SPSS, originally called the Statistical Package for the Social Sciences, is powerful, easy-to-use statistical software. When SPSS users need to perform data analysis, one of the most common first steps is the transformation of data. The most common form of data transformation is normalisation. To normalise data, you must subtract the mean from the data and then rescale the data using a statistic related to the variance of the data. This can be done conveniently in SPSS.

Open your dataset in SPSS's data editor. Select "File" and choose "Open" from the drop-down menu. Select the file that contains your data. It will then open in the data editor.

  • SPSS, originally called the Statistical Package for the Social Sciences, is powerful, easy-to-use statistical software.
  • It will then open in the data editor.

Open the "Descriptives" dialogue box. Select "Analyze" on the top menu. Choose "Descriptive Statistics" from the resulting menu. Choose "Summarize." A drop-down box will appear. Here, select "Descriptives" and the dialogue box will appear.

  • Open the "Descriptives" dialogue box.
  • Here, select "Descriptives" and the dialogue box will appear.

Get the mean and standard deviation (the square of the variance) of your dataset. Select the variable for the data that you want to normalise and click on the arrow to the right. Click on the "Options" button to bring up a new dialogue box. In this dialogue box, check the "Mean" and "Std. Deviation" options, and then click "Continue." Click "OK" to display the mean and standard deviation of the data.

Compute the normalised data. Click on "Transform" at the top SPSS menu and select "Compute" from the drop-down box. Type the "normalised" in the "Target Variable" box. Under "Numerical Expression," type "(", the name of the variable for which you want to compute normalised data, "-", the mean that was displayed earlier, ")", "/" and the standard deviation that was displayed earlier. Click "OK" and the normalised data will appear in the data editor as a column under the name "normalised."