The following code (in google colaboratory / jupyter lab) was used to generate this plot.

from matplotlib import pyplot as plt values = ["power", "achievement", "hedonism", "stimulation", "self-direction", "universalism", "benevolence", "tradition", "conformity", "security"]

1. Values taken from 10.1177/1948550620933434

harm = -0.23, -0.15, -0.13, -0.08, 0.06, 0.24, 0.19, 0.08, 0.07, 0 fairness = -0.21, -0.12, -0.11, -0.07, -0.01, 0.24, 0.15, 0.03, 0.05, 0.02 loyalty = -0.03, -0.06, -0.16, -0.12, -0.21, -0.15, 0.06, 0.23, 0.23, 0.19 authority = -0.03, -0.01, -0.19, -0.17, -0.25, -0.14, 0.04, 0.27, 0.30, 0.19 purity = -0.05, -0.07, -0.20, -0.17, -0.24, -0.14, 0.06, 0.30, 0.26, 0.17 assert len(harm) == len(fairness) == len(loyalty) == len(authority) == len(purity) plt.plot(values, harm, label="harm") plt.plot(values, fairness, label="fairness") plt.plot(values, loyalty, label="loyalty") plt.plot(values, authority, label="authority") plt.plot(values, purity, label="purity") plt.xticks(values, rotation=45) plt.ylabel("coefficient") plt.title("Point estimates of correlations between moral foundations\ndimensions and basic values") plt.grid(axis='x') plt.axhline(0.0) plt.legend() plt.savefig("moral-foundations-value-correlation.svg") from google.colab import files