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The Fog Index

Posted on Wednesday, September 29, 2010 at 1:27 PM

Assessing the readability of a book excerpt published on NPR.com.

This month, we examine a passage from NPR.com (an excerpt from Tom Bissell's book, Extra Lives: Why Video Games Matter):

"For a while I hoped that my inability to concentrate on writing and reading was the result of a charred and overworked thalamus. I knew the pace I was on was not sustainable and figured my discipline was treating itself to a Rumspringa. I waited patiently for it to stroll back onto the farm, apologetic but invigorated. When this did not happen, I wondered if my intensified attraction to games, and my desensitized attraction to literature, was a reasonable response to how formally compelling games had quite suddenly become. Three years into my predicament, my discipline remains AWOL. Games, meanwhile, are even more formally compelling."

--Word count: 105
--Average sentence length: 18 words (23, 20, 14, 32, 9, 7)
--Words with 3+ syllables: 22 percent (23/105 words)
--Fog Index: (18+22) x .4 = 16 (no rounding)

In this case, the average sentence length is within reasonable range. The 32-word sentence skews the average -- without it, average sentence length is 15 words. However, it is the high percentage of long words that makes this Fog score so high.

Let's try revising the sample to improve our score:

"For a while, I hoped that my trouble concentrating on reading and writing was the result of a charred brain. I knew my pace was unsustainable and figured my discipline had treated itself to a Rumspringa. I waited patiently for its return to the farm, contrite but refreshed. When this didn't happen, I wondered if my intense attraction to games and waning attraction to books was a response to how formally compelling games had become. Three years into my problem, my discipline remains AWOL. Games, meanwhile, are even more formally compelling."

--Word count: 92
--Average sentence length: 15 words (20, 16, 13, 27, 9, 7)
--Words with 3+ syllables: 13 percent (12/92 words)
--Fog Index: (15+13) x .4 = 11 (no rounding)

Overall, we were able to trim word count by 13. The language in the original sample was quite dense, so reducing our Fog score was largely a matter of eliminating longer words and trimming excess language.

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