How do you interpret skewness and kurtosis values in SPSS?

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 leptokurtik. If the value is less than -1.0, the distribution is platykurtik.

How do you interpret skewness and kurtosis results?

A general guideline for skewness is that if the number is greater than +1 or lower than –1, this is an indication of a substantially skewed distribution. For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked.

What is the acceptable range of skewness and kurtosis SPSS?

A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable. Skewness: the extent to which a distribution of values deviates from symmetry around the mean.

What are acceptable values for skewness and kurtosis?

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 does skewness and kurtosis tell us?

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers.

How do you interpret kurtosis value?

If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails). If the kurtosis is less than 3, then the dataset has lighter tails than a normal distribution (less in the tails).

What is considered a high kurtosis value?

It measures the amount of probability in the tails. The value is often compared to the kurtosis of the normal distribution, which is equal to 3. If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails).

What does a positive kurtosis mean?

Positive values of kurtosis indicate that a distribution is peaked and possess thick tails. A leptokurtic distribution has a higher peak and taller (i.e. fatter and heavy) tails than a normal distribution.

What is acceptable level of kurtosis?

The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.

What is an acceptable kurtosis value?

How do you interprete kurtosis and skewness value in SPSS?

In SPSS, the skewness and kurtosis statistic values should be less than ± 1.0 to be considered normal. 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.

What does it mean to have sample skew in SPSS?

Skewness in SPSS First off, “skewness” in SPSS always refers to sample skewness: it quietly assumes that your data hold a sample rather than an entire population. There’s plenty of options for obtaining it.

What does the mean, the kurtosis, and the skewness mean?

SPSS computes SE for the mean, the kurtosis, and the skewness A small value indicates a greater stability or smaller sampling err Measures of the shape of the distribution (measures of the deviation from normality) Kurtosis: a measure of the “peakedness” or “flatness” of a distribution. A kurtosis value near zero indicates a shape close to normal.

What does the skewness of the standard error mean?

Standard error is designed to be a measure of stability or of sampling error. SPSS computes SE for the mean, the kurtosis, and the skewness A small value indicates a greater stability or smaller sampling err Measures of the shape of the distribution