Correlations

Sometimes, researchers may choose to more passively interact with a phenomenon, rather than to intervene and manipulate the behaviors of interest.

For instance, perhaps a scientist wants to know if there’s an association between eating a plant-based diet and sleep. In this case, she quantitatively measures two variables by asking participants to report how many vegetables they consumed that day, and later, how many hours they slept.

This design is known as correlational research—examining whether relationships exist between two variables.

After collecting both series of measurements, the researcher can visualize the data for each unit, in this case each person, on a graph—a scatterplot—with the variables—daily vegetable consumption and amount of sleep—placed on either axis.

Statistically, correlations are determined by calculating the correlation coefficient, commonly denoted as r—a number between -1 and 1 that indicates the direction, the sign, of the association and its overall strength, how tight the points align.

Here, the correlation could be positive, which means that the two variables move in the same direction. That is, people who consumed very few veggies slept less, whereas others who ate more, slept more.

In addition, the correlation could be strong, with a value close to 1, which indicates that the data points cluster linearly, with very few exceptions across individuals.

When the association has more exceptions, the linear pattern can disappear as the data points are spread out: the absolute value becomes closer to zero, and the correlation is considered weak to non-existent.

Now, if the two variables move in the opposite direction of each other, the correlation would be considered negative. That is, the more vegetables people consumed, the less they slept and vice versa. The strength is relatively strong, given the scatter is rather linear.

Importantly, correlation does not mean causation! Researchers would need to follow-up on observations to determine just how changes in one variable cause changes in another.