# 2014 Blog stats, and why Blogging >> Articles

The readership of the blog has continued to grow. Here are the total site views per month since the beginning in December 2011.

At this point we can start to see some seasonal patterns. I take a big hit in December and January, and increases when school is in session. I get quite a bit of my traffic from SPSS searches, so I presume much of the traffic are students using SPSS.

I do not worry too much about posting regularly, but I like to take some time if I have not published anything in around 2 weeks. I just enjoy taking a break from a specific work projects, and often I blog about something I have dealt with multiple times (or answered peoples questions multiple times) so I like making a blog post for my own and others reference.

Now, one of the more popular posts I have written is Odds Ratios NEED To Be Graphed On Log Scales. This I published in October 2013, recieved around 100 referrals from twitter the day I published it, and since has averaged about 5-10 views per day (it has accumulated a total of near 3,000 total). It is one of the first sites returned for odds ratio graph from a google search.

I don’t take my work as venerable as Ken Rothman’s (obviously he is a well respected and influential epidemiologist or methodologist more generally for his books), but I disagree with his reasoning for using linear scales in some circumstances in the referenced article. My general response to the Rothman example is that if you want to show absolute risk differences then show them. Plotting the ratios on an arithmentic scale is misleading, and while close for his example is still not as accurate as just plotting the risk differences. In Rothman et al.’s example plotting the odds ratios would result in an overestimate of the absolute risk differences by over 10%! (The absolute risk difference is `90 - 1 = 89`, whereas the linear difference between the odds is `10 - .01 = 9.99`. The former mapped onto a scale from 0 to 10 would result in a length of 8.9, so an over estimate of `(9.99 - 8.9)/8.9 ~ 12%`.)