What are acceptable skewness and kurtosis values?

Both skew and kurtosis can be analyzed through descriptive statistics. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 when utilizing SEM (Brown, 2006).

What is significant skewness and kurtosis?

You can interpret the values as follows: “Skewness assesses the extent to which a variable’s distribution is symmetrical. For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked. Likewise, a kurtosis of less than –1 indicates a distribution that is too flat.

What is critical measure skewness?

If skewness is less than −1 or greater than +1, the distribution is highly skewed. If skewness is between −1 and −½ or between +½ and +1, the distribution is moderately skewed. If skewness is between −½ and +½, the distribution is approximately symmetric.

What does a kurtosis of 5 mean?

Distributions with large kurtosis exhibit tail data exceeding the tails of the normal distribution (e.g., five or more standard deviations from the mean). Distributions with low kurtosis exhibit tail data that are generally less extreme than the tails of the normal distribution.

How do you calculate skewness and kurtosis?

1. Formula & Examples

Sample Standard deviation S=√∑(x-ˉx)2n-1.
Skewness =∑(x-ˉx)3(n-1)⋅S3.
Kurtosis =∑(x-ˉx)4(n-1)⋅S4.

How do you report skewness and kurtosis?

For skewness, if the value is greater than + 1.0, the distribution is right skewed. If the value is less than -1.0, the distribution is left skewed. For kurtosis, if the value is greater than + 1.0, the distribution is leptokurtic. If the value is less than -1.0, the distribution is platykurtic.