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# Understanding the settings in Variable View

last edited by 5 years, 7 months ago

In SPSS, variable view (the tab located on the bottom left-hand corner next to data view) is the location where one will find information on their specific variables in the data set. Under the variable view tab, one will find eleven column headings: name, type, width, decimals, label, values, missing, columns, align, measure, and role. Each section tells a different thing about the variables. For the most part, when using variable view (which should be any time when using SPSS) one should double check and make sure that all sections match up with the data, and that the variables are correctly labeled for the data one is using. For an elementary statistics course, the tab sections that are the most relevant are: name, measure, type, and - depending on what type of data - decimals

For most data sets, one will focus on the measure tab. The measure tab tells the statistician what measure the variable will be in, nominal, scale, or ordinal. When checking variables in variable view it is extremely important to check and make sure the variables are correctly defined by the measures. This setting affects a lot of what SPSS can and cannot do. Putting the wrong level of measurement can affect the results one receives, such as graphs, charts, and other outputs, so it is a critical step in all statistical analyses. The name tab will usually stay unaffected unless one wants to change the name of the the variable (which cannot contain spaces), and has no effect on the actual data. The type column tells you what type of data one is working with, which is usually string or numeric. Numeric variables are strictly numbers, while string variables can be letters or numbers. Something to watch out for with the string variable is that it may contain only numbers, but functions such as finding the mean, standard deviation, etc., cannot be calculated. If one is working with nominal data, but wants to find the mean for example, then you simply change the type to numeric. The numeric type allows you to calculate more information on the data than string does. The decimals section of variable view will change the amount of decimal places the data has, but has no effect on the values of the data.

For example, data was collected about the fuel economy of multiple type of cars, the horsepower, and the miles per gallon. As one can see, there is both categorical and quantitative data in this set, so one would go to the Variable View to make sure that SPSS categorizes them correctly.

The cars are categorical data, and the horsepower and the miles per gallon quantitative. As the pictures above shows, SPSS has categorized cars as nominal and horsepower and mpg as scale variables. As nominal is a measure for categorical data and scale is a measure for quantitative, SPSS has done its job correctly. Had it put scale as the measure for cars and nominal as the measure for horsepower and mpg, then one would need to change the measures.

In the decimal tab, there are 0's for each variable. As categorical data has no decimals, and as all of the quantitative data in this set are whole numbers, this is exactly what one would want to see in this tab for this example.

In the type tab, cars are classified as string variables and horsepower and mpg as numeric. String variables are the ones containing letters (although they can contain numbers), which categorical data tends to. Numeric variables are strictly numbers. This is what one should expect to see for this data in this tab.

How to Use the Variable Tab in SPSS

• In the bottom bar, next to the Data View, click Variable View.
• Check the name of each variable in the Name column.
• Go over to the Measure Column.
• The categorical data should be listed as nominal, and the quantitative data should be listed as scale.
• If these are inaccurate, click on the little box, and then the blue arrow.
• When the list pops up, change the measure to the correct one.
• When finished, go back to the Data View.