TY - JOUR
T1 - Coh-Metrix measures text characteristics at multiple levels of language and discourse
AU - Graesser, Arthur C.
AU - McNamara, Danielle
AU - Cai, Zhiqang
AU - Conley, Mark
AU - Li, Haiying
AU - Pennebaker, James
N1 - Publisher Copyright:
© 2014 by The University of Chicago. All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84928104195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928104195&partnerID=8YFLogxK
U2 - 10.1086/678293
DO - 10.1086/678293
M3 - Article
AN - SCOPUS:84928104195
SN - 0013-5984
VL - 115
SP - 211
EP - 229
JO - Elementary School Journal
JF - Elementary School Journal
IS - 2
ER -