Assessing the readability of a Newsweek.com excerpt.

This month, we're calculating the Fog Index of a sample from a July 26 Newsweek.com article ("Can Google Build the Perfect Human Being?" by Paula Mejia). Here's the clip:

"Once all of the thousands of samples are gathered, Google will use its state-of-the-art software to find molecular sequences -- or, biomarkers -- within them. These biomarkers can signal a range of specific data points about a given body; for example, a biomarker could identify a protein excess or a mutated gene. Biomarkers aren't inherently bad. They can either be a biological advantage or disadvantage depending on the person and the specifics of the biomarker. Either way, they can be a boon to medical researchers, who could, for example, cross-reference the database in order to identify those people with, say, a biomarker linked to potential diabetes risk. Those people could then be flagged, and worked with to take steps to lower their risk for developing the disease."

--Word count: 125
--Average sentence length: 21 words (23, 27, 4, 19, 32, 20)
--Words with 3+ syllables: 18 percent (22/125 words)
--Fog Index (21+18)*.4 = 15 (no rounding)

The Fog Index is fairly low for a sample of this nature. Because we're dealing with highly scientific subject matter, we can expect a higher score than, say, a sample from a pop culture piece. The author has done a good job of breaking it down for the lay reader, but perhaps we can improve the Fog score a bit:

"Once the thousands of samples are gathered, Google will use its advanced software to find molecular sequences -- or, biomarkers -- within them. These biomarkers can signal a range of precise data points about a given body. For example, a biomarker could find a protein excess or a mutated gene. Biomarkers aren't inherently bad. They can either be a biological advantage or disadvantage, depending on the person and the specifics of the biomarker. Either way, they can be a boon to medical researchers. For example, they could cross-reference the database in order to detect those people with, say, a biomarker linked to diabetes risk. Those people could then be flagged, and worked with to take steps to lower their risk of getting the disease."

--Word count: 122
--Average sentence length: 15 words (21, 14, 13, 4, 19, 10, 21, 20)
--Words with 3+ syllables: 14 percent (17/122 words)
--Fog Index (15+14)*.4 = 11 (no rounding)

We made very few changes to this piece beyond a word tweak here and there. We cut through the fog primarily by turning 6 sentences into 8. We also replaced a few longer words where possible. Overall, these minor changes reduced our sentence length average by 6 and our percentage of longer words by 4 points. Thus, we were able to cut our 15-point Fog score down to 11.

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