Next
The product difference test is not a separate statistical test in its own right. Instead, it enables you to apply statistical testing (using the column proportions or column means tests) to all combinations of categories in a number of variables. One use for this is to identify those attributes of tested products that show significant differences between products.
The test creates a table specification by breaking down a number of variables, known as difference attributes, added to the side of the table, and creating a separate row for each category in each variable, and for each combination of categories from the variables.
For example, if you use the variables education and biology as difference attributes, the test first creates one row for each category (for simplicity, these examples omit the Not Answered categories, but if you choose to include them the table will also include rows for those categories):
education Yes education No biology Yes biology No
If you request two combinations of difference attributes, it also creates a row for each combination of categories in the two variables to give the following rows:
education Yes education No biology Yes biology No education Yes biology Yes education Yes biology No education No biology Yes education No biology No
Having created the side of the table, the test applies the column proportions and/or column means test to the table, using the columns in the variable specified on the top of the table. The test also hides any rows that do not contain significant results, and sorts the rows by significance. The end result is a table that displays a detailed breakdown of significant results by combination of categories.
You can further break down the analysis of the difference attributes by placing another variable (known as the inner variable) on the side of the table. Instead of creating one row for each single category and combination of categories, IBM® SPSS® Data Collection Survey Reporter creates a whole section. For example, you could add gender as the inner variable to give the following rows:
education Yes gender Base education Yes gender Male education Yes gender Female education No gender Base education No gender Male education No gender Female biology Yes gender Base biology Yes gender Male biology Yes gender Female biology No gender Base biology No gender Male biology No gender Female education Yes biology Yes gender Base education Yes biology Yes gender Male education Yes biology Yes gender Female education Yes biology No gender Base education Yes biology No gender Male education Yes biology No gender Female education No biology Yes gender Base education No biology Yes gender Male education No biology Yes gender Female education No biology No gender Base education No biology No gender Male education No biology No gender Female
Rows are also created for the base of the inner variable and for non-categorical items such as means.
You can configure the test so that it displays all results, or only those results that are statistically significant.
The statistical formulae for the test are as shown for the column proportions and the column means test. See Statistical Formula for the Column Proportions Test and Statistical Formula for the Column Means Test.