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Summary

Abstract

Introduction

Protocol

Representative Results

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Behavior

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published: October 11th, 2018

DOI:

10.3791/58557

1Florida Center for Reading Research, Florida State University

Here we present a protocol for decomposing the variance in reading comprehension into the unique and common effects of language and decoding.

The Simple View of Reading is a popular model of reading that claims that reading is the product of decoding and language, with each component uniquely predicting reading comprehension. Although researchers have argued whether the sum rather than the product of the components is the better predictor, no researchers have partitioned the variance explained to examine the extent to which the components share variance in predicting reading. To decompose the variance, we subtract the R2 for the language-only model from the full model to obtain the unique R2 for decoding. Second, we subtract the R2 for the decoding-only model from the full model to obtain the unique R2 for language. Third, to obtain the common variance explained by language and decoding, we subtract the sum of the two unique R2 from the R2 for the full model. The method is demonstrated in a regression approach with data from students in grades 1 (n = 372), 6 (n = 309), and 10 (n = 122) using an observed measure of language (receptive vocabulary), decoding (timed word reading), and reading comprehension (standardized test). Results reveal a relatively large amount of variance in reading comprehension explained in grade 1 by the common variance in decoding and language. By grade 10, however, it is the unique effect of language and the common effect of language and decoding that explained the majority of variance in reading comprehension. Results are discussed in the context of an expanded version of the Simple View of Reading that considers unique and shared effects of language and decoding in predicting reading comprehension.

The Simple View of Reading1 (SVR) continues as a popular model of reading because of its simplicity-reading (R) is the product of decoding (D) and language (L)-and because SVR tends to explain, on average, approximately 60% of explained variance in reading comprehension2. SVR predicts that correlations between D and R will decline over time and that correlations between L and R will increase over time. Studies generally support this prediction3,4,5. There are disagreements, however, about the functional form of SVR, with additiv....

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Note: The steps below describe decomposing total variance in a dependent variable (Y) into unique variance, common variance, and unexplained variance components based on two selected independent variables (called Equation 1 and Equation 2 for this example) using software with a graphical user interface and data management software (see Table .......

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The objective of this study was to investigate the contributions of unique and common variance of language (L) and decoding (D) to predicting reading comprehension (R) in grades 1, 7, and 10 in Florida, a state whose demographics are representative of the nation as a whole. There were two hypotheses regarding predictions of the variance explained in reading comprehension. First, after the primary grades, the unique contribution of D will significantly decrease, and the unique contribution.......

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There are three critical steps in the protocol for decomposing the variance in R into unique and common variance due to L and D. First, subtract the R2 in the L-only model from the full model to obtain the unique R2 for D. Second, subtract the R2 for the D-only model from the full model to obtain the unique R2 for L. Third, to obtain the common variance explained by L and D, subtract the sum of the two unique R2 from the R2 for the full model.

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The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through a subaward to Florida State University from Grant R305F100005 to the Educational Testing Service as part of the Reading for Understanding Initiative. The opinions expressed are those of the authors and do not represent views of the Institute, the U.S. Department of Education, the Educational Testing Service, or Florida State University.

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Name Company Catalog Number Comments
IBM SPSS Statistics Software IBM
Microsoft Office Excel Microsoft

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