Last modified on 29 June 2010, at 03:06

Using SPSS and PASW/Understanding the Variable View

The Variable View tab is another tab in the Data Editor window in addition to the Data View tab, which was discussed in the last chapter. Again, you can select between the tabs at the bottom left corner of the Data Editor Window:

05variableview01.png

This tab does not show raw data but rather shows information about the variables included in the data set. In fact, after examining the Data View, it may seem a little counter-intuitive to look at the Variable View window because the rows now show variables, not cases.

05variableview02.png

The columns provide information about the various characteristics of variables.

05variableview03.png

There are 10 columns total. Each column and its significance for variables is discussed in the table below:

Column What it Means
Name This column provides the name of the variable. Older versions of SPSS were limited to 8 character names, which is why you often find rather intriguing names for variables in data sets. New versions of SPSS are not limited to 8 characters, but lengthy descriptions should not be included in the Name. They go in the Label column.
Type This column indicates the type of variable that is reflected in this particular row. There are 8 options to choose from: Numeric, Comma, Dot, Scientific notation, Date, Dollar, Custom currency, and String. Most variables beginning users will encounter are either Numeric or String variables. Numeric variables are numbers that either represent a value (e.g., 1=Catholic) or are the value of interest (height=73 inches). String numbers are text and can only be treated as such. As a result, very few manipulations can be performed on them in SPSS.
Width This column indicates the number of spaces available for the variable values.
Decimals This column allows you to control the number of characters after the decimal place.
Label This column allows you to provide a more extensive description of the variable.
Values This column allows you to provide a key for what the numbers of a numeric variable may represent (e.g., 1=Catholic, 2=Protestant).
Missing This column allows you to indicate whether there are any missing values in a variable. Values marked as missing are excluded from analyses in SPSS.
Columns This column indicates the total number of columns a variable's values may have.
Align This column indicates the alignment of the variable in the Data View.
Measure This last column indicates the level of measurement of the variable. There are three from which you can choose: Nominal, Ordinal, and Scale.

Every variable in your data set should have all of the columns filled out such that it is clear exactly what the variable's characteristics are. However, not every column may be relevant for a variable. For instance, if you have a variable "ID" that simply provides a random indicator of a case in your data set, there is no reason to create "Values" for that variable as each case value should be unique.