Many tanks to Jon Peck. It looks as if there might be something peculiar with my installation, but at any rate his offline suggestion seems to have addressed the problem.
djr
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Daniel Robertson
Senior Research and Planning Associate
Institutional Research and Planning
Cornell University / irp.cornell.edu
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Daniel J. Robertson
Sent: Friday, March 18, 2011 3:13 PM
To: [hidden email]
Subject: [SPSSX-L] Overriding Automatic Assignment of Measurement Level to New Variables?
When SPSS computes a new variable, it assigns a measurement level to that variable based on a set of conditions which are described in Help (I’m on 19, but I believe this automatic assignment has been in place for several releases). However, we’ve noticed that SPSS often classifies new variables as Nominal when, based on the documented conditions, they seem like they should be Continuous.
For example, with Employee Data.sav open, if I run the command “compute varNew = sum(salary, salbegin)”, the resulting variable is assigned a measurement level of Nominal, even though both of the source variables are Continuous and the new variable appears to meet the documented conditions for being automatically assigned a Continuous measurement level. One of my colleagues thinks this behavior changed with v19. However, I can’t find any documentation of a change in v19, and I can’t find anything in the Knowledgebase on the support site to suggest a bug (resolution 59054 is similar but relates to importing Excel files, and this is occurring with SAV files).
So, assuming this is behaving as designed, is there a way to override this automatic assignment of measurement level, and tell SPSS to set all new variables to Continuous by default? I’ve tried in Options setting the unique value variable under Reading External Data to ‘1’, but it doesn’t appear to be making a difference. I suppose I could get in the habit of explicitly setting the measurement level of all imported and computed variables, which in some ways might be good practice, but it would be nice if there were a simpler solution.
Thanks,
Dan
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Daniel Robertson
Senior Research and Planning Associate
Institutional Research and Planning
Cornell University / irp.cornell.edu
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