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MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment
Abstract
The main purpose of this study was to examine the validity of the approach to lexical diversity assessment known as the measure of textual lexical diversity (MTLD). The index for this approach is calculated as the mean length of word strings that maintain a criterion level of lexical variation. To validate the MTLD approach, we compared it against the performances of the primary competing indices in the field, which include vocd-D, TTR, Maas, Yule's K, and an HD-D index derived directly from the hypergeometric distribution function. The comparisons involved assessments of convergent validity, divergent validity, internal validity, and incremental validity. The results of our assessments of these indices across two separate corpora suggest three major findings. First, MTLD performs well with respect to all four types of validity and is, in fact, the only index not found to vary as a function of text length. Second, HD-D is a viable alternative to the vocd-D standard. And third, three of the indices--MTLD, vocd-D (or HD-D), and Maas--appear to capture unique lexical information. We conclude by advising researchers to consider using MTLD, vocd-D (or HD-D), and Maas in their studies, rather than any single index, noting that lexical diversity can be assessed in many ways and each approach may be informative as to the construct under investigation.
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“We identify vocabulary richness by using a well-established measure of textual, lexical diversity (MTLD)46 which is often used in the field of automated essay grading25,45,47. It takes into account the number of unique words but unlike the best-known measure of lexical diversity, the type-token ratio (TTR), it is not as sensitive to the difference in the length of the texts.”
From A large-scale comparison of human-written versus ChatGPT-generated essays (2023) · cites this paper as [46]
In the citing paper
“Particularly relevant to the present study is Moon et al. 42 , who found that in a corpus of college admissions essays, each additional humanwritten essay introduced more novel ideas than each additional GPT-4-generated essay.”
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 [42]
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Metadata
- Authors
- McCarthy, P. M., & Jarvis, S.
- Affiliations
- University of Memphis (US)
- Ohio University (US)
- Corresponding author
- McCarthy, Philip M.
- Venue
- Behavior Research Methods
- Published
- 2010-05
- Volume
- 42
- Issue
- 2
- Pages
- 381-392
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2087388117
- Semantic Scholar
- 70085f28eb99e5bbba6f5abf7ea964f17eba26ea
- Cited by
- 905
- References
- 50
- Citations by year
- 2026: 147 · 2025: 103 · 2024: 85 · 2023: 82 · 2022: 92 · 2021: 70 · 2020: 71 · 2019: 53 · 2018: 24 · 2017: 35 · 2016: 38 · 2015: 26 · 2014: 27 · 2013: 17 · 2012: 23
- FWCI
- 128.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Arts and Humanities › Language and Linguistics › Linguistic research and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- lexical diversity, hypergeometric distribution, convergent validity, discriminant validity, incremental validity
- MeSH terms
- Humans, Language Tests, Psychology, Experimental, Reproducibility of Results
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