- How do you read kurtosis in SPSS?
- What does a positive kurtosis mean?
- What does excess kurtosis mean?
- Is high kurtosis good or bad?
- What does a negative kurtosis value mean?
- How do you interpret kurtosis in descriptive statistics?
- What does kurtosis mean in SPSS?
- How do you interpret descriptive statistics in SPSS?
- How do you know if kurtosis is significant?
- What is the range for kurtosis?
- How do you interpret skewness and kurtosis in SPSS?
- How do you solve kurtosis?

## How do you read kurtosis in SPSS?

How to Calculate Skewness and Kurtosis in SPSSClick on Analyze -> Descriptive Statistics -> Descriptives.Drag and drop the variable for which you wish to calculate skewness and kurtosis into the box on the right.Click on Options, and select Skewness and Kurtosis.Click on Continue, and then OK.Result will appear in the SPSS output viewer..

## What does a positive kurtosis mean?

Positive values of kurtosis indicate that a distribution is peaked and possess thick tails. … An extreme positive kurtosis indicates a distribution where more of the values are located in the tails of the distribution rather than around the mean.

## What does excess kurtosis mean?

Excess kurtosis means the distribution of event outcomes have lots of instances of outlier results, causing fat tails on the bell-shaped distribution curve. Normal distributions have a kurtosis of three. Excess kurtosis can, therefore, be calculated by subtracting kurtosis by three.

## Is high kurtosis good or bad?

Kurtosis is only useful when used in conjunction with standard deviation. It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good). Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).

## What does a negative kurtosis value mean?

A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value.

## How do you interpret kurtosis in descriptive statistics?

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 does kurtosis mean in SPSS?

Kurtosis – Kurtosis is a measure of the heaviness of the tails of a distribution. In SAS, a normal distribution has kurtosis 0. … Kurtosis is positive if the tails are “heavier” than for a normal distribution and negative if the tails are “lighter” than for a normal distribution.

## How do you interpret descriptive statistics in SPSS?

Interpret the key results for Descriptive StatisticsStep 1: Describe the size of your sample.Step 2: Describe the center of your data.Step 3: Describe the spread of your data.Step 4: Assess the shape and spread of your data distribution.Compare data from different groups.

## How do you know if kurtosis is significant?

The same numerical process can be used to check if the kurtosis is significantly non normal. A normal distribution will have Kurtosis value of zero. So again we construct a range of “normality” by multiplying the Std. Error of Kurtosis by 2 and going from minus that value to plus that value.

## What is the range for 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).

## How do you interpret skewness and kurtosis 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 solve kurtosis?

x̅ is the mean and n is the sample size, as usual. m4 is called the fourth moment of the data set. m2 is the variance, the square of the standard deviation. The kurtosis can also be computed as a4 = the average value of z4, where z is the familiar z-score, z = (x−x̅)/σ.