Skewness, and Kurtosis in SPSS

Introduction

This tutorial will guide you through the process of checking skewness and kurtosis using SPSS. Understanding these two statistical concepts is essential for analyzing the distribution of your data. Skewness measures the asymmetry of the distribution, while kurtosis assesses the "tailedness." Both metrics help in determining the normality of your data, which is crucial for many statistical analyses.

Step 1: Prepare Your Data in SPSS

1. Open SPSS: Launch the SPSS software on your computer.
2. Import Data:
   • Click on File in the top menu.
   • Select Open and choose your data file (e.g., .sav, .csv).
3. Check Data Structure: Ensure your data is properly organized with variables in columns and observations in rows.

Step 2: Access the Descriptive Statistics Functions

1. Navigate to Analyze Menu:
   • Click on Analyze in the top menu.
2. Select Descriptive Statistics:
   • Hover over Descriptive Statistics.
   • Click on Descriptives.

Step 3: Configure Descriptive Statistics

1. Select Variables:
   • In the dialog box, select the variable(s) for which you want to calculate skewness and kurtosis.
   • Move them to the right box using the arrow button.
2. Set Options:
   • Click on the Options button.
   • In the new dialog, check the boxes for Skewness and Kurtosis.

Step 4: Run the Analysis

1. Execute the Command:
   • Click Continue to return to the main dialog.
   • Then click OK to run the analysis.
2. View Output:
   • SPSS will generate an output window with the results, including skewness and kurtosis values.

Step 5: Interpret Results

1. Understand Skewness:
   • A skewness value of 0 indicates a symmetrical distribution.
   • Positive values indicate a right skew (tail on the right), while negative values indicate a left skew (tail on the left).
2. Understand Kurtosis:
   • A kurtosis value of 0 (using excess kurtosis) indicates a normal distribution.
   • Positive values suggest a distribution with heavier tails (leptokurtic), and negative values indicate lighter tails (platykurtic).

Practical Tips

• Check Normality: Use skewness and kurtosis values to assess the normality of your data. Generally, a skewness value between -2 and +2 and a kurtosis value between -7 and +7 suggests normality.
• Visualize Data: Complement your analysis with visualizations like histograms or Q-Q plots for a better understanding of data distribution.
• Common Pitfalls: Ensure your data does not contain outliers, as they can significantly affect skewness and kurtosis values.

Conclusion

In this tutorial, you learned how to check skewness and kurtosis in SPSS. By following these steps, you can assess the normality of your data effectively. Understanding these concepts is vital for proper statistical analysis, so consider applying these techniques to your datasets. For further exploration, practice with different datasets or delve into other SPSS functionalities related to data distribution analysis.