  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.
