How to Collapse Ordinal Data in SPSS

Written by katina coleman | 13/05/2017
How to Collapse Ordinal Data in SPSS
Collapsing ordinal data affects the interpretation of your results. ( Images)

Ordinal data is a level of measurement with an implied ranking but no distinction to the magnitude between levels. Collapsing ordinal data can increase the interpretability of your results and analyses. You may also want to collapse or combine categories with a low sample size with an adjacent value. Typically, you want to have a strong justification for collapsing your data and should include the rationale in your write-ups. Statistical Package for the Social Sciences (SPSS) is statistical software that permits simple and advanced analyses, such as collapsing data.

Establish the new categories and values for your collapsed data and their connection with your original ordinal data values.

Open your SPSS software.

Select "Transform" from the main menu, scroll down and select either "Recode into Same Variables" or "Recode into Different Variables." Choosing "Recode into Same Variables" overwrites your current ordinal data, whereas "Recode into Different Variables" creates a new variable for your collapsed data.

Select the variable name of the ordinal data you want to collapse. Drag to the "Input Variable -> Output Variable:" box or use the arrow button.

Click "Old and New Values."

Enter the originally coded ordinal data values and their new values individually. Enter the original ordinal data code value or select a range of values under "Old Value." Also, enter the "New Value." Click "ADD." Repeat process until all new codes are added. Click "Continue" when you finish.

Enter the new variable name and label, a short description of the variable, if you selected "Recode Into Different Variable" under "Output Variable." Click "Change."

Select "OK" and SPSS will recode and collapse your ordinal data as specified. .


  • Check the new values of your collapsed ordinal data for accuracy

Tips and Warnings

  • Check the new values of your collapsed ordinal data for accuracy


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