Working with multiple-response data (SPSS Modeler)
You can analyze multiple-response data using a number of comparison
functions.
Available comparison functions include:
value_at
first_index / last_index
first_non_null / last_non_null
first_non_null_index / last_non_null_index
min_index / max_index
For example, suppose a multiple-response question asked for the first, second,
and third most important reasons for deciding on a particular purchase (for example, price, personal
recommendation, review, local supplier, other). In this case, you might determine the importance of
price by deriving the index of the field in which it was first included:
first_index("price", [Reason1 Reason2 Reason3])
Copy to clipboardCopied to clipboard
Similarly, suppose you asked customers to rank three cars in order of
likelihood to purchase and coded the responses in three separate fields, as follows:
Table 1. Car ranking example
customer id
car1
car2
car3
101
1
3
2
102
3
2
1
103
2
3
1
In this case, you could determine the index of the field for the car they like
most (ranked #1, or the lowest rank) using the min_index function:
The special @MULTI_RESPONSE_SET function can be used to
reference all of the fields in a multiple-response set. For example, if the three car fields
in the previous example are included in a multiple-response set named car_rankings, the
following would return the same result:
About cookies on this siteOur websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising.For more information, please review your cookie preferences options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.