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Index nameFormula NameFormula Variable derived fromReferences
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Variable (i)Coefficient
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Flesch-Reading-EaseFlesch Reading Ease FormulaConstant206.835N/AFlesch, R. (1948). A new readability yardstick. Journal of applied psychology, 32(3), 221.
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Average number of words per sentence - (i x 1.015)spaCy
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Average number of syllables per word- (i x 84.6)custom Python code
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Flesch-Kincaid-ReadabilityFlesch Kincaid Grade Level FormulaConstant-15.59N/AKincaid, J. P., Fishburne Jr, 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.
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Average number of words per sentence i x 0.39spaCy
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Average number of syllables per wordi x 11.8custom Python code
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Automated-Reading-IndexAutomated Readability IndexConstant-21.43N/AKincaid, J. P., Fishburne Jr, 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.
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Average number of words per sentence i x 0.5spaCy
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Average number of characters per wordi x 4.71custom Python code
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SMOG Readability FormulaSMOG GradingConstant3N/AMc Laughlin, G. H. (1969). SMOG grading-a new readability formula. Journal of reading, 12(8), 639-646.
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Square root of pollysyllabic words per 30 sentencei x 1spaCy
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New Dale-Chall Readability FormulaNew Dale-Chall Readability FormulaAverage number of words per sentence i x 0.0496spaCyChall, J. S., & Dale, E. (1995). Readability revisited: The new Dale-Chall readability formula. Brookline Books.
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Percentage of difficult wordsi x 0.1579ReaderBench
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CARECCrowdsourced algorithm of reading comprehensionConstant1.811N/A
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: new methods and new models. Journal of Research in Reading, 42(3-4), 541-561.
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Average age of acquisition (Kuperman) for all content wordsi x 0.022TAALES (Kuperman_AoA_CW)
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior research methods, 44(4), 978-990.
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Average bigram range score (COCA) for all wordsi x 0.746TAALES (COCA_Academic_Bigram_Range)
Davies, M. (2009). The Corpus of Contemporary American English (COCA): 400+ million words, 1990-present (2008). Available online at http://www. americancorpus. org.
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Average trigram proportion score (BNC-written) for all words- (i x 0.742)TAALES (BNC_Written_Trigram_Proportion)
BNC Consortium. (2007). The British national corpus, version 3 (BNC XML Edition). Distributed by Oxford University Computing Services on behalf of the BNC Consortium, 5(65), 6.
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Average imageability score (MRC) for all content words- (i x 0.001)TAALES (MRC_Imageability_CW)Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A, 33(4), 497-505.
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Average frequency score (Brown) for all wordsi x 0.0000625TAALES (Brown_Freq_AW)
Brown, G. D. (1984). A frequency count of 190,000 words in theLondon-Lund Corpus of English Conversation. Behavior research methods, instruments, & computers, 16(6), 502-532.
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Average type token ratio of lemma trigrams for all trigrams- (i x 0.699)TAACO (trigram_lemma_ttr)Crossley, S. A., Kyle, K., & Dascalu, M. (in press). The Tool for the Automatic Analysis of Cohesion 2.0: Integrating Semantic Similarity and Text Overlap. Behavioral Research Methods.
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Proportion of lemma types that occur in the next paragraph for all paragraphs- (i x 0.111)TAACO (adjacent_overlap_all_para)
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Number of temporal connectives divided by number of words in text- (i x 2.067)TAACO (all_temporal)
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Proportion of noun lemma types that occur in the next paragraph for all paragraphsi x 0.035TAACO (adjacent_overlap_noun_sent_div_seg)
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Number of content word lemma typesi x 0.002TAACO (nlemma_content_types)
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Positive adjective scores derived from 4 different corpora- (i x 0.08)SEANCE (positive_adjectives_component)
Crossley, S. A., Kyle, K., & McNamara, D. S. (2017). Sentiment analysis and social cognition engine (SEANCE): An automatic tool for sentiment, social cognition, and social order analysis. Behavior Research Methods 49(3), pp. 803-821. doi:10.3758/s13428-016-0743-z.
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Average standard deviation of word length for all wordsi x 0.047ReaderBench (RB.WdLettStdDev)Dascalu, M., Dessus, P., Trausan-Matu, Ş., Bianco, M., & Nardy, A. (2013, July). ReaderBench, an environment for analyzing text complexity and reading strategies. In International Conference on Artificial Intelligence in Education (pp. 379-388). Springer, Berlin, Heidelberg.
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Average character entropy for all characters- (i x 0.395)ReaderBench (RB.CharEnt)
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CAREC_MCrowdsourced algorithm of reading comprehension modifiedConstant1.811N/A
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: new methods and new models. Journal of Research in Reading, 42(3-4), 541-561.
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Average age of acquisition (Kuperman) for all content wordsi x 0.022TAALES (Kuperman_AoA_CW)
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior research methods, 44(4), 978-990.
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Average bigram range score (COCA) for all wordsi x 0.746TAALES (COCA_Academic_Bigram_Range)
Davies, M. (2009). The Corpus of Contemporary American English (COCA): 400+ million words, 1990-present (2008). Available online at http://www. americancorpus. org.
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Average trigram proportion score (BNC-written) for all words- (i x 0.742)TAALES (BNC_Written_Trigram_Proportion)
BNC Consortium. (2007). The British national corpus, version 3 (BNC XML Edition). Distributed by Oxford University Computing Services on behalf of the BNC Consortium, 5(65), 6.
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Average imageability score (MRC) for all content words- (i x 0.001)TAALES (MRC_Imageability_CW)Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A, 33(4), 497-505.
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Average frequency score (Brown) for all wordsi x 0.0000625TAALES (Brown_Freq_AW)
Brown, G. D. (1984). A frequency count of 190,000 words in theLondon-Lund Corpus of English Conversation. Behavior research methods, instruments, & computers, 16(6), 502-532.
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Average type token ratio of lemma trigrams for all trigrams- (i x 0.699)TAACO (trigram_lemma_ttr)Crossley, S. A., Kyle, K., & Dascalu, M. (in press). The Tool for the Automatic Analysis of Cohesion 2.0: Integrating Semantic Similarity and Text Overlap. Behavioral Research Methods.
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Proportion of lemma types that occur in the next paragraph for all paragraphs- (i x 0.111)TAACO (adjacent_overlap_all_para)
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Number of temporal connectives divided by number of words in text- (i x 2.067)TAACO (all_temporal)
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Proportion of noun lemma types that occur in the next paragraph for all paragraphsi x 0.035TAACO (adjacent_overlap_noun_sent_div_seg)
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Number of content word lemma types divided by number of content wordsi x 0.2TAACO (nlemma_content_types)
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Positive adjective scores derived from 4 different corpora- (i x 0.08)SEANCE (positive_adjectives_component)
Crossley, S. A., Kyle, K., & McNamara, D. S. (2017). Sentiment analysis and social cognition engine (SEANCE): An automatic tool for sentiment, social cognition, and social order analysis. Behavior Research Methods 49(3), pp. 803-821. doi:10.3758/s13428-016-0743-z.
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Average standard deviation of word length for all wordsi x 0.047ReaderBench (RB.WdLettStdDev)Dascalu, M., Dessus, P., Trausan-Matu, Ş., Bianco, M., & Nardy, A. (2013, July). ReaderBench, an environment for analyzing text complexity and reading strategies. In International Conference on Artificial Intelligence in Education (pp. 379-388). Springer, Berlin, Heidelberg.
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Average character entropy for all characters- (i x 0.395)ReaderBench (RB.CharEnt)
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CARESCrowdsourced algorithm of reading speedConstant-0.862N/A
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: new methods and new models. Journal of Research in Reading, 42(3-4), 541-561.
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Average word naming response time for all wordsi x 0.003TAALES (WN_Mean_RT)
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., ... & Treiman, R. (2007). The English lexicon project. Behavior research methods, 39(3), 445-459.
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Average concreteness score (MRC) for all words- (i x 0.001)TAALES (MRC_Concreteness_AW)Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A, 33(4), 497-505.
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Average semantic distinctiveness scores for all words- (i x 0.461)TAALES (Sem_D)
Hoffman, P., Ralph, M. A. L., & Rogers, T. T. (2013). Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words. Behavior research methods, 45(3), 718-730.
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Number of content word lemmasi x 0.004TAACO (nlemma_content_words)Crossley, S. A., Kyle, K., & Dascalu, M. (in press). The Tool for the Automatic Analysis of Cohesion 2.0: Integrating Semantic Similarity and Text Overlap. Behavioral Research Methods.
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Number of function words i x 0.002TAACO (nfunction_words)
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Complex nominals per T-uniti x 0.011TAASSC (CN_T)
Lu, X. (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15(4):474-496.
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Number of dependents per direct objecti x 0.023TAASSC (av_dobj_deps)
Kyle, K. (2016). Measuring syntactic development in L2 writing: Fine grained indices of syntactic complexity and usage-based indices of syntactic sophistication (Doctoral Dissertation).
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Average number of sentences per paragraph- (i x 0.015)ReaderBench (RB.BlStDevSen)Dascalu, M., Dessus, P., Trausan-Matu, Ş., Bianco, M., & Nardy, A. (2013, July). ReaderBench, an environment for analyzing text complexity and reading strategies. In International Conference on Artificial Intelligence in Education (pp. 379-388). Springer, Berlin, Heidelberg.
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Average number of characters per wordi x 0.062ReaderBench (RB.WdLettStdDev)
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CML2RICoh-Metrix L2 Readability Index (approximated)Constant-43.142N/ACrossley, S. A., & McNamara, D. S. (2008). Assessing L2 reading texts at the intermediate level: An approximate replication of Crossley, Louwerse, McCarthy & McNamara (2007). Language Teaching, 41(3), 409-429.
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Number of sentences in texti x 0.642spaCy
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Average frequency score (SUBTLEXus) for all content words loggedi x 12.671TAALES (SUBTLEXus_Freq_CW_Log)Brysbaert, M., & New, B. (2009). Subtlexus: American word frequencies. Http:/Subtlexus. Lexique. Org.
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Proportion of noun and pronoun lemma types that occur in the next two sentences for all sentences
i x 29.619TAACO (adjacent_overlap_2_argument_sent)
Crossley, S. A., Kyle, K., & Dascalu, M. (in press). The Tool for the Automatic Analysis of Cohesion 2.0: Integrating Semantic Similarity and Text Overlap. Behavioral Research Methods.
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