What are best practices to compute an audio spectrogram? The spectrogram if generally defined with the squared magnitude of the fft. However, in lots of implementation, it seems that people just use the magnitude without square.

Moreover an audio signal is by convention scale between -1 and 1. This scaling often needs a supplementary step in implementations, in python language for example, which is not always do.

Finally, what are best practices to compute an audio spectrogram?
- square magnitude of the fft / magnitude of the fft ?
- Integer audio values / scaling (-1 to 1) audio values

EDIT

As the comments tell, these are questions without consequences if the aim is to plot an image of the spectrogram. 

However, I would like to use the matrix of the spectrogram as an entry point for sound analysis and recognition. In this case, the computation process matters and I find curious that the implementations differ so often.
I think, perhaps, that the statement should be reversed: that a lot of implementations "just" use the square magnitude. This is because the DFT of a real-valued signal is typically complex, so that the square magnitude is just the product of complex conjugates, e.g.:


  X*(f) &middot; X(f)


This requires less computation than the magnitude, which would follow the same algorithm, but with a subsequent square-root. 

The square magnitude of a DFT is formally known as the Power Spectrum.