Hi aldus,

When you say "nonparametric multiple regression", the main actual analysis that springs to mind is quantile regression. This isn't available in SPSS though.

You mention your data not being parametric... really "parametric" and "nonparametric" are labels we usually apply to tests rather than data as such. In terms of your data there may be two distinct sets of concerns that might lead you to be hesitant about using a parametric test:

1) The distributional assumptions of multiple linear regression - most notably that the residuals from the regression model are independently and identically distributed. You may also wish to assume that the residuals are normally distributed in order to perform inferential tests, although your fairly sizeable sample provides some robustness to this assumption. Likert data is arguably not fully conducive to some of these assumptions since it isn't truly continuous, but note that *you can only really evaluate these assumptions once you've actually run your analysis*. You can't evaluate them on the basis of your raw data alone. (There are some other possibly relevant assumptions I haven't mentioned above, like linearity and absence of multicollinearity).

2) Measurement level issues. Some researchers argue that Likert scale data represents ordinal data according to the S.S Stevens measurement levels. Other researchers argue that it can safely be treated as interval (you can find plenty of articles on this with a Google Scholar search). *If* we follow the logic of the S.S Stevens measurement levels and the associated measurement theory (representationalism), *and* believe that Likert scale data is only ordinal, then performing parametric analysis with Likert data would not be appropriate. Note that "interval data" is not an intrinsic assumption of multiple linear regression; rather it's representational measurement theory that suggests we should not use such a test with ordinal data.

However, not all researchers believe that the S.S. Stevens measurement levels are actually useful or important. There are certainly other measurement theories than representationalism (e.g. latent variable theory, classical test theory), which lead us to different concerns.

Hopefully that helps clarify things a little!