Linsear Write Formula

John O’Hayre developed Linsear Write in 1966 for the US Bureau of Land Management as a tool to help government writers produce plainer prose. It classifies every word as easy or hard based on syllable count, then calculates a grade level from the ratio of hard words to sentence length.

At a Glance


Output	US grade level
Best for	Technical and government documents, polysyllabic jargon assessment
Method	`r.linsear_write()`
What it counts	Easy words (1–2 syllables) and hard words (3+ syllables) per sentence
Minimum text	100 words (default)

When to Use This Metric

You are assessing technical or government documents where sentence complexity and polysyllabic word density are the main concerns. Linsear Write was designed for exactly this context — bureaucratic writing that uses long, technical words when shorter ones would work.
You want a fast check on jargon density without needing a word-familiarity list. Unlike Dale-Chall and Spache, Linsear Write uses no word list. It treats all 3+ syllable words as hard, regardless of domain.
You are comparing two drafts of a technical document against each other. Linsear Write is consistent within a domain, which makes it useful for relative comparisons even when the absolute grade level is approximate.
You want a secondary check to validate results from another metric. Use it alongside ARI or Flesch-Kincaid rather than as the sole measure for important decisions.

How It Works

Linsear Write labels every word as easy (1–2 syllables) or hard (3+ syllables). Easy words contribute 1 point each; hard words contribute 3 points each. The formula sums those points, divides by the number of sentences, then converts the result to a grade level.

intermediate = (easy_words × 1 + hard_words × 3) / sentences

if intermediate > 20:  grade level = intermediate / 2
if intermediate ≤ 20:  grade level = (intermediate − 2) / 2

The two-branch conversion was added to the original formula at some point after O’Hayre published it. The reasoning behind the split at 20 is not documented.

O’Hayre’s original 1966 formula was actually a writing-quality scale — he called it a measure of “writeability,” not readability. A score of 70–80 on his scale meant the prose was appropriate for a general adult reader; 80 was ideal. The plain-language context matters: his goal was to move government writers away from passive voice and bureaucratic phrasing by rewarding monosyllabic words. The Air Force later adopted the formula for its own technical writing programs, which is why many sources attribute it to the Air Force. O’Hayre and the Bureau of Land Management are the documented origin.

Score Interpretation

Linsear Write returns a grade level directly. The table below gives context for what each level represents in practice.

Score	Grade Level	Reading Context
5–6	Grades 5–6	Elementary school
7–8	Grades 7–8	Middle school
9–10	Grades 9–10	Early high school
11–12	Grades 11–12	Late high school
13+	College+	Post-secondary

Government plain-language guidelines generally recommend grade 8 or lower for public-facing documents. A Linsear Write score above 12 on a technical document suggests a high density of polysyllabic terms that may slow non-specialist readers.

Return Values

r.linsear_write() returns a LinsearWriteResult object:

Field	Type	Description
`score`	`float`	Raw Linsear Write score (grade level as a float)
`grade_levels`	`list[str]`	Grade level rounded to nearest integer, e.g. `["10"]`
`grade_level`	`str`	The primary grade level (first item in `grade_levels`)

Minimum Requirements

The library defaults to a 100-word minimum. Passing fewer words raises a ValueError. You can lower this threshold with Readability(text, min_words=50), but scores from short texts are even less reliable for Linsear Write than for other metrics. The original formula requires a 100-word sample to operate as designed.

Limitations

The grade-level conversion has unknown authorship and limited validation. The formula that maps the intermediate score to a grade level was added after O’Hayre’s original work. Who created it and how it was validated against actual reading comprehension is not documented. Independent testing has found it can be off by up to two grade levels.
Full-text statistics replace the 100-word sample. O’Hayre’s formula operates on a 100-word passage. This library computes the same calculation across the entire text. This is a practical approximation that reduces variance but deviates from the original specification. Long texts will produce more stable scores; short texts near the 100-word minimum will behave closest to the original design.
Syllable count treats all polysyllabic words the same. “Beautiful” (three syllables) and “perpendicular” (five syllables) both count as hard words, even though most readers find “beautiful” easy. Linsear Write cannot distinguish between common long words and rare technical terms. Dale-Chall handles this better for general-audience texts.
Not designed as a comprehension predictor. O’Hayre built this formula to change writer behavior, not to measure reader comprehension. Using it to guarantee that readers of a specific grade level will understand the text goes beyond what the formula was designed to do. SMOG and Flesch-Kincaid have substantially more comprehension validation research.

Example

from readscore import Readability

text = """
Federal land managers must weigh the environmental consequences of proposed
actions before approving new permits. The review process requires coordination
between multiple agencies and public comment periods that can extend for months.
Applicants should submit documentation demonstrating compliance with applicable
regulations and environmental standards.

When an application involves activities with significant potential impacts, an
Environmental Impact Statement may be required. This document must analyze
alternatives, assess cumulative effects, and identify mitigation measures.
The review process is structured to ensure transparency and public participation
at each stage. Agencies must respond to substantive comments and explain how
those comments influenced the final decision.
"""

r = Readability(text)
result = r.linsear_write()

print(f"Score: {result.score:.1f}")        # Score: ~11.2
print(f"Grade: {result.grade_levels}")    # ['11']
print(f"Grade level: {result.grade_level}")  # 11

The text above contains many polysyllabic words common to government documents: “environmental,” “consequences,” “coordination,” “documentation,” “compliance.” These push the score into the high school range, which matches what a plain-language review would flag for revision.