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Flesch-Kincaid scores estimate how readable a passage may be from average sentence length and syllables per word. Lexical diversity measures vocabulary variation instead, so it is a separate dimension—not another readability score. Neither measure directly tests whether readers understand a text.
What do the Flesch-Kincaid scores measure?
Both formulas use two averages: average sentence length (ASL), calculated as words divided by sentences, and average syllables per word (ASW), calculated as syllables divided by words. They combine those inputs differently and their score directions are not the same.
| Measure | Formula | How to read it |
|---|---|---|
| Flesch Reading Ease | 206.835 − (1.015 × ASL) − (84.6 × ASW) | Higher scores indicate easier text. |
| Flesch-Kincaid Grade Level | (0.39 × ASL) + (11.8 × ASW) − 15.59 | A U.S. school-grade estimate; higher values indicate text with greater sentence-length and syllable demands. |
Microsoft says standard files commonly target a Reading Ease score of 60–70 and a Grade Level of about 7.0–8.0. These are Microsoft’s general guidelines, not universal cutoffs or proof that a passage suits a particular audience. Microsoft Support explains the formulas and its guidance.
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Word can show both Flesch scores in its document statistics. The exact interface can vary by Word version, so use Microsoft’s current instructions for your version. Typically, enable the option to show readability statistics in Word’s proofing settings, run spelling and grammar checking, then review the statistics when checking finishes. The report can include counts and averages as well as the readability scores.
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Language configuration matters. Microsoft says that for a document containing multiple languages, Word displays statistics for the last language checked. For some European languages in an English document, Word may show counts and averages without readability scores. When reporting or comparing results, record the tool and relevant language settings.
What is lexical diversity, and what is type-token ratio?
Lexical diversity describes variation in the vocabulary used in a text. One named measure is type-token ratio (TTR): the number of distinct word types divided by the number of word tokens. A 2020 Association for Computational Linguistics paper on readability assessment also lists MTLD, or measure of textual lexical diversity, among available measures. The paper describes these lexical-diversity measures.
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A lexical-diversity result is meaningful only with its method identified. Different measures are not interchangeable, and tokenization and preprocessing—such as how repeated words, punctuation, or word forms are handled—can affect the result. The cited paper establishes examples of measures, not one best method for every text length, genre, or purpose.
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How should you automate a readability analysis?
For a useful comparison, keep the analysis method consistent and document what the software actually counted. Before interpreting a score, decide how the text is extracted and segmented, which content is included, and how language-specific syllables and words are handled.
- Define the input. Specify whether the analysis includes headings, lists, captions, quotations, and other non-paragraph text.
- Set segmentation rules. Establish how sentences and words are identified, including how abbreviations, numbers, and punctuation are treated.
- Record language and tool settings. Note the tool, language configuration, and any syllable or tokenization rules it documents.
- Handle short or empty passages deliberately. Decide how these inputs are reported rather than assuming every tool calculates a meaningful score for them.
- Compare like with like. Use the same extraction, segmentation, and settings across drafts. Investigate a score change in the text; do not treat it alone as evidence of improvement.
These choices affect word, sentence, and syllable counts—and therefore the Flesch calculations. Do not assume that a tool handles every edge case unless its documentation says so.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What readability scores can—and cannot—tell you
Flesch-Kincaid formulas focus mechanically on average sentence length and word syllables. The Agency for Healthcare Research and Quality cautions that readability formulas have this narrow focus and that different formulas can produce noticeably different grade-level scores for the same text. The U.S. Environmental Protection Agency also cautions about formula reliability at score extremes and recommends using multiple methods for some health communication materials. AHRQ explains the limits of readability formulas; EPA discusses their use in health communication.
Use scores to flag passages for editorial review or to compare drafts under a consistent method. They do not assess whether explanations are logical, whether key ideas are clear, or whether a particular audience actually understands the text. Short words and sentences can improve a formula result without resolving those problems. For consequential materials, combine formula scores with editorial judgment and, where appropriate, audience testing.
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