So continuing from my last post, the central question posed earlier is, “Does the Alexa ranking influence the amount of comments that a blog will receive?”
The Calculation
Connections such as this can be determined statistically. There’s a little tool in statistics called a “coefficient of correlation.” What this handy little tool does is tell us if there is a statistically valid connection between two or more sets of numbers. In business things that are statistically valid are generally accepted as gospel, so naturally I thought this would be the best approach.
Embarking on a project such as this can be somewhat formidable. After all, at the time of this writing Technorati, the blog tracking system, is tracking over 113 million blogs. That’s a lot of blog territory and even more comments to cover. I can’t even reasonably imagine how many comments are attached to all those blogs. Nor is there any research I consider to be reliable that tells us how many blog comments are out there. So let’s just say there are a billion comments out there.
I can’t correlate the traffic of 113 million blogs to a billion comments. The task is just too onerous. So I took a sample instead. In market research picking a sample size is always a problem. But I did it carefully and with attention to market research rules.
Being that our focus is on business blogging, my sample consisted of 20 blogs in business related fields such as finance, investing, the economy, and general business. I even threw in a few of the top corporate blogs.
Picked at random from within the above business categories, I made sure that I got blogs with both a high and low Alexa ranking. By picking blogs with high and low rankings I raised my chances of being able to answer my question. Then the tedious work began.
I noted each blog’s Alexa ranking, and then I counted all the posts and comments for a two-week period prior to my research, which I performed back on April 2nd and April 3rd of 2008. The results are shown in the table below.
|
Alexa Rank |
Number of Comments |
Number of Posts |
Average Number of Comments Per Post |
|
| web-strategist.com |
42,610 |
276 |
20 |
13.80 |
| psfk.com |
72,817 |
123 |
99 |
1.24 |
| doshdosh.com |
10,181 |
258 |
4 |
64.50 |
| mashable.com |
2,048 |
1900 |
315 |
6.03 |
| conversationagent.com |
166,142 |
91 |
13 |
7.00 |
| coca-colaconversations.com |
949,014 |
12 |
9 |
1.33 |
| Jnjbtw.com |
1,866,554 |
4 |
3 |
1.33 |
| stonyfield.typepad.com/babybabble/ |
2,594,348 |
6 |
5 |
1.20 |
| blog.delta.com/ |
2,899 |
21 |
7 |
3.00 |
| fastlane.gmblogs.com/ |
224,713 |
34 |
3 |
11.33 |
| 1000words.kodak.com/ |
6,194 |
70 |
11 |
6.36 |
| econtalk.org |
847,199 |
42 |
2 |
21.00 |
| blog.acton.org/ |
353,567 |
51 |
27 |
1.89 |
| wishfulthinking.co.uk/blog/ |
563,848 |
14 |
4 |
3.50 |
| punny.org |
191,578 |
59 |
6 |
9.83 |
| brandstrategy.wordpress.com |
2,587,030 |
0 |
3 |
0.00 |
| wallstreetexaminer.com |
281,341 |
35 |
47 |
0.74 |
| getrichslowly.org/blog |
25,545 |
1299 |
24 |
54.13 |
| natewhitehill.com |
128,358 |
8 |
2 |
4.00 |
| careerramblings.com |
938,310 |
0 |
1 |
0.00 |
| lifehack.org |
15,369 |
263 |
24 |
10.96 |
| gapingvoid.com |
46,241 |
21 |
2 |
10.50 |
So what does all this mean?
Well, if you know correlation analysis you can crunch the numbers yourself. Or you can simply return for my next post where I’ll explain the meaning of the above data.
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