Readability indices have been widely used in order to measure textual difficulty. They can be useful for the automatic classification of texts, especially in language teaching. Among other applications, they allow for the previous determination of the difficulty level of texts without the need of reading them through. The aim of this research is twofold: first, to examine the degree of accuracy of the six most commonly used readability indices, and second, to present a new optimized measure. The main problem is that these readability indices may offer disparity, and this is precisely what has motivated our attempt to unite their potential. A discriminant analysis of all the variables under examination has enabled the creation of a much more precise model, improving the previous best results by 15%. Furthermore, errors and disparities in the difficulty level of the analyzed texts have been detected.
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