title: Toward a theory relating text complexity, reader ability, and reading comprehension.
abstract: Validity of specification equations used by auto-text processors to estimate theoretical text complexity have increased importance because of the Common Core State Standards. Theoretical estimates of text complexity will inform (a) setting standards for college and career readiness, (b) grade-level standards, matching readers to text, and (d) creating a daily diet of stretch and targeted text designed to grow reading ability and content knowledge. The purpose of this research was to investigate the specification equation used in the Lexile Framework for Reading to measure text complexity. The Lexile Reading Analyzer contains a specification equation that uses proxies for the semantic difficulty and syntactic complexity to estimate the theoretical complexity of professionally-edited text. Differences between theoretical and empirical estimates of text complexity were examined for a set of 446 professionally authored, previously published passages. Students in grades 2-12 read these passages using A Learning Oasis, a web-based technology, to ensure that most of the articles read were well-targeted to student ability (+100L). Each article was response illustrated using an auto-generated semantic cloze item type embedded into passages. Observed student performance on this item type was used to derive an empirical estimate of text complexity for each passage. Theoretical estimates of text complexity accounted for approximately 90 percent of the variance in empirical estimates of text complexity. These findings suggest that the specification equation contains powerful predictors of empirical text complexity, speculation remains on what additional variables might account for the 10 percent of unexplained variation.
authors.affiliation: MetaMetrics, 1000 Park Forty Plaza Dr., Ste. 120, Durham, NC 27713, USA, cswartz@lexile.com.
authors.forename: Carl W
authors.initials: CW
authors.lastname: Swartz
authors.forename: Donald S
authors.initials: DS
authors.lastname: Burdick
authors.forename: Sean T
authors.initials: ST
authors.lastname: Hanlon
authors.forename: A Jackson
authors.initials: AJ
authors.lastname: Stenner
authors.forename: Andrew
authors.initials: A
authors.lastname: Kyngdon
authors.forename: Harold
authors.initials: H
authors.lastname: Burdick
authors.forename: Malbert
authors.initials: M
authors.lastname: Smith
mesh_terms: D000293:Adolescent; D002648:Child; D032882:Comprehension; D003198:Computer Simulation; D004521:Educational Measurement / Q000379:methods*; D005260:Female; D006801:Humans; D007807:Language Tests / Q000706:statistics & numerical data*; D008297:Male; D015233:Models, Statistical; D009323:Natural Language Processing; D011594:Psychometrics / Q000379:methods* / Q000706:statistics & numerical data; D011932:Reading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis