187: Kurtosis in JASP

In this video, we go over the Kurtosis statistic in JASP. The kurtosis statistic will tell us if our data are not normally distributed, or how kurtotic our data is.

A positive statistic will mean a leptokurtic distribution (peaked), a negative one will mean a platykurtic distribution (heavy-tailed), and zero represents no kurtosis.  

When it comes to the magnitude of this statistic there are a few different cutoffs or conventions in the literature. Some are more strict and put the cut-off at -1 to 1 (Hair et al., 2017).

Others are less strict and put that cut off at -2 to 2 (Gravetter & Wallnau, 2014) or -1.5 to 1.5 (Tabachnick & Fidell, 2014).

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd Ed. Thousand Oaks, CA: Sage

Gravetter, F. and Wallnau, L. (2014) Essentials of Statistics for the Behavioral Sciences. 8th Edition, Wadsworth, Belmont, CA.

Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics. 6th Edition, Pearson.

Related Videos:
186: Skewness in JASP 186: Skewness in JASP
188: Shapiro-Wilk test of normality in JASP 188: Shapiro-Wilk test of normality i...

0:00 Intro
0:19 Calculating kurtosis
0:43 Interpreting kurtosis statistica

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