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Coh-Metrix analyzes texts on multiple measures of language and discourse that are aligned with multilevel theoretical frameworks of comprehension. Dozens of measures funnel into five major factors that systematically vary as a function of types of texts (e.g., narrative vs. informational) and grade level: narrativity, syntactic simplicity, word concreteness, referential cohesion, and deep (causal) cohesion. Texts are automatically scaled on these five factors with Coh-Metrix-TEA (Text Easability Assessor). This article reviews how these five factors account for text variations and reports analyses that augment Coh-Metrix in two ways. First, there is a composite measure called formality, which increases with low narrativity, syntactic complexity, word abstractness, and high cohesion. Second, the words are analyzed with Linguistic Inquiry and Word Count, an automated system that measures words in texts on dozens of psychological attributes. One next step in automated text analyses is a topics analysis that scales the difficulty of conceptual topics.
Graesser et al. (Fri,) studied this question.