Computes a Monte Carlo p-value (250,000 replications) for the null hypothesis that the sample data come from a normal distribution. If the p-value is small (e.g., less than .05) and excess kurtosis is positive, then the normality assumption can be rejected due to leptokurtosis. If the p-value is small (e.g., less than .05) and excess kurtosis is negative, then the normality assumption can be rejected due to platykurtosis.
test.kurtosis(y)vector of quantitative scores
Returns a 1-row matrix. The columns are:
Kurtosis - estimate of kurtosis coefficient
Excess - estimate of excess kurtosis (kurtosis - 3)
p - Monte Carlo two-sided p-value
y <- c(30, 20, 15, 10, 10, 60, 20, 25, 20, 30, 10, 5, 50, 40, 95)
test.kurtosis(y)
#> Kurtosis Excess p
#> 4.8149 1.8149 0.0394
# Should return:
# Kurtosis Excess p
# 4.8149 1.8149 0.0385