<Research Paper>The Simple View of Reading and Multiple Regression: Comparing and Compromising Multiplicative and Additive Models

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  • <研究論文>読解モデルとしてのSimple View of Readingと重回帰分析: 乗法モデルと加法モデルの比較と互換性
  • The Simple View of Reading and Multiple Regression : Comparing and Compromising Multiplicative and Additive Models

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Controversies abound regarding the choice between linear regression and the Simple View of Reading (SVR) to describe reading comprehension (R), particularly in relation to decoding (D) and linguistic comprehension (C) (Høien-Tengesdal, 2010; Savage, 2006; Savage & Wolforth, 2007). This is despite the stark qualitative difference between multiplicative SVR and additive linear regression. In this comparative study, I (1) examine the different fit criteria, validity, and merits as well as demerits of each instrument, (2) explain why they are mathematically equivalent with optimal zero-point adjustments for D and C in SVR and the inclusion of D×C in linear regression, and (3) generate simulated datasets to examine the contribution of D×C based on significance levels and effect sizes. The equivalence in (2) means that SVR with optimal zeropoint adjustments can always explain more variance in R than multiple regression with only D and C as the predictors. However, the mathematically optimal adjustments may not always make good linguistic sense. These issues are discussed algebraically followed by an illustrative numerical example. This paper concludes with a set of criteria involving the significance, effect size, nullity adjustments, and commonality analysis to assess the applicability of SVR in a given situation.

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