Skewness and Kurtosis are two different measures of shapes and distribution of a dataset in qualitative methods.
Skewness is a measure of the asymmetry of a distribution. It describes the degree to which the distribution deviates from a symmetric shape. A skewness value of 0 indicates a perfectly symmetric distribution. Positive skewness indicates a distribution with a longer tail on the right side, while negative skewness indicates a longer tail on the left side.
Kurtosis is a measure of the “tailedness” or “peakedness” of a distribution. It describes how the distribution’s tails and peak compare to a normal distribution. A kurtosis value of 0 indicates a distribution with a similar shape to a normal distribution. Positive kurtosis indicates a distribution with heavier tails and a more peaked shape than a normal distribution, while negative kurtosis indicates lighter tails and a less peaked shape.
We can use the same
psych package to generate skewness and Kurtosis. The
describe() function also provides other descriptive statistics, such as mean, median, standard deviation, and range. If you only want to see the skewness and kurtosis, you can use the
kurtosis() functions separately.