Hi Stan,
I found that Chi^2 and R-factor values are not provided by many EXAFS papers. However, according to standards of The International XAFS Society (http://ixs.iit.edu/) both these values should be reported.
Yep, many papers do not repot chi-square and r-factor. I've not always reported these myself.
My understanding is that the reduced Chi^2 should be close to 1 in ideal case. In practice however, the value is always greater than 1 because of systematic errors.
This is the convential wisdom. It's probably even right. We almost always try to measure until statistical errors are not significant.
For example, my reduced Chi^2 values are around 300. However, the R-factor values are about 3% which doesn't look as "ugly" as Chi^2 reduced. On the other hand, ifeffit overestimates uncertainties using reduced Chi^2 value. Thus, all errors are taken into account.
Well, I wouldn't say 'overestimates'. It does the equivalent of adjusting the measurement uncertainty until reducded-chi-square=1. Then it does the normal estimate of parameter uncertainties of increasing (non-reduced) chi-square by 1.
The final question: Is it appropriate to report just R-factor calculated by ifeffit and the obtained uncertainties (which look reasonable) without reporting the "ugly" Chi^2/reduced Chi^2 values?
Speaking for myself (not the IXS!!): I would say this can be appropriate. I'd also say that it can be appropriate to present _qualitative_ EXAFS analysis ("spectra A does not look at all like spectra B or C") in a paper, and not report any statistics at all -- the IXS recommendation completely ignores this, and seems to say should not happen. I want to see chi-square and/or r-factor in a paper if it's really part of the story... that is, if you're comparing two models or the quality of the fit is actually in question (generally, the gentle reader takes it on faith that you're presenting the best fit and the most reasonable model you could find). If you're reporting fits to a reasonably clear model and the point is something along the lines of "see, in this one sample, the near neighbor distance is different by 0.08Ang", I probably wouldn't care too much about r-factor and chi-square, unless the parameter uncertainties were unusual (say, +/- 0.07Ang). Then again, in a case like that, I'd probably even be able that something was different by looking at k*chi(k). Basically, if the story NEEDS the detailed statistical parameters, the paper needs them. If the story doesn't need the detailed statistical parameters, the paper is probably fine without them. Depending on the journal and the story, it may be appropriate to put such details (chi-square, epsilon, fit ranges, etc) into the 'supplemental material'. --Matt