The Past, Problems, and Potential of Readability Analysis

Abstract

Readability analysis combines statistical modeling, theoretical linguistics, and psychological theory to determine the accessibility level of writing samples. This study has a long history and broad impact, yet typically uses extremely simple statistical tools (in particular linear regressions). This article briefly reviews key stages in the history of readability, and discusses present issues and potential future benefits these tools offer.

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Notes on contributors

Nicholas A. Lines

Nicholas A. Lines is an applied research mathematician for the US Department of Defense. His research is focused on data science and text mining. He is pursuing a master’s degree in applied and computational mathematics at Johns Hopkins University.