Formula to detect the ease of reading a text according to the Coleman-Liau index (1975)
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
.editorconfig
.gitignore
.npmrc
.prettierignore
.travis.yml
index.js
license
package.json
readme.md
test.js

readme.md

coleman-liau Build Status Coverage Status

Formula to detect the ease of reading a text according to the Coleman-Liau index.

Installation

npm:

npm install coleman-liau

Usage

var colemanLiau = require('coleman-liau')

// For:
//
// Existing computer programs that measure readability are
// based largely upon subroutines which estimate number of
// syllables, usually by counting vowels. The shortcoming
// in estimating syllables is that it necessitates
// keypunching the prose into the computer. There is no
// need to estimate syllables since word length in letters
// is a better predictor of readability than word length
// in syllables. Therefore, a new readability formula was
// computed that has for its predictors letters per 100
// words and sentences per 100 words. Both predictors can
// be counted by an optical scanning device, and thus the
// formula makes it economically feasible for an
// organization such as the U.S. Office of Education to
// calibrate the readability of all textbooks for the
// public school system.
//
// Containing 5 sentences, 119 words, and 639 letters or digits.
colemanLiau({sentence: 5, word: 119, letter: 639}) // => 14.53042...

API

colemanLiau(counts)

Given an object containing the number of words (word), the number of sentences (sentence), and the number of letters (letter) in a document, returns the grade level associated with the document.

Related

  • automated-readability — Uses character count instead of error-prone syllable parser
  • dale-chall-formula — Uses a dictionary, suited for higher reading levels
  • flesch — Uses syllable count
  • flesch-kincaid — Like flesch-formula, returns U.S. grade levels
  • gunning-fog — Uses syllable count, hard to implement with a computer (needs POS-tagging and Named Entity Recognition)
  • smog-formula — Like gunning-fog-index, without needing advanced NLP
  • spache-formula — Uses a dictionary, suited for lower reading levels

License

MIT © Titus Wormer