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

Summary

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

Abstract

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.

Introduction

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 prediction^3,4,5. There are disagreements, however, about the functional form of SVR, with additiv....

Protocol

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 and for this example) using software with a graphical user interface and data management software (see Table .......

Discussion

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.

Materials

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