Impact factors -- useless or worse?

Eugene Garfield introduced the impact factor to the world of bibliometrics in the 1960s. Unfortunately, a lot of my colleagues don't seem to understand (a) how it is calculated or (b) how useless it is. Various alternatives have been proposed. altmetrics is one approach that gets a lot of play on Twitter. I mentioned another a few months ago that is less well known but seems even more promising, since it relies on textual analysis to see how a work is used and how significant that use is.

I mention this for two reasons. First, Stephen Curry posted another analysis of impact factors a few days ago (follow-up post here), and that led to a flurry of responses. Here's his conclusion:

[C]onsider all that we know of impact factors and think on this: if you use impact factors you are statistically illiterate.

• If you include journal impact factors in the list of publications in your cv, you are statistically illiterate.
• If you are judging grant or promotion applications and find yourself scanning the applicant's publications, checking off the impact factors, you are statistically illiterate.
• If you publish a journal that trumpets its impact factor in adverts or emails, you are statistically illiterate. (If you trumpet that impact factor to three decimal places, there is little hope for you.)
• If you see someone else using impact factors and make no attempt at correction, you connive at statistical illiteracy.

(Stephen Curry)

If you don't understand why Stephen is right, read the whole post and then read this one:

There is no evidence in the published literature that journal rank has any persuasive predictive property for any measure of scientific quality, be it expert review, citations, sound methodology, anything. (Bjorn Brembs)

The second reason I bring up impact factors again is that Jonathan Eisen has created a public Mendeley group, Impact Factor, that collects published analyses of impact factors or commentaries on them. Take a look, and ignore impact factors.