The Flesch Reading Ease (FRES) score says how easy something is to read. J. Peter Kincaid and others this formula for the U.S. Navy in 1975.[1]

How it works

The FRES test works by counting the number of words, syllables, and sentences in the text. It then calculates the average number of words per sentence and the average number of syllables per word. The idea is that shorter words and shorter sentences are easier to read. The higher the score, the easier the text is to understand. The formula is:

${\displaystyle 206.835-1.015\left({\frac {\text{total words}}{\text{total sentences}}}\right)-84.6\left({\frac {\text{total syllables}}{\text{total words}}}\right)}$[2]

Some points of reference for the score are:[3]

Score School level Notes
100.00-90.00 5th grade Very easy to read. Easily understood by an average 11-year-old student.
70.0–60.0 8th & 9th grade Plain English. Easily understood by 13- to 15-year-old students.

The highest score possible is 121.22. It is gained if every sentence only has a one-syllable word. "The cat sat on the mat" scores 116. There is no lower limit to this score. Some very complicated sentences can have negative scores.

The Flesch score is usually lower for technical documentation because the topic itself is complicated. Someone who uses the test regularly will develop a sense of a reasonable score for this type of writing. They can then aim to align with this score.

The Flesch score for this subsection is 69.

Tools

Tools to calculate the Flesch Reading Ease include:

References

1. Kincaid, J.P., Fishburne, R.P., Rogers, R.L., & Chissom, B.S. (1975). Derivation of new readability formulas (automated readability index, fog count, and flesch reading ease formula) for Navy enlisted personnel. Research Branch Report 8–75. Chief of Naval Technical Training: Naval Air Station Memphis.
2. Flesch, Rudolf. "How to Write Plain English". University of Canterbury. Archived from the original on July 12, 2016. Retrieved 12 July 2016.
3. Flesch, Rudolf. "How to Write Plain English". University of Canterbury. Retrieved 5 February 2016.
4. michalke, m eik; Brown, Earl; Mirisola, Alberto; Brulet, Alexandre; Hauser, Laura (2017-03-02), koRpus: An R Package for Text Analysis, retrieved 2017-03-28