How To Calculate Kurtosis In SPSS

SPSS Command for Kurtosis:

DESCRIPTIVES /STATISTICS=MEAN,STDDEV,MIN,MAX,SKEWNESS,KURTOSIS

Variable Name:

variable

Dataset Size:

observations

Analysis Type:

Output Format:

SPSS Command Output:

Unit Converter ▲

Unit Converter ▼

From:	To:

1. What Is Kurtosis?

Kurtosis is a statistical measure that describes the shape of a distribution's tails in relation to its overall shape. It measures whether the data are heavy-tailed or light-tailed relative to a normal distribution.

2. SPSS Command For Kurtosis

The primary SPSS command for calculating kurtosis:

DESCRIPTIVES /STATISTICS=MEAN,STDDEV,MIN,MAX,SKEWNESS,KURTOSIS

Where:

DESCRIPTIVES - Main command for descriptive statistics
/STATISTICS= - Specifies which statistics to calculate
KURTOSIS - Includes kurtosis calculation in output
MEAN, STDDEV, MIN, MAX, SKEWNESS - Additional descriptive statistics

Explanation: This command generates comprehensive descriptive statistics including kurtosis, which is dimensionless and helps understand the tail behavior of your data distribution.

3. Interpreting Kurtosis Values

Details:

Mesokurtic (Kurtosis ≈ 0): Normal distribution tails
Leptokurtic (Kurtosis > 0): Heavy tails, more outliers
Platykurtic (Kurtosis < 0): Light tails, fewer outliers

Kurtosis helps identify potential outliers and understand the risk in statistical modeling.

4. Using The SPSS Calculator

Tips: Enter your variable name, dataset size, select analysis type and output format. The calculator will generate the appropriate SPSS syntax for your analysis needs.

5. Frequently Asked Questions (FAQ)

Q1: What is the difference between skewness and kurtosis?
A: Skewness measures asymmetry of the distribution, while kurtosis measures the tail heaviness relative to a normal distribution.

Q2: What does a kurtosis value of 3 mean?
A: In some statistical packages, a kurtosis of 3 indicates a normal distribution. SPSS typically reports excess kurtosis (actual kurtosis minus 3).

Q3: When should I be concerned about kurtosis?
A: High kurtosis (>2) may indicate outliers that could affect parametric tests. Low kurtosis (<-2) may suggest lack of extreme values.

Q4: Can kurtosis affect statistical tests?
A: Yes, extreme kurtosis can violate normality assumptions required for many parametric tests like t-tests and ANOVA.

Q5: How do I handle high kurtosis in my data?
A: Consider data transformations, non-parametric tests, or robust statistical methods if kurtosis is extreme.