Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel
Open accessAbstract
Three readability formulas were recalculated to be more suitable for Navy use. The three formulas are the Automated Readability Index (ARI), Fog Count, and Flesch Reading Ease Formula. They were derived from test results of 531 Navy enlisted personnel enrolled in four technical training schools at two Navy bases: Naval Air Station at Memphis, and Great Lakes Navy Training Center. Personnel were tested for their reading comprehension level according to the comprehension section of the Gates-McGinitie reading test. At the same time, they were tested for their comprehension of 18 passages taken from Rate Training Manuals.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An anonymous survey of over 1000 British undergraduate students found that 30% reported using GenAI tools to generate text for coursework in November 2023, rising to 64% in December 2024.
“Second, an anonymous survey 29 of over 1000 British undergraduate students revealed that 30% reported using GenAI tools to generate text for their coursework in November 2023, a figure that rose to 64% in December 2024 2 .”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [29]
The Introduction describes a study of readability formulas for Navy materials, and the Subjects section describes Navy enlistees tested in 1974. Searches for the British undergraduate GenAI survey and its 30% and 64% figures found no passage addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 9
- Farr, J. N., Jenkins, J. J., & Paterson, D. G. Simplification of Flesch Reading Ease Formula. Journal of Applied Psychology, 1951, 35, 333-357.
- Flesch, R. F. A new readability yardstick. Journal of Applied Psychology, 1948, 32, 221- 223.
- Johnson, K. A., Relova, R. P. & Stafford, J. P. An analysis of the relationship between Air Force Procedural Manuals and discrepancies involving noncompliance with the procedures, M. A. Thesis, Air Force Institute of Technology, 1972 (AD 750 917).
- Kincaid, J. P., & Delionbach, L. J. Validation of the Automated Readability Index: A follow-up. Human Factors, 1973, 15, 17-20.
- Klare, G. R. The measurement of readability. Ames, Iowa: Iowa State University Press, 1963.
- McDaniel, W. C. Inter-analyst reliability and time measures for the Automated and Manual Flesch Counts. M. A. Thesis, Georgia Southern College, 1974.
- Pinning, J. Proposed training specifications for new weapon systems procurement and relationship to technical manuals. In C. R. Vest (Ed.), Naval education and training: a symposium (January 10-11, 1974;
- Rankin, E. F., & Culhane, J. O. Comparable cloze and multiple-choice comprehension test scores. Journal of Reading, 1969, 13, 193-198.
- Thomas, G., Hartley, R. D., & Kincaid, J. P. Test-retest and inter-analyst reliability of the Automated Readability Index, Flesch Reading Ease Score, and the Fog Count. Journal of Reading Behavior. (In Press)
Cited by
Sorted by citations
A six-item short-form survey for measuring headache impact: The HIT-6™
Kosinski, M., Bayliss, M. S., Bjorner, J. B., Ware, J. E., Garber, W. H., Batenhorst, A., Cady, R., Dahlöf, C. G. H., Dowson, A., & Tepper, S. (2003) · Quality of Life Research · doi:10.1023/a:1026119331193 · Cited by 1,376 · see citing works
Development and validation of the user version of the Mobile application rating scale (uMARS)
Stoyanov, S. R., Hides, L., Kavanagh, D. J., & Wilson, H. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.5849 · Cited by 872 · see citing works
Neumann, A., van Lier, P. A. C., Gratz, K. L., & Koot, H. M. (2010) · Assessment · doi:10.1177/1073191109349579 · Cited by 521 · see citing works
Calamaro, C. J., Mason, T. B. A., & Ratcliffe, S. J. (2009) · Pediatrics · doi:10.1542/peds.2008-3641 · Cited by 499 · see citing works
Rolffs, J. L., Rogge, R. D., & Wilson, K. G. (2018) · Assessment · doi:10.1177/1073191116645905 · Cited by 504 · see citing works
Revisiting readability: a unified framework for predicting text quality
Pitler, E., & Nenkova, A. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613742 · Cited by 375 · see citing works
Self-Affirmation Reduces Biased Processing of Health-Risk Information
Reed, M. B., & Aspinwall, L. G. (1998) · Motivation and Emotion · doi:10.1023/a:1021463221281 · Cited by 374 · see citing works
The Perinatal Anxiety Screening Scale: Development and preliminary validation
Somerville, S., Dedman, K., Hagan, R., Oxnam, E., Wettinger, M., Byrne, S., Coo, S., Doherty, D., & Page, A. C. (2014) · Archives of Women's Mental Health · doi:10.1007/s00737-014-0425-8 · Cited by 302 · see citing works
Batterham, P. J., Ftanou, M., Pirkis, J., Brewer, J. L., Mackinnon, A. J., Beautrais, A., Fairweather-Schmidt, A. K., & Christensen, H. (2015) · Psychological Assessment · doi:10.1037/pas0000053 · Cited by 264 · see citing works
Best practices for the human evaluation of automatically generated text
van der Lee, C., Gatt, A., van Miltenburg, E., Wubben, S., & Krahmer, E. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8643 · Cited by 205 · see citing works
“…conceivable that these criteria can be assessed using more objective measures, similar to existing readability measures (e.g., Ambati et al., 2016; Kincaid et al., 1975; Pitler and Nenkova, 2008; Vajjala and Meurers, 2014), where objective text metrics (e.g. average word length, average parse…”
BackgroundSentiment Analysis in the Age of Generative AI
Krugmann, J. O., & Hartmann, J. (2024) · Customer Needs and Solutions · doi:10.1007/s40547-024-00143-4 · Cited by 189 · see citing works
“We extract features including word count, the frequency of lengthy words (more than seven letters), the proportion of capitalized words (e.g., “GREAT”), netspeak (a measure of informal language usage), and the grade level score (an indicator of text complexity) [37].”
Opportunities and challenges of text mining in materials research
Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182 · see citing works
Mei, X., Meng, C., Liu, H., Kong, Q., Ko, T., Zhao, C., Plumbley, M. D., Zou, Y., & Wang, W. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2024.3419446 · Cited by 150 · see citing works
Do Massively Pretrained Language Models Make Better Storytellers?
See, A., Pappu, A., Saxena, R., Yerukola, A., & Manning, C. D. (2019) · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/k19-1079 · Cited by 139 · see citing works
Development and Initial Validation of the Duke Misophonia Questionnaire
Rosenthal, M. Z., Anand, D., Cassiello-Robbins, C., Williams, Z. J., Guetta, R. E., Trumbull, J., & Kelley, L. D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.709928 · Cited by 117 · see citing works
Measuring the complexity of the law: the United States Code
Katz, D. M., & Bommarito, M. J. (2014) · Artificial Intelligence and Law · doi:10.1007/s10506-014-9160-8 · Cited by 117 · see citing works
Plagiarism Detection Without Reference Collections
Meyer zu Eissen, S., Stein, B., & Kulig, M. (2007) · Studies in Classification, Data Analysis, and Knowledge Organization · doi:10.1007/978-3-540-70981-7_40 · Cited by 106 · see citing works
An Assessment of Attitudes, Behaviors, and Outcomes of Patients with Type 2 Diabetes
Daly, J. M., Hartz, A. J., Xu, Y., Levy, B. T., James, P. A., Merchant, M. L., & Garrett, R. E. (2009) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2009.03.080114 · Cited by 101 · see citing works
Carrasco-Farré, C. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01174-9 · Cited by 84 · see citing works
Readability of radiology reports: implications for patient-centered care
Martin-Carreras, T., Cook, T. S., & Kahn, C. E. (2019) · Clinical Imaging · doi:10.1016/j.clinimag.2018.12.006 · Cited by 87 · see citing works
Assessment of length and readability of informed consent documents for COVID-19 vaccine trials
Emanuel, E. J., & Boyle, C. W. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.10843 · Cited by 78 · see citing works
An online stress management workbook for breast cancer
Carpenter, K. M., Stoner, S. A., Schmitz, K., McGregor, B. A., & Doorenbos, A. Z. (2014) · Journal of Behavioral Medicine · doi:10.1007/s10865-012-9481-6 · Cited by 80 · see citing works
Benson, T., Sladen, J., Liles, A., & Potts, H. W. W. (2019) · BMJ Open Quality · doi:10.1136/bmjoq-2018-000394 · Cited by 77 · see citing works
Gershon, R. R. M., Magda, L. A., Qureshi, K. A., Riley, H. E. M., Scanlon, E., Carney, M. T., Richards, R. J., & Sherman, M. F. (2010) · Journal of Occupational & Environmental Medicine · doi:10.1097/jom.0b013e3181f43872 · Cited by 77 · see citing works
Paragraph-level Simplification of Medical Texts
Devaraj, A., Marshall, I., Wallace, B., & Li, J. J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.395 · Cited by 79 · see citing works
Classification of human- and AI-generated texts: Investigating features for ChatGPT
Mindner, L., Schlippe, T., & Schaaff, K. (2023) · arXiv · doi:10.48550/arxiv.2308.05341 · Cited by 73
An objective analysis of quality and readability of online information on COVID-19
Wrigley Kelly, N. E., Murray, K. E., McCarthy, C., & O’Shea, D. B. (2021) · Health and Technology · doi:10.1007/s12553-021-00574-2 · Cited by 69 · see citing works
Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ill
Hickman, R. L., Pignatiello, G. A., & Tahir, S. (2018) · Western Journal of Nursing Research · doi:10.1177/0193945917723828 · Cited by 69 · see citing works
Fergus, T. A., Valentiner, D. P., Gillen, M. J., Hiraoka, R., Twohig, M. P., Abramowitz, J. S., & McGrath, P. B. (2012) · Psychological Assessment · doi:10.1037/a0025776 · Cited by 68 · see citing works
Computerized Adaptive Tests for Rapid and Accurate Assessment of Psychopathology Dimensions in Youth
Gibbons, R. D., Kupfer, D. J., Frank, E., Lahey, B. B., George-Milford, B. A., Biernesser, C. L., Porta, G., Moore, T. L., Kim, J. B., & Brent, D. A. (2020) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.08.009 · Cited by 60 · see citing works
Public understanding of climate change terminology
Bruine de Bruin, W., Rabinovich, L., Weber, K., Babboni, M., Dean, M., & Ignon, L. (2021) · Climatic Change · doi:10.1007/s10584-021-03183-0 · Cited by 62 · see citing works
PANDORA Talks: Personality and Demographics on Reddit
Gjurković, M., Karan, M., Vukojević, I., Bošnjak, M., & Snajder, J. (2021) · Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media · doi:10.18653/v1/2021.socialnlp-1.12 · Cited by 58 · see citing works
Ante, L. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101252 · Cited by 52 · see citing works
Shah, A., Ranka, P., Dedhia, U., Prasad, S., Muni, S., & Bhowmick, K. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01410110 · Cited by 47 · see citing works
Beadman, M., Das, R. K., Freeman, T. P., Scragg, P., West, R., & Kamboj, S. K. (2015) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.03.013 · Cited by 45 · see citing works
Tepe, M., & Emekli, E. (2024) · Patient Education and Counseling · doi:10.1016/j.pec.2024.108307 · Cited by 42 · see citing works
Cicero: Multi-Turn, Contextual Argumentation for Accurate Crowdsourcing
Chen, Q., Bragg, J., Chilton, L. B., & Weld, D. S. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300761 · Cited by 41 · see citing works
Vercell, A., Gasteiger, N., Yorke, J., & Dowding, D. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104931 · Cited by 36 · see citing works
de Winter, J. (2024) · Scientometrics · doi:10.1007/s11192-024-04939-y · Cited by 34 · see citing works
“…classical readability indexes, including the Automated Readability Index (Senter & Smith, 1967), SMOG grade (McLaughlin, 1969), Flesch-Kincaid grade level (Kincaid et al., 1975), Flesch Reading Ease (Flesch, 1948), Coleman-Liau index (Coleman & Liau, 1975), and Gunning fog index (Gunning, 1952).”
Forensic scientists’ conclusions: How readable are they for non-scientist report-users?
Howes, L. M., Kirkbride, K. P., Kelty, S. F., Julian, R., & Kemp, N. (2013) · Forensic Science International · doi:10.1016/j.forsciint.2013.04.026 · Cited by 34 · see citing works
de Jong, S., Paananen, V., Tag, B., & van Berkel, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710946 · Cited by 32 · see citing works
Toward Clinical-Grade Evaluation of Large Language Models
Moreno, A. C., & Bitterman, D. S. (2024) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.11.012 · Cited by 28 · see citing works
Adapting a sexual violence primary prevention program to Ghana utilizing the ADAPT-ITT framework
Munro-Kramer, M. L., Rominski, S. D., Seidu, A.-A., Darteh, E. K. M., Huhman, A., & Stephenson, R. (2020) · Violence Against Women · doi:10.1177/1077801219828533 · Cited by 27 · see citing works
Writing to Learn from Multiple-Source Inquiry Activities in History
Wiley, J., Steffens, B., Britt, M. A., & Griffin, T. D. (2014) · Writing as a Learning Activity · doi:10.1163/9789004265011_007 · Cited by 25 · see citing works
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23 · see citing works
How well informed is the informed consent for cancer clinical trials?
Malik, L., Kuo, J., Yip, D., & Mejia, A. (2014) · Clinical Trials · doi:10.1177/1740774514548734 · Cited by 18 · see citing works
Gasteiger, N., Vercell, A., Davies, A., Dowding, D., Khan, N., & Davies, A. (2022) · Journal of Community Genetics · doi:10.1007/s12687-022-00579-y · Cited by 16 · see citing works
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
Joseph, S., Chen, L., Trienes, J., Göke, H., Coers, M., Xu, W., Wallace, B., & Li, J. J. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.459 · Cited by 17 · see citing works
Yang, R., Zeng, Q., You, K., Qiao, Y., Huang, L., Hsieh, C.-C., Rosand, B., Goldwasser, J., Dave, A., Keenan, T., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/60601 · Cited by 16 · see citing works
Carvalho, F. A., Elkins, M. R., Franco, M. R., & Pinto, R. Z. (2019) · Physiotherapy · doi:10.1016/j.physio.2018.11.003 · Cited by 15 · see citing works
Improvement in reading performance through training with simulated thalamic visual prostheses
Rassia, K. E. K., & Pezaris, J. S. (2018) · Scientific Reports · doi:10.1038/s41598-018-31435-0 · Cited by 13 · see citing works
Ulug, E., Gunesli, I., Acıkgoz Pinar, A., & Yildiz, B. O. (2025) · Nutrition Research · doi:10.1016/j.nutres.2024.11.005 · Cited by 12 · see citing works
(2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-72038-8 · Cited by 11 · see citing works
Deng, J., Li, L., Oosterhof, J. J., Malliaras, P., Silbernagel, K. G., Breda, S. J., Eygendaal, D., Oei, E. H., & de Vos, R.-J. (2025) · Musculoskeletal Science and Practice · doi:10.1016/j.msksp.2025.103275 · Cited by 12 · see citing works
Exploring the change in scientific readability following the release of ChatGPT
Alsudais, A. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101679 · Cited by 9 · see citing works
Agentic LLM Workflows for Generating Patient-Friendly Medical Reports
Sudarshan, M., Shih, S., Yee, E., Yang, A., Zou, J., Chen, C., Zhou, Q., Chen, L., Singhal, C., & Shih, G. (2024) · arXiv · doi:10.48550/arxiv.2408.01112 · Cited by 8 · see citing works
“This value is calculated using a predefined formula incorporating the average sentence length, number of syllables and number of words per sentence [16] and can be accessed by importing the readability module [17].”
MethodUgas, M., Calamia, M. A., Tan, J., Umakanthan, B., Hill, C., Tse, K., Cashell, A., Muraj, Z., Giuliani, M., & Papadakos, J. (2025) · Patient Education and Counseling · doi:10.1016/j.pec.2024.108560 · Cited by 8 · see citing works
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Tripto, N., Uchendu, A., Le, T., Setzu, M., Giannotti, F., & Lee, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.916 · Cited by 6 · see citing works
Improving Text Simplification with Factuality Error Detection
Ma, Y., Seneviratne, S., & Daskalaki, E. (2022) · Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) · doi:10.18653/v1/2022.tsar-1.16 · Cited by 3 · see citing works
Mak, M. H. C., & Walasek, L. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100507 · Cited by 1 · see citing works
“To assess formality, we pre-registered seven linguistic features: Four lexical: (1) lexical diversity, (2) nomi-nalisation, (3) lexical density, and (4) lexical sophistication, two syntactic: (5) syntactic depth and (6) clausal density, and one readability metric: (7) Flesch-Kincaid Grade Level.”
InfluentialAbstract Readability Across Knowledge Domains and Citation Impact in Forestry-Related Research
Jiang, C., Liu, L., Luo, G., & Wu, J. (2026) · Forests · doi:10.3390/f17090998 · Cited by 1 · see citing works
Tadayonifar, M., & Entezari, M. (2026) · Language Teaching Research · doi:10.1177/13621688261452314
All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs?
Kim, S., Janssen, M. A., Wang, J., Min-Venditti, A., Karanjia, N., & Anderies, J. M. (2026) · arXiv · doi:10.48550/arxiv.2604.17247
PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing
Żurawicki, K., Farganus, J., Gaweł, A., Bystroński, M., & Kajdanowicz, T. J. (2026) · arXiv · doi:10.48550/arxiv.2605.29815
- Authors
- Kincaid, J. P., Fishburne, Jr., Robert P., R., Richard L., C., & Brad, S.
- Publisher
- Defense Technical Information Center
- Published
- 1975-02-01
- OpenAlex
- W1507711477
- Cited by
- 3,497
- References
- 0
- Citations by year
- 2026: 231 · 2025: 358 · 2024: 283 · 2023: 285 · 2022: 220 · 2021: 309 · 2020: 223 · 2019: 252 · 2018: 226 · 2017: 161 · 2016: 165 · 2015: 129 · 2014: 133 · 2013: 108 · 2012: 85
- Type
- report
- Language
- en
- Open access
- Locations
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text Readability and Simplification
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Keywords
- readability formulas, Automated Readability Index, Navy enlisted personnel, reading comprehension
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review