Extending the Simple View of Reading to Account for Variation Within Readers and Across Texts: The Complete View of Reading (CVRi). (September 2018)

• Record Type: Journal Article
• Authors: Francis, David J.; Kulesz, Paulina A.; Benoit, Julia S.
• Other Names: Vaughn Sharon guest-editor.
• Is Part Of: Remedial and special education. Volume 39:Number 5(2018)
• Page Start: 274
• Page End: 288
• Publication Date: 2018-09
• Subjects: Simple View of Reading -- reading comprehension -- oral reading fluency -- multilevel models -- cross-classified random effects models; Special education -- Periodicals; Learning disabilities -- Periodicals; 371.905
• DOI: 10.1177/0741932518772904
• Languages: English
• ISSNs: 0741-9325

Abstract

This study leverages advances in multivariate cross-classified random effects models to extend the Simple View of Reading to account for variation within readers and across texts, allowing for both the personalization of the reading function and the integration of the component skills and text and discourse frameworks for reading research. We illustrate the Complete View of Reading (CVR i ) using data from an intensive longitudinal design study with a large sample of typical ( N = 648) and struggling readers ( N = 865) in middle school and using oral reading fluency as a proxy for comprehension. To illustrate the utility of the CVR i, we present a model with cross-classified random intercepts for students and passages and random slopes for growth, Lexile difficulty, and expository text type at the student level. We highlight differences between typical and struggling readers and differences across students in different grades. The model illustrates that readers develop differently and approach the reading task differently, showing differential impact of text features on their fluency. To be complete, a model of reading must be able to reflect this heterogeneity at the person and passage level, and the CVR i is a step in that direction. Implications for reading interventions and 21st century reading research in the era of "Big Data" and interest in phenotypic characterization are discussed.