How to Run a Data Normality Test in SPSS for a Thesis

One of the most crucial stages in statistical analysis for a thesis is testing classical assumptions, where the data normality test plays an important role. This test ensures that the regression model you use meets the assumption of normal distribution, which is a prerequisite for many parametric statistical methods.

If the data is not normally distributed, the interpretation of your analysis results may become biased or invalid. That is why understanding how to perform a proper normality test using SPSS is an essential skill for every student.

In this guide, we will comprehensively discuss the steps for conducting a data normality test in SPSS, from data preparation to interpreting the results.

Before going further, let us first understand the essence of a normality test. A normality test is a statistical procedure used to determine whether your research data has a normal or approximately normal distribution.

A normal distribution is often called a Gaussian distribution or bell curve because of its bell-shaped graph. Normally distributed data is symmetric around the mean, with most observations close to the average and fewer observations farther away.

Why does this matter? Because many parametric statistical tests, such as the t-test, ANOVA, and linear regression, assume that sample data comes from a normally distributed population so that the inference is accurate.

In SPSS, there are several methods you can use to test data normality. The two most common methods, and the ones most often recommended for thesis work, are:

• Kolmogorov-Smirnov (K-S) test: Commonly used for large samples, usually more than 50 observations.
• Shapiro-Wilk test: Generally recommended for small samples, usually 50 observations or fewer.

Both tests produce a significance value (Sig.) or p-value, which becomes the basis for deciding whether the data is normally distributed.

Follow these steps to run a data normality test in SPSS:

Make sure you have entered all of your research variable data into SPSS. Each variable should be in a different column, and each row should represent one observation or respondent.

For example, if you have data for “Learning Interest” and “Academic Achievement,” ensure both variables are correctly entered in the SPSS “Data View.”

The Shapiro-Wilk test is often used for small samples. Here are the steps:

1. Click Analyze > Descriptive Statistics > Explore...
2. In the “Explore” dialog box, move the variable you want to test from the list on the left into the Dependent List box on the right.
3. Click the Plots... button.
4. In the “Explore Plots” dialog box, check Normality plots with tests.
5. Click Continue, then OK.

SPSS will display several tables, including the “Tests of Normality” table.

The Kolmogorov-Smirnov test is suitable for large samples. Follow this guide:

1. Click Analyze > Nonparametric Tests > Legacy Dialogs > 1-Sample K-S...
2. In the “One-Sample Kolmogorov-Smirnov Test” dialog box, move the variable you want to test into the Test Variable List.
3. Make sure the Normal option under “Test Distribution” is checked.
4. Click OK.

SPSS will display the “One-Sample Kolmogorov-Smirnov Test” table.

After getting the output from SPSS, the next step is understanding what it means. Our main focus is the Significance (Sig.) value or p-value.

The decision rule is the same for both tests, Kolmogorov-Smirnov and Shapiro-Wilk:

• If Sig. > 0.05, then the data is normally distributed.
• If Sig. < 0.05, then the data is not normally distributed.

The value 0.05 is the standard significance level (alpha) commonly used in research. You can think of Sig. as a decision indicator. If the Sig. value is greater than 0.05, it is a green light that the normality assumption is met.

Example interpretation:

Suppose that in the “Tests of Normality” table, the Shapiro-Wilk Sig. value for the “Learning Interest” variable is 0.125. Because 0.125 > 0.05, you can conclude that the “Learning Interest” data is normally distributed.

Sometimes your data may not meet the normality assumption. Do not worry, there are several options to consider:

• Data transformation: Apply transformations such as logarithm, square root, or inverse to move the data closer to a normal distribution.
• Use non-parametric statistical tests: If transformation does not help, consider non-parametric tests that do not require the normality assumption, such as the Mann-Whitney test, Wilcoxon test, or Kruskal-Wallis test.
• Increase the sample size: In some cases, non-normal data may be caused by a sample that is too small.

The best option depends on your data characteristics and research objectives.

A normality test is an important foundation in statistical data analysis, especially when you use parametric methods. By following this step-by-step SPSS guide, you now have a stronger understanding of how to conduct and interpret a normality test for your thesis data.

Making sure your data is normally distributed, or taking the right corrective steps when it is not, will significantly improve the validity of your research results.

If you are still facing difficulties or need further guidance in the SPSS data analysis process, Bimbingan Informal is ready to help. Our expert team provides professional consultation and thesis guidance services to help keep your research accurate and on track. Feel free to contact us for the support you need.