Measuring Lexical Diversity in Texts: The Twofold Length Problem
This addresses a methodological issue for researchers analyzing lexical diversity in texts, but it is incremental as it reviews and critiques existing approaches.
The paper tackles the problem of text length affecting lexical diversity estimation in language learning studies, finding that while indices reducing texts to the same length solve length dependency, they remain sensitive to the reduction parameter.
The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. The analysis of three datasets of English language-learners' texts revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.