CoChat
Sign Up
Cited by 908Computational and Text Analysis MethodsLinguistic research and analysisAuthorship Attribution and Profiling

The DOI matches its Crossref record; 46 of the 50 references it cites were found in a registry or an index.

MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment

McCarthy, Philip M., Jarvis, Scott·2010·Behavior Research Methods
DOI 10.3758/brm.42.2.381
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.3758/brm.42.2.381

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

Show all 50 references

4 of the 50 references couldn’t be matched to a known paper.

Research works printed but not linked: 8

  1. Biggs, A., Daniel, L., Feather, R. M., Ortleb, E., Rillero, P., Snyder, S. L., & Zike, D. (2003). Glencoe science: Science level green. New York: Glencoe/McGraw-Hill.
  2. Crossley, S. A., & McNamara, D. S. (in press). Predicting second language writing proficiency: The role of cohesion, readability, and lexical difficulty. Journal of Research in Reading.
  3. Dugast, D. (1978). Sur quoi se fonde la notion d’étendue théoretique du vocabulaire? Le Français Moderne, 46, 25–32.
  4. Honoré, A. (1979). Some simple measures of richness of vocabulary. Association for Literary & Linguistic Computing Bulletin, 7, 172–177.
  5. Maas, H. D. (1972). Zusammenhang zwischen Wortschatzumfang und Länge eines Textes. Zeitschrift für Literaturwissenschaft und Linguistik, 8, 73–79.
  6. McCarthy, P. M., Dufty, D., Hempelman, C., Cai, Z., Graesser, A. C., & McNamara, D. S. (in press). Evaluating givenness/newness. Discourse Processes.
  7. McEnery, T. (2003). Corpus linguistics. In R. Mitkov (Ed.), Handbook of computational linguistics (pp. 448–463). Oxford: Oxford University Press.
  8. Orlov, Y. K. (1983). Ein Model der Häufigekeitsstruktur des Vokabulars. In H. Guiter & M. V. Arapov (Eds.), Studies on Zipf’s law (pp. 154–233). Bochum: Brockmeyer.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
McCarthy, P. M., & Jarvis, S.
Affiliations
  • University of Memphis (US)
  • Ohio University (US)
Corresponding author
McCarthy, Philip M.
ORCID
Venue
Behavior Research Methods
Publisher
Springer Science and Business Media LLC
Published
2010-05
Volume
42
Issue
2
Pages
381-392
ISSN
  • 1554-351X (ISSN-L)
  • 1554-3528
DOI
10.3758/brm.42.2.381
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

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