How Simple is the Simple View of Reading?

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How Simple is the Simple View ofReading?Stefan Gustafson a , Christina Samuelsson a , Ellinor Johansson b &Julia Wallmann ca Linköping University , Swedenb County Council of Östergötland , Swedenc Daneryd Hospital , SwedenPublished online: 10 Apr 2012.

To cite this article: Stefan Gustafson , Christina Samuelsson , Ellinor Johansson & Julia Wallmann(2013) How Simple is the Simple View of Reading?, Scandinavian Journal of Educational Research,57:3, 292-308, DOI: 10.1080/00313831.2012.656279

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How Simple is the Simple View of Reading?

Stefan Gustafson and Christina SamuelssonLinkoping University, Sweden

Ellinor JohanssonCounty Council of Ostergotland, Sweden

Julia WallmannDaneryd Hospital, Sweden

According to the Simple View of Reading, reading ability can be divided into decoding andlanguage comprehension. In the present study, decoding and comprehension’s contributionto reading ability was studied both in children with reading difficulties and in children withtypical reading ability. Decoding and comprehension was further divided into sub-components, and the contribution from non-verbal ability and general processing speedwas also studied. The results demonstrated that decoding made the largest contribution toreading ability for children with reading difficulties, while language comprehensioncontributed the most for children with typical reading ability. The contribution of non-verbal ability was not significant, and general processing speed only made a significantcontribution to decoding for typical children. The two factors in the Simple View ofReading, decoding and comprehension, together explained less of the variance in readingability for children with reading difficulties than for children with typical reading ability.

Keywords: reading ability, decoding, language comprehension, Simple View of Reading

The Simple View of Reading was put forth more than two decades ago by Gough andTunmer (1986). According to this model, reading ability (that is, reading comprehension)can be broken down into two components: decoding and language comprehension (Gough& Tunmer, 1986; Hoover & Gough, 1990). This may be presented as a formula R ¼ D ×C, where reading (R) is the product of decoding (D) and comprehension (C). Each variableranges from 0 (inability) to 1 (perfection) (Gough & Tunmer, 1986). According to the SimpleView of Reading (SVR), the components are independent of each other, and each componentis necessary for successful reading, but neither component is sufficient in itself (Gough &Tunmer, 1986).

# 2013 Scandinavian Journal of Educational Research

Stefan Gustafson, Department of Behavioural Sciences and Learning, Linkoping University; Chris-tina Samuelsson, Department of Clinical and Experimental Medicine, Linkoping University; EllinorJohansson, Habilitation Centre in eastern Ostergotland, County Council of Ostergotland, Sweden;Julia Wallmann, Department of Communication Disorders, Daneryd Hospital, Sweden.

We gratefully acknowledge the assistance of the participating schools, special education teachersand children. The procedures followed when carrying out this study were in accordance with theethical standards of the responsible committee of human experimentation and with the HelsinkiDeclaration of 1975 as revised in 1983.

Correspondence concerning this article should be addressed to Dr Stefan Gustafson, Department ofBehavioural Sciences and Learning, Linkoping University, Linkoping, 58183 Sweden. E-mail:[email protected]

Scandinavian Journal of Educational Research, 2013Vol. 57, No. 3, 292–308, http://dx.doi.org/10.1080/00313831.2012.656279

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Research has provided strong support for the general validity and reliability of the SVRwhen describing components of reading (Aaron, 1997; Catts, Hogan, & Fey, 2003; Joshi,Williams, & Wood, 1998; Kendeou, Savage, & van den Broek, 2009; Kirby & Savage,2008; Roberts & Scott, 2006). In a recent review by Kirby and Savage (2008, p. 75), itwas concluded that the SVR “provides a good fit to much scientific data on typical and aty-pical development, and variation among students across the school age range.”

Using multiple regression analysis, studies have shown that decoding and comprehensionmake individual contributions to reading ability, and thus that the combination of these twovariables predicts reading ability better than each variable does alone (Gough & Tunmer,1986; Joshi & Aaron, 2000; Joshi et al. 1998). Hoover and Gough (1990) concluded that71–83% of the variance in reading ability may be explained by decoding and comprehensionamong bilingual children in the fourth grade. Other studies report results of 48% (Joshi &Aaron, 2000) and 62% (Joshi et al., 1998) explained variance.

Previous research has also shown that there is a discrepancy in the extent to which decod-ing and comprehension predict reading ability, depending on skill level. For individuals withlimited reading capacity, decoding is a better predictor, while comprehension is a better pre-dictor of the variance in reading ability among skilled readers (Curtis, 1980; Hoover &Gough, 1990). These findings indicate that language comprehension processes cannot func-tion sufficiently in reading until the individual has reached a functional level of decoding(Vellutino et al., 2007).

The SVR is useful in different areas, such as speech and language pathology, psychology,and education (Roberts & Scott, 2006). Furthermore, there is evidence of different geneticand environmental influences in the two components, decoding and comprehension(Keenan, Betjemann, Wadsworth, DeFries, & Olson, 2007). The SVR has been used toexplain and categorize different types of reading disabilities, suggesting different interven-tions for different subgroups of reading disabilities (Aaron, 1997; Aaron, Joshi, Gooden,& Bentum, 2008; Gough & Tunmer, 1986; Hoover & Gough, 1990; Tunmer & Hoover,1992).

The present study is mainly focused on the SVR itself, rather than subgroups of readingdisability. The sample size was not large enough to allow separate regression analyses for thethree different subgroups of reading disabilities suggested by the SVR: that is, poor decoders,poor comprehenders, and those who have both problems (Aaron, 1997; Aaron et al., 2008;Hoover & Gough, 1990). In the present study, the sample of children with reading difficultiestherefore consists of (almost) the full range of children who receive special instruction inreading in regular schools (see Participants section), thus representing a heterogeneoussample, but one with high external validity.

Although the SVR is simple insofar as it contains only two different components, the twoconcepts of decoding and comprehension are themselves quite complex (Kirby & Savage,2008; Tunmer & Greaney, 2010). In the SVR, decoding has been described as effectiveword recognition (Hoover & Gough, 1990). It refers to the ability to quickly derive a rep-resentation of the written, visual stimuli that gives access to adequate retrieval of informationfrom the mental lexicon. Based on this view, decoding is regarded as the retrieval of semanticinformation on the word level (Hoover & Gough, 1990). In dual route models of word decod-ing, the reader gains access to the meaning of words either by phonological decoding (gra-pheme-phoneme conversion), or by more direct visual-orthographic decoding (see forexample Ellis & Young, 1988; Gustafson, Ferreira, & Ronnberg, 2007). According toHoover and Gough (1990), a skilled reader uses the orthographical strategy and decodes

HOW SIMPLE IS THE SIMPLE VIEW OF READING? 293

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isolated words quickly, correctly and quietly. However, the orthographical strategy is basedon the phonological strategy (Høien & Lundberg, 1999) and there is an important develop-mental aspect that needs to be considered when examining word decoding skills in childrenboth with and without reading difficulties (Tunmer & Greaney, 2010). Although there areindividual differences in early decoding development, there is a general gradual shift fromreliance on phonological word decoding to reliance on orthographic word decoding(Ehri & Wilce, 1987; Juel, Griffith, & Gough, 1986; Samuelsson, Gustafson, & Ronnberg,1996).

Thus, in order to get a comprehensive picture of decoding ability in different groups ofchildren, there should be an examination of both irregularly spelled words, where a moreorthographic decoding strategy is needed, and of non-words, where a more phonologicaldecoding strategy is required (Gustafson et al., 2007; Hoover & Gough, 1990). Therefore,the present study included a general word decoding test as well as tests of phonologicaland orthographic word decoding.

Also note that skilled decoding involves both fluency and accuracy. Kirby and Savage(2008, p. 80) stated that “in virtually all of the studies of SVR, decoding has been measuredas accuracy,” and measures of decoding which also take into account speed and fluency werecalled for. Thus, in the present study, timed decoding tests measuring both speed and accu-racy were used.

The other component, comprehension, is also a complex concept (Kirby & Savage,2008). In the SVR, comprehension is viewed as the ability to use lexical information to inter-pret spoken language (Hoover & Gough, 1990). Irrespective of modality, language compre-hension concerns all different aspects of language – phonology, semantics, grammar andpragmatics – and it is therefore part of several linguistic domains at the same time. Languagecomprehension within different linguistic domains is needed for the satisfactory comprehen-sion of an utterance (Nettelbladt, Samuelsson, Sahlen, & Ors, 2008). At the phonologicallevel, the input signal must be interpreted and the different linguistic segments must be dif-ferentiated. In order to achieve semantic and lexical comprehension, the phonologicalsequence needs to be matched to previously stored representations in the mental lexicon.Receptive grammar relates to the comprehension and interpretation of the internal relation-ship between words and phrases in complex sentences. At the pragmatic level, comprehen-sion concerns connecting previously acquired knowledge to comprehension of words andsentences, and also interpretation of underlying intentions (Bishop, 1997). Pragmatic com-prehension is closely linked to semantic skills and also involves inference making. To thisend, it is well established that there is a relationship between children’s reading comprehen-sion and their ability to generate inferences from text, so that children with weak readingcomprehension are less likely to make a text cohesive by integrating information in sentences(Cain & Oakhill, 1999). Poor comprehenders also have problems with generating coherenceand elaborative inferences (Cain, Oakhill, Barnes, & Bryant, 2001), as well as with semanticbootstrapping to understand novel words (Cain, Oakhill, & Elbro, 2003). It has also beendemonstrated that children with poor reading comprehension, when interpreting opaqueidioms, did not benefit from contextual information to the same extent as their same-agedpeers with good reading comprehension (Cain, Oakhill, & Lemmon, 2005). The importanceof the ability to understand causality in narrations in becoming a skilled reader has also beendemonstrated by Kendeou, Savage, and van den Broek (2009).

In order to cover the above described relationship between different aspects of language,the different linguistic domains which may contribute to language comprehension are

294 GUSTAFSON, SAMUELSSON, JOHANSSON AND WALLMANN

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acknowledged in the present study by using tests targeting comprehension at the word, sen-tence, and text levels.

Given the simple nature of the SVR, it is not surprising that some researchers havesuggested more complex theories. In some studies the model has been extended by dividingdecoding and comprehension into subcomponents, as suggested above (see for example Cattset al., 2003; Vellutino, Tunmer, Jaccard, & Chen, 2007). Vellutino et al. (2007) took the SVRas a starting point and divided decoding and language comprehension into several underlyingabilities. Decoding was divided into phonological coding, phonological awareness, phonolo-gical decoding and spelling. Language comprehension was divided into word comprehensionand receptive grammar. Tests to investigate visual coding and visual analysis were alsocarried out. Vellutino et al. (2007) then investigated the relations between these different abil-ities and their relative contributions to reading ability. The model presented in the study wasclaimed to be useful for descriptions of the relationship between the factors and for predictionof reading ability. An unexpected result was that receptive grammar did not significantly con-tribute to language comprehension. In a study by Catts et al. (2003), language comprehensionwas assessed by auditory tests at word, sentence, and text level, while decoding was assessedby tests for phonological processing and letter identification. These authors argued thatlanguage comprehension and decoding, as well as factors related to these abilities, shouldbe assessed when examining reading disorders related to the SVR.

Other studies have examined whether additional components should be added to decod-ing and comprehension in the SVR model (Høien-Tengesdal, 2010; Joshi & Aaron, 2000;Tiu, Thompson & Lewis, 2003). A study by Tiu et al. (2003) investigated whether theSVR should also include the variables processing speed and/or intelligence. The authors con-cluded that both of these abilities made a significant contribution to reading ability. Joshi andAaron (2000) found that adding general processing speed to the formula increased theexplained variance in reading ability by 10% in addition to what was already explained bydecoding and language comprehension. They therefore suggested that processing speedshould complement the original model.

Some researchers have also suggested that an additive model (R ¼ C + D) wouldprovide a better estimate of reading than a product model, meaning that either D or Ccould be bypassed and it would still be possible to achieve adequate reading ability(Conners, 2009; Savage, 2006; Savage & Wolforth, 2007). A recent study explored this sug-gestion further (Høien-Tengesdal, 2010). In order to explore other abilities that may explainvariance in reading comprehension, phonemic awareness and rapid naming were included inthe assessment. The results demonstrated that the additive model explained more of the var-iance in reading comprehension, and that only rapid naming contributed to explain variancein reading comprehension beyond that explained by the SVR (Høien-Tengesdal, 2010). Theissue of whether a product or an additive model is preferable is not a focus of the presentstudy, but the contribution of two additional factors that may have an impact on reading isexplored.

Previous research thus suggests that it is important to further investigate possible contri-buting subcomponents of decoding and comprehension and possible additional factors, andto relate the results to the level of reading skills. Based on previous research, phonologicaland orthographic decoding were identified as critical subcomponents of general decodingability (Gustafson et al., 2007; Tunmer & Greaney, 2010) and it was decided that measuresof comprehension at the word, sentence, and text levels should be included (Kirby & Savage,2008; Nettelbladt et al., 2008). Based on previous findings, two additional variables –


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processing speed and non-verbal ability (Joshi & Aaron, 2000; Singer & Crouse, 1981; Tiuet al., 2003) – were included.

Aims

The main purpose of the study was to further investigate the general theoretical model ofthe Simple View of Reading by studying the contribution to reading ability of decoding andcomprehension. In addition, the relative contributions to decoding and language comprehen-sion from possible subcomponents of these two variables were assessed. The question ofwhether non-verbal skill and general processing speed contributed to reading ability, decod-ing, and language comprehension was also examined.

In order to examine the model’s generalizability to different groups of readers, we pre-formed separate analyses for children who were typical readers and children who hadreading difficulties. It is important to investigate the relationship between decoding and com-prehension in groups with different abilities, since this has a great bearing on intervention.This approach may also have theoretical implications for the explanation of reading pro-blems. In line with the SVR, which includes both decoding problems (developmental dys-lexia) and poor comprehension (or both) as possible causes of reading disability (Aaronet al., 2008; Hoover & Gough, 1990), participants represented the full range of reading pro-blems found among children who receive special instruction in reading in regular schools.

Method

Participants

The study comprised 72 participants: 36 children with reading difficulties and 36 childrenwith typical reading ability matched on age and gender, from fourth-grade classes in elevenregular Swedish schools. There were 16 girls and 20 boys in each group. Children withreading difficulties were selected by including all children who received special teachingin reading in the participating schools at the time of the study. The selected children receivedspecial teaching for reading problems only, not because of other problems such as attentiondifficulties or behaviour problems. Children with hearing disabilities and/or whose mothertongue was not Swedish were excluded, since these factors could have had effects onlanguage development and/or vocabulary as well as on their ability to participate intesting. Children with language impairments were not excluded, since developmentallanguage impairment may be manifested as reading difficulties later on, when the childrenno longer have apparent problems with expressive phonology, grammar and/or expressivevocabulary (Magnusson, Naucler, & Reuterskiold, 2008; Naucler & Magnusson, 2000; Net-telbladt, 1997). The children with typical reading ability were selected to match the childrenwith reading difficulties according to age (+- three months) and gender. They did not receiveany special teaching and according to their parents and teachers they did not have any historyof reading problems. In order to obtain an equal group size, only 36 children with typicalreading development were included in the study. All children and parents gave written,informed consent to their participation.

Variables

In accordance with the SVR, the present study examined reading ability, decoding andlanguage comprehension. Decoding was further divided into phonological decoding and


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orthographic decoding according to the two-way model (Ellis & Young, 1988). Similar to thestudy by Catts et al. (2003), language comprehension was examined at word, sentence, andtext level. Measurements of non-verbal ability and general processing speed were alsoincluded, since some previous studies have indicated that these factors also contribute toreading ability (Joshi & Aaron, 2000; Singer & Crouse, 1981; Tiu et al., 2003).

Materials

The tests used to assess each factor examined in the study are presented below.

Reading ability. Angelholmsprovet [the Angelholm test] (Franzen, 1994) is a silentreading test for Swedish school year four. This reading comprehension test contains threetexts with different levels of difficulty, in which one out of three words is picked out tofill in the blanks. For this test, the time limit was five minutes, and the maximum scorewas 27. The testing was carried out in a group setting. Correlation between the scores ofthe first and the second section of the test (7 items in each) was r ¼ .66.

Decoding. Ordkedjor A [Word chains A] (Jacobson, 1993) is a test where words in a wordchain should be separated by drawing a line between them. It was carried out groupwise andthe time limit was five minutes. Every word chain solved correctly scored one point. The testconsisted of so many word chains that it was not possible to finish all of them within the timelimit; therefore there was no maximum score. Jacobson (1993) reported test-retest corre-lations with an interval of 12 months between measurements of r ¼ .80–.90 in differentgroups of children in grades 1–6 (Jacobson, 1993). In a recent study by Gustafson, Falth,Svensson, Tjus and Heimann (2011) the test-retest correlation between two test sessions ingrade 2 was r ¼ .88.

Phonological decoding. The test Vilket later ratt? [What sounds right?] (Olson, Kliegl,Davidson, & Foltz, 1985) consists of word pairs presented in columns, where one is a mis-spelled real word and the other one is a pseudo-word. It was performed groupwise. The par-ticipants were asked to read the word pairs, decide which one sounded like a real word andunderline that word with a pen. There was no maximum score since the test comprised somany items that it was not possible to finish them within the time limit of two minutes.The score was the number of correct answers minus the number of errors. Correlationbetween the scores of the first 10 items and the following 10 items of the test was r ¼ .67.

Orthographic decoding. The test Vilket ar ratt? [Which is right?] (Olson et al., 1985) con-tains word pairs that sound the same when pronounced. One word is correctly spelled and theother one is misspelled. The children were asked to read the words and underline the correctlyspelled word in each pair. The test was performed in small groups and the time limit was twominutes. As it was not possible to complete all test items there was no maximum score – thescore was the number of correct answers minus the number of errors. In a previous study, test-retest correlation between two test-sessions in grade 2 was r ¼ .91 (Gustafson et al., 2011).

Language comprehension. Three texts from Gustafsson and Rosen (2004) were used andin the present study the test was called Murmeldjur [Marmots]. The texts, the questions, and


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the alternatives to choose from were presented through the use of a previously recordedsoundtrack. The test had 15 questions altogether and was carried out in small groups. Toevery question there were four alternative answers: A, B, C, or D. The maximum scorewas 15. The internal consistency of the test, measured by Cronbach’s alpha, was .70.

Word comprehension. Peabody Picture Vocabulary Test 3rd ed., PPVT-III (Dunn &Dunn, 1997) is a test which assesses word comprehension. The participants were asked tochoose one of four pictures that matched a word read out loud by the test leader. Sevenparts of the test, containing twelve tasks each, were used, starting with the subtest designedfor ten-year-olds. The test was performed on an individual basis, and the maximum score was84. Test-retest reliabilities ranging from .91 to .94 have been reported (Williams & Wang,1997). Convergent validity, the extent to which PPVT-III scores correlate with othermeasures of verbal ability, ranges from .80 to .92 across various measures (Williams &Wang, 1997).

Receptive grammar. Three parts of the Swedish version of the Test for Reception ofGrammar, TROG (Bishop, 1989; Holmberg & Lundalv, 2002), were used. The participantslistened to sentences and then chose which of four pictures matched what they heard. Theparts used were “X, but not Y,” “Inserted clauses,” and “Post modified subject.” Thesewere the parts which, according to the test manual, did not result in any ceiling effects forchildren aged ten. The test was carried out individually and the maximum score wastwelve. For the British version of the test (Bishop, 1989), the split-half reliability based ontotal scores was .65. The internal consistency of the three parts of the Swedish versionused in the present study, as measured by Chronbach’s alpha, was .55, which is ratherpoor. This might however be explained by the fact that the three parts of the test tap differentabilities related to receptive grammar and that the level of difficulty differs between the parts.

Non-verbal ability. Ravens matrices – Coloured (Psykologiforlaget AB, 1995) was used.This is a test in which the participant is requested to choose one of six pieces that fits into thebigger pattern. Set A, set AB, and set B were used. Maximum score was 36. Chronbach’salpha was .72.

General processing speed. The Doodle matching test is a test created by the authors, usingFigure matching test (see Andersson & Lyxell, 2007) as a model. The doodles were designedto avoid association with any language concepts. The participants had to identify and markthe doodle that occurred twice in a line of seven doodles. The participants solved as manytasks as they could within one minute. There was no maximum score since the test comprisedso many items that it was impossible to finish them all within the time limit. This timed test ofprocessing speed resulted in very few errors, so no meaningful estimate of reliability could bereported.

Test Administration

Pilot testing was carried out to make sure that the tests would not result in any ceiling orfloor effects. All the testing was completed by two of the authors during four weeks in


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February and March 2008. The test battery consisted of nine different tests, six of which werecarried out in groups and three individually (see above). The group testing was always per-formed before the individual testing. Written instructions for each test were used to make surethe tests were presented the same way every time. The participants left the regular classes for90 minutes each to complete the indvidual tests in a separate room. Breaks were included toavoid fatigue and children were carefully monitored for any sign of distress during the testing.

Statistical Analyses

The results were analysed using the program SPSS for Windows, version 16.0. All thedata were considered to be on interval level. Independent two-tailed t-tests were used toexamine differences between children with reading difficulties and children with typicalreading ability. The contributions from the different factors were then calculated using mul-tiple regression analysis. First, inter-correlations were calculated. Correlations above .70would have been considered too high for predictors to be included in the same regressionmodel (Tabachnick & Fidell, 2001), but all correlations between predictors were lowerthan that. Since the sample sizes used in the multiple regression analyses were relativelysmall, adjusted R squared values were reported in order to provide a more reliable estimateof the amount of variance.

Separate multiple regression analyses were performed for each group. In the first set ofanalyses, the contributions of two predictors, decoding and language comprehension (thatis, the original SVR model) to reading ability were investigated. The second set of regressionanalyses examined the contributions of two predictors – phonological decoding and ortho-graphic decoding – to decoding. In the third set of regression analyses the contributionsmade to language comprehension by the predictors word comprehension and receptivegrammar were investigated. Finally, non-verbal ability and general processing speed wereadded to the multiple regression analyses described above to examine if these predictorsmade any significant contribution to explaining the variance of reading ability, decoding,and language comprehension, respectively.

Results

Differences Between Groups

Two-tailed independent t-tests were used to establish that the two groups exhibited differ-ent levels of reading skills, and to examine other differences (see Table 1).

Children with reading difficulties performed significantly worse than children withtypical reading development on all tests of reading ability and decoding (all ps , .01). Chil-dren with reading difficulties also obtained significantly lower results than children withtypical reading development on tests of language comprehension and non-linguistic skills(all ps , .05). Effect sizes (Cohen’s d) ranged from moderate for non-verbal ability,through large for general processing speed and the comprehension variables, to very largefor reading ability and the decoding variables (Cohen, 1988). In general, skewness wasacceptable, but children with reading difficulties had positively skewed results on ortho-graphic decoding, stemming from many low scores (but no severe floor effects: Thelowest score was 6) and a few very good scores. They also had negatively skewed resultson general processing speed. Children with typical reading ability had negatively skewedresults on language comprehension, stemming from a moderate ceiling effect.


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Contributions from Underlying Factors to Reading Ability, Decoding, and LanguageComprehension

Bivariate correlations. First, correlations were calculated using Pearson’s r (see Table 2).The results showed that variables could be included in the subsequent multiple regressionanalysis as intended, since none of the correlations between predictors were higher than.70 (Tabachnick & Fidell, 2001). The correlations between decoding and language

Table 1

Means, standard deviations and skewness for the group with reading difficulties, n ¼ 36, and thegroup with typical reading ability, n ¼ 36, observed t-values and effect sizes (Cohen’s d)

Reading difficulties Typical reading ability

M SD Skew M SD Skew tobs Cohen’s d

Reading ability 10.67 3.48 20.57 19.22 3.91 20.20 9.80∗∗ 2.31

Decoding 16.92 7.16 0.45 32.58 9.77 0.27 7.76∗∗ 1.83

Phonological decoding 12.36 6.23 0.28 23.17 9.10 20.96 5.88∗∗ 1.39

Orthographic decoding 18.97 9.62 1.52 38.94 13.65 0.42 7.18∗∗ 1.69

Language comprehension 9.25 3.04 20.41 11.33 2.20 21.21 3.33∗∗ 0.78

Word comprehension 54.11 7.43 20.54 60.44 6.65 20.08 3.81∗∗ 0.90

Receptive grammar 8.25 1.61 0.18 9.44 1.48 0.11 3.28∗∗ 0.77

Non-verbal ability 28.22 4.36 20.29 30.50 3.22 20.52 2.52∗ 0.59

General processing speed 13.17 2.61 21.07 15.67 2.86 0.08 3.87∗∗ 0.91

Note. ∗ p , .05. ∗∗ p , .01.

Table 2.

Correlations (Pearson’s r) between all variables for the group with typical reading ability at top right,n ¼ 36, and for the group with reading difficulties at bottom left, n ¼ 36

1 2 3 4 5 6 7 8 9

1. Reading ability 1 0.45∗∗ 0.73∗∗ 0.46∗∗ 0.56∗∗ 0.39∗ 0.44∗∗ 0.36∗ 0.04

2. Decoding 0.49∗∗ 1 0.62∗∗ 0.72∗∗ 0.28 0.10 0.30 0.06 0.43∗∗

3. Phonological

decoding

0.20 0.40∗ 1 0.38∗ 0.42∗ 0.33 0.19 0.16 0.10

4. Orthographic

decoding

0.48∗∗ 0.75∗∗ 0.34∗ 1 0.16 0.10 0.19 0.10 0.30

5. Language

comprehension

0.20 20.24 20.23 20.31 1 0.41∗ 0.43∗∗ 0.39∗ 0.02

6. Word

comprehension

0.30 20.23 20.16 20.20 0.61∗∗ 1 0.34∗ 0.48∗∗ 20.07

7. Receptive grammar 20.10 20.14 0.15 20.10 0.19 0.17 1 0.43∗∗ 0.06

8. Non-verbal ability 0.41∗ 0.09 0.08 0.22 0.39∗ 0.42∗ 0.33∗ 1 20.04

9. General processing

speed

0.51∗∗ 0.48∗∗ 0.18 0.43∗∗ 20.12 0.04 20.26 0.29 1

Note. ∗ p , .05. ∗∗ p , .01.


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comprehension were .28 and 2.24 for typical readers and children with reading difficulties,respectively. Correlations between phonological and orthographic decoding correlationswere .38 and .34 for the two groups, and between word comprehension and receptivegrammar they were .34 and .17.

Also note that the pattern of results differed between the two groups. For example, forchildren with typical reading ability all variables except general processing speed were sig-nificantly correlated with reading ability. For children with reading difficulties only decoding,orthographic decoding, non-verbal ability and general processing speed showed significantcorrelations with reading ability.

Results of multiple regression analysis for children with typical reading ability. Threeseparate multiple regression analyses were performed (summarized in Figure 1). In the firstanalysis, reading ability was the criterion variable, with decoding and language comprehen-sion as predictors. Results showed that decoding and language comprehension together weresignificant predictors of reading ability (R ¼ 0.64, p , .01) and explained 37% of the var-iance (R2

Adj. ¼ 0.37). Both decoding (b ¼ 0.31, p , .05) and language comprehension (b¼ 0.48, p , .01) were significant predictors of reading ability.

In the second analysis, decoding was the criterion variable and phonologicaldecoding and orthographic decoding were predictors. Phonological decoding and ortho-graphic decoding together were significant predictors of decoding (R¼ 0.81, p , .01) and64% of the variance in decoding was explained (R2

Adj. ¼ 0.64). Both orthographic decoding(b ¼ 0.57, p , .01) and phonological decoding (b ¼ 0.41, p , .01) were significantpredictors.

The third analysis showed that word comprehension and receptive grammar togetherwere significant predictors of language comprehension (R ¼ 0.51, p , .01) and explained21% of the variance (R2

Adj. ¼ 0.21). Neither word comprehension (b ¼ 0.30, ns.) nor recep-tive grammar (b ¼ 0.32, ns.) reached statistical significance as predictors.

Figure 1. Results of three regression analyses of the children with typical reading ability, n ¼ 36.The contributions of subcomponents to decoding and language comprehension, and of decodingand language comprehension to reading ability (standardized b-values). ∗ p , .05. ∗∗ p , .01.


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Results of multiple regression analysis for children with reading difficulties. The sameregression analyses were carried out for children with reading difficulties (see Figure 2). Forthis group of children, decoding and language comprehension together were significant pre-dictors of reading ability (R¼ 0.59, p , .01, R2

Adj. ¼ 0.30). Both decoding (b ¼ 0.57, p ,

.01) and language comprehension (b ¼ 0.33, p , .05) were statistically significantpredictors.

The second analysis showed that phonological decoding and orthographic decodingtogether were significant predictors of decoding (R ¼ 0.77, p , .01, R2

Adj. ¼ 0.56). Ortho-graphic decoding was a significant predictor of decoding (b ¼ 0.67, p , .01) but phonolo-gical decoding was not (b ¼ 0.16, ns.).

The third analysis showed that word comprehension and receptive grammar togetherwere significant predictors of language comprehension (R ¼ 0.62, p , .01, R2

Adj. ¼ 0.34).Word comprehension was a significant predictor of language comprehension (b ¼ 0.60, p, .01) but receptive grammar was not (b ¼ 0.09, ns.).

Contributions from non-verbal ability and general processing speed. In order to inves-tigate whether non-verbal ability and/or general processing speed contributed to the expla-nation of the variance in reading ability, decoding, and language comprehension, six newmultiple regression analyses were carried out, three for each group of children. In the newregression models, non-verbal ability and general processing speed were added to the predictorsused in the previous analyses. Thus, each regression analysis now contained four predictors.

For typical readers, the predictors of reading ability now together explained 38% of thevariance (R2

Adj. ¼ 0.38). Decoding (b ¼ 0.38, p , .05) and language comprehension (b ¼0.39, p , .05) were still significant predictors, but non-verbal ability (b ¼ 0.18) and generalprocessing speed (b ¼ 20.13) failed to reach statistical significance. In the second analysiswith decoding as the criterion, 68% of the variance was explained by the four predictors(R2

Adj. ¼ 0.68). Orthographic decoding (b ¼ 0.49, p , .01) and phonological decoding

Figure 2. Results of three regression analyses for the children with reading difficulties, n ¼ 36. Thecontributions of subcomponents to decoding and language comprehension, and of decoding andlanguage comprehension to reading ability (standardised b-values). ∗ p , .05. ∗∗ p , .01.


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(b ¼ 0.42, p , .01) were still significant predictors. General processing speed also reachedstatistical significance as a predictor, (b ¼ 0.24, p , .05) but non-verbal ability did not (b ¼20.04). The third analysis showed that only 18% of the variance in language comprehensionwas explained by the four predictors (R2

Adj. ¼ 0.18). (Since adjusted values of R2 were used,it may even show a slight decrease from the previous 21%). Neither word comprehension (b¼ 0.24), receptive grammar (b ¼ 0.28), non-verbal ability (b ¼ 0.15), nor general proces-sing speed (b ¼ 0.02) reached statistical significance as predictors of languagecomprehension.

For children with reading difficulties, the four predictors together explained 40% of thevariance in reading ability (R2

Adj. ¼ 0.40). Decoding (b ¼ 0.39, p , .05) was still a statisti-cally significant predictor, while language comprehension (b ¼ 0.25) was no longer signifi-cant. Non-verbal ability (b ¼ 0.19) and general processing speed (b ¼ 0.29) also failed toreach statistical significance. For decoding, 58% of the variance was explained by the newmodel with four predictors (R2

Adj. ¼ 0.58). Orthographic decoding (b ¼ 0.64, p , .01)remained a significant predictor and phonological decoding (b ¼ 0.15) was still non-signifi-cant. General processing speed (b ¼ 0.21) and non-verbal ability (b ¼ 20.13) did not reachstatistical significance as predictors of decoding. Finally, 36% of the variance in languagecomprehension was explained by the new model with four predictors (R2

Adj. ¼ 0.36).Word comprehension (b ¼ 0.52, p , .01) was still a significant predictor, receptivegrammar was still non-significant (b ¼ 20.04), and neither non-verbal ability (b ¼ 0.24)nor general processing speed (b ¼ 20.22) reached statistical significance as predictors.

Thus, the results of the six multiple regression analyses revealed that non-verbal ability didnot contribute significantly to reading ability, decoding, or language comprehension, whetherfor children with typical reading ability or for children with reading difficulties (all ps . .05).General processing speed contributed significantly (p , .05) to decoding for children withtypical reading ability, but not to reading ability or language comprehension, and for thegroup with reading difficulties there were no significant contributions from general processingspeed to reading ability, decoding or language comprehension (all ps . .05).

Discussion

The findings of the present study support the general validity of the SVR and reveal criti-cal differences between children with typical reading ability and struggling readers withregard to the associations between different components of reading. The two factors in theSVR – decoding and language comprehension – explained somewhat less of the variancein reading ability in children with reading difficulties than in children with typical readingability, suggesting that a more complex theory may be needed to explain reading for childrenwith reading difficulties than the original SVR proposed by Gough and Tunmer (1986). Thisis also in line with findings that children with more general reading difficulties benefit frombroad interventions combining several different bottom-up processes, as well as top-downprocesses (Gustafson et al., 2011).

The present study demonstrated that decoding made the largest contribution to readingability for children with reading difficulties, whereas language comprehension was themost important factor for children with typical reading development. This pattern ofresults is in accordance with the SVR predictions made by Hoover and Gough (1990) –that decoding is an important factor in early reading development, while language compre-hension is a better predictor of reading ability in skilled readers. It may also be the case


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that these two abilities develop rather independently – this has been suggested by otherresearchers (Kendeou et al., 2009), who have demonstrated that other language skills, suchas understanding of narrations and inference, are strongly related to comprehension. Inlight of both this and the results of the present study, in which reading comprehension isshown to be an important factor, it is reasonable to suggest that interventions to enlargethe vocabulary and to improve understanding of narrations in early development mayimprove reading comprehension. The findings of Cain et al. (2005), who demonstratedthat children with poor reading comprehension had problems with interpretation of idioms,alongside previous findings that poor comprehenders had difficulties with novel word learn-ing/understanding (Cain et al., 2001), also support the idea of providing interventions toenlarge the vocabulary and to enhance linguistic awareness.

However, one can also argue that decoding abilities constitute a prerequisite for the abilityto use language comprehension processes in reading (Vellutino et al., 2007). The childrenwith reading difficulties did not seem to have acquired a sufficient level of decoding skillin order to be able to apply their higher level language-processing skills to the materialbeing read. Here it should be noted that the children were in grade 4 and had receivedformal reading instruction throughout the several years they had been in school. Still, itwas clear that they lagged behind in many different areas compared to the children withtypical reading ability.

In the present study, decoding was further divided into orthographic decoding and pho-nological decoding. For children with typical reading abilities, phonological decoding andorthographic decoding provided significant contributions to reading, indicating that theywere able to use both strategies to reach a satisfactory level of general word-decodingability. In contrast, for the children with reading difficulties, orthographic decoding contrib-uted significantly to word decoding but phonological decoding did not. However, their per-formance on the orthographic decoding test was rather poor. The children with readingdifficulties might have used a primitive form of visual strategy, such as logographic or anunsuccessful type of orthographic word decoding. Some of these children might have aban-doned phonological decoding strategies in favour of more visually based strategies due topoor phonological awareness and poor phonological decoding skill (Gustafson, Samuelsson,& Ronnberg, 2000; Høien & Lundberg, 1999; Vellutino et al., 2007).

The importance of assessing the relative reliance on phonological and orthographic strat-egies was demonstrated in a longitudinal intervention study by Gustafson et al. (2000), whichfound that in order to benefit from a phonological intervention, children with reading disabil-ities had to show at least some reliance on phonological word decoding when reading. Inanother intervention study a double dissociation was obtained: Children with pronouncedphonological decoding deficits benefitted more from phonological than from orthographictraining, whereas children with pronounced orthographic decoding deficits benefitted morefrom orthographic than from phonological training (Gustafson et al., 2007). These two inter-vention studies also show that it is important to distinguish between the use of word-decodingstrategies in the assessment of reading and word-decoding skills. It is quite possible to haveproblems with something but still keep doing it out of habit, either because you have beentaught to do so or because you have not found a successful alternative.

For the typical readers, word comprehension and receptive grammar were together sig-nificant predictors for language comprehension, and 21% of the variance in language com-prehension was explained. However, neither word comprehension nor receptive grammarreached statistical significance as predictors by themselves; this can be regarded as a


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surprising result. Correlations between the two variables were only .34, so the problem ofcollinearity might have been relatively modest, but statistical power was limited and mighthave been insufficient to discover true relationships. Also, the result on language comprehen-sion was negatively skewed, with a low standard deviation (see Table 1) stemming from amoderate ceiling effect; this might lead to underestimation of the strength of correlations.It should also be noted that the obtained reliability on the test of receptive grammar(TROG) was rather poor, and this might contribute to the low correlation with wordcomprehension.

For children with reading difficulties, word comprehension was a significant predictor oflanguage comprehension but receptive grammar was not. This finding is in accordance withprevious research showing that receptive grammar does not contribute significantly tolanguage comprehension (Vellutino et al., 2007). It may be argued that this finding furthersupports the assumption that phonological skills are important to reading ability, sinceword comprehension heavily relies on phonological abilities. Here it should be noted thatlanguage comprehension is a complex phenomenon indeed and should not be reducedonly to the word level. According to Tunmer and Greaney (2010, p. 233), ‘vocabulary knowl-edge alone would not be a satisfactory measure of C because to understand a language is tounderstand the sentences of the language, not just the words (i.e., knowledge of thelanguage’s syntax is necessary).” The results obtained will also directly depend on the testmaterials used. A language comprehension test containing more complex syntax than theone used in the present study might yield a different pattern of result.

For children with typical reading abilities, general processing speed contributed signifi-cantly to decoding, but not to reading ability, or to language comprehension. According toJoshi and Aaron (2000), processing speed may enhance effective decoding, which seemsto be the case for good readers in the present study. The impact of rapid naming skillsshown by Høien-Tengesdal (2010) may also be an expression of the importance of processingspeed. However, processing speed did not contribute to the reading ability of the children inthe present study who had reading difficulties. Note that the measure of processing speed inthe present study was a visual matching task that did not require phonological processing.Rapid naming, which was used in the Høien-Tengesdal (2010) study, is also closelylinked to vocabulary and lexical access skills, which was not at all the case for the testused in the present study. Presumably poor phonological skills act as a bottleneck for childrenwith reading difficulties, reducing the influence of other variables.

Non-verbal ability (Raven’s) did not contribute significantly to reading ability, decoding,or language comprehension for any group of children. While it was expected that thismeasure would fail to contribute to decoding skill, it was interesting that it did not contributeto language comprehension or reading comprehension. This finding indicates that non-verbalability as measured by Raven’s provides very little additional information when assessingreading-related skills among typical as well as poor readers in grade 4.

Limitations

One limitation of the present study is that only single measures of each component wereused, due to time and resource constraints. Larger sample sizes would also have been prefer-able. To account for the limited number of participants, adjusted R squared values were pre-sented and only a limited number of predictors were included in the multiple regressionanalyses. Keenan, Betjeman, and Olson (2008) showed that the type of reading


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comprehension test used had an impact on the degree of variance explained by the factorslistening comprehension and decoding. This may also have had an impact on the results inthe present study. According to Keenan et al. (2008), decoding accounts for most of the var-iance if the reading comprehension tests use silent reading at sentence level. However, in thepresent study, reading comprehension was measured by silent reading at text level and bymultiple choice tasks, a test type that was not included in the previously mentioned study.

Sample characteristics also need to be considered when interpreting the results. It shouldbe noted that the children with reading difficulties had general rather than specific deficits inreading, and only some of these children would be characterized as having developmentaldyslexia. Thus, the findings should not be generalized to more specific reading disabilities.On the other hand, the selection implied a high external validity since the sample representedchildren who received special education in reading in those schools. The age and develop-mental level of the children as well as the nature of Swedish orthography, which is relativelytransparent (much more so than English), should also be taken into account when interpretingthe results. Another control group, consisting of younger children matched on reading skillwith the children with reading difficulties, would have been valuable to investigate the clini-cal and educational implications.

In general, the quality of data was acceptable but a few skewed results were obtained (seeTable 1), which might lead to an underestimation of the strength of correlations.

Finally, reading ability includes aspects of reasoning and drawing conclusions fromthings that are not explicitly expressed in the text, and therefore, texts with blanks may beconsidered as a somewhat limited measure of reading ability (Kirby & Savage, 2008).However, this type of test is frequently used (Catts & Kamhi, 2005) and allows for quanti-tative analysis, which made it appropriate.

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How Simple is the Simple View of Reading?