Predictors of literacy skills and developmental dyslexia ...

Manuscript under review

Predictors of literacy skills and developmental dyslexia in

six European orthographies

Franck Ramus,*,1 Karin Landerl,*,2,3 Kristina Moll,2,4 Heikki Lyytinen,5,6 Paavo H.

T. Leppänen,5,6 Kaisa Lohvansuu,5,6 Michael O'Donovan,7 Gary Hill,7 Julie

Williams,7 Jürgen Bartling,8 Jennifer Bruder,8 Sarah Kunze,8 Nina Neuhoff,8 Silke

Streiftau,8 Dénes Tóth,9 Ferenc Honbolygó,9 Valéria Csépe,9 Caroline Bogliotti,1

Stéphanie Iannuzzi,10,11 Yves Chaix,10,11 Jean-François Démonet,11 Emilie

Longeras,1,12,13 Sylviane Valdois,12,13 Camille Chabernaud,1,14 Florence Delteil-

Pinton,14 Catherine Billard,14 Florence George,15 Johannes Ziegler,16 Isabelle

Comte-Gervais,17 Isabelle Soares-Boucaud,17,18 Christophe-Loïc Gérard,19 Leo

Blomert,20 Anniek Vaessen,20 Patty Gerretsen,21 Michel Ekkebus,21 Daniel

Brandeis,22,23,24 Urs Maurer,22,25,26 Enrico Schulz,22,27 Sanne van der Mark,28

Gerd Schulte-Körne,8

*Equal contributors.

1 Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS, ENS, EHESS, Paris,

France ; 2 Department of Psychology, University of Salzburg, Austria; 3 Department of

Psychology, University of Graz, Austria; 4 Department of Psychology, University of York,

York, UK; 5 Department of Psychology, University of Jyväskylä, Finland; 6 Agora Center,

University of Jyväskylä, Finland; 7 MRC Centre for Neuropsychiatric Genetics and

Genomics, Department of Psychological Medicine and Neurology, Cardiff University,

Cardiff, UK; 8 Department of Child and Adolescent Psychiatry and Psychotherapy,

University Hospital Munich, Munich, Germany; 9 Institute for Psychology, Hungarian

Academy of Sciences; 10 Unité de Neurologie Pédiatrique, Hôpital des Enfants, Toulouse,

France ; 11 Inserm U825, Hôpital Purpan, Toulouse, France; 12 Laboratoire de Psychologie et

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NeuroCognition UMR 5105 CNRS, Université Pierre Mendès France, Grenoble, France ; 13

Centre référent pour le diagnostic des troubles du langage et des apprentissages, Département

de pédiatrie, CHU Nord, Grenoble, France ; 14 Centre de Référence sur les Troubles des

Apprentissages, Hôpital Bicêtre, Paris, France ; 15 Centre de Référence des Troubles

d’apprentissages, CHU Timone, Marseille, France ; 16 Laboratoire de Psychologie Cognitive,

CNRS, Aix-Marseille Université ; 17 Centre de Référence pour les Troubles des

Apprentissages, Hospices Civils de Lyon, Hôpital E. Herriot, Lyon, France ; 18 Centre

Hospitalier Le Vinatier, Bron, France; 19 Service de Psychopathologie de l’enfant et de

l’adolescent, Hôpital Robert Debré, APHP, Paris, France ; 20 Department of Cognitive

Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the

Netherlands; 21 Regional Institutes of Dyslexia, the Netherlands; 22 Department of Child and

Adolescent Psychiatry, University of Zurich, Zurich, Switzerland; 23 Center for Integrative

Human Physiology, University of Zurich, Zurich, Switzerland; 24 Department of Child and

Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim,

Germany; 25 Sackler Institute for Developmental Psychobiology, Weill Medical College of

Cornell University, New York, USA; 26 Institute of Psychology, University of Zurich,

Zurich, Switzerland; 27 Department of Neurology, Technische Universität München,

München, Germany; 28 MR-Center, University Children's Hospital, University of Zurich,

Zurich, Switzerland

Abbreviated title: Cross-linguistic predictors of literacy and dyslexia.

Total number of words: 7903

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Abstract

Background: This study investigates the relationship between phoneme awareness, rapid

naming, verbal short-term memory and literacy skills in control and dyslexic children, and the

extent to which it depends on orthographic complexity. Methods: General cognitive,

phonological and literacy skills were tested in 1138 control and 1114 dyslexic children living

in 8 European countries and speaking 6 different languages spanning the whole range of

alphabetic orthographic complexity (Finnish, Hungarian, Dutch, German, French, English).

Results: Across all languages, phoneme awareness was the best predictor of diagnostic status,

as well as of reading accuracy and spelling measures, while rapid naming was the best

predictor of reading speed measures. The influence of phoneme awareness on dyslexia and on

literacy skills increased with orthographic complexity, while that of rapid naming remained

constant. More than 80% of dyslexic children showed a deficit in phoneme awareness, rapid

naming and/or verbal short-term memory, with substantial overlap between deficits.

Conclusions: Findings are largely consistent with the literature on predictors of dyslexia and

literacy skills, while uniquely demonstrating how orthographic complexity exacerbates some

symptoms of dyslexia, and modulates the relationship between phonological and literacy

skills.

Keywords: dyslexia, reading, phonology, orthography, cross-linguistic

Abbreviations:

WISC: Wechsler intelligence scale for children

RAN: Rapid automatised naming

PA: phoneme awareness

vST/WM: verbal short-term/working memory

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Introduction

Developmental dyslexia is a common learning disorder affecting about 3 – 7 % of the

population (Lindgren, De Renzi, & Richman, 1985). It is defined as a specific deficit in

reading that cannot be accounted for by low IQ, poor educational opportunities, or obvious

sensory or neurological damage (World Health Organization, 2008). Research has made

tremendous progress in specifying the genetic and neuro-cognitive factors underlying this

disorder as well as relevant environmental factors that modulate its manifestations (Scerri &

Schulte-Korne, 2010; Sénéchal & LeFevre, 2002; Vellutino, Fletcher, Snowling, & Scanlon,

2004). The dominant theoretical account of dyslexia is that of a phonological core deficit

(Snowling, 2000), i.e. genetically determined problems in the development of brain

mechanisms concerned with processing speech sounds lead to marked difficulties to fully

understand and exploit the mappings between spoken and written language (Galaburda,

LoTurco, Ramus, Fitch, & Rosen, 2006; Roeske et al., 2009). Three subcomponents have

been identified that are typically deficient in dyslexic individuals, namely phonological

awareness (PA), verbal short-term and working memory (vST/WM), and rapid automatised

naming (RAN) (Wagner & Torgesen, 1987). For the latter component there is an ongoing

discussion whether the reduced naming speed that is characteristic for dyslexia is part of the

phonological core deficit or whether it constitutes a second, largely independent cognitive

deficit that causes dyslexia (Kirby, Georgiou, Martinussen, & Parrila, 2010; Powell,

Stainthorp, Stuart, Garwood, & Quinlan, 2007; Wolf & Bowers, 1999).

A central environmental factor that has been identified to influence reading acquisition

and dyslexia is the particular orthography the child is acquiring. All alphabetic orthographies

depict the sound structure of the language they represent, but there is considerable variability

in how transparent this relationship is for the learner and how consistently orthographic

symbols represent the sounds of a certain language. Numerous studies investigating typical

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development showed that so called shallow orthographies with high grapheme-phoneme

consistency are acquired more easily than deep orthographies with a high proportion of

inconsistent and irregular spellings. Most impressively, Seymour, Aro, and Erskine (2003)

investigated word and nonword reading skills in 1st graders learning 13 different European

orthographies. While reading accuracy was close to ceiling for most children acquiring a

consistent orthographic system (Italian, Icelandic, Norwegian, Spanish, German, Dutch,

Finnish), young readers acquiring more complex alphabetic orthographies (English, Danish,

French), were still struggling. In particular, the complex and opaque mapping system of

English orthography causes problems to the young learner. Ironically, the investigation of

reading acquisition in English strongly dominates the research field and the question arises,

how informative such an “Anglocentric view” (Share, 2008) is for all other orthographies.

Cross-linguistic studies can directly address this central issue by comparing reading

acquisition in different orthographies within the same research design. This approach can

certainly not eliminate all methodological problems that are inherent in comparisons across

different educational, cultural, and language backgrounds, but findings are still easier to

interpret within such designs than between studies that are carried out in different

orthographic systems independently. This approach has been exploited in a number of studies

on typical reading acquisition. However, cross-linguistic studies of dyslexia are scarce. Not

surprisingly, two out of three published cross-linguistic studies on dyslexia were carried out in

English and German, two languages that are highly similar, but differ critically with respect to

letter-sound consistency (low in English, high in German). Both studies (Landerl, Wimmer, &

Frith, 1997; Ziegler, Perry, Ma-Wyatt, Ladner, & Schulte-Korne, 2003) presented highly

similar words (e.g., ball – Ball; discussion – Diskussion) and nonwords (e.g., bloar – Blohr) to

typically developing and dyslexic children in the two languages and found marked deficits in

reading speed, especially for nonwords, for both dyslexia groups, while reading accuracy was

clearly more deficient in the English than in the German dyslexia groups, confirming that in

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consistent orthographies reading accuracy is high, even among dyslexic readers. However,

both studies investigated reading profiles in dyslexic children but did not address the

prediction of reading and dyslexia.

Caravolas, Volin and Hulme (2005, Study 2) compared English with Czech, a shallow

orthography that is clearly less similar to English than German. The authors reported similar

deficits on phoneme awareness relative to control groups in both languages, and concluded

that phoneme awareness appears to be a core component skill of alphabetic literacy, which is

equally important in consistent and inconsistent orthographies. However, note that in this

study children were selected based on poor spelling, and that the correlation of phoneme

awareness with spelling is typically higher than with reading (e.g., Moll, Fussenegger,

Willburger, & Landerl, 2009). Furthermore, the study only investigated PA and did not

include other predictors such as verbal ST/WM and RAN.

It has been suggested that PA may be less relevant in consistent than in inconsistent

orthographies as the high reliability of grapheme-phoneme correspondences and the phonics

oriented teaching approach that is typical for these orthographies may help dyslexic children

compensate for any early phonological deficits (Wimmer, Mayringer, & Landerl, 2000). On

the other hand, RAN may be a more relevant predictor in consistent than in inconsistent

orthographies as this component is closely associated with reading fluency which is the

central selection criterion of dyslexia in consistent orthographies (Kirby et al., 2010). Thus,

the question of the relative importance of the predictors of component reading skills and

dyslexia remains to be fully addressed cross-linguistically.

In a recent study of Grade 2 children, Ziegler et al. (2010) compared predictive

patterns across five different orthographies and came to similar conclusions as Caravolas et al

(2005): PA was the main factor associated with reading in each language. However, they

found some support for the hypothesis that PA may be less relevant in consistent

orthographies as its impact was indeed weaker. The association of RAN with reading fluency

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was surprisingly weak in this study. Unfortunately, this large scale analysis did not include an

English language group, thus did not capture the full variance of orthographic complexity.

Direct comparisons of reading acquisition in English and in more consistent orthographies

(Swedish/Norwegian: Furnes & Samuelsson, 2010; Greek: Georgiou, Parrila, &

Papadopoulos, 2008; German: Mann & Wimmer, 2002) also report evidence for language-

specific patterns: In general, PA seems to be a better predictor in English than in more

consistent orthographies, especially for reading accuracy, while RAN was found to be a

significant predictor of reading fluency in all orthographies. However, all these studies

investigated reading skills across the normal range and their findings may be of limited value

for our understanding of the deficits underlying dyslexia.

In the context of a large European research network on the genetic and neurobiological

foundations of dyslexia (Bosch, 2006), the unique opportunity emerged to investigate the

associative patterns of cognitive, phonological and reading skills across six orthographies,

representing the full range of orthographic complexity. English and French comprised the

highest level of orthographic complexity as both orthographies represent morphological rather

than phonological structures and are therefore often opaque for the learner. Dutch and

German represented a medium level of orthographic complexity. These two highly similar

Germanic languages have comparable orthographic structures with highly consistent

grapheme-phoneme correspondences (high feedforward consistency), but less consistent

phoneme-grapheme correspondences (low feedback consistency). Finally, Finnish and

Hungarian comprised the lowest level of orthographic complexity as both languages have

non-ambiguous 1:1 relationships between letters and sounds with equally high feedforward

and feedback consistency. Although this classification is relatively informal, it is perfectly

consistent with more quantitative analyses of letter-phoneme mappings (Borgwaldt, Hellwig,

& de Groot, 2005).

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Dyslexic and control children performed language-specific measures of word and

nonword reading, spelling, as well as tasks tapping the three central components of

phonological/cognitive skills, namely PA, verbal STM/WM and RAN. Due to the large

variability of languages involved, it was not feasible to apply strictly identical language and

literacy measures for all participants, however, the major advantage of this European

cooperation was the parallel structure of ascertainment and components assessed resulting in

comparable datasets from altogether 1114 dyslexic and 1138 typically developing school

children acquiring six different alphabetic orthographies in eight countries.

The central research questions we wanted to investigate in this sample were: (1) What

predicts dyslexia? Is PA the best predictor in all orthographies or is its impact lower in

consistent than in inconsistent orthographies? (2) What are the main predictors of literacy

measures across orthographies? Do they differ between various components of literacy? (3)

To what extent does the influence of these predictors depend on orthographic complexity? (4)

What is the relative prevalence of the cognitive deficits underlying dyslexia, and to what

extent do they overlap?

Methods

Participants

Informed consent was obtained for all participants according to each country’s

legislation. The same inclusion and exclusion criteria were applied in all partner countries.

The following criteria applied to all children:

• Age between 8 years and 12 years 11 months old.

• To have at least one and a half years of formal reading instruction in order to

differentiate serious problems in reading acquisition from early delays that are

not always persistent.

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• An age-appropriate scaled score of at least 7 on WISC Block Design, and of at

least 6 on WISC Similarities.

• An attention scale score within the 95th percentile of the age-appropriate norm,

either from the Child Behavior Check-List (Achenbach, 2001), or from the

Conners questionnaire (Conners, 1973), filled by parents.

• A parental questionnaire further asked a number of simple questions, on which

basis we applied the following exclusion criteria:

o Hearing loss

o Uncorrected sight problems

o Test language not spoken by at least one parent since birth.

o Child not schooled in the test language.

o Child missed school for any period of 3 months or more.

o A formal diagnosis of ADHD.

o Child on medication for epilepsy or behavioural problems.

Inclusion criterion for dyslexic children:

• More than 1.25 SDs below grade level on a standardized test of word reading

fluency (a composite reading accuracy and speed score, in order to allow for

different manifestations of reading disability in different languages).

Inclusion criterion for control children:

• Less than 0.85 SDs below grade level on the same standardised word reading

fluency test.

Table 1 shows participant numbers per grade level separately for each participating country.

There are 1138 control and 1114 dyslexic children in total, based in 8 different countries and

speaking 6 different languages. In the control group there were 598 boys and 540 girls, while

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in the dyslexic group there were 705 boys and 408 girls, which is consistent with the typical

sex-ratio of dyslexia.

INSERT TABLE 1 ABOUT HERE

Measures

IQ was estimated based on at least one verbal and one nonverbal WISC subtest:

Similarities (SIMILAR) and Block Design (BLOCKS). The 4th edition of the WISC was used

in countries where it was available, otherwise the 3rd edition was used (Wechsler, 1992,

2003).

Literacy skills were assessed using three language-appropriate tests yielding five

separate measures: word reading (accuracy WDREADAC and speed WDREADSP), nonword

reading (accuracy NWREADAC and speed NWREADSP), and spelling (accuracy

WDSPELL). Word reading accuracy and speed measures were combined to form a composite

word reading fluency measure (WDREADFL), for which national norms were available in

each relevant grade, and which was used as the main inclusion criterion, but not in subsequent

analyses.

Phonological awareness was assessed by a phoneme deletion task, requiring children

to delete a specified phoneme from a nonword (e.g., “Say /bli:k/ without the /k/”). Only

accuracy was measured (PHONDEL).

Verbal ST/WM were measured by WISC digit span (forwards and backwards). Raw

scores were transformed into age-appropriate scaled scores based on national norms

(DIGSPAN).

Naming speed was assessed via language specific RAN tasks requiring children to

sequentially name as quickly as possible lists of pictures depicting easily recognizable objects

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(PICRAN). Letter and digit naming speed (LETRAN, DIGRAN) were also tested in a similar

way. The dependent measure was the number of items named per minute, irrespective of

errors.

Data analysis

For all variables but the Word reading fluency inclusion variable and the WISC subtests

(which were already on a standardised scale), raw scores were converted into z-scores based

on the control group’s mean and standard deviation, within each country and each grade level.

Because some variables in some countries had highly skewed distributions, we further applied

the following procedure: Each variable in each country was converted into ranks, then

rescaled on a ]0-100[ interval, then applied the normal distribution function to convert them

into z-scores, and then finally rescaled based on the control group’s mean and standard

deviations.

Throughout this paper, we use as dependent variables Group (control, dyslexic) as well as

literacy skills (Word and Nonword reading accuracy and speed, Word spelling). The

independent variables of greatest interest are those indexing phonological and cognitive skills

that are known to have an impact on literacy (PA, verbal ST/WM and RAN). For the latter

measure we used Picture RAN as the main variable of interest, because we felt that for

theoretical reasons it is a better indicator of general naming fluency than Digit and even more

so Letter RAN which are very close to the reading process. Nevertheless the same analyses

were carried out with Digit instead of Picture RAN and this did not change the general pattern

of results. Finally, Similarities and Blocks were used as measures of general cognitive

abilities, and Age and Grade were also considered as independent variables, as they differ

slightly between groups and may moderate some of the effects observed. All analyses were

carried out using PASW 18 (SPSS Inc.).

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Results

Descriptive results

Table 2 reports means, number of observations, standard deviations, minima and maxima for

each variable and each group. Given the large sample size, all variables significantly differ

between groups at the p <=.001 level, even though in some cases the effect size is very small.

Among the smallest differences, dyslexic children are older by a little less than 3 months on

average. They are also slightly underrepresented in Grades 3 and 4 and overrepresented in

Grades 5, 6 and 7, thus have an average grade .23 points higher than controls. Performance on

Blocks is slightly higher in the control group, but the effect size is very small (.12 standard

deviation of the general population). As age, grade and performance IQ are normally matched

between dyslexic and control groups, these variables will be controlled in subsequent

analyses. Performance on Similarities differs to a larger extent between the groups, with an

effect size of .46 standard deviation of the population. This estimate of verbal IQ will also be

controlled in subsequent analyses. All other variables reflect either literacy or phonological

skills and logically show large differences between the groups.


Logistic regressions of diagnostic category

Across languages

A logistic regression was carried out with Group (control, dyslexic) as dependent variable,

and with Age, Grade, Blocks, Similarities, Digit span, Picture RAN, and Phoneme deletion

entered simultaneously as predictor variables, using all participants who had data in all of

these variables (N=2001). Table 3 gives the Wald statistic, which indexes the relative

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importance of each variable in the model, and the associated p value. The best predictors of

group membership are, in order of decreasing importance, Phoneme deletion, Picture RAN,

then to a much lower extent Digit span, Similarities, and Blocks. Age and Grade did not

significantly contribute to the prediction. The resulting model explains between 36.9 and

49.2% of the variance, and allows for a correct classification of 77.4% of the participants into

their diagnostic category.


In order to investigate whether Orthographic complexity affects the influence of the main

predictors, we re-ran the same logistic regression with, as additional regressors, Orthographic

complexity (i.e., the three language groups coded 1, 2, 3), its interaction with Phoneme

deletion, and its interaction with Picture RAN. Orthographic complexity did not have any

predictive value by itself (Wald=0.04, p=.84), however there was an interaction between

Orthographic complexity and Phoneme deletion (Wald=13.1, p<.001) but not with Picture

RAN (Wald=1.26, p=.26). Thus, the greater the orthographic complexity, the more Phoneme

deletion is a powerful diagnostic marker, as illustrated in Figure 1. On the other hand, the

predictive power of Picture RAN does not differ between languages.

INSERT FIGURE 1 HERE

Within language groups

The same logistic regression was repeated, restricted to each language group (see Table 3).

The relative importance of the predictor variables was largely replicated, at least within

medium and high complexity orthographies. Within low complexity orthographies, on the

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other hand, Phoneme deletion and Picture RAN seem to make a more equal contribution. The

relative contributions of the other predictor variables remain much smaller, and seem more

variable between language groups, maybe due to more idiosyncratic factors. The proportion

of participants correctly classified by this model is 75.3% for low, 78.6% for medium, and

86.8% for high complexity orthographies.

The general picture provided by these logistic regressions is that Phoneme deletion is the best

predictor of dyslexia, RAN is the second best, and the relative importance of RAN increases

with the transparency of the orthography. Nevertheless, the total predictive power of the entire

model seems to decrease with orthographic transparency.

Linear regressions of literacy measures

Across languages

A multiple linear regression was carried out with Word reading accuracy as dependent

variable, and with Age, Grade, Blocks, Similarities, Digit span, Picture RAN, and Phoneme

deletion entered simultaneously as predictor variables, using all participants who had data in

all of these variables (N=2005). Table 4 gives the Beta statistic, which indexes the relative

importance of each variable in the model, and the associated p value. The best predictors of

Word reading accuracy across all languages are, in order of decreasing importance, Phoneme

deletion, Grade, then to a much lower extent Digit span, Picture RAN, Age and Similarities.

Blocks did not significantly contribute to the prediction. The resulting model explained 38.9%

of the variance.

The perhaps unexpectedly high contribution of Grade level to this model is due to the fact that

the variance of Word reading accuracy abilities increases with Grade, on the side of negative

z-scores. This may in turn result from two non exclusive effects: 1) that the gap between

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dyslexic and control children increases with time, and 2) that Word reading accuracy

measures tend to show ceiling effects at older ages in at least some languages, thereby

reducing the control group’s variance and mechanically increasing negative z-scores for

dyslexic children.

INSERT TABLES 4, 5 AND 6 ABOUT HERE

Similar multiple linear regressions were run on each of the other main literacy variables:

Word reading speed, Word spelling (Tables 5-6), Nonword reading accuracy, and Nonword

reading speed (Online tables 1 and 2). For Nonword reading accuracy and Word spelling, the

results are largely consistent with those of Word reading accuracy, with Phoneme deletion as

single most important predictor, then Digit span, Grade, Picture RAN and Similarities as more

minor predictors. For Word and Nonword reading speed, on the other hand, Picture RAN is

the main predictor, followed by Phoneme deletion as a strong second predictor, and then other

variables of only minor importance.

Within language groups

The same multiple linear regressions were repeated for each of the language groups with the

five literacy variables. The results are presented on Tables 4-6 and Online tables 1 and 2. The

results are generally consistent with those obtained across all languages, at least with respect

to the main predictor in each regression. The main exception is that Phoneme deletion seems

to be a better predictor of Word and Nonword reading speed measures than Picture RAN in

medium-complexity orthographies. There is no straightforward interpretation for that, but it

should be noted that the beta values for the two variables are very close, so this result may not

be reliable and may simply reflect idiosyncrasies of our dataset.

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Overall, the general picture given by these multiple linear regressions is that Phoneme

deletion is the best predictor of reading and spelling accuracy measures, while RAN is the

best predictor of reading speed measures. Furthermore, in all our analyses, the predictive

power of the models increases with orthographic complexity.

Covariance analyses of literacy measures

In order to better understand the extent to which the effects of a given predictor of a literacy

measure may change depending on orthographic complexity, we ran general linear models

with a literacy measure as dependent variable, orthographic complexity as a fixed factor, and

the best predictor variable as covariate.

We first ran this analysis with Word reading accuracy as dependent variable and Phoneme

deletion as covariate. Beyond the obvious effect of Phoneme deletion (F(1, 2158)=832,

p<.001), there was a main effect of Orthographic complexity (F(2, 2158)=53,6), p<.001) and

an interaction between the two variables (F(2, 2158)=33.6, p<.001).


As Figure 2 shows (also confirmed by post-hoc tests), the main effect of Orthographic

complexity is due to the fact that dyslexic children reach lower Word reading accuracy z-

scores in high than in both medium and low complexity languages. Furthermore, the

interaction is explained by a stronger relationship between Phoneme deletion and Word

reading accuracy in high (R2=.38) than in medium (R2=.22) and low (R2=.21) complexity

languages.

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We ran a similar analysis with Word spelling as dependent variable, and Phoneme deletion as

covariate. There was again a main effect of Phoneme deletion (F(1, 2141)=778, p<.001), a

main effect of Orthographic complexity (F(2, 2141)=26,7, p<.001) and an interaction (F(2,

2141)=7.8, p<.001). Here, the Orthographic complexity effect differentiated both high from

medium, and medium from low complexity languages (all p values <.001). However, the

interaction was this time due to Phoneme deletion having a greater influence on Word

spelling in both high (R2=.30) and medium (R2=.30) than in low complexity languages

(R2=.23) (see Figure 3).


Finally, we ran a similar analysis with Word reading speed as dependent variable and its main

predictor, Picture RAN, as covariate. This time we found a main effect of Picture RAN (F(1,

2164)=694, p<.001), but no significant effect of Orthographic complexity (F(2, 2164)=1.04,

p=.35) and no significant interaction (F(2, 2164)<1). Thus it seems that dyslexic children have

similar reading speed difficulties in all languages, and that Picture RAN influences Word

reading speed to the same extent in all languages (see Figure 4).


Prevalence of deficits

In order to investigate potential subtypes of dyslexia in the manner of Wolf et al. (2002), we

counted the number of dyslexic children with a deficit in PA, RAN, and also in verbal

ST/WM. A deficit was defined as scoring below -1 standard deviation below the control mean

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on Phoneme deletion, Picture RAN or Digit span respectively. Figure 5 shows the prevalence

of the three deficits among the 977 dyslexic children who had data in all three variables.


Using these criteria, 58.3% of dyslexic children had a PA deficit, 52.7% had a RAN deficit,

and 33.8% had a verbal ST/WM deficit. There were multiple overlaps between deficits, with

34.7% of dyslexic children having a single deficit, 34.2% having two, and 13.9% having

three. 17.2% had no detectable deficit by these criteria. Finally, it is worth noting that by the

same criteria, 64.4% of the 1025 control children had no deficit, 27.9% had a single deficit,

6.3% had two, and 1.4% had three.

Discussion

In this study we have investigated the cognitive abilities and literacy skills of more than 1000

dyslexic and 1000 control children aged from 8 to 12, from 8 European countries, learning to

read in 6 different languages varying widely in terms of orthographic complexity. We have

specifically tested to what extent various cognitive variables predicted children’s literacy

skills and diagnostic status, and to what extent this differs between languages of different

orthographic complexity.

What are the predictors of dyslexia?

We have found that there are two major predictors of developmental dyslexia: Phoneme

awareness and Rapid naming. Whereas the diagnostic power of Rapid naming is constant

across languages, that of Phoneme awareness increases with orthographic complexity.

Overall, Phoneme awareness has relatively greater predictive power than Rapid naming,

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except in low complexity orthographies where the two variables seem to have equal relative

importance. Verbal ST/WM and general verbal abilities play a comparatively minor role.

Given the paucity of cross-linguistic studies of dyslexia, these findings have little counterpart

in the literature, but they are certainly consistent with a number cross-linguistic studies of the

predictors of reading ability in the general population (Furnes & Samuelsson, 2010; Mann &

Wimmer, 2002; Ziegler et al., 2010)

What are the predictors of literacy skills?

We have studied five literacy skills: Word reading accuracy and speed, Nonword reading

accuracy and speed, and Word spelling. Overall we have found that PA is the best predictor of

reading and spelling accuracy variables, while RAN is the best predictor of reading speed

measures. This is consistent with the notion that RAN, being a timed measure, captures

mainly the fluency dimension of reading (e.g., Vaessen, Gerretsen, & Blomert, 2009).

Furthermore, we found that PA is a better predictor of reading and spelling accuracy in high

than in low complexity orthographies. In contrast, the influence of RAN on reading speed

seems more universal as its predictive power was similar across orthographies.

In general, our statistical models always predicted all the more variance (and classified more

participants) when orthography was more complex. This trend holds across every single of

our analyses, and is particularly large for reading accuracy measures (more than twice as

much total variance explained in high than in low complexity languages, see Table 4 and

Online Table 1). This may be a consequence of the larger variance in reading skills in high

than in low complexity orthographies. Indeed, despite our normalisation of all variables into

z-scores, dyslexic children often reached more extreme negative z-scores in high than in low

complexity languages (as can be seen in Figure 2 for instance). In low complexity

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orthographies, reading achievement is generally higher (Seymour et al., 2003), even for

dyslexic children (Lindgren et al., 1985). Thus, variance in reading skills is reduced,

particularly so in reading accuracy measures where ceiling effects are common. The little

variance that remains to be explained, amplified by rescaling into z-scores, may be much

noisier and reflect more idiosyncratic factors than in high complexity orthographies, where

variance in phonological skills can show its full impact and capture a relatively higher

proportion of variance in reading skills.

Overall these results are consistent with previous cross-linguistic studies of reading

acquisition (Furnes & Samuelsson, 2010; Mann & Wimmer, 2002; Ziegler et al., 2010). Our

finding that RAN is the best predictor of reading speed is at odds with Ziegler et al. (2010),

however it fits well with the rest of the literature (Kirby et al., 2010).

Prevalence of deficits

Finally, we identified, among dyslexic children, those with deficits in PA, RAN, and Verbal

ST/WM. Like Wolf et al. (2002), we found a predominance of multiple deficits, although

about one third of the children seemed affected by just one deficit. PA was the most common

deficit. In the tradition of Wagner and Torgesen (1987), we considered the possibility of a

triple instead of a “double deficit”. We did find that about one third of dyslexic children had a

verbal ST/WM deficit, although very few in isolation (4.3%).

Furthermore, 17% of dyslexic children did not seem to show any deficit by our criteria. This

should be moderated by the fact that we had only one task per deficit. Having several tasks

per construct would have increased construct validity and would likely have increased our

chances of detecting children with a given deficit. Furthermore, not all of these children may

truly be dyslexic, especially given our relatively liberal inclusion criteria (-1.25 SD on a

20


single reading measure). Nevertheless, it is a common finding in the literature that not all

dyslexic children seem to have a phonological deficit, even in the broad sense of the triple

deficit (e.g., Bosse, Tainturier, & Valdois, 2007; Ramus, Pidgeon, & Frith, 2003; White et al.,

2006; Wolf et al., 2002). What exactly explains the reading disability of these children

remains to be investigated, but is beyond the scope of the present study.

Limitations and caveats

The present cross-linguistic study of dyslexia covers an unprecedented number of languages

(6) representing the full range of orthographic complexity, from the most regular (Finnish) to

the most irregular (English). Nevertheless, the conclusions of this study remain limited to

alphabetic languages, and indeed to those using the Latin alphabet and a left-to-right writing

direction. Any generalisation to other types of alphabetic languages, and all the more so to

logographic languages such as Chinese, would be very tentative.

As in any cross-linguistic study, a major issue is the extent to which linguistic tasks designed

in different languages tap similar cognitive processes and similar levels of difficulty. One

possibility is to choose languages that are sufficiently close (such as English and German) to

match all the material (e.g., Landerl et al., 1997; Ziegler et al., 2003). When one wants to

study a broader range of languages, this is not an option anymore. There is simply no way to

design equivalent lists of words or nonwords across languages as different as English and

Finnish. Furthermore, even if the material was matched, the difficulty of certain tasks (such as

word reading accuracy or phoneme deletion) might not be, as this is partly dependent on the

orthographic complexity of the language. Thus, it is inevitable that in the present study some

tasks tap different levels of performance and thus have different sensitivities in different

languages. Does that affect our conclusions in any significant way?

21


The issue may seem most crucial with respect to the role of phoneme deletion. Indeed, it has

been suggested that in languages with regular orthographies, both reading accuracy and

phoneme awareness are easily acquired, even by dyslexic children, so that the former is a

minor issue, and the latter is of minor importance for reading acquisition and dyslexia

(Wimmer et al., 2000). Nevertheless, here we found that phoneme awareness was the best

predictor of reading accuracy and spelling, even in the most transparent languages. Could that

be an artefact? There is no doubt that in transparent orthographies, both control and dyslexic

children reach higher absolute performance levels in reading accuracy and in phoneme

awareness. So it may be argued that variations in performance in these tasks are limited and

thus of minor clinical and social importance. Nevertheless, we found that, to the extent that

there is variation in performance in phoneme awareness and reading accuracy (and there is,

even in Finnish), this variation is still meaningful, both in terms of the prediction of diagnostic

category, and in terms of the relationship between phonological and reading skills. Even in a

language where dyslexic children reach near-perfect performance on phoneme deletion, the

fact that their performance is slightly less perfect than that of controls’ is a good predictor of

clinical status, reading accuracy and spelling.

However, we should acknowledge that the present cross-sectional study can only provide

suggestive information regarding causal connections between the variables we have

measured. Our use of the word “predictor” is meant in a strictly statistical sense, i.e., the

extent to which knowing the value of one variable allows one to predict the value of another

variable. We do not imply that the pattern of predictions observed here would necessarily hold

across different time points. Our present findings are most likely to reflect bi-directional

causality between phonological and reading skills.

22


Conclusions

In summary, this unique cross-linguistic study of dyslexia has found that PA and RAN are the

best predictors of both reading skills and developmental dyslexia. Verbal ST/WM and general

verbal ability also play a significant, but more minor role. PA is a better predictor of

diagnostic status and of reading accuracy and spelling skills, while RAN is a better predictor

of reading speed. Furthermore, the influence of PA on diagnostic status and on reading skills

increases with orthographic complexity, while that of RAN seems constant across languages.

Finally, more than 80% of dyslexic children can be described as having a combination of one

or more deficits in PA, RAN and/or verbal ST/WM, with substantial overlap between deficits.

Correspondence

Franck Ramus, LSCP, Ecole Normale Supérieure, 29 rue d’Ulm, 75005 Paris, France. Tel :

+33 144322619. Fax : +33 144322630. Email: [email protected].

Acknowledgements

This project was funded by the European Commission FP6 grant to the Neurodys consortium,

as well as by Agence Nationale de la Recherche, Ville de Paris, Austrian Science Fund

(project no. 18351-B02), SNSF grant 32-108130, and Stiftung für wissenschaftliche

Forschung an der Universität Zürich. We thank all the children and their families for their

participation. We further thank the following people for their help: Laure Bricout, Sophie

Jery, Geneviève Lina, Christine Nossent, Corinne Bruchet, Lionel Collet, Marie-Christine

Mouren.

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Tables

Table 1. Number of participants per country, per grade and per group.

Number of participants Group

Language Country Grade Control Dyslexic

Low orthographic complexity languages

Finnish Finland 2 68 95

3 131 123

4 1 2

Total 200 220

Hungarian Hungary 2 62 40

3 58 27

4 55 25

Total 175 92

Medium orthographic complexity languages

German Germany 3 110 80

4 92 78

Total 202 158

Switzerland 2 7 3

3 10 7

4 2 2

5 25 17

Total 44 29

Austria 2 42 31

3 83 52

4 62 57

Total 187 140

Dutch Netherlands 2 10 27

3 39 43

4 34 20

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29

5 2 8

Total 85 98

High orthographic complexity languages

French France 2 8 6

3 23 28

4 26 49

5 23 36

6 15 25

7 7 14

Total 102 158

English United Kingdom 3 10 17

4 25 36

5 31 43

6 39 54

7 38 69

Total 143 219

1138 1114


Table 2. Mean, number of observations, standard deviations, minimum and maximum for all variables in each group.

GROUP AGEa GRADE BLOCKSb SIMILARb DIGSPANb WDREADFLc WDREADAC WDREADSP NWREADAC NWREADSP WDSPELL LETRAN DIGRAN PICRAN PHONDEL

Mean 116.52 3.53 11.13 12.41 10.23 0.48 0.00 0.02 0.01 0.01 0.03 0.02 0.01 0.03 0.02

N 1138 1138 1138 1137 1065 1138 1119 1120 1107 1026 1104 978 1085 1086 1084

Std.

Deviation

12.45 1.22 2.56 2.94 2.73 0.88 0.99 1.00 1.00 1.00 0.99 0.99 1.00 1.00 0.99

Minimum 96 2 7 6 1 -.84 -2.83 -2.86 -3.16 -2.92 -3.30 -4.21 -4.26 -4.26 -3.30

Control

Maximum 155 7 19 19 19 6.89 3.31 3.43 2.72 3.24 3.12 2.77 2.83 2.75 2.98

Mean 119.37 3.76 10.78 11.02 8.38 -1.81 -1.79 -2.54 -1.31 -2.15 -1.66 -1.32 -1.09 -1.03 -1.24

N 1114 1114 1114 1114 1009 1114 1090 1092 1090 926 1084 869 1087 1087 1086

Std.

Deviation

14.52 1.43 2.42 2.78 2.58 0.45 1.42 1.12 1.19 1.21 1.17 1.06 1.08 1.08 1.14

Minimum 96 2 7 6 1 -3.45 -7.15 -7.20 -5.02 -7.02 -5.53 -4.44 -4.38 -4.26 -4.50

Dyslexic

Maximum 155 7 19 19 19 -1.25 2.58 .52 2.54 2.98 3.17 2.46 2.32 2.69 1.81

a months

b scaled scores

c z-scores based on national norms

All other variables: z-scores based on control groups

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Table 3. Relative importance of predictor variables of GROUP membership, across all orthographies and within each orthographic complexity group.

Orthographic Complexity:

All

(N=2001)

Low

(N=684)

Medium

(N=931)

High

(N=386)

Predictor variables Wald

PHONDEL 219*** 50.2*** 119*** 50.4***

PICRAN 195*** 52.1*** 105*** 34.7***

DIGSPAN 27.4*** 13.2*** 5.40* 3.16

SIMILAR 26.2*** 6.65** 9.35** 5.31*

BLOCKS 7.04** 2.17 10.3** 0.26

AGE 0.82 9.76** 0.24 0.94

GRADE 0.00 17.0*** 0.00 0.02

Cox & Snell R2 0.37 0.31 0.38 0.46

Nagelkerke R2 0.49 0.42 0.51 0.63

% correctly classified 77.4 75.3 78.6 86.8

***: p<.001; **: p<.01; *: p<.05

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Table 4. Relative importance of predictor variables of Word reading accuracy (WDREADAC), across all orthographies and within each orthographic

complexity group.

Orthographic

Complexity:

All

(N=2005)

Low

(N=681)

Medium

(N=931)

High

(N=386)

Predictor variables Beta

PHONDEL 0.38*** 0.39*** 0.40*** 0.41***

GRADE -0.33*** 0.12* -0.08 -0.14

DIGSPAN 0.15*** 0.11** 0.08* 0.23***

PICRAN 0.14*** 0.06 0.13*** 0.23***

AGE 0.12** -0.03 0.04 -0.02

SIMILAR 0.11*** 0.08* 0.05 0.18***

BLOCKS -0.03 -0.04 -0.04 0.00

Adjusted R2 0.39 0.24 0.25 0.54

***: p<.001; **: p<.01; *: p<.05

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Table 5. Relative importance of predictor variables of Word reading speed (WDREADSP), across all orthographies and within each orthographic

complexity group.

Orthographic

Complexity:

All

(N=2005)

Low

(N=681)

Medium

(N=931)

High

(N=386)


PICRAN 0.38*** 0.32*** 0.36*** 0.44***

PHONDEL 0.28*** 0.23*** 0.37*** 0.14***

GRADE 0.13*** .107 -0.11* 0.57***

SIMILAR 0.12*** 0.17*** 0.08** 0.16***

DIGSPAN 0.12*** 0.14*** 0.074** 0.13**

BLOCKS -0.052** -.047 -0.059* -.036

AGE -.077 -.095 .081 -0.48***

Adjusted R2 0.39 0.34 0.41 0.44

***: p<.001; **: p<.01; *: p<.05

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Table 6. Relative importance of predictor variables of Word spelling (WDSPELL), across all orthographies and within each orthographic complexity

group.

Orthographic

Complexity:

All

(N=2005)

Low

(N=680)

Medium

(N=917)

High

(N=386)


PHONDEL 0.38*** 0.33*** 0.43*** 0.36***

PICRAN 0.21*** 0.10** 0.20*** 0.28***

GRADE -0.16*** -.026 -0.11* .103

DIGSPAN 0.13*** 0.16*** 0.095*** 0.15***

SIMILAR 0.10*** 0.16*** 0.095*** 0.11**

AGE .019 -.036 .000 -.187

BLOCKS .007 .060 -.024 .011

Adjusted R2 0.39 0.31 0.37 0.42

***: p<.001; **: p<.01; *: p<.05


Figures

Figure 1. Phoneme deletion x Orthographic complexity interaction on Group prediction.

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Figure 2. Phoneme deletion x Orthographic complexity interaction on Word reading accuracy.

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Figure 3. Phoneme deletion x Orthographic complexity interaction on Word spelling.

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Figure 4. Picture RAN x Orthographic complexity interaction (n.s.) on Word reading speed.

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Figure 5. Number of dyslexic children having a given deficit (more than 1 SD below the control

mean). PA: Phoneme Awareness. RAN: Rapid Automatised Naming. vSTM: verbal short-term

memory.


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Appendix (online tables)

Online Table 1. Relative importance of predictor variables of Nonword reading accuracy

(NWREADAC), across all orthographies and within each orthographic complexity group.

Orthographic

Complexity:

All

(N=2005)

Low

(N=680)

Medium

(N=931)

High

(N=386)


PHONDEL 0.38*** 0.34*** 0.42*** 0.43***

DIGSPAN 0.16*** 0.13*** 0.099** 0.17***

GRADE -0.15*** .120 -.002 -.026

PICRAN 0.11*** .058 .014 0.26***

SIMILAR 0.05* .035 .024 .046

AGE 0.01 -.075 -.094 -.029

BLOCKS -0.02 -.025 -.027 .003

Adjusted R2 0.31 0.20 0.24 0.45

***: p<.001; **: p<.01; *: p<.05


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Online Table 2. Relative importance of predictor variables of Nonword reading speed

(NWREADSP), across all orthographies and within each orthographic complexity group.

Orthographic

Complexity:

All

(N=1925)

Low

(N=657)

Medium

(N=931)

High

(N=331)


PICRAN 0.40*** 0.37*** 0.34*** 0.53***

PHONDEL 0.29*** 0.21*** 0.39*** 0.18***

DIGSPAN 0.087*** 0.10** 0.068* -.001

BLOCKS -0.046* -.040 -0.07** .010

AGE .077 -.038 0.15** -0.49***

SIMILAR .032 .033 .031 0.10*

GRADE -.013 .110 -.083 0.62***

Adjusted R2 0.36 0.28 0.38 0.48

***: p<.001; **: p<.01; *: p<.05

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