Republican Primary Candidates Grammar Power Rankings

At Grammarly, we believe that every time we write, we make a statement. Technology encourages fast-paced typing and textspeak—and while we don’t think that’s always bad—we do think it can fuel misconceptions and get out of hand.

In the interest of fun and a little gamesmanship, we’ve started a series of studies to award Grammar Power Rankings to different categories of commenters across the web. After a quick look at NFL and MLB fans, we’ve decided to focus on the upcoming presidential race, starting with the Republicans (who begin their GOP primary debates this week).

Stay tuned for a potential study about Democratic candidates’ supporters in the near future. For now, check out this infographic for the results and methodology from the GOP study:

GOP Debate Grammar Power Rankings To share this infographic with your blog readers, embed this in your blog post by pasting the following HTML snippet into your web editor:

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We began by taking a large sample of Facebook comments containing at least fifteen words from each candidate’s official page. Next, we created a set of guidelines to help limit (as much as possible) the subjectivity of categorizing the comments as positive or negative. Since the point of the study was to analyze the writing of each candidate’s supporters, we considered only obviously positive or neutral comments. Obviously negative or critical comments, as well as ambiguous or borderline negative comments, were disqualified.

We then randomly selected two hundred fifty of these positive and neutral comments to analyze for each candidate. Using Grammarly, we identified the errors in the comments, which were then verified and tallied by a team of live proofreaders. For the purposes of this study, we counted only black-and-white mistakes such as misspellings, wrong and missing punctuation, misused or missing words, and subject-verb disagreement. We ignored stylistic variations such as the use of common slang words, serial comma usage, and the use of numerals instead of spelled-out numbers.

Finally, we calculated the average number of mistakes per one hundred words by dividing the total word count of the comments by the total number of mistakes for each candidate.


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