happiness. To deal with outliers and non-normality, you can use robust methods, such as trimming or transforming the data.

Third, correlation is not a comprehensive measure of relationship. It only captures the linear or monotonic aspect of the relationship, but not the nonlinear or complex aspect. For example, if you plot the relationship between age and happiness, you may find a U-shaped curve, where happiness decreases in midlife and increases in later life. However, the correlation coefficient may be close to zero, because it does not account for the curvature. To capture the nonlinear or complex aspect of the relationship, you can use other methods, such as regression or curve fitting.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?