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Who will develop dyslexia? Cognitive precursors in parents and children
van Bergen, E.
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Download date: 02 Apr 2020
omslag_Elsje-van-Bergen_v2.indd 1 20-12-12 19:37
Who will develop dyslexia?
Cognitive precursors in parents and children
Elsje van Bergen
23629 Bergen, Elsje van V.indd 1 19-12-12 12:24
The Dutch Dyslexia Programme was supported by the Netherlands Organisation
for Scientific Research (NWO) under project number 200-62-304.
This thesis was sponsored by Stichting Kohnstamm Fonds
Copyright © 2013 Elsje van Bergen
ISBN/EAN: 978-90-6464-625-6
NUR: 773
Cover design by Esther Ris, Proefschriftomslag.nl, Koog a/d Zaan
Layout by Ferdinand van Nispen, Citroenvlinder-dtp.nl, Bilthoven
Printed by GVO | Ponsen & Looijen drukkers & vormgevers BV, Ede
23629 Bergen, Elsje van V.indd 2 19-12-12 15:59
Who will develop dyslexia?
Cognitive precursors in parents and children
ACADEmISCh PROEFSChRIFT
ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam
op gezag van de Rector magnificus
prof. dr. D.C. van den Boom
ten overstaan van een door het college voor promoties
ingestelde commissie,
in het openbaar te verdedigen in de Agnietenkapel
op donderdag 14 februari 2013, te 14:00 uur
door
Elisabeth van Bergen
geboren te Landsmeer
23629 Bergen, Elsje van V.indd 3 19-12-12 12:24
Promotiecommissie
Promotores: Prof. dr. P.F. de Jong
Prof. dr. D.A.V. van der Leij
Co-promotor: Prof. dr. F.J. Oort
Overige Leden: Dr. B. Boets
Dr. J.E. Rispens
Prof. dr. m.J. Snowling
Prof. dr. L.T.W. Verhoeven
Prof. dr. F.N.K. Wijnen
Faculteit der maatschappij- en Gedragswetenschappen
23629 Bergen, Elsje van V.indd 4 19-12-12 12:24
Voor Yves
23629 Bergen, Elsje van V.indd 5 19-12-12 12:24
Contents
Chapter 1General Introduction 11
1.1 Dyslexia 121.2 Theoretical Framework 14
1.2.1 The multiple deficit model 141.2.2 The generalist genes hypothesis 191.2.3 The hybrid model 21
1.3 Precursors in Children 231.3.1 IQ 241.3.2 Preliteracy skills 25
1.4 Precursors in Families 281.4.1 Home literacy environment 281.4.2 Parental skills 29
1.5 The Current Studies 301.5.1 The Dutch Dyslexia Programme 301.5.2 Outline of the thesis 30
1.6 References
Chapter 2Dutch children at family risk of dyslexia: Precursors, reading development, and parental effects
39
2.1 Introduction 412.2 Methods 44
2.2.1 Participants 442.2.2 Measures 462.2.3 Procedure 482.2.4 Analytic approach 48
2.3 Results 482.3.1 Parent characteristics and home-literacy environment 482.3.2 Child characteristics 51
2.4 Discussion 532.5 References 58
23629 Bergen, Elsje van V.indd 6 19-12-12 15:59
Chapter 3Child and parental literacy levels within families with a history of dyslexia
63
3.1 Introduction 653.1.1 FR children without dyslexia 653.1.2 Intergenerational transfer 67
3.2 Methods 683.2.1 Participants 683.2.2 Measures 693.2.3 Procedure 71
3.3 Results 723.3.1 Data screening 723.3.2 Children 723.3.3 Intergenerational transfer 74
3.4 Discussion 793.5 Key Points 833.6 References 84
Chapter 4The effect of parents’ literacy skills and children’s preliteracy skills on the risk of dyslexia
87
4.1 Introduction 894.1.1 Risk factors in families 894.1.2 Risk factors in children 904.1.3 Specificity of precursors 91
4.2 Method 924.2.1 Participants 924.2.2 Measures 94
4.3 Results 964.3.1 Family characteristics in relation to children’s group 1014.3.2 Children’s characteristics in relation to children’s group 1014.3.3 Predictors of children’s reading skills 101
4.4 Discussion 1044.4.1 Intergenerational transfer 1064.4.2 Home literacy environment 1074.4.3 Preliteracy skills and their specificity 107
4.5 References 112
23629 Bergen, Elsje van V.indd 7 19-12-12 15:59
Chapter 5IQ of four-year-olds who go on to develop dyslexia 117
5.1 Introduction 1195.2 Method 121
5.2.1 Participants 1215.2.2 Measures 1225.2.3 Procedure 125
5.3 Results 1255.3.1 Structure of the IQ test 1255.3.2 The relationship of IQ at age 4 and second-grade school
achievement128
5.3.3 Differences among reading groups 1285.4 Discussion 1315.5 References 135
Chapter 6General Discussion 139
6.1 Precursors in Children 1416.1.1 IQ 1416.1.2 Preliteracy skills 143
6.2 Literacy Skills in Children 1466.3 Precursors in Families 147
6.3.1 Home literacy environment 1486.3.2 Parental skills 150
6.4 The Intergenerational Multiple Deficit Model 1536.5 Future Directions 1566.6 Concluding Remarks 1586.7 References 159
Summary 164Samenvatting (Summary in Dutch) 167Acknowledgements 173Dankwoord (Acknowledgements in Dutch) 177List of Publications 182Curriculum Vitae 184
23629 Bergen, Elsje van V.indd 8 19-12-12 15:59
23629 Bergen, Elsje van V.indd 9 19-12-12 12:24
Writing – the art of communicating thoughts to the mind, through the eye – is
the great invention of the world. Great, very great in enabling us to converse
with the dead, the absent, and the unborn, at all distances of time and space;
and great, not only in its direct benefits, but greatest help, to all other inventions.
- Abraham Lincoln -
General Introduction
Chapter
chapter-intros_Elsje-van-Bergen_v2.indd 1 18-12-12 20:4523629 Bergen, Elsje van V.indd 10 23-12-12 12:49
General Introduction
Chapter
chapter-intros_Elsje-van-Bergen_v2.indd 1 18-12-12 20:4523629 Bergen, Elsje van V.indd 11 19-12-12 12:24
Chapter 1
12
At this very moment, precisely controlled eye movements make you process a
few black marks on a white page from the general introduction of this thesis.
The message I convey from my brain to your brain is in fact coded as just a
collection of straight and curved lines. Yet such a collection of tiny lines can
transfer knowledge or bring a story to life. Our ability to read and write is an
impressive feat and life today is impossible to imagine without it.
From an evolutionary perspective, reading and writing are relatively
recent inventions. homo sapiens have existed for 200,000 years, but reading
and writing dates back roughly 5.000 years (Powell, 2009). Nevertheless,
it has been only some 100 years since nearly all people in Western societies
have become literate. It probably does not come as a surprise that the start of
compulsory education, and hence reading education for all, roughly coincides
with the birth of its study, the science of reading.
From a neuroscientific perspective, simply reading a single word is
already a very complex task, let alone reading this thesis. Learning to read is not
something that comes naturally to us, like learning to walk or talk. Instead, it takes
a couple of years of deliberate practice. But once mastered, we recognize words
instantly. A 10-year old skilled reader can read a word that is flashed on a screen
for just 200 milliseconds (Yap & van der Leij, 1993). On seeing the word a cascade
of orchestrated neural firing is triggered, making us recognize the black marks
as a meaningful word. The beauty of it is that this all happens very quickly and
automatically; you cannot help but read.
1.1 Dyslexia
In view of the complexity of word recognition it is perhaps not surprising that
not all children learn to read without difficulty. Although the exact definition
and diagnostic criteria of dyslexia are still hotly debated (e.g., Brown Waesche,
Schatschneider, maner, Ahmed, & Wagner, 2011; Fletcher, Coulter, Reschly, &
Vaughn, 2004; Kleijnen et al., 2008; Snowling & hulme, 2012), there is consensus
among researchers and practitioners that individuals with dyslexia experience
difficulty in mastering word-level reading skills and show persistent problems
in the accuracy or fluency of word-reading. These problems in word-level
reading1 often go hand in hand with problems in word-level spelling.
Throughout this thesis I will use ‘reading’ to refer to word-level reading , rather than reading comprehension.
23629 Bergen, Elsje van V.indd 12 19-12-12 12:24
General IntroductIon
13
1 There are large individual differences in reading fluency, that is,
how quickly and accurately single words are read. Individuals with dyslexia
are at the bottom of the distribution of normal variation in reading fluency.
Slow speed of processing and relatively high error rates indicate their lack of
automatic decoding (van der Leij & van Daal, 1999). It can readily be seen that
the prevalence rate of dyslexia reflects the cut points established as criteria
for identification, as well as the exclusion criteria that are used. According to
the criteria proposed in the Dutch Dyslexia protocol, it is estimated that 4% of
Dutch primary school children have dyslexia (Blomert, 2006).
Children with dyslexia might experience great difficulty in education.
After the initial stage of learning acquisition, an educational transition takes
place from learning to read to reading to learn. Struggling with the task of
reading itself hampers a child with dyslexia to employ reading to learn about
for example science or history. Indeed, dyslexia negatively impacts long-term
academic outcomes (Willcutt et al., 2007). moreover, dyslexia is associated with
increased risks of anxiety disorders, depression, attention-deficit/hyperactivity
disorder (ADhD), conduct disorder (Bosman & Braams, 2005; Carroll, maughan,
Goodman, & meltzer, 2005; Willcutt et al., 2007), and even criminal behaviour
(Kirk & Reid, 2001; macdonald, 2012).
Given the trouble that dyslexia might cause, it is very important to study
in what way individuals with dyslexia differ from their peers without dyslexia.
The ultimate goal is to unravel the causal pathway of aetiological risk factors
leading to dyslexia. Insight into these pathways will inform the development
of effective interventions to ameliorate the impact of dyslexia.
From research comparing individuals with and without dyslexia, we
know that those with dyslexia generally show deficits in specific cognitive areas,
like the processing of speech sounds in spoken words. Finding a cognitive deficit
linked to dyslexia raises the question as to whether the deficit is a consequence
of children’s reading problems or whether it was already present before they
came to the task of learning to read. If so, the deficit is said to be a precursor
of dyslexia. Furthermore, to better understand the developmental pathways we
need to find out whether a precursor of reading ability or disability is specifically
linked to reading development or whether it is shared with for instance arithmetic
development. Beyond studying these cognitive characteristics intrinsic to
the child, the current research will explore a new avenue by investigating the
cognitive risks that parents pass on to their child.
23629 Bergen, Elsje van V.indd 13 19-12-12 12:24
Chapter 1
14
1.2 Theoretical Framework
The issues pursued in the current thesis were guided by previous empirical
work on cognitive deficits associated with dyslexia and by theoretical work,
specifically the multiple deficit model and the generalist genes hypothesis.
This theoretical framework will be discussed in the following two sections,
concluded by a third section about the proposed hybrid model.
1.2.1 The Multiple Deficit ModelResearch into dyslexia was dominated for a long time by the quest for the holy
Grail: the single cognitive deficit that is necessary and sufficient to cause all
behavioural characteristics of the disorder. In the case of dyslexia the dominant
hypothesis about a single causal factor has been the phonological-deficit
hypothesis (e.g., Snowling, 1995; Wagner, 1986); other hypotheses include
core deficits in rapid auditory processing (Tallal, 1980), amplitude-envelope
rise-time discrimination (Goswami et al., 2002), visual attention span (Bosse,
Tainturier, & Valdois, 2007), visuo-spatial attention (Vidyasagar & Pammer, 2010),
magnocellular processing of fast sensory information (Stein & Walsh, 1997), the
ability to perform skills automatically due to cerebellar deficits (Nicolson, Fawcett,
& Dean, 2001), and the forming of stimulus-specific anchors (Ahissar, 2007).
however, single cognitive deficit models have a number of
shortcomings (see Pennington, 2006, for a comprehensive overview). First,
there is no single cognitive deficit found that can explain all behavioural
symptoms of all cases with dyslexia. For example, not all individuals with
dyslexia show a phonological deficit (e.g., Pennington et al., 2012; Valdois et
al., 2011), which is the main candidate for a single cognitive deficit explanation.
Conversely, not all individuals with a phonological deficit have dyslexia (e.g.,
Bekebrede, van der Leij, Schrijf, & Share, 2010; Snowling, 2008). This questions a
one-to-one mapping and points to the possibility that various constellations of
underlying cognitive deficits can lead to the behavioural symptoms of dyslexia.
Second, these models cannot readily explain the phenomenon of
comorbidity. In the case of dyslexia, comorbidity refers to the fact that dyslexia
co-occurs more often than expected by chance with other developmental
disorders, like dyscalculia, specific language impairment, speech-sound
disorder, or ADhD. To illustrate this point, suppose disorder A and B each
have a prevalence of 5% in the general population. If disorder A and B were
23629 Bergen, Elsje van V.indd 14 19-12-12 12:24
General IntroductIon
15
1independent, then the chance that A and B co-occur would only be 0.05 * 0.05
= 0.0025, or 0.25%. however, comorbidity rates for developmental disorders
are commonly in the order of 30%, for example between dyslexia and speech-
sound disorder or dyslexia and ADhD (Pennington, 2006). The huge discrepancy
between these figures (0.25% vs. 30%) implies that developmental disorders
are not independent.
The single deficit model requires for each comorbidity (pair of disorders)
a distinct account. Pennington (2006) discusses as an example the comorbidity
between dyslexia and speech-sound disorder. Speech-sound disorder is
defined by difficulties in the development of spoken language, especially
problems with the intelligible production of speech sounds. Approximately
30% of children with early language or speech problems go on to develop
dyslexia. A parsimonious single deficit model to explain this comorbidity is the
severity hypothesis. The severity hypothesis states that speech-sound disorder
and dyslexia have the same underlying phonological deficit, with speech-
sound disorder being an earlier developmental manifestation of this deficit
than dyslexia. Comorbid cases will have the most severe phonological deficit.
If the phonological deficit is less severe, speech-sound disorder will not reach
clinical boundaries but dyslexia will. To account for cases with early speech-
sound disorder but without later dyslexia, the model must pose a subtype
of speech-sound disorders that is caused by a phonological deficit distinct
from the phonological deficit as seen in cases with dyslexia. Alternatively, the
phonological deficit in such cases must be resolved by the time they come to
the task of learning to read. however, Snowling, Bishop, and Stothard (2000)
followed a group of former language-impaired children into adolescence.
Those with early speech-sound disorder (isolated phonological impairments
at 4 years of age) had normal reading skills at age 15, but continued to show
phonological deficits. Similar results were obtained by Peterson, Pennington,
Shriberg, and Boada (2009). In their study many children with early speech-
sound disorder went on to learn to read normally despite a lasting phonological
deficit. Thus, in both studies the children with early speech-sound disorder
had a phonological deficit similar to children with dyslexia. This conclusion is
inconsistent with the single cognitive deficit severity hypothesis.
While research at the cognitive level of explanation was still searching
for a single deficit, studies at the genetic level converged on the conclusion
that the aetiology of dyslexia and other developmental disorders is genetically
23629 Bergen, Elsje van V.indd 15 19-12-12 12:24
Chapter 1
16
complex (Pennington, 2006). So instead of a single gene determining dyslexia,
many genes act probabilistically, each having only a small contributory effect
to the aetiology of dyslexia (Bishop, 2009). moreover, behavioural genetic
studies showed for certain developmental disorders that the relation between
two traits (like reading ability and inattention) is larger in monozygotic twin
pairs than in dizygotic twin pairs (Willcutt, Pennington, & DeFries, 2000). Such
a bivariate heritability supports genetic overlap between the conditions, in this
example between dyslexia and ADhD. The partly shared aetiology of dyslexia
and ADhD does not yet rule out the possibility of a distinct single cognitive
deficit for each disorder. however, studies have demonstrated that a processing
speed impairment is not only a characteristic of dyslexia, but also of ADhD (e.g.,
Willcutt, Pennington, Olson, Chhabildas, & hulslander, 2005), suggesting that
processing speed is a shared cognitive risk factor (mcGrath et al., 2011). The
accumulating evidence for aetiological and cognitive overlap between dyslexia
and ADhD speaks against a single deficit model for explaining their frequent
co-occurrence. Also for other dyslexia comorbidities, shared cognitive deficits
are found, for example a phonological deficit in specific language impairment
(e.g., Bishop, mcDonald, Bird, & hayiou-Thomas, 2009) and a processing-speed
deficit in dyscalculia (e.g., van der Sluis, de Jong, & Leij, 2004).
It seems that single deficit models are untenable and must give way to
multiple cognitive deficit models for understanding developmental disorders.
The multiple cognitive deficit model proposed by Pennington (2006) is depicted
schematically in Figure 1.1. In his model, multiple genetic and environmental
risk factors operate probabilistically by increasing the liability to a disorder;
conversely, protective factors decrease the liability. These aetiological factors
produce the behavioural symptoms of developmental disorders by influencing
the development of relevant neural systems and cognitive processes.
Importantly, there is no single aetiological or cognitive factor that is sufficient
to cause a disorder. Instead, multiple cognitive deficits (each due to multiple
aetiological factors) need to be present to produce a disorder at the behavioural
level. Some of the aetiological and cognitive risk factors are shared with other
disorders. Consequently, comorbidity among developmental disorders is to be
expected, rather than something that requires additional explanations. Finally,
from Pennington’s multiple deficit model it follows that the liability distribution
for a given disorder is continuous and quantitative, rather than being discrete
and categorical. Therefore, thresholds to define disorders are rather arbitrary.
23629 Bergen, Elsje van V.indd 16 19-12-12 12:24
general introduction
17
1
Behavioural disorders
Cognitive processes
Neural systems
Aetiological risk and protective factors
Level of analysis
GG1 EE1 GG2
NN1 NN2 NN3
CC1 CC2 CC3
DD1 DD2 DD3
Figure 1.1. Pennington’s multiple de cit model. Double headed arrows indicate
interactions. Causal connections between levels of analysis are omitted. G = genetic risk or protective factor
E = environmental risk or protective factor N = neural system
C = cognitive process D = disorder
Figure 1.1. Pennington’s multiple defi cit model. Double headed arrows indicate interactions. Causal connections between levels of analysis are omitted.
G = genetic risk or protective factorE = environmental risk or protective factorN = neural systemC = cognitive processD = disorder
23629 Bergen, Elsje van V.indd 17 19-12-12 12:24
Chapter 1
18
Pennington (2006) concludes his paper by remarking that it remains
challenging to test the multiple cognitive deficit model. The model is much
more complex than single deficit models, which are attractively parsimonious,
but this complexity is needed to account for the observations at the different
levels of analysis. The model is universally applicable to developmental
disorders, but therefore remains abstract. It is not specified which aetiological
factors, neural systems, and cognitive processes interact to produce a given
disorder.
We would like to argue that a line of inquiry that can contribute to
testing and specifying the multiple deficit model are family-risk studies. In
family-risk studies, children are followed who are at risk of dyslexia by virtue
of having an immediate dyslexic family member (usually a parent). Such
studies have shown that 34 to 66% of them develop dyslexia (Elbro, Borstrøm,
& Petersen, 1998; Pennington & Lefly, 2001; Scarborough, 1990; Snowling,
Gallagher, & Frith, 2003; Torppa, Lyytinen, Erskine, Eklund, & Lyytinen, 2010),
depending on the stringency of the dyslexia criteria. The much higher
prevalence of dyslexia among offspring of parents with dyslexia is consistent
with twin studies showing moderate to strong heritability of dyslexia (e.g.,
Olson, Byrne, & Samuelsson, 2009).
From the multiple deficit model it follows that children at family risk
experience at least some of the aetiological risk factors: they inherit genetic
risk factors and might experience a less rich literacy environment. hence, it is
hypothesized that at-risk children have a higher genetic and environmental
liability than children without a family history of dyslexia (labelled control
children). Furthermore, the at-risk children who go on to develop dyslexia
are expected to show cognitive deficits in several processes. Some of these
cognitive processes are expected to be affected even before the onset of
reading instruction, as a consequence of aetiological risk factors and deficient
neural systems.
A key prediction of the multiple deficit model for family-risk studies
concerns the at-risk children who do not develop dyslexia. If liability to dyslexia
were discrete (as would happen if only one factor, say a gene, were involved), at-
risk non-dyslexic children would not differ from controls. however, according to
the multiple deficit model, liability is continuously distributed. This also follows
from the fact that reading ability is influenced by many genes of small effect,
producing normal distributions of phenotypes (Plomin, DeFries, mcClearn, &
23629 Bergen, Elsje van V.indd 18 19-12-12 12:24
General IntroductIon
19
1mcGuffin, 2008, p. 33). Consequently, the multiple deficit model predicts that at-
risk children without dyslexia also inherit at least some disadvantageous gene
variants from their dyslexic parents, giving them a higher liability than control
children, although still lower than at-risk dyslexic children. At the behavioural
and cognitive level this should translate into mild deficits in literacy skills and
some of its cognitive underpinnings. When plotting mean performances of the
three groups, a step-wise pattern (i.e., at-risk dyslexic < at-risk non-dyslexic <
controls) would support a continuum of liability, one of the characteristics of
the multiple deficit model.
Comparing the three groups of children on behavioural measures sheds
light on cognitive deficiencies and behavioural symptoms, the bottom two levels
in Figure 1.1. These three groups have also been compared on neural processing
of visual and auditory stimuli (e.g., Leppänen et al., 2010; Plakas, van Zuijen, van
Leeuwen, Thomson, & van der Leij, 2012; Regtvoort, van Leeuwen, Stoel, & van
der Leij, 2006), the second level of the multiple deficit model. Some family-risk
studies have also examined aspects of the home environment, which belong
to the aetiological level. however, specific genetic risk factors remain hidden in
family-risk studies. As genetic screening of children for their dyslexia susceptibility
is still far away, we propose an indicator of their genetic risk. Since reading ability
is moderately to highly heritable and children receive their genetic material from
their parents, we argue that cognitive abilities of parents can partly reveal their
offspring’s liability. The parents of at-risk children will have weaker reading skills
than those of control children, reflecting selection criteria in family-risk studies,
but the key issue is whether parental reading skills differentiate between at-risk
children with and without dyslexia. Based on the multiple deficit model it is
expected that at-risk children who develop dyslexia have inherited more genetic
risk variants than at-risk children without dyslexia and that this difference can be
revealed by lower reading performance of parents of the at-risk dyslexic children.
In Section 1.4.2 I will elaborate upon parental effects.
1.2.2 The Generalist Genes HypothesisIn our family-risk study we seek to identify cognitive processes playing a role in
the developmental pathways that lead to dyslexia. The multiple deficit model
states that some cognitive deficits are shared among disorders. This raises
the question of which cognitive precursors of dyslexia are distinct and which
are shared with other disorders. With regard to learning abilities, like reading
23629 Bergen, Elsje van V.indd 19 19-12-12 12:24
Chapter 1
20
ability, there is a hypothesis that addresses this specificity issue: the generalist
genes hypothesis (Kovas & Plomin, 2007; Plomin & Kovas, 2005).
The generalist genes hypothesis stems from behavioural genetic studies
employing the twin design. The twin design is the major method to quantify
genetic and environmental influences on a trait. It compares the resemblance of
monozygotic twins, who are genetically identical, with dizygotic twins, who are
on average 50% similar genetically. If for a certain trait monozygotic twins are
more similar than dizygotic twins, genetic factors must play a role. If there is no
difference in resemblance heritability is negligible. For dyslexia, the concordance
rate (that is, the likelihood that one twin will be affected if the other twin is affected)
is 75% for monozygotic twins and 43% for dizygotic twins (Stromswold, 2001),
suggesting substantial heritability. Estimates for the heritability of reading ability
are in the range of .47 to .84 (Taylor, Roehrig, hensler, Connor, & Schatschneider,
2010, and Byrne et al., 2009, respectively).
Recently, the field of behavioural genetics has moved beyond
quantifying genetic and environmental influences on a trait to studying genetic
and environmental overlap between traits. For the three learning abilities
reading, arithmetic, and language, empirical data have shown that the genes
important for one learning ability largely overlap with the genes important for
the other learning abilities. The genetic correlation is the measure that quantifies
this: it indexes the extent to which genetic influences on one trait correlate with
the genetic influences on another trait (independently of the heritability of the
traits). The genetic correlation between learning abilities is about .70 (Kovas &
Plomin, 2007; Plomin & Kovas, 2005). This suggests that half (.702) of the genes
associated with reading ability are generalists: they also influence other learning
abilities. hence Plomin and Kovas (2005) named their hypothesis the ‘generalist
genes hypothesis’. As genetic correlations are not 1.0, there are also specialist
genes: genes that contribute to dissociations among learning abilities. however,
most genes associated with reading are generalists, also in another respects:
twin studies have demonstrated that genetic influences on the lower end of the
reading distribution (containing children with dyslexia) are largely the same as
the genetic influences on the entire distribution (Kovas, haworth, Dale, & Plomin,
2007; Plomin & Kovas, 2005).
Observed differences in learning abilities among individuals are also
partly due to differences between the environments in which individuals
were born, were brought up and live. Behavioural genetics subdivides
23629 Bergen, Elsje van V.indd 20 19-12-12 12:24
General IntroductIon
21
1environmental influences into those that make family members similar (called
shared environmental effects) and those that do not contribute to resemblance
among family members (called non-shared environmental effects). Shared
environmental effects include shared family experiences or sharing the same
school or community. Examples of non-shared environmental effects are
accidents, illnesses, hobbies, and influences of individuals such as peers and
teachers. Also for these environmental components statistics exist analogous
to genetic correlation. Shared environmental correlations among learning
abilities are as high as genetic correlations, so shared environmental effects
are also largely general effects (Kovas & Plomin, 2007). In contrast, non-shared
environmental correlations are low. This indicates that these effects primarily
act as specialists, contributing to performance differences in learning abilities
within a child (Kovas & Plomin, 2007).
1.2.3 The Hybrid ModelThe generalist genes hypothesis and the multiple cognitive deficit model
complement each other well. The multiple deficit model is more general
because it holds for all common developmental disorders, while the generalist
genes hypothesis specifically pertains to learning abilities and disabilities.
Furthermore, the multiple deficit model includes four levels of explanation,
whereas the generalist genes hypothesis only concerns the aetiological level.
Nevertheless, the generalist genes hypothesis details for learning abilities the
degree of overlapping and unique influences in each of the three aetiological
components (genetical, shared environmental, and non-shared environmental
influences). I have visualised the generalist genes hypothesis and incorporated
it into the multiple deficit model, yielding the hybrid model depicted in Figure
1.2. In this model only the first and the fourth layer are further specified
because the generalist genes hypothesis only deals with these two levels. The
aetiological factors of the first level influence the behavioural manifestations at
the fourth level by acting through the second and third level.
The hybrid model quantifies the overlap in aetiological factors between
learning abilities: genetic and shared environmental effects are largely shared
by the three learning domains, whereas the non-shared environmental effects
are largely distinct. These differential overlaps are visualised in the hybrid
23629 Bergen, Elsje van V.indd 21 19-12-12 12:24
cHaPter 1
22
Behavioural disorders
Cognitive processes
Neural systems
Aetiological risk and protective factors
Level of analysis
N1 N2 N3
C1 C2 C3
RD AD LD
Figure 1.2. Hybrid model for learning (dis)abilities, consisting of the generalist
genes hypothesis (Kovas & Plomin, 2007) and the multiple de cit model (Pennington, 2006). Double headed arrows indicate interactions. Causal
connections between levels of analyses are omitted. N = neural system
C = cognitive process RD = reading disorder; AD = arithmetic disorder; LD = language disorder
Genetic Shared environment
Non-shared environment
Figure 1.2. hybrid model for learning (dis)abilities, consisting of the generalist genes hypothesis (Kovas & Plomin, 2007) and the multiple de!cit model (Pennington, 2006). Double headed arrows indicate interactions. Causal connections between levels of analyses are omitted.
N = neural systemC = cognitive processRD = reading disorder; AD = arithmetic disorder; LD = language disorder
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1model as the degree of overlap between the circles. Despite this quantification
of aetiological overlap, the hybrid model does not specify which aetiological
factors are relevant. Regarding genetic factors, molecular genetic studies will
ultimately inform us which genes are implicated in dyslexia. Knowledge of
specific genes contributing to dyslexia susceptibility promises to help bridge
the gap from genes to neural systems, cognitive processes, and behavioural
outcomes (Fisher & Francks, 2006). Until now, only a handful of candidate
genes are identified, each of small effect. As an example of the small effects, the
most common form of the candidate gene KIAA0319 is found equally frequent
in people with and without dyslexia. The high-risk version is found in 35% of
those with dyslexia and 27% of those without, whereas the figures for the low-
risk version are 24% versus 36% (Bishop, 2009).
Insight into which specific neural systems, cognitive skills, and
behavioural symptoms are implicated in dyslexia can be gained from family-
risk studies. The hybrid model points to the opportunity to study reading in
combination with arithmetic or language to increase insight into shared and
distinct factors. We chose to focus on reading and arithmetic, both basic
school skills central during early primary school. As the model suggests, its
disorders, dyslexia and dyscalculia, indeed often co-occur (Landerl & moll,
2010). moreover, this pair of comorbidity is under-researched compared to the
comorbidity of dyslexia with ADhD or language disorders. We aimed to study
the comorbidity issue at the cognitive level of explanation. Which cognitive
factors are specific and which are shared between the development of reading
and arithmetic?
1.3 Precursors in Children
This thesis will deal with two types of cognitive skills: general cognitive skills
and preliteracy skills. The key questions regarding cognitive precursors in
children are: Do the children who go on to develop dyslexia show cognitive
deficits before they come to the task of learning to read? And do the at-risk
children who do not develop dyslexia perform at the same level as the control
children?
Reading performance is the outcome of the combined action of
multiple factors, as depicted in Figure 1.2. Knowledge about these factors can
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be obtained by studying predictors of reading outcome. In addition, knowledge
about predictors of dyslexia is of clinical importance because it enables early
identification and therefore remedial teaching of children at heightened risk of
dyslexia.
1.3.1 IQReading ability and IQ are correlated. Therefore, when selecting a group of
children with dyslexia based on low reading achievement, it can be expected
that their group mean on IQ falls below that of a group of children who do
not show reading problems. however, scores on IQ tests might be adversely
affected by reading problems and, as a result, less print exposure or print
exposure to less complex texts (Cunningham & Stanovich, 1997). Consider
for instance conceivably poorer performance on the Verbal Comprehension
Index (including vocabulary, similarities, comprehension, information, and
word-reasoning subtests) of the WISC (Wechsler, 2004), a world-wide used
IQ battery. Thus, the correlation between reading ability and IQ might partly
be a consequence of reading ability. Therefore, longitudinal data from before
and after reading onset are required. The only two previous family-risk studies
that report IQ before Grade 1 (Snowling et al., 2003; Torppa et al., 2010) report
that the at-risk group with later dyslexia performed lower on verbal IQ, but the
studies are inconsistent regarding nonverbal IQ. moreover, IQ scores are given
but the papers are silent about their interpretation.
A partly causal relation between early verbal IQ and later reading
development is plausible, since the cognitive system for written language
builds on the cognitive system for oral language (hulme, Snowling, Caravolas,
& Carroll, 2005). Regarding nonverbal IQ, if it is associated with later reading, a
causal interpretation is less self-evident. It might be that nonverbal IQ indicates
a child’s ability to acquire cognitive skills in general. It cannot be readily seen
how this general ability translates into a specific problem in learning to decode
words. Alternatively, a possible relation between early nonverbal IQ and later
reading might be explained by a third common underlying variable. It might be
that both abilities share aetiological factors, as do different domains of learning
abilities according to the generalist genes hypothesis (see Figure 1.2). Chapter
5 and Chapter 6 (General Discussion) will further discuss the interpretation of
longitudinal relations between IQ and reading.
Chapter 5 aims to investigate the longitudinal relations between IQ
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1and reading (dis)ability by examining IQ performance at age 4 and reading
outcome at age 8 years. Another goal of this chapter is to investigate the
specificity of the potential predictive relation between early IQ and later reading.
Are early verbal and nonverbal IQ uniquely related to later fluency in word
reading or also to later fluency in mental arithmetic? As mentioned previously,
word reading and mental arithmetic are arguably the most important basic
school skills acquired during the early school years. moreover, impairments in
acquiring these skills (i.e., dyslexia and dyscalculia, respectively) often co-occur
(Landerl & moll, 2010), making them a good pair of skills to study shared and
distinct links with early IQ. As can be seen in the hybrid model (Figure 1.2),
the aetiological and cognitive factors influencing reading and arithmetic are
assumed to be partly shared and partly unique. Investigating the relationship
of reading and arithmetic with early IQ offers insight into whether verbal and
nonverbal ability are cognitive factors related to these school skills and if so,
whether they are shared between the school skills.
1.3.2 Preliteracy SkillsBesides studying general cognitive abilities we investigated specific cognitive
abilities before the start of reading education. The cognitive skills that are
thought of as causally linked to subsequent development of decoding skills and
later development of fluent reading are phonological skills and knowledge of
grapheme-phoneme –or letter-sound– correspondence (e.g., hulme, Bowyer-
Crane, Carroll, Duff, & Snowling, in press). A third ability found to be a powerful
longitudinal (and concurrent) predictor of reading outcome is the ability to
rapidly name a matrix of items such as colours, objects, or once mastered,
digits or letters (e.g., de Jong & van der Leij, 1999; de Jong & van der Leij, 2003;
Lervåg, Bråten, & hulme, 2009).
The main indicator of phonological skills is phonological awareness,
which refers to the ability to detect and manipulate sounds in spoken words,
like phonemes or rimes. The first task to measure phonological awareness,
phoneme deletion, was developed in 1964 (Bruce) and is still widely used (see
e.g., Chapter 3). In phoneme deletion the participant is required to identify
how a spoken word would sound if one sound were omitted. For example,
“What is smile without /s/?” Other phonological-awareness tasks include
phoneme blending (e.g., “What does /c/ /a/ /t/ say?”) and segmentation (e.g.,
“What sounds do you hear in the word /s/ /u/ /n/?”), both of which are used in
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Chapter 2 and 4. Rapid naming is sometimes subsumed under the umbrella
of phonological skills (Boets et al., 2010; Wagner & Torgesen, 1987), since fast
retrieval of phonological codes is one of its task components, alongside the
intermodal coupling of visual input to phonological output (Warmington &
hulme, 2012). Others see rapid naming as a separate cognitive underpinning
of reading ability (Wolf & Bowers, 1999).
The mentioned trio of preliteracy skills –phonological awareness, rapid
naming, and letter knowledge– will be investigated in this thesis. These skills
were measured in kindergarten in both the Amsterdam sample (Chapter 2)
and the national sample (Chapter 4; see for the sample descriptions Section
1.5.1). Furthermore, Chapter 3 (national sample) will present findings on the
concurrent relations of phonological awareness and rapid naming with reading
skills, all assessed at the end of second grade. As literacy is acquired, letter
knowledge loses its correlations with reading ability (halfway through Grade 1
all letters are taught, producing ceiling effects on letter-knowledge measures),
but phonological awareness and rapid naming continue to show concurrent
correlations with reading ability (e.g., de Jong, 2011; Vaessen & Blomert, 2010),
even into adulthood (e.g., Bekebrede, van der Leij, Plakas, Share, & morfidi,
2010). The three groups of children in our family-risk studies, at-risk dyslexia,
at-risk non-dyslexia, and controls, will be compared on these preliteracy or
underlying cognitive skills of reading.
A couple of previous family-risk studies compared the groups of
children at kindergarten age. In general, the at-risk children who later become
dyslexic are found to have difficulty with phonological-awareness tasks, are
slower on rapid-naming tasks, and are less familiar with the letters of the
alphabet compared to their typically developing peers (Elbro et al., 1998;
Pennington & Lefly, 2001; Scarborough, 1990; Snowling et al., 2003; Torppa et
al., 2010). moreover, after kindergarten at-risk children with dyslexia perform
poorly on tests of phonological awareness and rapid naming, in addition to the
known difficulties in reading and spelling (Boets et al., 2010; de Bree, Wijnen, &
Gerrits, 2010; Pennington & Lefly, 2001; Snowling, muter, & Carroll, 2007).
The at-risk children who do not meet dyslexia criteria nevertheless
typically perform less well than control children on reading and spelling tasks
(Boets et al., 2010; Pennington & Lefly, 2001; Snowling et al., 2003; but see Torppa
et al., 2010 for an exception). When group means are visualized in a graph this
yields the mentioned stepwise pattern on literacy tasks: the at-risk dyslexics
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1perform weakest, the at-risk non-dyslexics take up an intermediate position,
and the controls perform best. This pattern of results fits with the continuity
of family risk (Pennington & Lefly, 2001; Snowling et al., 2003) and with a
multifactorial aetiology of dyslexia (Pennington, 2006, Figure 1.1). Previous
research has shown mixed results, however, regarding the performance of the
at-risk non-dyslexic group on the cognitive underpinnings of reading. During
the early school years this group either performs somewhat lower or equally
well as the controls (Boets et al., 2010; Pennington & Lefly, 2001; Snowling et
al., 2003), but no significant differences have been reported on phonological
awareness and rapid naming during kindergarten (Boets et al., 2010; Elbro et
al., 1998; Pennington & Lefly, 2001; Snowling et al., 2003; Torppa et al., 2010).
Our national sample is larger, and hence, analyses have more statistical power
than those in previous studies. Therefore we have the opportunity to detect
also subtle impairments.
Returning to the multiple deficit model (Figure 1.1), this model predicts
a normally distributed liability continuum. Relating this liability continuum to
our three groups, we hypothesize the at-risk dyslexics to be in the high tail
of the distribution, the at-risk non-dyslexics to be somewhat above average
(inheriting some disadvantageous gene variants from their dyslexic parents),
and the controls to have a somewhat below average liability profile (as their
parents are average to good readers). This outlined model is in line with
the reported phenotypic findings of a step-wise pattern for literacy skills.
Combining this model with the fact that literacy, phonological-awareness, and
rapid-naming skills are normally distributed and interrelated, it follows that a
step-wise pattern for phonological awareness and rapid naming is also most
likely.
As for the relation between general cognitive ability and later reading,
we also investigated the specificity question for the relation between the
preliteracy skills and later reading. Chapter 4 examines whether the predictive
value of the trio of preliteracy skills –phonological awareness, rapid naming,
and letter knowledge– is specific for later word-reading fluency or whether
these skills are also predictive for later mental-arithmetic fluency. De Smedt
and colleagues (Boets & De Smedt, 2010; De Smedt, Taylor, Archibald, & Ansari,
2010) have proposed an hypothesis to explain the correlation between word
reading and mental arithmetic, which states that performance on these
tasks are related because both depend upon the quality of phonological
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representations in long-term memory. mental arithmetic involves fast
retrieval of phonological-based arithmetic facts. mobilizing those facts
inefficiently limits memory resources available for selecting and carrying out
suitable arithmetic procedures (hecht, Torgesen, Wagner, & Rashotte, 2001).
Therefore, we expected that both phonological awareness and rapid naming
in kindergarten would be significant predictors of third-grade arithmetic,
although not as strong as for reading, since these skills are the core precursors
of literacy, while the core precursor of arithmetic is early number competence
(e.g., Jordan, Kaplan, Ramineni, & Locuniak, 2009).
1.4 Precursors in Families
The previous section focused on an individual child (with or without family risk)
and his or her cognitive characteristics or risk factors for dyslexia. In addition,
the current thesis encompasses a broader domain of plausible risk factors
present before formal reading instruction starts. We will investigate whether
characteristics of children’s home literacy environment are related to children’s
reading outcome. Last but not least, we will study parent-child resemblance,
which signifies intergenerational transfer of cognitive skills of parents to their
offspring.
1.4.1 Home Literacy EnvironmentDoes the literacy environment that parents provide at home before school
entry influence children’s subsequent reading development? This question
will be addressed for both the Amsterdam (Chapter 2) and the national
sample (Chapter 4). We tested whether individual differences in home literacy
environment before first grade were related to individual differences in reading
performance some years later. home literacy environment is usually quantified
as the availability of books and other reading material in the home and how
often parents read to their children. Parents are often told that they should
read to their child to promote reading development. however, the effect of
shared-reading interventions on eventual reading performance is surprisingly
under-researched and those studies addressing this issue failed to demonstrate
that parent reading enhances reading ability (Sénéchal & Young, 2008).
Correspondingly, the family-risk studies that looked into literacy characteristics
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1of the family environment did not show effects on later reading achievement
(Elbro et al., 1998; Snowling et al., 2007; Torppa et al., 2007). These findings are
in agreement with findings from behavioural genetic studies showing strong
genetic and small shared-environmental influences on reading ability (Byrne et
al., 2009; haworth et al., 2009).
1.4.2 Parental SkillsAs mentioned earlier, in family-risk studies two samples are followed, differing in
family risk by virtue of having or not having a parent with dyslexia. Accordingly,
these studies are implicitly based on a dichotomy of family risk. however, it
might well be that within the at-risk sample, the groups of children who do and
do not go on to develop dyslexia differ in their degree of family risk for dyslexia,
despite the fact that they all have an affected parents.
As briefly touched upon before, we propose that the assumption that
the at-risk children with and without dyslexia have equal liability to dyslexia is
testable by considering the cognitive skills of their parents. multiple genetic
and environmental factors act probabilistically, leading to a continuum (rather
than a dichotomy) of liability to dyslexia. Since parents provide both genes and
environments for their child, variability among children must be associated
at least partly with variability among their parents. Therefore, we studied the
relation between parents and offspring in their reading and reading-related
skills.
To return to the two groups of at-risk children, we investigated
whether the reading outcome of children is related to the severity of affected
parent’s dyslexia. This would indicate that within the at-risk sample the
affected children have a higher liability than the unaffected children, as the
multiple deficit model predicts: affected children inherit and experience more
of the numerous aetiological risks. This is tested for the Amsterdam sample
in Chapter 2 and for the national sample in Chapter 3 and 4. Replication of
findings across two independent samples is important as we are the first to
investigate this issue. Furthermore, Chapter 4 also investigates the importance
of the literacy levels of the parent without dyslexia. Chapter 6 concludes by
proposing an extended version of the multiple deficit model of Pennington
(2006; Figure 1.1): the intergenerational multiple deficit model, which includes
cognitive risks that parents pass on to their offspring.
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1.5 The Current Studies
The current set of studies are based on data of the Dutch Dyslexia Programme
(DDP). In what follows I will introduce the DDP and I will conclude this chapter
with a brief overview of the issues addressed in the chapters to come.
1.5.1 The Dutch Dyslexia ProgrammeThe Dutch Dyslexia Programme (DDP) is a multidisciplinary and multicentre
research programme, funded by The Netherlands Organisation for Scientific
Research (NWO), and initiated and executed by the University of Amsterdam,
the University of Groningen, and the Radboud University Nijmegen. It brings
researchers together from a variety of disciplines, including educational
sciences, psychology, linguistics, neurology, and genetics. The DDP consists
of three components, namely a prospective longitudinal study, intervention
studies, and genetic studies.
The present thesis presents findings of two family-risk samples, which
I will refer to as the Amsterdam and the national sample. Chapter 2 presents
the Amsterdam family-risk study. This sample of children originates from the
two intervention studies of the DDP which were conducted in Amsterdam
(Regtvoort & van der Leij, 2007; van Otterloo & van der Leij, 2009). Children
in this sample were followed from 5 (kindergarten) till 11 years of age (Grade
5). The other three studies of my thesis employ data of the ongoing national
longitudinal study of the three DDP universities. In this larger-scale family-risk
study, couples with and without a family history of dyslexia who expected a
baby were recruited. In short, children were considered at family risk if (at least)
one of their parents and one other family member had dyslexia. The children
have been followed from infancy. Chapter 3, 4, and 5 belong to this prospective
longitudinal study, covering the development of 3½ to 9 years of age (Grade 3).
1.5.2 Outline of the Thesismy thesis aims to shed light on the constellation of risks that ultimately leads
to dyslexia. In Section 1.2 and 1.3 I have introduced the topics that will be
covered. As there is no one-to-one relation between the four topics and the
four following chapters, I will provide an overview of the topics per chapter.
Chapter 2 (van Bergen et al., 2011) concerns the Amsterdam sample, which is
followed from kindergarten to Grade 5. It examines home literacy environment,
23629 Bergen, Elsje van V.indd 30 19-12-12 12:24
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1preliteracy skills, reading development, and parental skills. Chapter 3 to 5 are
part of the ongoing national study. The focus of Chapters 3, 4, and 5 will be
children’s cognitive skills at age 8 (Grade 2), 6 (kindergarten), and 4 years,
respectively. Chapter 3 (van Bergen, de Jong, Plakas, maassen, & van der Leij,
2012) discusses group differences on literacy and its cognitive underpinnings
at the end of Grade 2. moreover, the effect of the literacy skills of the dyslexic
parent on children’s reading outcome are examined. Chapter 4 (van Bergen,
de Jong, maassen, & van der Leij, submitted) the intergenerational transfer
from both parents to their offpspring is investigated. moreover, reading and
arithmetic skills in Grade 3 will be predicted from home literacy environment
and preliteracy skills measured before learning to read. In Chapter 5 (van
Bergen et al., in revision) focusses on the relation between general cognitive
ability at 4 years of age and reading and arithmetic four years later, at the end
of Grade 2. Finally, in Chapter 6 the key findings and theoretical implications of
the present thesis will be discussed.
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23629 Bergen, Elsje van V.indd 37 19-12-12 12:24
The great tragedy of science – the slaying of a beautiful hypothesis by an ugly
fact.
- Thomas Huxley -
Dutch children at family risk of dyslexia: Precursors, reading development, and
parental effects
van Bergen, E., de Jong, P.F., Regtvoort, A., Oort, F., van Otterloo, S., & van der Leij, A. (2011). Dutch children at family risk of dyslexia: Precursors, reading development, and
parental effects. Dyslexia, 17(1), 2-18.
Chapter
chapter-intros_Elsje-van-Bergen_v2.indd 2 18-12-12 20:4523629 Bergen, Elsje van V.indd 38 19-12-12 12:24
Dutch children at family risk of dyslexia: Precursors, reading development, and
parental effects
van Bergen, E., de Jong, P.F., Regtvoort, A., Oort, F., van Otterloo, S., & van der Leij, A. (2011). Dutch children at family risk of dyslexia: Precursors, reading development, and
parental effects. Dyslexia, 17(1), 2-18.
Chapter
chapter-intros_Elsje-van-Bergen_v2.indd 2 18-12-12 20:4523629 Bergen, Elsje van V.indd 39 19-12-12 12:24
Chapter 2
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Abstract
The study concerns reading development and its precursors in a
transparent orthography. Dutch children differing in family risk for
dyslexia were followed from kindergarten through 5th grade. In 5th grade,
at-risk dyslexic (n = 22), at-risk non-dyslexic (n = 45), and control children
(n = 12) were distinguished. In kindergarten, the at-risk non-dyslexics
performed better than the at-risk dyslexics, but worse than the controls
on letter knowledge and rapid naming. The groups did not differ on
phonological awareness. At-risk dyslexics read less fluently from 1st
grade onwards than the other groups. At-risk non-dyslexics’ reading
fluency was at an intermediate position between the other groups at
the start of reading. By 5th grade they had reached a similar level as
the controls on word reading, but still lagged behind on pseudoword
reading. Results further showed that the parents of the groups of at-
risk children differed in educational level and reading skills. Overall,
the groups of at-risk children differed on pre-reading skills as well as
on reading development. These differences do not seem to stem from
differences in intellectual abilities or literacy environment. Instead, the
better reading skills of parents of at-risk non-dyslexics suggest that
these children might have a lower genetic liability.
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2.1 Introduction
It is well established that reading and spelling problems of dyslexics are
accompanied by deficits in phonological processing. Dyslexics’ impairments
have been found in phonological awareness, verbal short-term memory,
and rapid naming (see for reviews Elbro, 1996; Vellutino, Fletcher, Snowling,
& Scanlon, 2004). most of the evidence is based on studies in which the
phonological deficits were examined after reading problems had become
manifest. There is, however, a small number of studies that started before
reading onset and continued to follow children until an age that reading
problems could be established. Such longitudinal studies can provide stronger
evidence for a causal relationship between deficits in phonological processing
and the development of dyslexia.
most prospective studies involved children who have a dyslexic parent
and, hence, have a family risk of becoming dyslexic. Such studies provide the
opportunity to examine the differential development of at-risk children who
do and do not become dyslexic (hereafter called dyslexics and at-risk non-
dyslexics, respectively), and controls (i.e., not at-risk non-dyslexics). The majority
of studies concerned children learning to read in an opaque orthography. The
first issue of importance in the present study is that it involved children learning
to read in a relatively transparent orthography: Dutch. Several studies suggest
that the differential importance of precursors of later reading success or failure
is moderated by orthographies’ transparencies (de Jong & van der Leij, 2003;
Puolakanaho et al., 2008; Wimmer, mayringer, & Landerl, 2000). The second
issue that is addressed in this study is the impact of parental reading status
on their offspring’s reading success or failure. Although differences between
the three groups on child characteristics are well documented, differences on
parental characteristics have largely been neglected.
Several earlier prospective studies showed that phonological deficits
were already present before reading started (Boets, Wouters, van Wieringen, &
Ghesquière, 2007; Elbro, Borstrøm, & Petersen, 1998; Pennington & Lefly, 2001;
Scarborough, 1990; Snowling, Gallagher, & Frith, 2003). For example, Elbro,
Borstrøm, and Petersen (1998) followed the progress of Danish children with
and without family risk from kindergarten through the beginning of second
grade, when dyslexia was determined. In kindergarten, the at-risk dyslexics
showed deficits as compared to the control group in phoneme awareness,
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verbal short-term memory, and distinctness of phonological representations,
whereas the at-risk non-dyslexics did not. In contrast, the at-risk non-dyslexics
did perform as poorly as the at-risk dyslexics on morpheme awareness and
articulation, whereas they performed better than the at-risk dyslexics but less
well than the controls on letter knowledge.
Snowling and colleagues carried out a similar study with children
learning to read in English (Gallagher, Frith, & Snowling, 2000; Snowling,
Gallagher, & Frith, 2003; Snowling, muter, & Carroll, 2007) . Children’s reading
status was determined at the age of 8. At 3 years and 9 months, the at-risk
non-dyslexics performed better than the at-risk dyslexics but poorer than the
controls on non-word repetition and letter knowledge. The dyslexic, but not
the non-dyslexic at-risk readers, exhibited early deficits in vocabulary, narrative
skills, and verbal short-term memory. Snowling et al. (2003; 2007) concluded
that the children with a family risk that did not become dyslexic had probably
compensated for their deficits in grapheme-phoneme knowledge with strong
oral language skills.
Pennington and Lefly (2001) also conducted an at-risk study with
English-speaking children. Dyslexia was assessed in second grade. At the start
of first grade, the at-risk dyslexics had, compared to both groups of future
normal readers, impairments on emergent literacy skills and verbal short-term
memory. The at-risk dyslexics also performed poorer on phoneme awareness
and speech perception than at-risk non-dyslexics, who performed poorer than
controls. Interestingly, Pennington and Lefly also assessed rapid naming skills,
on which the at-risk dyslexics also scored weakest and the controls highest.
Generally, learning to read in an opaque orthography like Danish or
English poses extra challenges to the beginning reader and might affect the
various manifestations of dyslexia (Seymour, Aro, & Erskine, 2003; Ziegler et al.,
2005). In a study of dyslexics (with and without risk combined), at-risk non-
dyslexics, and controls in a relatively transparent orthography (Dutch), Boets
et al. (2007; 2010) found that the dyslexic readers performed as kindergartners
worse than the not at-risk non-dyslexic readers on letter knowledge,
verbal short-term memory, phonological awareness and rapid naming. The
performance of the at-risk non-dyslexic group was at an intermediate position
between that of the other two groups, but did not differ significantly from
either of them. These results are roughly in accordance with studies in opaque
orthographies, but the results on phonological awareness are at odds with
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findings that kindergarten phonological awareness predicts reading skills less
well in transparent orthographies (de Jong & van der Leij, 2003; Puolakanaho
et al., 2008; Wimmer, mayringer, & Landerl, 2000).
Regardless of the subtle differences between orthographies in the
cognitive profile of dyslexic readers, the question remains in what respect
at-risk children that do not develop dyslexia differ from those that do, and
especially, where these differences stem from. One possibility is that at-risk
non-dyslexic children are able to compensate their impaired reading potential
with other skills, as suggested by Snowling et al. (2003). Another possibility
is that this group is at lower genetic risk. Finally, it could be that this group
experiences more advantageous environmental factors.
Torppa et al. (2007) assessed the relation between home literacy,
reading interest, phonological awareness, vocabulary, and emergent literacy
variables. They found a direct effect of home-literacy environment (shared
reading) on vocabulary growth, but not on emergent literacy. Similarly,
Elbro et al. (1998) did not find differences between dyslexic and non-dyslexic
parents, nor between parents of dyslexic and non-dyslexic children in the
amount of shared reading in kindergarten. Finally, Snowling et al. (2007) did
not find differences between the two at-risk groups in parent reading behavior,
family literacy behavior, and socioeconomic background. Overall, there is no
strong evidence that children who develop dyslexia grow up in a relatively
disadvantageous literacy environment.
Dyslexia is commonly seen as a complex multifactorial disorder
with numerous genes involved that interact with one another and with the
environment (Bishop, 2009). Each gene affects the probability of the disorder,
but is in itself not necessary and sufficient for causing it. The involvement of
multiple genes may function as a normally distributed genetic liability (Rutter,
2006; van den Oord, Pickles, & Waldman, 2003): as more genes are affected the
probability increases that dyslexia becomes manifest. A multifactorial etiological
conceptualization resulting from a normally distributed genetic liability is in
accordance with the view from behavioral studies (Snowling, Gallagher, & Frith,
2003) that the family risk of dyslexia is continuous. This model may be useful
to speculate about the question why some at-risk children develop dyslexia
whereas others do not.
Surprisingly, possible differences in reading ability between the
dyslexic parents of the two at-risk groups have been largely neglected. Such
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differences might suggest that at-risk dyslexic children have a higher genetic
liability than at-risk non-dyslexics. As an exception, Snowling et al. (2007)
reported that the parents of at-risk dyslexics did not differ from the parents of
at-risk non-dyslexics on spelling and word-reading accuracy. however, it should
be noted that in these analyses the pairs of parents were divided according to
gender instead of according to reading status. Furthermore, within the at-risk
group the literacy levels of the children and their parents were not associated.
It is of interest whether similar results will be found for word-reading fluency
or rate. Word-reading fluency is seen as the most important feature of reading
ability in transparent orthographies (Wimmer, 2006).
In conclusion, studies following children at-risk of dyslexia indicate
that pre-readers who become dyslexic show early phonological deficits
and broader language difficulties. In addition, at-risk non-dyslexics are not
completely unaffected but show a milder phenotype. What is missing, though,
are long-term studies in orthographically transparent languages. In addition,
attention should be devoted to the reading skills of the parents.
In our study, the literacy development of Dutch children with and
without family risk for dyslexia was followed from kindergarten through fifth
grade. In fifth grade groups of at-risk dyslexics, at-risk non-dyslexics, and controls
were distinguished. Next we considered, retrospectively, differences in the
development of (pre-)reading skills among these groups. In addition, differences
in parental characteristics and home-literacy environment were examined.
2.2 Method
2.2.1 ParticipantsTwenty-two children were included in the at-risk dyslexic group and 45 in
the at-risk non-dyslexic group. The control group included 12 not at-risk non-
dyslexic children (see Table 2.1).
The children were recruited earlier (Regtvoort & van der Leij, 2007; van
Otterloo & van der Leij, 2009) . The original sample consisted of 98 children
(82 at-risk). None of the parents reported (suspicion of ) developmental
disorders. In addition, the children were recruited from regular education. In
the Netherlands children with developmental disorders are usually referred
to special education. Fifty-nine at-risk children received intervention before
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2
they received formal reading instruction. The intervention group showed a
beneficial effect on phonological awareness and letter knowledge at the end
of kindergarten, but this head start did not lead to better literacy levels in first
grade1. The only measurement occasion with an intervention effect present
(i.e., end of kindergarten) was not included in the present study.
Table 2.1 Characteristics of the Children from the Three Groups
Characteristic At-risk dyslexic
At-risk non-dyslexic
Control F (2, 76) p η2p
Sample size 22 45 12No (%) of boys 16
a (73%) 28
a (62%) 6
a (50%)
No (%) of children in intervention
14a (64%) 32
a (71%) 0 (0%)
Child characteristicsAge in months (KG) 70.43
a (4.56) 70.61
a (3.80) 69.95
a (4.12) 0.126 .882 .003
Vocabulary (KG) 72.00a (9.90) 72.22
a (8.95) 73.00
a (7.89) 0.049 .952 .001
Nonverbal IQ (Gr1) 25.05a (5.19) 26.00
a (4.30) 27.00
a (4.97) 0.714 .493 .018
Note. Characteristics are given in raw scores, standard deviations in parentheses. Numbers and means in the same row that do not share subscripts differ at p < .05 on the χ2-test and the Tukey honestly significant difference comparison. No = number; KG = kindergarten; Gr = Grade.
Children were considered at-risk if at least one of the parents reported
to be dyslexic and if this was confirmed by tests measuring word and
pseudoword-reading fluency (Brus & Voeten, 1972; van den Bos, lutje Spelberg,
Scheepstra, & de Vries, 1994) . Also, the subtest similarities of the Wechsler Adult
Intelligence Scale was administered to measure verbal reasoning (Wechsler,
1997). For inclusion of a child in the at-risk group, at least one parent had to
score below or at the 20th percentile on both reading tests, or below or at the
10th percentile on one of them (with the other ≤ 40th percentile). Children of
parents who showed a large discrepancy (≥ 60 percentiles) between verbal
reasoning and one of the reading-tests were also included, with the restriction
that neither reading-test percentile score exceeded 402. Of not at-risk children
both parents had to score ≥ 40th percentile on both reading tests. 1 T-tests (comparing at-risk children with and without kindergarten intervention) on
January Grade 1 measures showed no significant differences: for letter-naming fluency, mean difference = 0.008, t(65) = 0.13, p = .896; for word-reading fluency, mean difference = -1.56, t(65) = -0.67, p = .504.
2 There were 14 at-risk children for whose parents only the discrepancy criterion applied. Excluding this group of children did not alter the pattern of results.
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Chapter 2
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One of the 16 not at-risk and 15 of the 82 at-risk children dropped out
of the study. Within the at-risk group, children who dropped out had a smaller
vocabulary than the others, t (80) = 2.21, p = .030, but they did not differ in
age, t (80) = 1.59, p = .146. Their parents did not differ on word-reading fluency,
t (80) = -0.71, p = .481, pseudoword- reading fluency, t (80) = -0.95, p = .346,
and verbal reasoning, t (80) = -1.05, p = .298. The attrition rate in the not at-
risk group was too small to do group comparisons. One not at-risk child was
excluded from the study because an elder sibling was diagnosed as dyslexic.
The child itself also became dyslexic.
Reading status was determined in fifth grade (at a mean age of 10 years
and 10 months). Children with a reading score on a word-reading fluency task
(WRF2, see below) corresponding to the weakest ten percent in the population
were considered dyslexic. The not at-risk dyslexic group consisted of only two
children and was excluded in the study. The remaining three groups did not differ
on sex, age, vocabulary, and nonverbal IQ (Table 2.1). The proportion of children
in the at-risk groups who had received intervention in kindergarten did not differ.
2.2.2 Measuresmeasures were selected to investigate intelligence, phonological awareness,
letter knowledge, rapid naming, and (pseudo)word-reading skills. Test
reliabilities ranged from .73 to .97.
Intelligence
Vocabulary. In the receptive vocabulary test (Verhoeven & Vermeer,
1996) the child had to choose among four alternatives the picture that best
matched a given word. The 98 items were of increasing difficulty. Administration
was stopped when the child failed six out of the last eight items.
Nonverbal IQ. Coloured Progressive matrices (J. C. Raven, Court, &
Raven, 1984) were used to measure nonverbal IQ.
Home-literacy environment
Parents were given a short questionnaire when their child attended
kindergarten. They were asked to indicate whether their child was a library
member, to estimate their shared reading frequency on a scale from 1 (never)
to 5 (more than five times a week) and the number of books they had at home
scaled from 1 (less than 50) to 5 (over 200). Next, the level of education of
both parents was ranked on a scale ranging from 1 (primary school only) to 7
(university degree).
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Phonological Awareness
Alliteration (Irausquin, 2001). The word with a different onset sound
from three words had to be named (‘odd one out’). All words had a consonant-
vowel-consonant (CVC) structure. The experimenter named the three words
and repeated them when necessary. There were 3 practice items (with feedback)
and 10 test items, all of approximately equal difficulty.
Phoneme blending and segmentation. In phoneme-blending
(Verhoeven, 1993a) the child was required to blend aurally presented phonemes
into a word. For example, children listened to the successive phonemes /r/ /u/
/p/ /s/, after which they had to merge this into /rups/ (caterpillar). Phoneme-
segmentation (Verhoeven, 1993b) was the reverse of phoneme-blending. Now
the child had to segment a given word into its constituent phonemes. Both
tasks began with three practice trials (with feedback). Test items consisted of 20
monosyllabic words per test, increasing from two to five phonemes, with four
to six items per number of phonemes. The tests were stopped when all items
with the same number of phonemes were blended or segmented incorrectly.
Rapid Naming
Serial rapid naming (van den Bos, 2003) was measured for colors (black, yellow,
red, green, and blue) and objects (bike, tree, duck, scissors, and chair). Each of
the tasks consisted of 50 randomly ordered symbols arranged in five columns
of 10 symbols each. Before test administration, the child practiced by naming
the last column of symbols. Children were instructed to name the symbols
column-wise as quickly as possible. For both tasks, the time to completion
was transformed to number of symbols per second to normalize the score
distribution.
Letter Knowledge
Receptive letter-knowledge. The test (Verhoeven, 2002) required the child
to point from six alternative lowercase letters to the letter that matched a given
sound. For instance, the child was asked “Where do you see the /m/ of mooi
(beautiful)?” The knowledge of 32 graphemes (including digraphs) was tested.
Letter-naming fluency. Children were asked to provide the sound of 34
graphemes (including digraphs) correctly as quickly as possible (Verhoeven,
1993a). When the child gave letter names instead, the child was corrected
and the test was started once again. The randomly ordered graphemes were
printed in lowercase in two columns of 17 items each. Scores were transformed
to number of correctly-read letters per second.
23629 Bergen, Elsje van V.indd 47 19-12-12 12:24
Chapter 2
48
Reading Skills
Word-reading fluency (WRF). This was measured as how many
disconnected words one can read correctly within a certain time allowed. Three
lists were used (Verhoeven, 1995; Verhoeven & van Leeuwe, 2009). The first
and second list (WRF1 and WRF2, respectively) consisted of 150 monosyllabic
words each. WRF1 included words with a CV, VC, or CVC pattern. All words
in WRF2 contained at least one consonant cluster. WRF3 comprised 120
polysyllabic words with various orthographic complexities. The words had to
be read correctly as quickly as possible. The score per list was the number of
words read correctly within one minute. WRF2 was used to assess dyslexia in
Grade 5, because it differentiates best between normal and dyslexic readers.
Pseudoword-reading fluency. The test (van den Bos, lutje Spelberg,
Scheepstra, & de Vries, 1994) consisted of a list of 116 pseudowords of increasing
difficulty. The child was asked to correctly read aloud as many pseudowords as
possible within two minutes.
2.2.3 ProcedureTests were administered individually in a fixed order by trained graduate
students in a separate room at school, in January of the second kindergarten
year, in January and June of Grade 1, June of Grade 2, and January of Grade 5.
2.2.4 Analytic ApproachOne-way analyses of variance (ANOVAs) and χ2-tests were used to compare
groups. Contrasts were evaluated post-hoc with Tukey’s procedure to correct
for multiple testing. When the same ability was measured on several occasions,
repeated-measures ANOVAs were performed with Group as between subject-
factor and Time as within-subject factor.
2.3 Results
2.3.1 Parent Characteristics and Home-Literacy EnvironmentThe characteristics of the children’s parents and home environment are
presented in Table 2.2. In both at-risk groups, almost 75% of the children had
a dyslexic father. Regarding the level of education for mothers and fathers,
control families had the highest level whereas the lowest level was found in
23629 Bergen, Elsje van V.indd 48 19-12-12 12:24
Dutch chilDren at family risk of Dyslexia
49
2
Tab
le 2
.2 C
hara
cter
istic
s of
the
Par
ents
and
hom
e En
viro
nmen
t fr
om t
he T
hree
Gro
ups,
with
χ2 a
nd A
NO
VA
Resu
lts
Cha
ract
eris
ticA
t-ris
k dy
slex
icA
t-ris
k no
n-dy
slex
icC
ontr
olF
dfp
η2 p
Pare
nt c
hara
cter
istic
s
No
(%) o
f dys
lexi
c fa
ther
s16
a (73%
)32
a (71%
)
Leve
l of e
duca
tion
of
mot
her
4.32
a (1.4
3)5.
07ab
(1.5
7)5.
92b (1
.31)
4.58
(2, 7
6).0
13.1
08
Fath
er3.
86a (1
.96)
4.91
ab (1
.85)
6.17
b (1
.53)
6.27
(2, 7
6).0
03.1
42
Dys
lexi
c p
aren
t4.
00a (1
.98)
4.73
a (1.8
3)2.
24(1
, 65)
.139
.033
Non
-dys
lexi
c p
aren
t4.
18a (1
.44)
5.24
b (1
.55)
7.25
(1, 6
5).0
09.1
00
Verb
al re
ason
ing
17.5
0 a (5.2
0)16
.22 a (4
.78)
18.4
2 a (4.1
4)1.
13(2
, 76)
.329
.029
Wor
d-re
adin
g flu
ency
60.4
1 a (11.
96)
69.0
0 b (1
3.61
)97
.83 c (1
1.08
)34
.33
(2, 7
6)<
.001
.475
Pseu
dow
ord-
read
ing
fluen
cy38
.77 a (1
5.32
)53
.22 b
(17.
85)
101.
75c (8
.45)
61.5
5(2
, 76)
< .0
01.6
18
hom
e-lit
erac
y en
viro
nmen
t
No
(%) o
f lib
rary
mem
ber
s16
a (73%
)32
a (71%
)6 a (5
0%)
Shar
ed re
adin
g4.
05a (1
.29)
4.39
a (0.9
1)4.
83a (0
.39)
2.55
(2, 7
6).0
85.0
63
Book
s at
hom
e3.
05a (1
.53)
3.31
a (1.4
9)4.
00a (1
.65)
1.54
(2, 7
6).2
20.0
39
Not
e. P
aren
tal r
eadi
ng fl
uenc
y an
d re
ason
ing
are
scor
es o
f the
wea
kest
-rea
ding
par
ent.
Num
ber
s an
d m
eans
in th
e sa
me
row
th
at d
o no
t sh
are
sub
scrip
ts d
iffer
at
p <
.05
on t
he χ
2 -tes
t an
d th
e Tu
key
hone
stly
sig
nific
ant
diff
eren
ce c
omp
aris
on. N
o =
nu
mb
er.
23629 Bergen, Elsje van V.indd 49 19-12-12 12:24
Chapter 2
50
Table 2.3 Descriptive Statistics and ANOVA Results for the measures of Phonological Awareness, Rapid Naming, and Letter Knowledge
At-risk dyslexic
At-risk non-
dyslexic Control
measure M SD M SD M SD F (2, 76) p η2p
Phonological awareness (Jan. KG)
Alliteration 4.15a 2.99 4.53
a 3.08 4.83
a 3.10 0.21 .808 .006
Phoneme blending 6.18a 6.63 7.27
a 6.15 7.67
a 6.51 0.29 .748 .008
Phoneme segmentation 4.86a 4.84 5.22
a 5.35 4.58
a 5.55 0.09 .918 .002
Rapid naming (Jan. KG)
Colors 0.48a 0.15 0.63
b 0.18 0.74
b 0.21 9.85 < .001 .206
Objects 0.53a 0.14 0.62
ab 0.17 0.74
b 0.17 6.72 .002 .150
Letter knowledge
Receptive (Jan. KG) 10.36a 4.86 14.42
b 6.70 18.00
b 5.94 6.51 .002 .146
Naming fluency (Jan. Gr.1) 0.82a 0.21 0.96
b 0.24 1.18
c 0.23 9.52 < .001 .200
Naming fluency (June Gr.1) 1.01a 0.26 1.24
b 0.28 1.24
b 0.29 5.72 .005 .131
Note. maximum scores are 10, 20, and 20 for the phonological-awareness tasks, and 32 for the receptive letter-knowledge task. Jan. = January. Numbers and means in the same row that do not share subscripts differ at p < .05 on the Tukey honestly significant difference comparison. KG = kindergarten; Gr = Grade.
the families of the at-risk dyslexics. Within at-risk families, we also investigated
level of education for dyslexic and non-dyslexic parents. An ANOVA (with
Group as between-subject and Parent as within-subject factor) revealed that
the parents of the at-risk non-dyslexics had a higher educational level than
those of the at-risk dyslexics (main effect of Group: F (1, 65) = 5.26, p = .025, η2p
= .075). The main effect of Parent (F (1, 65) = 2.73, p = .103, η2p = .040) and the
interaction effect (F < 1) were not significant. In contrast to level of education,
no differences were found for parental verbal reasoning. Interestingly, reading
fluency of the parents of at-risk dyslexics was significantly lower than of at-
risk non-dyslexics for both words and pseudowords. This finding was further
endorsed by a correlation of .27 (p = .026) between reading fluency scores (a
composite of words and pseudowords) of the dyslexic parents and their offspring.
With respect to home-literacy environment in kindergarten (Table
2.2), the proportion of children who were member of the library did not differ
significantly among the groups, nor did the groups differ significantly in the
amount of shared reading. Finally, the Group effect was not significant for the
number of books at home.
23629 Bergen, Elsje van V.indd 50 19-12-12 12:24
Dutch chilDren at family risk of Dyslexia
51
2
2.3.2 Child CharacteristicsPre-Reading Skills
The scores on the measures of phonological awareness, rapid naming, and
receptive letter knowledge (Table 2.3) were subjected to a multivariate analysis
of variance (mANOVA). The three groups differed significantly: Wilks’ λ = .668,
F (12, 142) = 2.65, p = .003, η2p = .183. The univariate results revealed that the
groups did not differ on phonological-awareness tasks. Conversely, differences
were found on rapid naming of colors and objects, and on letter knowledge.
The at-risk dyslexic children were slower on rapid naming and knew fewer
letters than the controls. The at-risk non-dyslexics also seemed to perform more
poorly than controls, but this did not reach significance at the (conservative)
Tukey post-hoc comparisons.
Reading Development
Letter-naming fluency (Table 2.3) was measured at the middle and end of
first grade. There was a main effect of both Time, F (1, 76) = 21.23, p < .001,
Table 2.4 Descriptive Statistics and ANOVA Results for the Reading Fluency measures
measure
At-risk dyslexic At-risk non-dyslexic Control
F (2, 76) η2p
M SD M SD M SD
Grade 1, January
Words-WRF1 11.14a 4.52 20.76
b 8.62 37.75
c 15.02 33.81 .471
Grade 1, June
Words-WRF1 22.68a 11.12 41.42
b 15.48 58.33
c 15.20 25.70 .403
Words-WRF2 10.68a 7.10 26.78
b 13.17 43.92
c 17.18 28.49 .428
Words-WRF3 6.77a 7.10 16.58
b 9.44 29.67
c 11.91 23.99 .387
Pseudowords 11.32a 8.91 20.82
b 9.71 36.58
c 11.91 25.58 .402
Grade 2, June
Words-WRF1 42.09a 17.10 66.29
b 15.30 77.58
b 11.81 26.15 .409
Words-WRF2 30.18a 16.57 56.53
b 18.48 72.50
c 12.52 27.72 .422
Words-WRF3 19.05b 13.34 41.33
b 16.40 52.17
b 12.81 23.38 .381
Pseudowords 19.41a 11.27 35.64
b 12.86 47.50
c 8.93 24.24 .389
Grade 5, January
Words-WRF1 63.45a 15.78 93.04
b 12.44 95.67
b 13.21 39.45 .509
Words-WRF2 55.00a 16.20 89.76
b 11.48 93.25
b 12.68 58.44 .606
Words-WRF3 43.82a 16.70 75.71
b 12.05 80.75
b 11.15 48.81 .562
Pseudowords 36.55a 15.94 61.87
b 14.35 78.50
c 11.71 37.77 .498
Note. The maximum scores for the word-reading fluency (WRF)-lists are 150, 150, and 120, respectively; for pseudoword-reading 116. Numbers and means in the same row that do not share subscripts differ at p < .05 on the Tukey honestly significant difference comparison.
23629 Bergen, Elsje van V.indd 51 19-12-12 12:24
Chapter 2
52
January Gr5June Gr2June Gr1January Gr1
Num
ber o
f wor
ds
100
80
60
40
20
0
At-risk DyslexicAt-risk Non-dyslexicControl
January Gr5June Gr2June Gr1
Num
ber o
f pse
udow
ords
80
60
40
20
At-risk DyslexicAt-risk Non-dyslexicControl
Figure 2.1. Development of reading. First panel: words (i.e., WRF1); second panel: pseudowords.
23629 Bergen, Elsje van V.indd 52 19-12-12 12:24
Dutch chilDren at family risk of Dyslexia
53
2
η2p = .218, and Group, F (2, 76) = 9.71, p < .001, η2
p = .203. The Time by Group
interaction approached significance, F (2, 76) = 2.59, p = .082, η2p = .064. Pairwise
comparisons revealed that halfway through Grade 1 the at-risk non-dyslexics
were significantly slower than the controls, but significantly faster than the at-
risk dyslexics. By the end of the school year, this group had reached the same
level as the controls.
The statistics for reading fluency are presented in Table 2.4. In addition,
the results for WRF1 and pseudowords are presented graphically in Figure
2.1. On all word and pseudoword reading tests, the main effects of both
Group and Time, as well as their interaction were significant (ps < .001, results
available on request). On all word and pseudoword reading tests and on all
occasions, the at-risk dyslexic group lagged significantly behind the other
two. moreover, the performance on both word and pseudoword reading
of the at-risk non-dyslexics was significantly below that of controls in first
and second grade. In Grade 5, however, the at-risk non-dyslexics still read
pseudowords significantly less fluently than the controls, but performed at
the same level on word reading.
2.4 Discussion
The reading development of children born to families with or without a history
of dyslexia was followed from kindergarten to fifth grade. In fifth grade reading
status (dyslexic or not dyslexic) was determined. Then, the development of
early reading-related abilities and reading in the groups differing in family-risk
status and reading status were compared.
In kindergarten, we found that the amount of letter-knowledge and
rapid naming differed among the groups of at-risk dyslexic, at-risk non-dyslexic,
and not at-risk normal-reading children (controls). however, differences among
the groups in the development of phonological awareness were not found.
The finding that the level of phonological awareness in kindergarten
was similar in dyslexic and normal reading children is in accordance with results
reported by de Jong and van der Leij (2003). They also did not find differences
between groups of kindergartners that later did or did not develop dyslexia.
De Jong and van der Leij attributed this null-result to Dutch kindergartners’
low level of phonological awareness (Wesseling & Reitsma, 1998). Apparently,
23629 Bergen, Elsje van V.indd 53 19-12-12 12:24
Chapter 2
54
as long as instruction pertaining to phonological awareness is sparse, there are
no group differences (de Jong & van der Leij, 1999).
however, in a recent prospective study, Boets et al. (2007; 2010) did find
deficits in a Dutch speaking dyslexic group (with and without risk combined)
relative to controls on phonological awareness assessed in kindergarten.
There are a number of reasons that might account for the different findings
on phonological awareness between the current study and the study of
Boets et al. (2010). To start with, it should be acknowledged that most of the
phonological-awareness performance in our study was at floor level, which
makes it difficult to detect differences. Another possibility concerns the
moment of assessing reading status. Boets et al. assessed reading status at the
start of third grade, whereas we assessed it mid-fifth grade. It is plausible that,
in general, groups will differ more on a correlate of reading when the time span
between the assessment of the correlate and the categorization into reading
groups is shorter. Indeed, when we repeated the analyses with the classification
based on reading scores at the end of second grade, the group differences on
phonological awareness were larger, although still not significant. In addition,
the relationship of phonological awareness and reading might change
over time. Vaessen and Blomert (2010) showed that the relation between
phonological awareness and word-reading fluency decreases as a function of
reading experience, so assessing reading status earlier might lead to groups
that differ more in kindergarten phonological awareness.
In contrast to phonological awareness, risk status and reading
outcome were related to the kindergartners’ knowledge of letters, as well as
their ability to rapidly name colors and objects. In line with previous studies
(Pennington & Lefly, 2001; Snowling, Gallagher, & Frith, 2003), rapid naming
and letter knowledge were impaired in the at-risk children who were later
classified as dyslexic, whereas the at-risk children who were later classified
as normal readers seemed to show milder impairments. These outcomes
support the notion that it is rapid naming rather than phonological awareness
that discriminates between reading groups in transparent orthographies,
as dyslexics in transparent orthographies are characterized by sufficient
reading accuracy but deficient reading fluency (de Jong & van der Leij, 2003;
Georgiou, Parrila, & Papadopoulos, 2008; Landerl & Wimmer, 2008). Since the
genes contributing to the manifestation of phonological awareness and rapid
naming seem to be only partially shared (Naples, Chang, Katz, & Grigorenko,
23629 Bergen, Elsje van V.indd 54 19-12-12 12:24
Dutch chilDren at family risk of Dyslexia
55
2
2009), a consequence of this notion is that the set of genes involved in the
etiology of dyslexia might differ somewhat between language environments
that differ in their orthography’s transparency. At present, this intriguing idea
remains speculative and warrants further investigation.
After half a year of reading instruction, the at-risk dyslexics were slower
in the naming of letters compared to the at-risk non-dyslexics, who were slower
than the controls. By the end of first grade the future dyslexics still lagged
behind, but the at-risk non-dyslexics performed at the level of controls. The
development of word-reading fluency showed a similar pattern, although over
a longer time period. Throughout the primary-school years, at-risk children
later classified as dyslexic read less fluently than the other two groups. The
group of at-risk non-dyslexics performed better than the at-risk dyslexics but
poorer than the controls in first and second grade, but in fifth grade they had
closed the gap and reached the same level of word reading as the controls.
meanwhile, the group differences in pseudoword-reading fluency were stable
over the five years studied, with the at-risk dyslexics performing less well and
the controls better than the at-risk non-dyslexics at all times.
Taken together, the results of the development of letter knowledge
and reading suggest that the at-risk non-dyslexics are genetically affected by
their dyslexic parent. Although they become normal readers in the long run,
they show subclinical deficits: first in pre-reading skills and later in reading
itself. These findings at the behavioral level are in line with an earlier report on
the present sample, showing atypical brain functioning during the processing
of simple visual stimuli at the age of five in the at-risk non-dyslexics (Regtvoort,
van Leeuwen, Stoel, & van der Leij, 2006). Recently, a study of Finnish researchers
revealed atypical processing of simple auditory stimuli of at-risk non-dyslexics
at birth (Leppänen et al., 2010). Despite these subtle deficits of at-risk non-
dyslexics, they catch-up with the controls on letter-naming and word-reading
fluency. Together with the finding that this group remains to lag behind in
pseudoword reading, these results might be taken to suggest that at-risk non-
dyslexics need more exposure to letters and words to reach the same level as
the controls. As a related possibility, it could be that this group has a larger
orthographic competence, the ability to store associations between written
and spoken word forms at the word and subword level (Bekebrede, van der Leij,
& Share, 2009), than their at-risk dyslexic peers. At the beginning of learning
to read differences between the at-risk dyslexic and non-dyslexic groups are
23629 Bergen, Elsje van V.indd 55 19-12-12 12:24
Chapter 2
56
relatively small, as word reading is highly dependent on phonological recoding.
however, as reading development proceeds and orthographic competence
becomes more important, at-risk non-dyslexic children outperform dyslexic
children and reach, though somewhat later, a similar level of word reading as
the controls.
The main focus of our study is to elucidate why some of the at-risk
children, about one-third, develop dyslexia whereas others with a similar
family background do not. Snowling et al. (2003) suggest that this latter group
compensates their phonological deficits with a higher general cognitive ability
or with good oral language skills. The current study does not provide support
for this explanation, as the groups neither differed in nonverbal IQ nor in
vocabulary. Likewise, there was no evidence of differences in nonverbal IQ and
vocabulary between the two at-risk groups in the studies of Elbro et al. (1998)
and Pennington and Lefly (2001). Still, we do not rule out that mild impairments
could be observed when broader oral language skills are assessed.
Similar to previous studies (Elbro, Borstrøm, & Petersen, 1998;
Snowling, muter, & Carroll, 2007), we also did not find evidence for the second
possible explanation that the at-risk non-dyslexic children experience a
more advantageous literacy environment compared to the at-risk dyslexic
children, though admittedly none of the studies (including ours) measured the
environment thoroughly (Torppa et al., 2007). however, in the present study
the parents of the non-dyslexic at-risk children had a higher level of education
compared to those of the dyslexic children, which has not been found before.
It is ambiguous whether this indicates a difference in the quality of their
offspring’s home literacy environment and/or a difference in their offspring’s
genetic potential.
A major finding of the current study is that within the group of dyslexic
parents, those whose child developed dyslexia performed worse in both word
and pseudoword reading. This might suggest that parents with more severe
dyslexia are genetically more affected and have offspring at higher genetic
risk, which fits with a continuous view of the family risk of dyslexia. Although
individual differences in parental reading skills cannot be attributed to genetic
influences alone, we suggest that it could be seen as an indication of their
offspring’s genetic liability for reading failure. The present finding needs
replication, but it shows that examining the relation between children’s reading
outcome and parents’ literacy levels is intriguing in itself.
23629 Bergen, Elsje van V.indd 56 19-12-12 12:24
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2
One of the limitations of our study is that our control group is rather
small. however, the focus of this study is the difference between the two at-
risk groups which are of substantial size. moreover, our finding that the control
group outperforms the at-risk groups in letter knowledge, rapid naming, and
reading skills is consistent with previous longitudinal at-risk studies (Boets,
Wouters, van Wieringen, & Ghesquière, 2007; Elbro, Borstrøm, & Petersen,
1998; Pennington & Lefly, 2001; Scarborough, 1990; Snowling, Gallagher, &
Frith, 2003). Furthermore, the small effect sizes on differences in phonological-
awareness suggest that the non-significant results can hardly be attributed to
a lack of power.
It should be noted that about two-thirds of the at-risk children received
intervention to promote literacy development during kindergarten, which
might hamper interpretation of the data for the current aim. But although
there were intervention effects observable directly after the intervention had
finished, the differences between the trained and untrained at-risk children
vanished when all children started formal reading instruction in first grade
(Regtvoort & van der Leij, 2007; van Otterloo & van der Leij, 2009). The absence
of long-term intervention effects is also shown in a meta-analysis of Bus and van
IJzendoorn (1999). Finally, it is reassuring that the children who had received
intervention spread equally over the dyslexic and non-dyslexic groups.
In sum, our data show that about two-third of the children born to
families with a history of dyslexia acquire in the end adequate word-reading
skills, despite mild impairments in rapid naming and letter knowledge in
kindergarten and mild difficulties in pseudoword reading throughout middle
childhood. The profile of reading and reading-related skills of the at-risk dyslexic
group indicates stable and persistent impairments. Overall, our findings
suggest that it is not likely that at-risk children who do and do not develop
dyslexia differ in their intellectual abilities or literacy environment. Rather, they
might differ in genetic liability.
23629 Bergen, Elsje van V.indd 57 19-12-12 12:24
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58
2.5 References
Bekebrede, J., van der Leij, A., & Share, D. L. (2009). Dutch dyslexic adolescents: Pho-nological-core variable-orthographic dif-ferences. Reading and Writing, 22(2), 133-165. doi:10.1007/s11145-007-9105-7
Bishop, D. V. m. (2009). Genes, cognition, and communication. Annals of the New York Academy of Sciences, 1156 (The Year in Cognitive Neuroscience 2009), 1-18. doi:10.1111/j.1749-6632.2009.04419.x
Boets, B., De Smedt, B., Cleuren, L., Vandewalle, E., Wouters, J., & Ghesquière, P. (2010). To-wards a further characterization of phono-logical and literacy problems in Dutch-spea-king children with dyslexia. British Journal of Developmental Psychology, 28, 5-31.
Boets, B., Wouters, J., van Wieringen, A., & Ghesquière, P. (2007). Auditory proces-sing, speech perception and phonologi-cal ability in pre-school children at high-risk for dyslexia: A longitudinal study of the auditory temporal processing theory. Neuropsychologia, 45(8), 1608-1620.
Brus, B. T., & Voeten, m. J. m. (1972). Eén-mi-nuut-test [One-minute-test]. Lisse: Swets & Zeitlinger.
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Pennington, B. F., & Lefly, D. L. (2001). Early reading development in children at family risk for dyslexia. Child Development, 72(3), 816-833.
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I wish you to gasp not only at what you read
But at the miracle of its being readable.
- Vladimir Nabokov -
Child and parental literacy levels within families with a history of
dyslexia
van Bergen, E., de Jong, P.F., Plakas, A., Maassen, B., & van der Leij, A. (2012). Child and parental literacy levels within families with a history of dyslexia. Journal of
Child Psychology and Psychiatry, 53(1), 28-36.
Chapter
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Child and parental literacy levels within families with a history of
dyslexia
van Bergen, E., de Jong, P.F., Plakas, A., Maassen, B., & van der Leij, A. (2012). Child and parental literacy levels within families with a history of dyslexia. Journal of
Child Psychology and Psychiatry, 53(1), 28-36.
Chapter
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Abstract
Background: The present study concerns literacy and its underlying
cognitive skills in Dutch children who differ in familial risk (FR) for
dyslexia. Previous studies with FR-children were inconclusive regarding
the performance of FR-children without dyslexia as compared to the
controls. moreover, van Bergen et al. (2011) recently showed that FR-
children with and without dyslexia differed in parental reading skills,
suggesting that those who go on to develop dyslexia have a higher
liability. The current study concerned 1) the comparison of three groups
of children at the end of 2nd grade and 2) the intergenerational transfer
of reading and its underlying cognitive skills from parent to child.
Method: Three groups of children were studied at the end of 2nd grade:
FR-dyslexia (n = 42), FR-no-dyslexia (n = 99), and control children (n = 66).
Parents and children were measured on naming, phonology, spelling,
and word and pseudoword reading.
Results: The FR-dyslexia children were severely impaired across all tasks.
The FR-no-dyslexia children performed better than the FR-dyslexia
children, but still below the level of the controls on all tasks; the only
exception was RAN, on which they were as fast as the controls. Focusing
on the FR sub-sample, parental reading and RAN were related to their
offspring’s reading status.
Conclusions: We replicated and extended van Bergen et al.’s study in
showing that the FR-children who develop dyslexia are likely to have
a higher liability. Both the group comparisons and the parent-child
relations highlight the importance of good RAN skills for reading
acquisition.
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3.1 Introduction
There is a fair amount of evidence to support the observation that dyslexia
tends to run in families. In studies with children at familial risk (FR) of dyslexia,
i.e. children with at least one dyslexic parent, approximately 33% to 66% have
been reported to become dyslexic (Boets et al., 2010; Pennington & Lefly,
2001; Scarborough, 1990; Snowling, Gallagher, & Frith, 2003; Torppa, Lyytinen,
Erskine, Eklund, & Lyytinen, 2010; van Bergen et al., 2011). In contrast, the
percentage of dyslexic children of parents without dyslexia has been found
to range from 6% to 16%. In combination with the results from quantitative
genetic studies (e.g., Olson, Byrne, & Samuelsson, 2009; Pennington & Olson,
2005), such results suggest a strong hereditary basis of dyslexia.
Reading ability and disability is the end product of the effects of many
genes in interaction with the environment. From this multifactorial etiology
it follows that the underlying liability (see Falconer, 1965) for reading failure
is continuously distributed. Plomin, DeFries, mcClearn and mcGuffin (2008, p.
33) show that the involvement of only a few genes already lead to continuous
variation at the phenotypic level. From a liability continuum two propositions
can be deduced. Firstly, children with a FR of dyslexia but without dyslexia
themselves (FR non-dyslexics) have a higher position on the liability continuum
than control children. That is, they inherit and/or experience more risk factors
and therefore are expected to show weaknesses in literacy and its underlying
cognitive skills. Secondly, within the group of FR children there might be a
difference in liability to dyslexia between children with and without dyslexia.
Insight into this liability might be gained by the skills of their parents. The
current study explores both of these propositions.
3.1.1 FR Children Without DyslexiaSeveral FR-studies demonstrated that not only FR children who went on to
become dyslexic were impaired on precursors of reading. Those who became
normal readers performed in general better than the FR-dyslexia children, yet
poorer than the controls. This is in accordance with a continuum of liability to
dyslexia, in which FR-no-dyslexia children have a higher liability than controls.
however, there are only a few studies in which groups of FR-dyslexia, FR-no-
dyslexia, and control children were compared on reading and its underlying
cognitive skills after a few years of reading instruction. The first focus of the
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current study was on the differences among these three groups when the
children were 8 years of age, that is, after two years of formal reading instruction.
Specifically, the emphasis was on the status of the FR-no-dyslexia children as
compared to the controls.
Previous studies have provided a fairly clear picture of the performance
of the groups regarding literacy skills, but nevertheless, they have been
inconclusive regarding the cognitive skills underlying reading. Underlying
skills are of interest because they represent endophenotypes, that is, the
layer between the genotype and the phenotype of the reading behavior itself
(Snowling, 2008). The most important cognitive skills underlying reading are
assumed to be rapid naming (RAN) and phonological awareness (PA) (Snowling
& hulme, 2000). RAN is the rapid naming of highly familiar visual symbols and
PA refers to the ability to identify and manipulate the sounds in spoken words.
Verbal short-term memory is sometimes seen as a third underlying cognitive
skill (Boets et al., 2010), but in literate individuals it tends to be closely tied
to PA (de Jong & van der Leij, 1999), which suggests that both measures tap
phonological processing.
Comparing at age 8 (i.e., after two years of reading instruction)
FR-children classified as dyslexic with controls, studies report pronounced
difficulties in reading, spelling, PA, verbal short-term memory, and RAN (Boets
et al., 2010; de Bree, Wijnen, & Gerrits, 2010; Pennington & Lefly, 2001; Snowling,
muter, & Carroll, 2007). These studies did not agree, however, on whether the
two groups of normal readers differ from each other. It is particularly intriguing
that the FR-children classified as non-dyslexic showed mild impairments
relative to the controls on reading and spelling, while the findings regarding the
difference between these two groups on the underlying skills are inconsistent.
In the study of Pennington and Lefly (2001) the FR-no-dyslexia children were
comparable to the controls on PA, but showed difficulties in verbal short-term
memory, RAN, and all literacy measures. Snowling et al.(2003) also found that
FR-no-dyslexia children and controls differed on the literacy tasks. In addition,
there was a trend for the FR-no-dyslexia children to perform somewhat weaker
on PA. however, they performed equally well on the repetition of nonwords,
a phonological task that measures verbal short-term memory. more recently,
Boets et al.(2010) reported mild impairments in the FR-no-dyslexia group
relative to controls on two out of the six literacy measures, although it seemed
that larger groups would yield a significant difference on the other literacy
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measures as well. The FR-no-dyslexia children were comparable to the controls
on RAN and verbal short-term memory, whereas there was a trend, as in the
study of Snowling et al., for PA to be lower. Taken together, it seems that
difficulties of FR-children without dyslexia are subtle and may not be detected
in studies with small samples.
3.1.2 Intergenerational TransferSince the etiology of dyslexia is multifactorial, involving both genetic and
environmental influences, individual differences between children in dyslexic
families must be associated at least in part with variations in their parents’
reading and cognitive skills. As an extension to earlier FR-studies, the second
focus of our study was the relation between the skills of the children and those
of their parents. In particular, we examined the relation between child and
parental literacy achievement and their underlying cognitive skills.
FR-studies start from the premise that children with and without a
FR of dyslexia differ in their liability of dyslexia. Implicitly, the assumption has
been that within the FR-subsample, children do not differ in their FR. Since
the reading skills of dyslexic parents all fall in the lower tail of the distribution,
differences in parental reading skills between FR-children with and without
dyslexia are not expected. To our knowledge, only two studies have tested
this assumption. Snowling et al.(2007) looked at the relation between
parents’ and children’s reading skills when the children were 12-13 year old.
Although reading skills of parents and children were related within the control
subsample, they were not within the FR-subsample. Based on these findings,
Snowling et al. conclude that “it does not seem to be the case that more severely
affected parents have more severely affected offspring”. This conclusion was
contradicted by the study of van Bergen et al. (2011). Despite the fact that
the dyslexic parents performed on average close to the fifth percentile, they
found that the dyslexic parents whose child manifested dyslexia at age 11 were
more severely impaired on word and nonword reading than those whose child
acquired reading skills within the normal range. This finding suggests that the
two groups of FR-children are not equal in their FR, i.e. the FR-children who
become dyslexic have a higher liability of developing dyslexia. Differences
in parental reading skills raise the question as to whether the FR-groups also
differ in parents’ cognitive skills underlying reading.
In the present study we compared groups of children differing in FR-
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status and reading outcome on literacy and its underlying skills. In addition,
we assessed literacy and its underlying skills in their parents, which allowed
us to investigate intergenerational transfer of skills from parent to child.
Given a liability continuum and the high heritability of both reading and its
underpinnings, we expected the FR-dyslexia children to show deficits across all
tasks and the FR-no-dyslexia children to display mild weaknesses. Based on the
findings of van Bergen et al. (2011), we predicted the FR-dyslexia children to
have more severely affected dyslexic parents than the FR-no-dyslexia children.
3.2 Method
3.2.1 ParticipantsThree groups of children were involved in this study. The FR-dyslexia group
consisted of 42 dyslexic children with a FR for dyslexia. The FR-no-dyslexia
group comprised 99 children with a FR for dyslexia but without dyslexia. Finally,
the control group included 66 children without a FR for dyslexia and without
dyslexia.
All children participated in the Dutch Dyslexia Programme (DDP):
a longitudinal multi-center study including the universities of Amsterdam,
Nijmegen, and Groningen. Couples with and without a history of dyslexia who
expected a baby were recruited between 1999 and 2002. They were recruited
via advertisements in newspapers and leaflets at GPs and midwives. Only
healthy, full-term babies were admitted to the program. The program was
approved by the ethical commission and all parents gave informed consent
for participation. About two-thirds of the sample consisted of families with a
history of dyslexia.
Children were considered to have a FR for dyslexia if at least one of
the parents and a close relative reported to have dyslexia. The dyslexia of the
parent had to be confirmed by tests measuring word and nonword-reading
fluency (see below). moreover, the subtest Similarities of the Wechsler Adult
Intelligence Scale was administered to measure verbal reasoning (Wechsler,
1997). For inclusion of a child in the FR-group, the parent with dyslexia had to
fulfill at least one out of the following criteria: 1) scores on both reading tests
≤ 20th percentile, 2) one reading score ≤ 10th percentile and the other reading
score ≤ 40th percentile, or 3) a discrepancy of ≥ 60 percentiles between the
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verbal-reasoning score and one of the reading scores, with the restriction that
neither reading-test percentile score exceeded 40. On average, the dyslexic
parents of the FR-children scored on the reading tests close to the 5th percentile.
For inclusion in the no-FR-group both parents had to score ≥ 40th percentile on
both reading tests.
Dyslexia in children was assessed at the end of second grade. Children
were considered dyslexic when their reading score on a word-reading fluency
task (WRF2, see below) corresponded to the weakest ten percent in the
population (see Boets et al., 2010 for a similar criterion). In the no-FR-group
only two children turned out to be dyslexic. These children were excluded from
the study. The characteristics of the three groups are presented in Table 3.1.
Percentages of boys were similar across groups. however, compared to the
other two groups, the FR-dyslexia group had a lower nonverbal IQ (SON-R,
Tellegen, Winkel, Wijnberg-Williams, & Laros, 1998) at 48 months of age.
Furthermore, the FR-dyslexia group was slightly older than the controls.
Table 3.1 Children’s Characteristics by Group
FR
Characteristic Dyslexia No-dyslexia Control F(2, 204)
Sample size 42 99 66
No (%) of boys 25a (60%) 56
a (57%) 39
a (59%)
Nonverbal IQ (at 48 months) 105.90a (15.27) 113.70
b (14.31) 114.14
b (15.38) 4.75
At assessment end Grade 2
Age in months 98.60a (5.52) 97.67
ab (4.47) 96.41
b (4.34) 3.02
No of months reading instruction 18.69a (0.68) 18.87
a (0.74) 18.80
a (0.66) 0.96
Note. The group means are given with standard deviations in parentheses. Numbers and means in the same row that do not share subscripts differ at p < .05 on the χ2-test or Tukey’s test. No = number.
3.2.2 MeasuresChildren
The children were measured at the end of second grade on reading accuracy
and fluency, spelling, PA, and RAN.
Word and nonword-reading accuracy. In these tests (de Jong & Wolters,
2002) the child was required to read a list of (non)words without time pressure.
Each test consisted of 40 items increasing in difficulty from one to four
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syllables. Testing was discontinued when six out of the last eight items were
read incorrectly.
Word-reading fluency (WRF). This was assessed using three lists
(Three-minutes-Test, Verhoeven, 1995). The first and second (WRF1 and WRF2,
respectively) consisted of 150 monosyllabic words each. WRF1 included
words with a CV, VC, or CVC pattern. All words in WRF2 contained at least
one consonant cluster. WRF3 comprised 120 polysyllabic words with various
orthographic complexities. The score per list was the number of words read
correctly within one minute. WRF2 was used to assess dyslexia, as it has a high
reliability (.96 at the end of Grade 2, moelands, Kamphuis, & Verhoeven, 2003)
and differentiates well between children at this age.
Nonword-reading fluency. This test (Klepel: van den Bos, lutje Spelberg,
Scheepstra, & de Vries, 1994) consisted of a list of 116 pseudowords of increasing
difficulty. The child was asked to read as many pseudowords as possible within
two minutes, with the score being the number correctly read.
Spelling. The child had to write down (without time pressure) mono-
and disyllabic words presented in a sentence (van den Bosch, Gillijns, Krom, &
moelands, 1993). The test had two versions, an easier and a more difficult one,
consisting of 38 and 36 items. The easier version was given at two centers, the
difficult version at the third. According to the test manual the scores of the tests
could be transformed to one scale (based on Rasch analyses). The scores on this
scale ranged from 80 to 144.
PA. PA was measured using a phoneme-deletion test (de Jong & van
der Leij, 2003). On each item of the test a phoneme, always a consonant, had
to be deleted from a nonword resulting in another nonword. The test consisted
of two parts. The first part comprised nine monosyllabic and nine disyllabic
nonwords. The second part consisted of nine disyllabic nonwords, with the
phoneme that had to be deleted occurring twice. Testing was discontinued
when six consecutive items were answered incorrectly in the first part or three
in the second part. Each attempt was scored as either correct or incorrect. Both
parts of the test started with two items for practice.
RAN. Serial RAN (RAN: van den Bos, 2003) was measured for numbers (2, 4,
5, 8, and 9) and colors (black, yellow, red, green, blue). Each of the tasks consisted of
fifty randomly ordered symbols arranged in five columns of 10 symbols (numbers
or colors) each. Before test administration, the child practiced by naming the last
column of symbols. Children were instructed to name the symbols column-wise
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as quickly as possible. The time to completion was transformed to number of
symbols per second to normalize the score distribution.
Parents
The dyslexic parent of the FR-children and both parents of the control children
were assessed on reading fluency, spelling, nonword repetition, and RAN.
The data presented are the data of the weakest-reading parent. In addition,
the level of education of both parents was ranked on a scale ranging from 1
(primary school only) to 5 (university degree).
Word-reading fluency. The test (One-minute-Test: Brus & Voeten, 1972)
consisted of a list of 116 words of increasing difficulty. The parent was asked to
correctly read as many words as possible within one minute.
Nonword-reading fluency. The same test as for the children was used
(see above).
Spelling. The ability to apply phonological analysis and spelling rules
was tested with a test that required writing nonwords to dictation. The 26
nonwords ranged from one to three syllables.
Nonword repetition. The test (de Jong & van der Leij, 1999) consisted
of 48 randomly ordered nonwords ranging from one to four syllables. The
nonwords were presented twice on audiotape. After the nonword was
presented the parent had to repeat back the nonword. The task started with
three practice items. Each attempt was scored as either correct or incorrect.
RAN. Three serial RAN tasks were developed for the present study:
one for upper case letters (all, except I, Q, and Y), one for numbers (2 up to
and including 11), and one for colors (green, red, yellow, and blue). Each of the
tasks consisted of 50 randomly ordered symbols arranged in five columns of
10 symbols each. Before test administration, the parent practiced by naming
the last column of symbols. Parents were instructed to name the symbols
column-wise as quickly as possible. For all three tasks, the time to completion
was transformed to number of symbols per second to normalize the score
distribution.
3.2.3. ProcedureParents and children were tested individually by trained graduate students.
The parents were tested in a quiet room at home or at the hospital around the
birth of their child. The children were tested in a quiet room at the university
between may and July of Grade 2.
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3.3 Results
3.3.1 Data ScreeningOne dyslexic parent and their dyslexic child were removed from the analyses
because of missing scores and scores on literacy measures below 3.3 SD’s of the
mean of their group. Data were missing for just a couple of the parents’ tests
(1-2%) and educational levels (4%). The analyses of these variables therefore
included somewhat fewer cases. All score distributions were approximately
normal. The one exception was for word-reading accuracy, which was too
easy for the two groups of normal readers. These scores were logarithmically
transformed before analysis.
3.3.2 ChildrenTable 3.2 displays the performance of the three groups of children (FR-dyslexia,
FR-no-dyslexia, and controls) on the literacy and cognitive measures. One-
way ANOVAs followed up with Tukey’s test were used to investigate group
differences. Effect sizes (Cohen’s d’s) were computed using the SD’s of the
control group.
All literacy measures showed the same pattern: the FR-no-dyslexia
group scored considerably higher than the FR-dyslexia group (with effect sizes
between 1.22 and 2.41), but was still impaired relative to the control group
(with effect sizes between 0.37 and 0.74). On average, the children with dyslexia
could only read the mono- and disyllabic words and monosyllabic nonwords of
the accuracy tests.
Similar group differences were observed for PA, but not for RAN. The
FR-dyslexia group was significantly impaired on both RAN tasks; however, the
FR-no-dyslexia group performed similarly to the control group.
Finally, some extra analyses were performed to investigate whether the
observed group differences on literacy(-related) skills were genuine rather
than due to group differences on children’s non-verbal IQ (Table 3.1) or
parental educational level (Table 3.5). Nonverbal IQ correlated between .24 and
.32 with the reading measures, .37 with spelling, .28 with PA and .07 and .20
with RAN digits and colors. Controlling for IQ in a series of one-way ANCOVAs
yielded virtually the same results as the reported ANOVAs (Table 3.2), as did
ANOVAs on a subset of data with groups matched for IQ. The level of education
of the weakest-reading parent only correlated significantly with the outcome
23629 Bergen, Elsje van V.indd 72 19-12-12 12:24
Literacy LeveLs within famiLies with a history of dysLexia
73
3
Tab
le 3
.2 C
hild
ren’
s Pe
rfor
man
ce o
n Li
tera
cy a
nd C
ogni
tive
mea
sure
s by
Gro
up
FR
Con
trol
Effec
t siz
e (C
ohen
’s d)
Dys
lexi
aN
o-dy
slex
ia
mea
sure
max
.M
SDM
SDM
SDF
(2, 2
04)
FRD
vs.
FRN
DFR
D v
s. C
FRN
D v
s. C
Read
ing
accu
racy
Wor
ds40
20.8
6 a10
.52
34.6
0 b5.
7337
.64 c
2.69
71.5
31.
732.
460.
74N
onw
ords
408.
19a
6.22
19.3
2 b10
.16
24.8
3 c8.
9943
.07
1.24
1.85
0.61
Read
ing
fluen
cyW
ords
- W
RF1
150
32.5
2 a11
.83
68.6
4 b16
.85
75.0
6 c15
.01
109.
422.
412.
830.
43W
ords
- W
RF2
150
19.0
7 a7.
1957
.51 b
19.4
867
.70 c
18.9
510
4.77
2.03
2.57
0.54
Wor
ds -
WRF
312
012
.95 a
7.02
42.6
8 b18
.17
53.5
6 c18
.21
79.4
91.
632.
230.
60N
onw
ords
116
13.0
0 a4.
5530
.37 b
12.6
037
.48 c
14.0
654
.73
1.24
1.74
0.51
Spel
ling
144
108.
50a
6.38
119.
04b
8.57
122.
21c
8.61
37.7
61.
221.
590.
37
PA27
9.26
a4.
8014
.47 b
6.00
18.1
7 c4.
8034
.16
1.09
1.86
0.77
RAN
Dig
its1.
22a
0.29
1.56
b0.
351.
61b
0.34
19.8
11.
001.
150.
15C
olor
s0.
83a
0.20
0.94
b0.
190.
96b
0.21
5.63
0.52
0.62
0.10
Not
e. C
hild
ren’
s sk
ills
wer
e as
sess
ed a
t th
e en
d of
Gra
de 2
. max
. = m
axim
um s
c ore
; WRF
= w
ord-
read
ing
fluen
cy. N
umb
ers
and
mea
ns in
the
sam
e ro
w t
hat
do n
ot
shar
e su
bsc
ripts
diff
er a
t p <
.05
on T
ukey
’s p
ost-
hoc
test
. Coh
en’s
d is
cal
cula
ted
usin
g th
e SD
s of
the
cont
rols
. max
. = m
axim
um s
c ore
, FRD
= F
R-dy
slex
ia, F
RND
= F
R no
-dys
lexi
a, C
= c
ontr
ol.
23629 Bergen, Elsje van V.indd 73 19-12-12 12:24
Chapter 3
74
variable PA: r = .16. Adjusting for parental education in ANCOVAs did not alter
the pattern of results. Although equating for group differences can be disputed
(miller & Chapman, 2001), the fact that the ANCOVAs yield the same picture as
the ANOVAs strengthen our findings.
3.3.3 Intergenerational TransferTables 3.3 and 3.4 display correlations between children’s and parents’ measures
of literacy and their underlying skills. As an illustration, the relation between
nonword-reading fluency in children and parents is shown graphically in
Figure 3.1. This measure was chosen since it was assessed with the same test in
children and parents. Due to our strict selection criteria (i.e., percentile scores
of the dyslexic parents ≤ 40 and those of the control parents ≥ 40), correlations
within the FR and control subsamples suffered from restriction of range in the
parental measures, which suppressed correlations. The correlations presented
are therefore of the FR and control subsamples combined. In the control
sample only the data of the weakest-reading control parent was selected.
Correlations are given separately for the weakest-reading mothers and their
children and the weakest-reading fathers and their children. The correlations
show that reading skills of parents and children were moderately correlated. As
expected, mothers’ nonword repetition correlated strongest with children’s PA.
mothers’ RAN, however, correlated stronger with children’s reading than with
children’s RAN. No such clear pattern was observed for father-child correlations.
Children’s reading fluency seemed to correlate higher with the performance of
mothers than of fathers. The significance of this difference was tested using
mx (Neale, 2004). It appeared that an unconstrained two-group model fitted
the data better than one with the set of eight correlations (i.e., two reading-
fluency measures times four parental measures) constrained to be equal across
mothers and fathers, Δχ2(8, N=205) = 15.55, p = .049. Children’s PA and RAN,
on the other hand, did not correlate differently with test scores of mother and
fathers, Δχ2(8, N=205) = 7.86, p = .447. The proportion of dyslexic parents was
virtually equal in the two groups, as was the variability in test scores. hence,
these factors could not explain the differences between maternal and paternal
correlations.
23629 Bergen, Elsje van V.indd 74 19-12-12 12:24
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75
3
Table 3.3 Correlations among skills of mothers and their children
mother
Reading fluency
Words NonwordsNonwordrepetition RAN digits
ChildrenReading fluency
Words .43 .47 .23 .38Nonwords .51 .56 .21 .49
Phonological awareness .54 .50 .34 .31RAN digits .13 .12 .10 .19
Note. Word-reading fluency of children is WRF2. n’s [83, 87]. For n = 85: p < .05 for r ≥ .21 and p < .001 for r ≥ .35.
Table 3.4 Correlations among skills of fathers and their children
Father
Reading fluency
Words NonwordsNonword repetition RAN digits
ChildrenReading fluency
Words .39 .38 .22 .27Nonwords .30 .32 .15 .15
Phonological awareness .22 .29 .22 .16RAN digits .28 .26 .17 .19
Note. Word-reading fluency of children is WRF2. n’s [119, 120]. For n = 119: p < .05 for r ≥ .18 and p < .001 for r ≥ .30.
23629 Bergen, Elsje van V.indd 75 19-12-12 12:24
Chapter 3
76
Figure 3.1. Scatterplots (with regression lines) illustrating the relation for nonword-reading fluency between children and mothers (top panel, r = .56) and children and fathers (lower panel, r = .32).
23629 Bergen, Elsje van V.indd 76 19-12-12 12:24
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77
3
Differences among the FR-dyslexia, FR-no-dyslexia, and control children
were also investigated for parental characteristics and skills. There was no difference
between the two FR-groups in gender of the dyslexic parent: The dyslexic parent
was the father in 55% of the cases in the FR-dyslexia group compared to 62%
in the FR-no-dyslexia group (χ2 < 1). The other parental characteristics and test
scores by group are shown in Table 3.5. The effect sizes of the comparisons with
the control group were large; the comparisons of the two FR-groups yielded
small effect sizes, except for word-reading fluency and alphanumeric RAN with
medium effect sizes. Regarding background characteristics, the mothers and
fathers of both FR-groups had a lower educational level compared to the control
group. The two FR-groups differed in educational level of the non-dyslexic
parent, but not in that of the dyslexic parent. The general Dutch population
has an average level of education of 3.20 (with SD = 0.94, Statline, 2010), which
indicates that the parents in the FR and control samples were slightly (Cohen’s d
= .31) and considerably (Cohen’s d = 1.01) above average, respectively.
The parental skills investigated included verbal reasoning, literacy, and
literacy correlates. The parents in the control group obtained the highest scores
on verbal reasoning, followed by the FR-dyslexia and FR-no-dyslexia groups,
respectively. By definition, the dyslexic parents in the FR-groups were weaker
readers than the parents in the control group, but they scored also significantly
lower than the controls on spelling. Since we hypothesized that the parents of
the FR-dyslexia children would score lower than those of the FR-no-dyslexia
children on literacy(-related) tasks, we performed planned t-tests. As predicted,
the dyslexic parents of the FR-dyslexia group scored significantly lower than
those of the FR-no-dyslexia group on word-reading fluency (t(139) = 1.69, p =
.047, one-tailed). however, the FR-groups did not differ in nonword-reading
fluency and spelling. The reading measures correlated similarly with educational
level (within FR-sample: word reading: r = .38; nonword reading: r = .42).
Concerning the correlates of reading, the parents of the controls were
better than those of the FR-children on nonword repetition and RAN. The two
FR-groups did not differ significantly on nonword repetition and RAN of colors,
but they did on alpha-numeric RAN: the dyslexic parents of the FR-no-dyslexia
children were significantly better on both letters (t(137) = 2.16, p = .016, one-
tailed) and digits (t(137) = 2.52, p = .006, one-tailed). Parental RAN hardly
correlated with their educational level (within FR-sample: .14, .19, and .10 for
letters, digits, and colors, respectively).
23629 Bergen, Elsje van V.indd 77 19-12-12 12:24
Chapter 3
78
Tab
le 3
.5 P
aren
ts’ C
hara
cter
istic
s an
d Pe
rfor
man
ce o
n Li
tera
cy a
nd C
ogni
tive
mea
sure
s by
Gro
up
FR
Dys
lexi
aN
o-dy
slex
iaC
ontr
olEff
ect s
ize
(Coh
en’s
d)
Cha
ract
eris
ticm
ax.
MSD
MSD
MSD
Fdf
FRD
vs.
FRN
DFR
D v
s. C
FRN
D v
s. C
Leve
l of e
duca
tion
ofm
othe
r5
3.40
a0.
873.
56a
0.89
4.14
b0.
7212
.69
(2, 1
96)
0.22
1.03
0.81
Fath
er5
3.34
a0.
883.
51a
0.91
4.16
b0.
8813
.66
(2, 1
96)
0.19
0.93
0.74
Dys
lexi
c p
aren
t5
3.41
a0.
873.
41a
0.90
< 1
(1, 1
35)
0.00
Non
-dys
lexi
c p
aren
t5
3.33
a0.
893.
67b
0.89
4.26
(1, 1
32)
0.38
Verb
al re
ason
ing
2616
.98 ab
3.04
16.4
3 a3.
7918
.43 b
3.16
6.63
(2, 2
02)
-0.1
70.
460.
63
Read
ing
fluen
cyW
ords
116
61.8
1 a12
.19
65.8
6 a13
.35
96.6
1 b8.
4816
7.62
(2, 2
04)
0.48
4.10
3.63
Non
wor
ds11
646
.17 a
17.9
447
.73 a
15.0
010
0.95
b9.
8431
8.42
(2, 2
04)
0.16
5.57
5.41
Spel
ling
2612
.98 a
4.79
13.4
7 a3.
9618
.66 b
3.12
41.5
4(2
, 200
)0.
161.
821.
66
Non
wor
d re
pet
ition
4836
.50 a
5.99
37.2
4 a5.
6742
.83 b
3.50
28.0
7(2
, 200
)0.
211.
811.
60
RAN Le
tter
s1.
91a
0.45
2.10
a0.
502.
79b
0.41
60.3
7(2
, 200
)0.
462.
151.
68
Dig
its1.
91a
0.47
2.14
b0.
532.
72c
0.44
41.2
3(2
, 200
)0.
521.
841.
32
Col
ors
1.41
a0.
271.
45a
0.29
1.87
b0.
2950
.48
(2, 1
99)
0.14
1.59
1.45
Not
e. T
est
scor
es b
elon
g to
the
wea
kest
-rea
ding
par
ent.
Num
ber
s an
d m
eans
in t
he s
ame
row
tha
t do
not
sha
re s
ubsc
ripts
diff
er a
t p
< .0
5 on
Tuk
ey’s
pos
t-ho
c te
st.
Coh
en’s
d is
cal
cula
ted
usin
g th
e SD
s of
the
cont
rols
. max
. = m
axim
um s
c ore
, FRD
= F
R-dy
slex
ia, F
RND
= F
R no
-dys
lexi
a, C
= c
ontr
ol.
23629 Bergen, Elsje van V.indd 78 19-12-12 12:24
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Finally, it was investigated whether the effect of parental characteristics
on children’s literacy outcome was fully mediated by children’s characteristics.
Therefore, a set of hierarchical regression analyses focusing on the FR-group
was performed. In predicting children’s literacy outcome (i.e., WRF2), first child
predictors were entered (nonverbal IQ, PA, and RAN) and then one of the
parental characteristic. It was found that parental word-reading fluency (β = .136,
p = .031) and alphanumeric RAN (letters: β = .149, p = .018; digits: β = .123, p =
.054) added further variance to the model, indicating that they affected children’s
literacy outcome (partly) independent from children’s cognitive skills. Parental
characteristics that did not improve the model were nonword-reading fluency
(β = .096, p = .128), RAN colors, nonword repetition, and level of education of the
dyslexic and non-dyslexic parent (all ps > .40). The predictive value of parental
RAN on children’s literacy outcome is nicely illustrated when comparing groups
of dyslexic parents with and without a RAN deficit (using the scores marking the
bottom 10% in the control group as cut-offs). In the group without a RAN deficit
in letters or digits 14% and 16%, respectively, manifested dyslexia, while these
proportions where 39% and 41%, respectively, for the parents with a RAN deficit.
3.4 Discussion
After two years of reading instruction, 30% of the children with a family history
of dyslexia had developed dyslexia, compared to only 3% of the children
without such a history. These figures agree with other studies using a similarly
(strict) criterion to identify dyslexia.
The FR-dyslexia children were significantly impaired relative to the FR-
no-dyslexia children on PA, RAN, spelling, and reading accuracy and fluency of
both words and nonwords. This is in line with previous longitudinal FR-studies
contrasting groups of 8-year-olds (Boets et al., 2010; Pennington & Lefly, 2001;
Snowling et al., 2003; Torppa et al., 2010; van Bergen et al., 2011). In contrast
to several earlier studies in transparent orthographies (e.g., de Jong & van der
Leij, 2003; Wimmer, 1993), but in accordance with Boets et al. (2010), reading
accuracy differentiated dyslexic from normal readers and even FR-no-dyslexia
children from controls. This shows that differences in accuracy are observable
until at least second grade, provided that the task is not restricted to one- or
two-syllable items.
23629 Bergen, Elsje van V.indd 79 19-12-12 12:24
Chapter 3
80
In accordance with previous studies (Boets et al., 2010; Snowling et al.,
2003), the FR-no-dyslexia group performed better than the FR-dyslexia group,
but weaker than the controls on PA and literacy, although their performance
was well within the normal range. In fact, compared to the national norm
scores for the reading-fluency and spelling tests, the performance of the FR-no-
dyslexia group was just below average, while the performance of the controls
was just above.
Regarding PA, this study is the first study that shows reliable differences
between FR-no-dyslexia children and controls. This confirms the earlier
reported trends observed by Snowling et al. (2003) and Boets et al. (2010).
Strikingly, the FR-no-dyslexia children were unimpaired on RAN. Of the two
other FR-studies that also included RAN, our result resembles that of Boets et
al. (2010), but contrasts with that of Pennington and Lefly (2001), who found
the FR-no-dyslexia group to perform slower. Unlike Boets et al. and our study,
Pennington and Lefly used reading accuracy to diagnose dyslexia. however,
reanalyzing RAN differences with reading status based on word-reading
accuracy still yielded equivalent performances of our two non-dyslexic groups.
The poor performance on RAN of the FR-children with dyslexia does
not seem to be a consequence of their poor reading. The current study showed
that the FR-children without dyslexia were faster on RAN than their relatively
weak reading fluency suggested. Furthermore, longitudinal studies have
demonstrated that FR-children who later manifested dyslexia were already
slow at RAN before they came to the task of learning to read (Boets, Wouters,
van Wieringen, & Ghesquière, 2007; Pennington & Lefly, 2001; van Bergen et al.,
2011). Finally, Lervåg and hulme (2009) found RAN to be a predictor of reading
development, but failed to find support for a reciprocal influence of reading
on RAN. These findings argue against RAN performance as a consequence of
reading performance.
Besides the skills of the children in the three groups, we aimed to
investigate the relation between child and parental skills. Child and parental
reading skills were moderately correlated: ~.35 for fathers and ~.50 for
mothers. These correlations are higher than the correlation of .28 reported by
Bynner and Parsons (2006), although this may well be explained by the fact
that parental literacy skills in their cohort study were self-reported instead
of tested. Interestingly, children’s reading fluency, but not their PA and RAN,
correlated higher with mothers’ than with fathers’ skills. This is suggestive of an
23629 Bergen, Elsje van V.indd 80 19-12-12 12:24
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3
environmental influence: it could be that mothers are in general more involved
in parenting and that this has an effect on their offspring’s reading skill but not
on its underlying cognitive skills. On the other hand, we cannot rule out the
possibility that some of the fathers in our sample are not the biological fathers.
This would suppress father-child correlations. moreover, the finding should be
interpreted with caution since it involves only data from the weakest-reading
parent. Replication with complete parent data is therefore warranted.
Importantly, we found that the dyslexic parents whose child manifested
dyslexia were even more impaired on word-reading fluency than those whose
child had an age-appropriate reading level. however, the difference was
smaller (i.e., a medium effect size of d = .48) than the difference observed by
van Bergen et al. (i.e., a large effect size of d = .78). moreover, the current study
failed to replicate a difference in nonword-reading fluency, as reported by van
Bergen et al.. Since reading ability is not yet fully stabilized after two years of
reading instruction, the moment of assessing reading status could partially
explain the differences between the studies. Indeed, when we reanalyzed van
Bergen et al.’s data with reading status based on children’s reading skills at
the end of second grade (instead of fifth grade, as in the original study), the
difference in parental reading ability between the two FR-groups decreased,
although it remained larger than in the current sample. Furthermore, we
wondered whether the presence of a word-reading difference and the absence
of a nonword-reading difference in the current sample could be subscribed to
a difference in the amount the two groups of dyslexic parents read. however,
the two reading measures correlated similarly with educational level, which
argues against this notion.
As expected, the dyslexic parents were impaired on all underlying
cognitive skills. Although nonword repetition (as a proxy for PA) discriminated
well between parents with and without dyslexia, within the FR-subsample
parental nonword repetition appeared to be unrelated to children’s reading
outcome. Interestingly, the dyslexic parents of the FR-dyslexia children were
somewhat slower on RAN than those of the FR-no-dyslexia children. Parental
differences in reading and RAN might be related to differences in liability of
their offspring. Compared with parental reading, parental RAN might be a
cleaner indicator of their genetic potential, because it is less influenced by
reading experience (as shown by the correlations with educational level).
Another indicator of children’s liability is that of the parents’ level of
23629 Bergen, Elsje van V.indd 81 19-12-12 12:24
Chapter 3
82
education. As also observed by van Bergen et al. (2011), the FR-no-dyslexia
children have non-dyslexic parents with a higher educational level compared
to FR-dyslexia children. This might be an additional factor which affects
the outcome of children, operating via inheritance and the home-literacy
environment.
Subsequently, we examined possible intergenerational transfer by
examining whether cognitive skills of the dyslexic parents were related to
children’s reading outcome. It appeared that differences in parental word-
reading fluency and alphanumeric RAN were predictive of differences in
children’s reading ability, beyond the effect of children’s cognitive skills. The
predictive value of parental RAN is also shown by the fact that of the children of
a dyslexic parent without a RAN deficit just 15% developed dyslexia, compared
to as many as 40% of those with a RAN deficit. This agrees with the finding that
the two groups of FR-children differed in parental RAN. Both underline that
RAN deficits in dyslexic parents added to their offspring’s risk for dyslexia, over
and above their reading status. Furthermore, parental RAN correlated stronger
with children’s reading than with children’s RAN. Apparently, RAN of parents
and RAN of children somehow measure different aspects of children’s reading
ability.
The mild deficits in PA and literacy displayed by the FR no-dyslexia
group corroborate a liability continuum. Additionally, both children’s data and
parent-child relations highlight that – at least within transparent orthographies
– RAN plays a more important role in reading acquisition than PA. A parallel can
be drawn between the present findings and those of Bishop, mcDonald, Bird,
and hayiou-Thomas (2009), who studied another group at-risk for dyslexia:
children with language impairment. They also found that those who achieved
adequate reading skills despite their risk showed remarkable good RAN skills.
They suggest that what RAN taps might protect against reading failure. Our
data support this hypothesis, although longitudinal studies are required to
elucidate whether and how the skills underlying RAN performance might
protect children at FR for dyslexia.
23629 Bergen, Elsje van V.indd 82 19-12-12 12:24
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3
3.5 Key Points
• Prospective studies with children at FR of dyslexia demonstrate the
multifactorial etiology of dyslexia.
• The current study shows that FR-children who do not meet criteria for
dyslexia are mildly impaired on literacy and PA, but not on RAN.
• Dyslexic parents of dyslexic children tended to be more severely impaired
on word-reading and RAN skills than dyslexic parents of non-dyslexic
children.
• Parental characteristics which affect the reading outcome of their offspring
are reading, RAN, and level of education. These factors can be used to
improve a child’s risk assessment for developing dyslexia.
23629 Bergen, Elsje van V.indd 83 19-12-12 12:24
Chapter 3
84
3.6 References
Bishop, D. V. m., mcDonald, D., Bird, S., & hayiou-Thomas, m. E. (2009). Children who read words accurately despite lan-guage impairment: Who are they and how do they do it? Child Development, 80(2), 593-605. doi: 10.1111/j.1467-8624.2009.01281.x
Boets, B., De Smedt, B., Cleuren, L., Van-dewalle, E., Wouters, J., & Ghesquière, P. (2010). Towards a further characterization of phonological and literacy problems in Dutch-speaking children with dyslexia. British Journal of Developmental Psycho-logy, 28, 5-31.
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Lervåg, A., & hulme, C. (2009). Rapid auto-matized naming (RAN) taps a mechanism that places constraints on the develop-ment of early reading fluency. Psycho-logical Science, 20(8), 1040-1048. doi: 10.1111/j.1467-9280.2009.02405.x
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Pennington, B. F., & Lefly, D. L. (2001). Early reading development in children at family risk for dyslexia. Child Development, 72(3), 816-833.
Pennington, B. F., & Olson, R. K. (2005). Gene-tics of dyslexia. In m. J. Snowling, & C. hulme (Eds.), The science of reading: A handbook (pp. 453-472). malden: Blackwell Publishing.
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Scarborough, h. S. (1990). Very early langua-ge deficits in dyslexic children. Child Deve-lopment, 61(6), 1728-1743.
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Snowling, m. J., muter, V., & Carroll, J. (2007). Children at family risk of dys-lexia: A follow-up in early adolescence. Journal of Child Psychology and Psychia-try, 48(6), 609-618. doi: 10.1111/j.1469-7610.2006.01725.x
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Torppa, m., Lyytinen, P., Erskine, J., Eklund, K., & Lyytinen, h. (2010). Language de-velopment, literacy skills, and predictive connections to reading in Finnish children with and without familial risk for dyslexia. Journal of Learning Disabilities, 43(4), 308-321. doi: 10.1177/0022219410369096
van Bergen, E., de Jong, P. F., Regtvoort, A., Oort, F., van Otterloo, S., & van der Leij, A. (2011). Dutch children at family risk of dyslexia: Precursors, reading develop-ment, and parental effects. Dyslexia, 17(1), 2-18. doi: 10.1002/dys.423
van den Bos, K. P. (2003). Serieel benoemen en woorden lezen [Serial naming and word reading]. Groningen: Rijksunversiteit Gro-ningen.
van den Bos, K. P., lutje Spelberg, h. C., Scheepstra, A. J. m., & de Vries, J. R. (1994). De klepel: Een test voor de leesvaardigheid van pseudowoorden [The klepel: A test for the reading skills of pseudowords]. Lisse: Swets & Zeitlinger.
van den Bosch, L., Gillijns, P., Krom, R., & moe-lands, F. (1993). Schaal vorderingen in spel-lingvaardigheid [Scale progress in spelling skills]. Arnhem: Cito.
Verhoeven, L. (1995). Drie-minuten-toets. Handleiding [Three-minutes-test.Mmanu-al]. Arnhem: Cito.
Wechsler, D. (1997). Wechsler adult intelligen-ce scale-III (WAIS-III). San Antonio: Psycho-logical Corporation.
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23629 Bergen, Elsje van V.indd 85 19-12-12 12:24
The whole is more than the sum of its parts.
- Aristotle -
The effect of parents’ literacy skills and children’s preliteracy skills on the
risk of dyslexia
van Bergen, E., de Jong, P.F., Maassen, B., & van der Leij, A. (submitted). The effect of parents’ literacy skills and children’s preliteracy skills on the risk of dyslexia.
Chapter
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The effect of parents’ literacy skills and children’s preliteracy skills on the
risk of dyslexia
van Bergen, E., de Jong, P.F., Maassen, B., & van der Leij, A. (submitted). The effect of parents’ literacy skills and children’s preliteracy skills on the risk of dyslexia.
Chapter
chapter-intros_Elsje-van-Bergen_v2.indd 4 18-12-12 20:4523629 Bergen, Elsje van V.indd 87 19-12-12 12:24
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88
Abstract
This familial-risk study examines plausible preschool risk factors for
dyslexia. Participants included at-risk children with (n = 50) and without
(n = 82) dyslexia in Grade 3 and controls (n = 64). Firstly, children from
families in which both parents experienced literacy difficulties were 2½
times more likely to develop dyslexia than children from families with only
one affected parent. Secondly, we found impairments in phonological
awareness, rapid naming, and letter knowledge in at-risk kindergartners
who later developed dyslexia, and mild phonological-awareness deficits
in at-risk kindergartners without subsequent dyslexia. We also examined
the specificity of these preliteracy skills. Although these skills were also
related to later arithmetic, associations with later reading were stronger.
Finally, we propose an intergenerational multiple deficit model in which
both parents pass on cognitive risks.
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4.1 Introduction
Prospective studies in which children are followed from the preschool years
can provide valuable insights into the characteristics of children who go on to
develop dyslexia. however, it is still an open question whether the precursors of
dyslexia specifically predict dyslexia and reading development or whether they
additionally predict dyscalculia and arithmetic development. Furthermore, less
is known about the characteristics of the families in which children who go on
to develop dyslexia are born. Knowledge about risk factors of dyslexia sheds
light on the causal pathways that ultimately lead to dyslexia. These insights
might also be useful for tracing children who are at heightened risk of reading
failure and need timely support. The current study investigates plausible risk
factors for dyslexia present before reading instruction, both at the level of
cognitive skills of the child, as well as at the level of family characteristics.
4.1.1 Risk Factors in FamiliesPart of the prospective studies included children with an increased risk
of dyslexia because they are born into families with a history of dyslexia.
Depending on the definition of dyslexia, 33 to 66% of the children at familial
risk have been found to develop dyslexia (Elbro, Borstrøm, & Petersen, 1998;
Pennington & Lefly, 2001; Scarborough, 1990; Snowling, Gallagher, & Frith, 2003;
Torppa, Lyytinen, Erskine, Eklund, & Lyytinen, 2010; van Bergen et al., 2011).
The fact that the prevalence of dyslexia is consistently found to be higher in
children with a familial history of dyslexia compared to children without such
a history is in accordance with a difference between these samples in liability
or risk for dyslexia. Nevertheless, in such a design liability to dyslexia is in fact
dichotomized, which implies that implicitly the assumption has been made
that at-risk children with and without dyslexia have equal liabilities. Given
that dyslexia is influenced by a wide range of environmental and genetic risk
factors (Pennington, 2006), its underlying liability distribution, however, must
be continuously distributed.
In three recent papers (Torppa, Eklund, van Bergen, & Lyytinen, 2011;
van Bergen et al., 2011; van Bergen, de Jong, Plakas, maassen, & van der Leij,
2012) the equal-liability assumption has been tested by comparing the two
at-risk groups on the reading and reading-related skills of the parent with
dyslexia. Evidence was found against the equal-liability assumption, because
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the parents of the affected children were more severely dyslexic than those
of the unaffected children. On the other hand, information regarding possible
differences between the groups in reading skills of the spouse, the parent
without dyslexia, is as yet lacking. We will present data on parents’ self-reported
reading and spelling skills, a valid indicator of tested skills (Snowling, Dawes,
Nash, & hulme, 2012). We expected the unaffected parents of the affected
children to report more literacy difficulties than those of the unaffected
children, mirroring the findings in affected parents.
Differences between the two risk groups in parental reading skills
could suggest that these two groups of children differ in genetic predisposition.
however, the alternative explanation is that weaker reading parents offer their
child a less advantageous literacy environment. Such differences in literacy
stimulation might have been the primary cause of children’s differences in later
reading success. We will investigate several characteristics of the home literacy
environment when children were 3½ years old, and test whether differences
are related to children’s reading status five years later. The combination of
findings on parental literacy skills and home literacy environment sheds light
on whether the intergenerational transmission of risks is predominantly via
genetic or environmental pathways.
4.1.2 Risk Factors in ChildrenAlongside characteristics of families that might indicate heightened risk for
dyslexia, we investigated characteristics of kindergartners that could signify
increased risk. more specifically, we examined whether differences between
at-risk dyslexic, at-risk non-dyslexic, and control children are only present after
some years of reading instruction, or whether the groups already demonstrate
differences before the start of reading instruction. By comparing at-risk children
who go on to develop dyslexia with controls retrospectively, it has been shown
that phonological awareness, rapid naming, and letter knowledge are among
the most important precursors of dyslexia. Familial-risk studies consistently
found that children with dyslexia were impaired across these skills during
the preschool years (Elbro, Borstrøm, & Petersen, 1998; Pennington & Lefly,
2001; Scarborough, 1990; Snowling, Gallagher, & Frith, 2003; Torppa, Lyytinen,
Erskine, Eklund, & Lyytinen, 2010; van Bergen et al., 2011).
In addition, familial-risk studies offer the interesting possibility to
compare the at-risk children classified as non-dyslexic with their peers without
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4
familial risk (controls). Although these at-risk children do not meet dyslexia
criteria, they typically perform less well than controls on reading and spelling
tasks after a few years of reading instruction (Boets et al., 2010; Pennington &
Lefly, 2001; Snowling et al., 2003; van Bergen et al., 2011; van Bergen et al., 2012)
(but see Torppa et al., 2010 for an exception). These findings provide further
support for the continuity of familial risk (Pennington & Lefly, 2001; Snowling et
al., 2003; van Bergen et al., 2012), and are consistent with a multifactorial model
of the aetiology of dyslexia (Pennington, 2006).
The somewhat lower literacy skills of the at-risk children without
dyslexia raise the question whether, before the start of reading instruction, they
also exhibit mild deficiencies in the cognitive skills underpinning reading, or
whether they start off first grade performing as well as controls but experience
slower development in reading skills. In previous familial-risk studies the
performance of the at-risk non-dyslexics was not significantly weaker on
phonological awareness, rapid naming, and letter knowledge in kindergarten
(Boets et al., 2010; Elbro et al., 1998; Pennington & Lefly, 2001; Snowling et al.,
2003; Torppa et al., 2010; van Bergen et al., 2011); the only exception being
letter knowledge in the study of Elbro and colleagues. Based on the mentioned
studies, with no clear differences between at-risk children without dyslexia
and controls, we would expect to find no differences either. Yet we might find
differences for two reasons. First, in previous studies the at-risk non-dyslexics
performed similar or even worse, though not significantly, than controls.
As we have a larger sample in the present study, subtle differences might
reach significance. Second, in an earlier paper about the same sample as the
current paper we reported mild difficulties at the end of Grade 2 of the at-risk
children without dyslexia on literacy and phonological awareness, but not on
rapid naming (van Bergen et al., 2012). Accordingly, we expected to find mild
difficulties in kindergarten on letter knowledge and phonological awareness,
but not on rapid naming.
4.1.3 Specificity of PrecursorsAn additional aim of the current study was to consider the specificity of known
precursors for dyslexia. Comorbidity rates of dyslexia and dyscalculia are higher
than expected by chance (Landerl & moll, 2010). Accordingly, it is important
to examine whether the above mentioned trio of preliteracy skills are also
predictive of arithmetic skills. Put differently, are the precursors of reading
23629 Bergen, Elsje van V.indd 91 19-12-12 12:24
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specifically related to basic reading skills or are they also precursors of basic
arithmetic skills? According to De Smedt and colleagues (Boets & De Smedt,
2010; De Smedt, Taylor, Archibald, & Ansari, 2010), reading and arithmetic
ability are concurrently associated because word reading and arithmetic
fact retrieval both depend upon the quality of phonological representations
in long-term memory. If so, we should find a longitudinal relation between
phonological awareness and arithmetic. Building on this hypothesis, the speed
of retrieval of phonological representations form long-term memory (as tapped
by rapid naming) should be related to subsequent mental arithmetic skills,
since the latter requires quick retrieval of arithmetic facts. Inefficient retrieval
of phonologically coded arithmetic facts leaves limited memory resources for
selecting and carrying out appropriate procedures (hecht, Torgesen, Wagner,
& Rashotte, 2001).
The current paper will report new data of an ongoing longitudinal
study (see e.g. van Bergen et al., 2012). Parental literacy and home literacy
environment when children were 3½ years old and children’s cognitive skills
in kindergarten will be related to their reading and arithmetic skills in Grade 3.
Adding to previous studies, in the current study we also examined literacy skills
of the non-dyslexic parent and the specificity of known precursors of reading.
4.2 Method
4.2.1 ParticipantsThree groups of children of the Dutch Dyslexia Programme (see van Bergen et
al., 2012) were involved in this study. Children who had data present at ages
3½ (literacy questionnaire) and/or 6 (kindergarten), as well as at age 9 (third
grade) were eligible for inclusion in this study (N = 196). The at-risk dyslexic
group consisted of 50 dyslexic children at high familial risk for dyslexia. The
at-risk non-dyslexic group comprised 82 children at high familial risk but
without dyslexia. Finally, the control group included 64 children at low familial
risk and without dyslexia. Six children at low familial risk were categorized as
dyslexic and were omitted from the study. All at-risk children had at least one
parent and one close relative with dyslexia. In 13 cases (9.8%) both parents had
dyslexia. On average, the scores of the dyslexic parents (i.e. weakest reading
parent) belonged to the bottom five percent on reading fluency. A detailed
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4
description of the assessment of familial risk is given in van Bergen et al. (2012).
Assessment of dyslexia in the children was done in Grade 3. Children were
considered to have dyslexia when their score on the word-reading fluency task
(described below) corresponded to the weakest ten percent in the population
(equivalent to a Wechsler score of ≤6.2 norm scores taken from van den Bos,
lutje Spelberg, Scheepstra, & de Vries, 1994). The Grade 3 assessment took
place between January and may. Cut-off scores were adjusted according to the
month of assessment.
Table 4.1 shows the characteristics of the three groups. Percentages of
boys were similar across groups. The at-risk dyslexic children had lower IQs than
the non-dyslexic children. The groups did not differ in age at the questionnaire
administration. The control children were two months younger at the
kindergarten and Grade 3 sessions, but the groups did not differ in the number
of months of reading instruction when seen in third grade. Kindergarten
encompasses two years in the Netherlands. The kindergarten assessment took
place between April and August of the second year, when the children were
between 67 and 79 months old. Four children were kept down in Grade 1 or 2.
Like the other children, they were seen for the Grade 3 assessment after almost
three years of reading instruction, when these four children attended Grade 2.
Finally, parental education (averaged over both parents; scale 1-5) was lower in
the at-risk groups than in the control group.
Table 4.1 Group Characteristics
At-risk
Dyslexic Non-dyslexic Control p
Sample size 50 82 64No. (%) of boys 30
a (60%) 45
a (55%) 39
a (61%) .728
Full-scale IQ (at age 4) 105.37a (9.90) 111.08
b (10.75) 113.24
b (10.53) <.001
Age in months at assessments
Questionnaire 41.75a (2.03) 42.52
a (3.75) 41.08
a (2.73) .214
Kindergarten 73.02ab
(3.31) 73.29a (3.08) 71.85
b (3.11) .023
Grade 3 107.96a (4.65) 107.46
ab (3.99) 105.83
b (3.97) .014
No of months reading at assessment
Grade 3* 26.18a (1.00) 26.23
a (0.89) 25.92
a (1.12) .158
Parental education 3.41a (0.72) 3.42
a (0.77) 4.16
b (0.74) <.001
Note. The group means are given with standard deviations in parentheses. Numbers and means in the same row that do not share subscripts differ at p < .05 on the χ2-test or Tukey’s test. No. = number; * = 10 months instruction per year.
23629 Bergen, Elsje van V.indd 93 19-12-12 12:24
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4.2.2 MeasuresWhen children were 3½ years old, parents were asked to fill out a questionnaire
about their own literacy and the home literacy environment. Subsequently, the
children were tested on preliteracy skills at the end of kindergarten (at age 6)
and on school achievement in Grade 3 (at age 9).
Literacy Questionnaire at Age 3½
Parental literacy. Both fathers and mothers were asked about their
print exposure and literacy difficulties. Three questions were related to print
exposure: how many hours per week do you spend on average on reading for
1) work/study and 2) leisure? and 3) how many hours per week do you spend
on average on writing (e-mails, letters, postcards, diary etc.)? Each was scored
on a scale ranging from 1 (less than one hour a week) to 5 (more than 10 hours
a week). The print-exposure measure (range 3-15, Chronbach’s α .62) was the
sum of the scores for these items. Furthermore, three questions (on a scale of
1 to 3) concerned literacy difficulties: 1) Do you think you are a fast, average or
slow reader? 2) Do you have trouble following the subtitles on TV? and 3) Do
you think you have more, average or less difficulties with spelling than other
people? The sum of the scores for these items formed the literacy-difficulties
measure (range 3-9).
For 78 fathers and 70 mothers we had scores on both the literacy-
difficulties measure and reading-fluency tests of words and nonwords, which
allowed us to investigate criterion validity. The reading-fluency tests (see van
Bergen et al., 2012, for descriptions) were assessed around the time of their child’s
birth. The correlation between the literacy-difficulties measure and a composite
of word- and nonword-reading fluency was -.84 for fathers and -.85 for mothers.
Home Literacy Environment. The literacy questionnaire also contained
questions regarding home literacy environment. Both fathers and mothers were
asked to indicate the frequency of storybook reading in a typical week on a scale
from 1 (never) to 5 (more than five times a week) and whether they had a magazine
or newspaper subscription. Furthermore, parents were asked to estimate the
number of books available in the home (1 = fewer than 20 to 5 = more than
150). Finally, part of an existing questionnaire was used to measure cognitive
stimulation (Leseman, 1994; Sigel, 1982) with statements like ‘I ask my child
questions during storybook reading’. There were six statements (Chronbach’s α
.54), rated on a scale ranging from 1 (strongly disagree) to 6 (strongly agree).
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4
Preliteracy at Age 6
Rapid naming. Serial rapid naming (van den Bos, 2003) consisted of
50 randomly ordered patches of colours (black, yellow, red, green, and blue)
arranged in five columns of 10 symbols each. Before test administration,
children practiced by naming the last column. Children were instructed to
name the colours column-wise as quickly as possible. The time to completion
was transformed to number of colours per second to normalize the score
distribution. The reliability for 6-year-olds is .80.
Phonological awareness. Two tests measured phonological awareness:
phoneme blending and phoneme segmentation. In phoneme blending
(Verhoeven, 1993a) the child was required to blend aurally presented phonemes
into a word. For example, children listened to the successive phonemes /r/ /u/
/p/ /s/, after which they had to merge this into rups [caterpillar]. Phoneme
segmentation (Verhoeven, 1993b) was the reverse of phoneme-blending. Now
the child had to segment a given word into its constituent phonemes. Both
phoneme blending and phoneme segmentation began with three practice
trials (with feedback). Test items consisted of 20 monosyllabic words per test,
increasing from two to five phonemes, with four to six items for each specific
number of phonemes. The tests were stopped when all items with the same
number of phonemes were failed. Chronbach’s α for both tests is above .85
(Verhoeven, 2000).
Letter knowledge. Both receptive and productive letter knowledge
were assessed. The receptive test (Verhoeven, 2002) required the child to
point from six alternative lowercase letters to the letter that matched a given
sound. For instance, the child was asked “Where do you see the /m/ of mooi
(beautiful)?” The knowledge of 32 graphemes (including digraphs) was tested.
Chronbach’s α is .88 (Eleveld, 2005). In the productive knowledge test, children
were asked to provide the sound of 34 graphemes (including digraphs), but
letter names were also considered correct (Verhoeven, 1993a). The randomly
ordered graphemes were printed in lowercase in two columns of 17 items
each. Chronbach’s α is above .85 (Verhoeven, 2000).
School Achievement at Age 9
Reading. To assess word-reading fluency, children were given the
One-minute-Test (Brus & Voeten, 1972), which consists of a list of 116 words
of increasing difficulty. They were asked to read as many words as possible
correctly within one minute. The reliability is .90 for Grade 3.
23629 Bergen, Elsje van V.indd 95 19-12-12 12:24
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Arithmetic. Two subtests of the arithmetic tempo test (de Vos, 1992)
were administered: addition and subtraction. Each paper-and-pencil subtest
includes 40 problems of increasing difficulty. Per problem two operands have
to be added or subtracted (e.g. 13 + 4 = …). All operands and outcomes are
below 100. The number of correctly solved problems within one minute forms
the raw score. A total score was computed as the sum of the standard scores
over both subtests. The reliability of each subtest is .93 (Stock, Desoete, &
Roeyers, 2010).
4.3 Results
About half of the participants returned the questionnaire that was sent by mail.
however, percentage of data present was nicely distributed among the groups
(at-risk dyslexic: 56%, at-risk non-dyslexic: 51%, and controls: 50%). missing-
value analyses indicated that the subsamples with and without missing
questionnaire data did not differ significantly on parental education (t(189.9) =
-0.57, p = .572), word-reading fluency of the weakest-reading parent (t(192.2)
= -0.17, p = .865), or word-reading fluency of the child (t(193.0) = -1.52, p
= .131). hence, further analyses of the questionnaire data were deemed
appropriate. The preliteracy data were present for 96% of the children. Grade
3 data were complete. One outlier on rapid naming in the control group was
removed because the score was 3.4 standard deviations above the control
group’s mean.
Differences among the groups on continuous measures were
evaluated using one-way ANOVAs, followed by pairwise comparisons with
Tukey’s correction for multiple testing. Group means, group effects, and
effects sizes are presented in Tables 4.2, 4.3, and 4.4. Results are described
below for family and child characteristics in relation to the three groups, and
subsequently, results concerning the prediction of children’s reading skills are
given.
4.3.1 Family Characteristics in Relation to Children’s GroupParental print exposure and literacy difficulties in relation to children’s group
can be found in Table 4.2. The fathers of the control children spent significantly
more time on reading and writing than those of the at-risk children.
23629 Bergen, Elsje van V.indd 96 19-12-12 12:24
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4
Tab
le 4
.2 P
aren
tal L
itera
cy in
Rel
atio
n to
Chi
ldre
n’s
Gro
up
At-
risk
Effec
t siz
e(C
ohen
’s d)
Dys
lexi
cN
on-d
ysle
xic
Con
trol
Mea
sure
MSD
MSD
MSD
Fdf
FRD
vs.
FRN
DFR
D v
s. C
FRN
D v
s. C
Prin
t exp
osur
e
Fath
er7.
81a
3.36
7.53
a 3.
089.
81b
3.06
4.99
(2, 9
2)-0
.09
0.65
0.75
Mot
her
7.96
a 2.
798.
10a
3.00
9.03
a 2.
871.
24(2
, 95)
0.05
0.37
0.32
Dys
lexi
c p
aren
t7.
56a
2.98
7.08
a 3.
08<
1(1
, 64)
-0.1
6
Non
-dys
lexi
c p
aren
t8.
23a
3.15
8.56
a 2.
84<
1(1
, 63)
0.11
Lite
racy
diffi
cult
ies
Fath
er6.
68a
1.52
6.20
a 1.
833.
91b
0.93
29.9
1(2
, 94)
-0.5
2-2
.98
-2.4
6
Mot
her
6.00
a 1.
945.
10a
2.07
3.84
b
1.17
10.8
3(2
, 98)
-0.7
7-1
.85
-1.0
8
Dys
lexi
c p
aren
t7.
56a
0.96
7.24
a 1.
111.
39(1
, 64)
-0.3
0
Non
-dys
lexi
c p
aren
t5.
19a
1.57
4.02
b
1.31
10.8
8(1
, 66)
-1.1
1
Not
e. N
umb
ers
and
mea
ns in
the
sam
e ro
w t
hat
do n
ot s
hare
sub
scrip
ts d
iffer
at
p <
.05
on T
ukey
’s te
st. C
ohen
’s d
is c
alcu
late
d us
ing
the
SDs
of t
he c
ontr
ols.
FRD
=
fam
ilial
-ris
k dy
slex
ic; F
RND
= fa
mili
al-r
isk
non-
dysl
exic
; C =
con
trol
.
23629 Bergen, Elsje van V.indd 97 19-12-12 15:59
Chapter 4
98
Tab
le 4
.3 H
ome
Lite
racy
Env
ironm
ent i
n Re
latio
n to
Chi
ldre
n’s
Gro
up
At-
risk
Effec
t siz
e(C
ohen
’s d)
Dys
lexi
cN
on-d
ysle
xic
Con
trol
Mea
sure
MSD
MSD
MSD
Fdf
FRD
vs.
FRN
DFR
D v
s. C
FRN
D v
s. C
Book
s at
hom
e*4.
04a
1.14
4.07
a1.
114.
62a
0.75
Shar
ed re
adin
g
Fath
er3.
20a
1.26
3.29
a1.
293.
50a
1.91
< 1
(2, 9
5)0.
050.
160.
11
Mot
her
4.23
a0.
994.
48a
0.77
4.19
a1.
001.
09(2
, 97)
0.25
-0.0
4-0
.29
Dys
lexi
c p
aren
t3.
70a
1.27
3.67
a1.
32<
1(1
, 67)
-0.0
2
Non
-dys
lexi
c p
aren
t3.
75a
1.23
4.12
a1.
051.
67(1
, 63)
0.25
Cog
nitiv
e st
imul
atio
n29
.01 a
4.48
29.7
9 a3.
3529
.28 a
3.45
< 1
(2, 9
9)0.
230.
08-0
.15
Not
e. N
umb
ers
and
mea
ns in
the
sam
e ro
w t
hat
do n
ot s
hare
sub
scrip
ts d
iffer
at
p <
.05
on T
ukey
’s te
st. C
ohen
’s d
is c
alcu
late
d us
ing
the
SDs
of t
he c
ontr
ols.
FRD
=
fam
ilial
-ris
k dy
slex
ic; F
RND
= fa
mili
al-r
isk
non-
dysl
exic
; C =
con
trol
; * =
test
ed n
on-p
aram
etric
ally
and
eff
ect s
izes
not
giv
en, s
ee te
xt.
23629 Bergen, Elsje van V.indd 98 19-12-12 15:59
Preschool risk factors for dyslexia
99
4
Tab
le 4
.4 C
hild
ren’
s Pr
elite
racy
Ski
lls a
t 6 Y
ears
and
Sch
ool A
chie
vem
ent a
t 9 Y
ears
At-
risk
Effec
t siz
e(C
ohen
’s d)
Dys
lexi
cN
on-d
ysle
xic
Con
trol
Mea
sure
MSD
MSD
MSD
Fdf
FRD
vs.
FRN
DFR
D v
s. C
FRN
D v
s. C
Prel
itera
cy s
kills
(6yr
)
Rap
id n
amin
g co
lour
s0.
58a
0.16
0.72
b0.
180.
75b
0.18
14.1
9(2
, 182
)0.
790.
960.
17
Phon
olog
ical
aw
aren
ess
Blen
ding
7.57
a5.
7011
.56 b
6.25
14.4
5 c5.
1419
.51
(2, 1
86)
0.78
1.34
0.56
Segm
enta
tion
5.08
a4.
979.
46b
6.67
12.3
2 c6.
0919
.49
(2, 1
86)
0.72
1.19
0.47
Lett
er k
now
ledg
e
Rece
ptiv
e13
.61 a
6.58
21.2
7 b6.
5922
.73 b
7.20
27.9
1(2
, 186
)1.
061.
270.
20
Prod
uctiv
e9.
02a
6.28
17.6
6 b8.
1318
.50 b
8.22
24.8
9(2
, 185
)1.
051.
150.
10
Scho
ol a
chie
vem
ent (
9yr)
Read
ing
28.6
4 a8.
2154
.72 b
12.1
061
.53 c
12.2
412
9.98
(2, 1
93)
2.13
2.69
0.56
Arit
hmet
ic-1
.35 a
1.61
0.15
b1.
790.
90c
1.75
24.2
2(2
, 193
)0.
861.
290.
43
Not
e. N
umb
ers
and
mea
ns in
the
sam
e ro
w t
hat
do n
ot s
hare
sub
scrip
ts d
iffer
at
p <
.05
on T
ukey
’s te
st. C
ohen
’s d
is c
alcu
late
d us
ing
the
SDs
of t
he c
ontr
ols.
FRD
=
fam
ilial
-ris
k dy
slex
ic; F
RND
= fa
mili
al-r
isk
non-
dysl
exic
; C =
con
trol
.
23629 Bergen, Elsje van V.indd 99 19-12-12 15:59
Chapter 4
100
Although maternal print exposure seemed not to be related to children’s group,
a multivariate analyses (with data of both parents analysed simultaneously) did
not show a significant parent by group interaction, F(2, 90) = 1.63, p = .202, but
only an effect of group, F(2, 90) = 3.73, p = .028. Next, in the at-risk sample we
subdivided parents according to their reading status (dyslexic vs. non-dyslexic1).
In this sample, parental print exposure was unrelated to reading outcome of at-
risk children. Non-dyslexic parents (M = 8.43, SD = 2.95) appeared to read and
write approximately equally frequent as the control parents (M = 9.42, SD =
2.62; t(94) = 1.59, p = .115).
Concerning literacy difficulties, parents in the control group reported
less problems with reading and spelling, as was expected given the selection
criteria. Interestingly, within the at-risk group parental literacy difficulties
seemed to differentiate children with and without dyslexia. This difference was
large and significant (Cohen’s d = -1.11, p = .002) for the non-dyslexic parent.
The interaction between parental status (dyslexic vs. non-dyslexic) and child
outcome (dyslexic vs. non-dyslexic) approached significance, F(1, 63) = 3.85,
p = .054. The non-dyslexic parents of the non-dyslexic children (M = 4.02, SD =
1.31) reported similar levels of literacy as the control parents (M = 3.88, SD =
0.67; t(71) = 0.59, p = .560).
home literacy environment was measured at 3½ years by whether
parents had a subscription to a newspaper or magazine, how many books
they had at home, and how often they read to their child. The percentages
of fathers/mothers with a subscription were 68%/68% in the at-risk dyslexic
group, 73%/76% in the at-risk non-dyslexic group, and 100%/91% in the
control group. The differences were significant for fathers, suggesting more
subscriptions in control families, χ2(2, N=101) = 11.86, p = .003, but not for
mothers, χ2(2, N=101) = 4.82, p = .090. The variable about number of books
in the home (Table 4.3) was strongly skewed and therefore analysed with the
nonparametric Kruskal-Wallis test. Overall, the difference between groups was
significant, K(2, N=102) = 7.01, p = .030, suggesting more books in the homes
of control families, but pairwise comparisons with adjusted p-values missed
significance. The frequency of storybook reading was virtually the same over
groups; the only difference being that mothers read more than fathers. Parents
provided similar levels of cognitive stimulation.
1 In 9 out of the 70 at-risk children with questionnaire data present, both parents had dyslexia. ‘Dyslexic parent’ refers to the weakest reading parent and ‘non-dyslexic’ to the other parent.
23629 Bergen, Elsje van V.indd 100 19-12-12 12:24
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101
4
4.3.2 Children’s Characteristics in Relation to Children’s GroupGroup means on precursors of reading at the end of kindergarten are shown
in Table 4.4. All group effects on the preliteracy tasks were highly significant
(ps < .001). The at-risk dyslexic group was slower on rapid naming and knew
fewer letters compared to the two non-dyslexic groups, which were statistically
indistinguishable. however, the group means on phonological awareness
showed a stepwise pattern, with the at-risk dyslexic children performing
lowest, followed by the at-risk non-dyslexic, and thereafter by the control
children. Group differences were not attributable to the group differences on
parental education (Table 4.1), as analyses with parental education as covariate
(ANCOVA for rapid naming and mANCOVAs for phonological awareness and
letter knowledge, with each two indicators) also yielded highly significant group
effects (ps < .001) and insignificant covariate effects (ps < .333). Controlling for
IQ differences (Table 4.1) in (m)ANCOVAs as above showed significant effects
of IQ (ps < .001), but all group effects remained highly significant (ps < .001).
As can be seen in Table 4.4, groups did not only differ on reading but
also on arithmetic ability. When applying a similar criterion for dyscalculia
(≤10, or Wechsler scale score ≤6.2, norm scores taken from melis, 2002) as for
dyslexia, the percentages of children identified with dyscalculia were found
to differ significantly: 42% (21/50) in the at-risk dyslexic group, 20% (16/82)
in the at-risk non-dyslexic group, and 8% (5/64) in the control group. These
differences were confirmed by a chi-square test, χ2(2, N=196) = 19.79, p < .001.
4.3.3 Prediction of Children’s Reading Skills After confirming comorbidity, we examined in the full sample how much of the
variance in school achievement can be explained by the preliteracy skills, and
whether these skills are specifically related to reading or arithmetic (see for the
correlations Table 4.5). Results of regression analyses with all predictors included
are presented in Table 4.6. The preliteracy skills together explained 43.3% of
the variance in reading; letter knowledge (akin to an autoregressor) made the
strongest contribution. After controlling for arithmetic ability, the preliteracy
skills explained an additional 21.4% of the variance, confirming the specific
relation between preliteracy and literacy skills. With respect to arithmetic, the
preliteracy skills explained 22.5% of the variance, and even made a small but
significant unique contribution to arithmetic (ΔR2 = 3.2%, p = .039) after reading
was controlled. Phonological awareness did not explain variance in arithmetic
23629 Bergen, Elsje van V.indd 101 19-12-12 12:24
Chapter 4
102
above that accounted for by rapid naming and letter knowledge, as shown by
its insignificant β. however, when letter knowledge (which correlated .73 with
phonological awareness) was excluded, rapid naming (β = .33, p < .001) as well
as phonological awareness (β = .19, p = .008) were significant.
Table 4.5 Correlations between Children’s Preliteracy Skills at 6 Years and School Achievement at 9 Years
School achievement (9yr)
Reading Arithmetic
Preliteracy skills (6yr)
Rapid naming colours .49 .41
Phonological awareness
Blending .49 .31
Segmentation .51 .34
Letter knowledge
Receptive .57 .41
Productive .57 .41
Note. All ps < .001.
Table 4.6 β-weights and Total R2 in Regression models Predicting School Achievement at 9 Years from Preliteracy Skills at 6 Years
Reading Arithmetic
Predictor Without arithmetic With arithmetic Without reading With reading
Controlling variable
Arithmetic - .30*** - -
Reading - - - .42***
Preliteracy skills (6yr)
Rapid naming colours .26*** .17** .29*** .18*
Phonological awareness a .17* .16* .03 -.04
Letter knowledge a .36*** .29*** .24* .09
Total R2 .43*** .51*** .23*** .32***
Note. a Summed standardized scores of the two measures. *p<.05; **p<.01; ***p<.001.
23629 Bergen, Elsje van V.indd 102 19-12-12 12:24
Preschool risk factors for dyslexia
103
4
Lastly, we examined the utility of parental literacy difficulties in
predicting children’s reading skills. Therefore, three regression analyses were
conducted on the complete sample, followed by three in the at-risk sample.
In the complete sample (n = 97, Table 4.7) the literacy difficulties of fathers
and mothers together explained 21.3% of the variance in children’s word-
reading fluency. After controlling for parental education, literacy difficulties
of fathers and mothers explained an additional 15.3% of the variance. Even
after accounting for parental education and all preliteracy skills of the children,
the literacy difficulties of fathers and mothers made a unique contribution,
explaining 4.3% additional variance in children’s reading fluency.
Table 4.7 Regression models Predicting Children’s Reading at 9 Years from Parental Literacy difficulties in the Complete Sample
At-risk + Control Sample
Predictor ΔR2 β
model 1
Step 1 .213***
Father’s literacy -.32***
mother’s literacy -.37***
model 2
Step 1 .065*
Parental education .26*
Step 2 .153***
Father’s literacy -.30**
mother’s literacy -.34***
model 3
Step 1 .540***
Parental education .25**
Children’s preliteracy skills
Rapid naming .34***
Phonological awareness a .21
Letter knowledge a .30*
Step 2 .043*
Father’s literacy -.20**
mother’s literacy -.16
Note. a Summed standardized scores of the two measures. *p<.05; **p<.01; ***p<.001.
23629 Bergen, Elsje van V.indd 103 19-12-12 12:24
Chapter 4
104
In the at-risk sample (n = 65, Table 4.8) we again subdivided parents
according to reading status. We anticipated that knowledge of the self-
reported literacy difficulties of the dyslexic parent would not add much
over and above the fact that they have dyslexia. This is supported by the
significantly lower SDs of the dyslexic parents (Table 4.2; Levene’s test: F(1, 132)
= 9.15, p = .003). Therefore, in subsequent analyses literacy difficulties of the
dyslexic parent were entered before those of the non-dyslexic parent, allowing
to test whether literacy difficulties of the non-dyslexic parent are related to
their offspring’s reading skills, controlling for literacy difficulties of the dyslexic
parent. It appeared that literacy difficulties of the dyslexic parent indeed
did not explain a significant amount of variance in at-risk children’s reading
fluency, but differences in literacy difficulties of the non-dyslexic parent did
explain additional variance. Over and above parental education, and literacy
difficulties of the dyslexic parent, the literacy difficulties of the non-dyslexic
parent accounted for an additional 11.5% in children’s reading fluency. In a
final analysis, differences in parental education and children’s preliteracy skills
together explained an impressive 62.8% of the variance in the reading fluency
of at-risk children, but parental literacy difficulties did not significantly add to
this prediction.
To illustrate the effect of the non-dyslexic parent on their offspring’s
risk for dyslexia, we dichotomised the literacy-difficulty measure: ‘non-
dyslexic’ parents scoring ≥62 were categorized as having literacy difficulty. It
appeared that within the group of at-risk children with a non-dyslexic parent
without literacy difficulties 30% (16/54) developed dyslexia, compared to 79%
(11/14) in the group of at-risk children with a ‘non-dyslexic’ parent with literacy
difficulties.
4.4 Discussion
The current paper studied putative risk factors for dyslexia at the level of
literacy behaviours and skills of parents, and at the level of cognitive skills of
kindergartners. This was done utilizing data of an ongoing longitudinal study
in which children with and without familial risk are followed from infancy.
2 This cut-off score was chosen because parents scoring ≥6 indicated to have difficulties on at least one of the three questions. moreover, the score distribution showed a jump between score 5 and 6.
23629 Bergen, Elsje van V.indd 104 19-12-12 12:24
Preschool risk factors for dyslexia
105
4
Table 4.8 Regression models Predicting Children’s Reading at 9 Years from Parental Literacy difficulties in the At-risk Sample
At-risk Sample
Predictor ΔR2 β
model 1
Step 1 .023
Dyslexic parent’s literacy -.15
Step 2 .135**
Non-dyslexic parent’s literacy -.37**
model 2
Step 1 .026
Parental education .16
Step 2 .017
Dyslexic parent’s literacy -.13
Step 3 .115**
Non-dyslexic parent’s literacy -.37**
model 3
Step 1 .628***
Parental education .18*
Children’s preliteracy skills
Rapid naming .29**
Phonological awareness a .13
Letter knowledge a .50***
Step 2 .020
Dyslexic parent’s literacy -.14
Step 3 .003
Non-dyslexic parent’s literacy -.06
Note. a Summed standardized scores of the two measures. *p<.05; **p<.01; ***p<.001.
23629 Bergen, Elsje van V.indd 105 19-12-12 12:24
Chapter 4
106
4.4.1 Intergenerational TransferAn important issue addressed was the intergenerational transfer of reading
skills. We measured parental reading and spelling difficulties using a rating
scale (assessed when children were 3½), which demonstrated high validity:
self-reported and tested reading performance in a subsample correlated
.84-.85. First, we investigated within the at-risk sample the effect of the non-
dyslexic parent, whose contribution has been neglected in previous work.
We found that self-reported literacy difficulties (i.e., reading and spelling
difficulties) of the non-dyslexic parent differentiated at-risk children with
and without dyslexia. moreover, in regression analyses parental literacy
difficulties predicted at-risk children’s reading fluency, even after accounting
for differences in literacy difficulties of the dyslexic parent and the educational
level of both parents. This supports the view that children who go on to
develop dyslexia have a higher genetic predisposition towards dyslexia and
that parental skills are indicative of their children’s liability (van Bergen et al.,
2012). Second, the literacy difficulties of the dyslexic parent, conversely, did
not differentiate at-risk children with and without dyslexia and did not explain
variance in children’s reading. Put differently, their self-reported difficulties did
not add to the prediction of children’s reading beyond the fact that they have
dyslexia. however, in an earlier paper about this sample (van Bergen et al.,
2012) we showed that individual differences in an objective measure of word-
reading fluency did differentiate at-risk children with and without dyslexia and
did explain variance in children’s word-reading fluency. The group difference
on this objective measure was somewhat larger than on the self-reported
measure (Cohen’s d = 0.48 vs. 0.30, respectively), presumably reflecting that
measuring skills is still more reliable than asking about skills (Snowling et al.,
2012). Third, intergenerational transfer of literacy skills was also observed in
the at-risk and control samples combined: both maternal and paternal self-
reported literacy difficulties predicted children’s reading, even after accounting
for parental education and children’s preliteracy skills.
Only recently an interest has emerged in the predictive value of
parental skills for the development of children’s reading skills. Previous familial-
risk studies have found associations between reading accuracy, reading fluency,
spelling, phonological skills, and vocabulary of the dyslexic parent and their
offspring’s reading outcome (Torppa et al., 2011; van Bergen et al., 2011; van
Bergen et al., 2012). most importantly, our findings are the first to demonstrate
23629 Bergen, Elsje van V.indd 106 19-12-12 12:24
Preschool risk factors for dyslexia
107
4
the importance of the literacy skills of the parent without dyslexia. They
predicted children’s reading beyond the effect of the dyslexic parent. Within
the children with a dyslexic parent, the risk for dyslexia was 2½ times higher if
the other parent had literacy difficulties as well.
4.4.2 Home Literacy EnvironmentInvestigation of environmental influences did not indicate links between
children’s home literacy environment and children’s reading outcome. Before
school entry, the home environment of at-risk children with and without
dyslexia did not differ in terms of shared reading, cognitive stimulation, how
much parents read and write, newspaper subscriptions, and number of books,
although control families tended to have more newspapers and books. Likewise,
in previous familial risk studies no group differences have been found in shared
reading, neither in children’s access to print, like library membership or number
of books at home (Elbro et al., 1998; Torppa et al., 2007; van Bergen et al., 2011).
Although many educators and parents have strong beliefs about the impact
of story book reading on later reading success, research repeatedly fails to find
such an association (see for a review Sénéchal & Young, 2008), which is in line
with the present findings. The absence of a relation between preschool home
literacy environment and subsequent reading attainment found in the current
and previous familial-risk studies is in agreement with behavioural genetic
studies showing low levels of shared environment influence, whereas up to
80% is due to genetic influences (e.g., Byrne et al., 2009; haworth et al., 2009).
however, the possibility remains that environmental influences in familial-
risk studies are downplayed because they are not measured thoroughly, and
because familial-risk studies are based on voluntary samples and therefore
might include a somewhat restricted range of environments.
4.4.3 Preliteracy Skills and their SpecificityIn line with the literature, in kindergarten the children who went on to develop
dyslexia (assessed in Grade 3) were impaired on rapid naming, phonological
awareness, and letter knowledge. Interestingly, the at-risk children without
later dyslexia had age-adequate rapid naming and letter knowledge, but
were mildly impaired on phonological awareness. Despite adequate letter
knowledge and rapid naming before the start of formal reading, these children
read less fluently in Grade 2 (van Bergen et al., 2012) and Grade 3 (Table 4.4).
23629 Bergen, Elsje van V.indd 107 19-12-12 12:24
Chapter 4
108
The different results for letter knowledge and phonological awareness were
unexpected given the often observed reciprocal development between these
skills (e.g., de Jong, 2007; Wagner, Torgesen, & Rashotte, 1994). however,
teaching of letters by parents affects children’s letter knowledge (Torppa,
Poikkeus, Laakso, Eklund, & Lyytinen, 2006). The dyslexic families in our study
are possibly aware of their children’s risk for dyslexia and might have paid extra
attention to teaching letters, which might explain the good letter knowledge
of the at-risk non-dyslexic children. Alternatively, the learning capacity of these
children is less affected and more similar to that of control children, making
them more likely to pick up letter-sound knowledge in their environment.
The finding of weak phonological awareness in combination with
good rapid naming of the at-risk non-dyslexics in kindergarten mirrors the
pattern found in this group at the end of second grade (van Bergen et al., 2012),
indicating longitudinal stability. Our finding highlights that phonological
awareness and rapid naming have partially unique influences on reading
ability and disability, as proposed by the double-deficit theory (Wolf & Bowers,
1999). Although their correlation with later reading was remarkably similar
(.5), group differences showed different patterns for the two precursors and
the regression analyses revealed partly unique contributions to reading. In
line with this, van den Boer, de Jong, and haentjens-van meeteren (in press)
recently demonstrated differential effects of phonological awareness and
rapid naming on the developing reading system. In an experiment with words
of different lengths, they modeled the amount of serial (or letter-by-letter)
processing as indexed by the time needed to read each additional letter (the
word length effect). They disentangled the word length effect from overall
reading speed and showed that phonological awareness is associated with
the degree of serial processing, whereas rapid naming is related to overall
reading speed, irrespective of the degree of serial processing. Van den Boer
et al. argue that “poor phonological awareness results in poor build-up of
orthographic representations, and therefore in continued reliance on a serial
processing strategy”. Translating their conclusions to the current study, the at-
risk non-dyslexic group is hypothesized to have some difficulty with building
up orthographic knowledge (a requisite for sight-word reading), but not with
fast processing of orthographic knowledge and fast retrieval of phonological
codes. The at-risk dyslexic group seems to be impaired in all these elements.
Our study once more confirmed that poor rapid naming, phonological
23629 Bergen, Elsje van V.indd 108 19-12-12 12:24
Preschool risk factors for dyslexia
109
4
awareness, and letter knowledge in kindergarten are cognitive risk factors for
dyslexia. With regard to arithmetic, our study showed a clear stepwise pattern
of (co)morbidity with dyscalculia. Rates of dyscalculia in the at-risk dyslexic,
at-risk non-dyslexic, and control groups were 42%, 20%, and 8%, respectively.
The demonstrated association between reading (dyslexia) and arithmetic
(dyscalculia) raises the question as to whether the three cognitive risk factors
for dyslexia are specific for reading. Our analysis controlling for arithmetic
showed that all three precursors uniquely captured variance in later reading
skills (21% in all). Visa versa, only rapid naming had a small (3%) but significant
influence on arithmetic, while holding reading constant (in agreement with de
Jong & van der Leij, 1999; and hecht et al., 2001), despite the clear associations
between the three preliteracy skills and subsequent arithmetic achievement.
These results suggest that the preliteracy trio is rather specifically related to the
academic domain of reading. Note that the measurement methods of reading
(word decoding fluency) and arithmetic (mental arithmetic fluency) closely
resemble each other. As lower overlap between word reading and broader
mathematical skills are to be expected, this strengthens the finding that the
preliteracy skills, especially letter knowledge and phonological awareness, are
better predictors of reading.
Consistent with the hypothesis that the ease of arithmetic fact retrieval is
affected by the quality of phonological representations (Boets & De Smedt, 2010;
De Smedt et al., 2010) we found longitudinal relations between phonological
processing and arithmetic. It should be noted that this hypothesis, that explains
why reading and arithmetic are associated, is not directly testable with the
current arithmetic task, since this graded task does not differentiate between
problems solved by fact retrieval and by procedural strategies (although fact
retrieval might be an element in procedural strategies as well). Rather, children
likely started the task using fact retrieval and shifted somewhere along to
using procedures. Based on the findings of De Smedt and colleagues higher
correlations with arithmetic might be expected when arithmetic was tested
using only problems with a high probability of being solved by retrieval. Since
our findings showed higher relations for rapid naming than for phonological
awareness in predicting arithmetic, future arithmetic studies should include
rapid naming as well. The importance of efficient retrieval of phonological
codes (as tapped by rapid naming) for arithmetic efficiency has been shown
before (e.g., van der Sluis, de Jong, & van der Leij, 2007) and supports the view
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that efficient retrieval of arithmetic facts leaves sufficient memory resources
for the selection and implementation of appropriate procedures (hecht et al.,
2001). Although our data showed relations between phonological processing
and subsequent arithmetic, relations were stronger for subsequent reading.
Our findings fit well in Pennington’s multiple deficit model (2006).
Pennington’s model is built on the multifactorial and probabilistic aetiology of
developmental disorders. It postulates that a cognitive developmental disorder
is the behavioural outcome of multiple interacting risk and protective factors.
Some of these factors influence several disorders (causing comorbidity) and
some are specific to one particular disorder. The investigated preliteracy skills
were good predictors of primarily subsequent reading, although they also
predicted subsequent arithmetic to some extent, especially rapid naming.
hence, deficits in phonological awareness and rapid naming can be viewed as
two of the multiple cognitive deficits that increase the probability of dyslexia.
Poor letter knowledge can be seen as a third underlying cognitive deficit, or as
an earlier developmental manifestation of dyslexia (akin to an autoregressor of
reading). Because rapid naming was predictive of later reading and arithmetic
in the current and previous studies, slow rapid naming might well be a cognitive
deficit in common to dyslexia and dyscalculia. Shared deficits can account for
the frequent co-occurrence of these developmental disorders.
The parent-child resemblance reported in the current and other
recent literature seems to imply that the multiple cognitive deficit model of
Pennington (2006) can be extended to an intergenerational multiple cognitive
deficit model. As we argued in an earlier paper (van Bergen et al., 2012), literacy
abilities of parents might be viewed as indicators of their offspring’s risk or
liability for literacy difficulties, since parents provide their offspring with their
genetic and family endowment. In Pennington’s model the focus is on a specific
individual, whereas we propose to include cognitive factors of parents. In the
intergenerational multifactorial deficit model cognitive skills of both parents
can be seen as risk and protective factors for their offspring’s predisposition
towards developmental disorders. These cognitive factors may include skills
like reading, spelling, arithmetic, and language, or may include their cognitive
underpinnings like phonological awareness, rapid-naming ability, verbal short-
term memory, number sense, and executive functioning. On each of these
continua parents occupy a position in multivariate space. The constellation of
these factors of both parents gives an indication of their offspring’s liability to
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4
develop certain cognitive disorders. The findings from our studies suggest that
parents confer risk factors for dyslexia predominantly via genetic rather than
environmental pathways. more research investigating the cognitive risks that
parents pass on to their children is needed to further develop the proposed
intergenerational multiple deficit model. Such studies will help to contribute
to unravelling the aetiology of developmental disorders. Additionally, such
research could lead to better identifying the children at-risk for cognitive
developmental disorders and provide them timely with appropriate support.
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4.5 References
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Boets, B., De Smedt, B., Cleuren, L., Vandewalle, E., Wouters, J., & Ghesquière, P. (2010). To-wards a further characterization of phono-logical and literacy problems in Dutch-spea-king children with dyslexia. British Journal of Developmental Psychology, 28, 5-31.
Brus, B. T., & Voeten, m. J. m. (1972). Eén-mi-nuut-test [One-minute-test]. Lisse: Swets & Zeitlinger.
Byrne, B., Coventry, W. L., Olson, R. K., Samu-elsson, S., Corley, R., Willcutt, E. G., . . . De-Fries, J. C. (2009). Genetic and environmen-tal influences on aspects of literacy and language in early childhood: Continuity and change from preschool to Grade 2. Journal of Neurolinguistics, 22(3), 219-236.
de Jong, P. F. (2007). Phonological awareness and the use of phonological similarity in letter-sound learning. Journal of Experi-mental Child Psychology, 98(3), 131-152.
de Jong, P. F., & van der Leij, A. (1999). Speci-fic contributions of phonological abilities to early reading acquisition: Results from a Dutch latent variable longitudinal study. Journal of Educational Psychology, 91(3), 450-476.
De Smedt, B., Taylor, J., Archibald, L., & Ansari, D. (2010). how is phonological processing related to individual diffe-rences in children’s arithmetic skills? De-velopmental Science, 13(3), 508-520. doi: 10.1111/j.1467-7687.2009.00897.x
de Vos, T. (1992). Tempo test rekenen [Arith-metic speed test]. Lisse: Swets & Zeitlinger.
Elbro, C., Borstrøm, I., & Petersen, D. K. (1998). Predicting dyslexia from kindergarten: The importance of distinctness of pho-nological representations of lexical items. Reading Research Quarterly, 33(1), 36-60.
Eleveld, m. A. (2005). At risk for dyslexia: The role of phonological abilities, letter know-ledge, and speed of serial naming in early intervention and diagnosis. Antwerpen/Apeldoorn: Garant.
haworth, C. m. A., Kovas, Y., harlaar, N., hayiou-Thomas, m. E., Petrill, S. A., Dale, P. S., & Plomin, R. (2009). Generalist genes and learning disabilities: A multivariate genetic analysis of low performance in reading, mathematics, language and ge-neral cognitive ability in a sample of 8000 12-year-old twins. Journal of Child Psycho-logy and Psychiatry, 50(10), 1318-1325. doi: 10.1111/j.1469-7610.2009.02114.x
hecht, S. A., Torgesen, J. K., Wagner, R. K., & Ras-hotte, C. A. (2001). The relations between phonological processing abilities and emer-ging individual differences in mathematical computation skills: A longitudinal study from second to fifth grades. Journal of Expe-rimental Child Psychology, 79(2), 192-227.
Landerl, K., & moll, K. (2010). Comorbidity of learning disorders: Prevalence and fami-lial transmission. Journal of Child Psycho-logy and Psychiatry, 51(3), 287-294.
Leseman, P. P. m. (1994). Preventie van on-derwijsachterstand [Prevention of edu-cational backwardness]. In J. Rispens, P. Goudena & J. Groenendaal (Eds.), Van kindmodel naar modelkind. [From child model to model child.] (pp. 63-92). Gronin-gen: Stichting Kinderstudies.
melis, G. (2002). DLE boek [DLE book] Edufor-ce/Swets & Zeitlinger B.V.
Pennington, B. F. (2006). From single to mul-tiple deficit models of developmental disorders. Cognition, 101(2), 385-413.
Pennington, B. F., & Lefly, D. L. (2001). Early reading development in children at family risk for dyslexia. Child Development, 72(3), 816-833.
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Scarborough, h. S. (1990). Very early langua-ge deficits in dyslexic children. Child Deve-lopment, 61(6), 1728-1743.
Sénéchal, m., & Young, L. (2008). The effect of family literacy interventions on children’s acquisition of reading from kindergarten to Grade 3: A meta-analytic review. Review of Educational Research, 78(4), 880-907. doi: 10.3102/0034654308320319
Sigel, I. E. (1982). The relation between pa-rental distancing strategies and the child’s cognitive behavior. In L. m. Laosa, & I. E. Si-gel (Eds.), Families as learning environments for children. New York: Plenum Press.
Snowling, m. J., Dawes, P., Nash, h., & hulme, C. (2012). Validity of a protocol for adult self-report of dyslexia and related diffi-culties. Dyslexia, 18(1), 1-15. doi: 10.1002/dys.1432
Snowling, m. J., Gallagher, A. m., & Frith, U. (2003). Family risk of dyslexia is continuo-us: Individual differences in the precurs-ors of reading skill. Child Development, 74(2), 358-373.
Stock, P., Desoete, A., & Roeyers, h. (2010). Detecting children with arithmetic disa-bilities from kindergarten: Evidence from a 3-year longitudinal study on the role of preparatory arithmetic abilities. Journal of Learning Disabilities, 43(3), 250-268. doi: 10.1177/0022219409345011
Torppa, m., Eklund, K., van Bergen, E., & Lyytinen, h. (2011). Parental literacy pre-dicts children’s literacy: A longitudinal family-risk study. Dyslexia, 17(4), 339-355. doi: 10.1002/dys.437
Torppa, m., Lyytinen, P., Erskine, J., Eklund, K., & Lyytinen, h. (2010). Language de-velopment, literacy skills, and predictive connections to reading in Finnish children with and without familial risk for dyslexia. Journal of Learning Disabilities, 43(4), 308-321. doi: 10.1177/0022219410369096
Torppa, m., Poikkeus, A., Laakso, m., Eklund, K., & Lyytinen, h. (2006). Predicting delay-
ed letter knowledge development and its relation to Grade 1 reading achievement among children with and without familial risk for dyslexia. Developmental Psycho-logy, 42(6), 1128-1142.
Torppa, m., Poikkeus, A., Laakso, m., Tol-vanen, A., Leskinen, E., Leppänen, P. h. T., . . . Lyytinen, h. (2007). modeling the early paths of phonological awareness and factors supporting its development in children with and without familial risk of dyslexia. Scientific Studies of Reading, 11(2), 73-103.
van Bergen, E., de Jong, P. F., Plakas, A., maas-sen, B., & van der Leij, A. (2012). Child and parental literacy levels within families with a history of dyslexia. Journal of Child Psychology and Psychiatry, 53(1), 28-36. doi: 10.1111/j.1469-7610.2011.02418.x
van Bergen, E., de Jong, P. F., Regtvoort, A., Oort, F., van Otterloo, S., & van der Leij, A. (2011). Dutch children at family risk of dyslexia: Precursors, reading develop-ment, and parental effects. Dyslexia, 17(1), 2-18. doi: 10.1002/dys.423
van den Boer, m., de Jong, P. F., & haentj-ens-van meeteren, m. (in press). modeling the length effect: Specifying the relation with visual and phonological correlates of reading. Scientific Studies of Reading, doi: 10.1080/10888438.2012.683222
van den Bos, K. P. (2003). Serieel benoemen en woorden lezen [Serial naming and word reading]. Groningen: Rijksunversiteit Gro-ningen.
van den Bos, K. P., lutje Spelberg, h. C., Scheepstra, A. J. m., & de Vries, J. R. (1994). De klepel: Een test voor de leesvaardigheid van pseudowoorden [The klepel: A test for the reading skills of pseudowords]. Lisse: Swets & Zeitlinger.
van der Sluis, S., de Jong, P. F., & van der Leij, A. (2007). Executive functioning in children, and its relations with reasoning, reading, and arithmetic. Intelligence, 35(5), 427-449.
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Verhoeven, L. (1993a). Grafementoets. Toets voor auditieve synthese. Handleiding [Grap-heme test. Test for phoneme blending. Ma-nual]. Arnhem, the Netherlands: Cito.
Verhoeven, L. (1993b). Toets voor auditieve analyse. Fonemendictee. Handleiding [Test for phoneme segmentation. Dictation of phonemes. Manual]. Arnhem, the Nether-lands: Cito.
Verhoeven, L. (2000). Components in early second language reading and spelling. Scientific Studies of Reading, 4(4), 313-330.
Verhoeven, L. (2002). Passieve letterkennis [Receptive letter knowledge]. Arnhem: Cito.
Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-re-lated phonological processing abilities: New evidence of bidirectional causality from a latent variable longitudinal study. Developmental Psychology, 30(1), 73-87.
Wolf, m., & Bowers, P. G. (1999). The dou-ble-deficit hypothesis for the develop-mental dyslexias. Journal of Educational Psychology, 91(3), 415-438.
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Essentially, all models are wrong, but some models are useful.
- George E.P. Box -
IQ of four-year-olds who go on to develop dyslexia
van Bergen, E., de Jong, P.F., Maassen, B., Krikhaar, E., Plakas, A., & van der Leij, A. (in revision). IQ of four-year-olds who go on to develop dyslexia.
Chapter
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IQ of four-year-olds who go on to develop dyslexia
van Bergen, E., de Jong, P.F., Maassen, B., Krikhaar, E., Plakas, A., & van der Leij, A. (in revision). IQ of four-year-olds who go on to develop dyslexia.
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Abstract
Do children who go on to develop dyslexia show normal verbal and
nonverbal development before reading onset? According to the
aptitude-achievement discrepancy model, dyslexia is defined as a
discrepancy between intelligence and reading achievement. One of the
underlying assumptions is that the general cognitive development of
children who fail to learn to read has been normal. The current study
tests this assumption. Additionally, we investigated whether possible
IQ deficits are uniquely related to later reading or are also related to
arithmetic. Four-year-olds (N = 212) with and without familial risk for
dyslexia were assessed on 10 IQ subtests. Reading and arithmetic skills
were measured four years later, at the end of Grade 2. Relative to the
controls, the at-risk group without dyslexia had subtle impairments
only in the verbal domain, while the at-risk group with dyslexia lagged
behind across IQ-tasks. Nonverbal IQ was associated with both reading
and arithmetic, whereas verbal IQ was uniquely related to later reading.
The children who went on to develop dyslexia performed relatively
poorly in both verbal and nonverbal abilities at age 4, which challenges
the discrepancy model. Furthermore, we discuss possible causal and
epiphenomenal models explaining the links between early IQ and later
reading.
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5.1 Introduction
The first case reports of developmental dyslexia describe children who fail
to master literacy despite normal development in other cognitive domains
(e.g., Kerr, 1897; morgan, 1896). Therefore, the reading problems they faced
were unexpected within the context of their otherwise normal cognitive
development. This ‘unexpectedness’ is translated into diagnostic criteria that
take intelligence measures into account. According to the so-called aptitude-
achievement discrepancy model, dyslexia is defined as a discrepancy between
intelligence and reading achievement (Fletcher, Lyon, Fuchs, & Barnes, 2007).
Although several researchers have advised against the use of IQ-scores in the
diagnosis of learning disorders (e.g., Fletcher, Coulter, Reschly, & Vaughn, 2004;
Siegel, 1989), it is still commonly used.
One of the assumptions underlying a discrepancy model is that the
general cognitive development of children who fail to learn to read has been
normal. This assumption is rarely tested, however. After the start of reading,
the assumption cannot be adequately examined because the development
of verbal and nonverbal IQ of children with reading problems might have
been adversely affected by less print exposure as a consequence of their poor
reading (Cunningham & Stanovich, 1997). hence, longitudinal studies that start
before reading onset are required to investigate the issue of normal cognitive
development.
There are several prospective studies assessing cognitive precursors of
reading ability and disability, but these studies usually match groups of reading-
disabled and typically-reading children on IQ (e.g., de Jong & van der Leij, 2003)
and are therefore not suitable for the present purpose. A more appropriate
design is to follow children at-familial risk of dyslexia, that is, children with
dyslexic relatives. The current study is a familial-risk study that reports verbal and
nonverbal IQ-data of four-year-olds who went on to become dyslexic. Do these
children indeed show normal cognitive development? To pursue this question
these children were compared to children with and without a familial risk who
did not develop dyslexia on nonverbal and verbal IQ (i.e., language skills).
In a landmark study, Scarborough (1990) compared at-risk children who
later became dyslexic with control children. She found that the at-risk dyslexic
children were deficient in several language skills from age 2½ onwards. Slow
language development in the at-risk children with dyslexia was also shown by
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a prospective study of Snowling, Gallagher, and Frith (2003). When the children
were about 4 years old, they had deficiencies in vocabulary, narrative skills,
and verbal short-term memory. At this age, nonverbal IQ was not significantly
lower, despite the observed medium effect size of .5. Yet at 6 years of age both
verbal and nonverbal IQ of the dyslexic children were significantly lower than
that of their peers without familial risk by more than one standard deviation.
Furthermore, the at-risk children who did not become dyslexic showed at both
measurement occasions normal nonverbal IQ, but they had a slightly lower
verbal IQ than the children without familial risk. This latter difference had a
medium effect size, but missed significance possibly due to a lack of power.
Recently, Torppa, Lyytinen, Erskine, Eklund, and Lyytinen (2010)
examined very early language markers of dyslexia in a familial-risk study. Various
receptive and expressive language abilities were examined in this group between
1½ and 5½ years of age. From age 2 onwards, the at-risk and non at-risk children
who later became dyslexic showed impairments relative to the controls. At-risk
children who became normal readers tended to show weak expressive syntax
and vocabulary at age 5, although this difference (d = .4) was not significant.
Torppa et al. also found that at age 2, the future dyslexic children had significantly
lower full-scale IQ than the other two groups, with a medium effect size. At age
5 they performed significantly lower (d = 1) than the control group on verbal IQ.
however, at this age nonverbal IQ was similar in all groups.
Taken together, these studies show that children with dyslexia typically
had problems with language acquisition in the preschool years. however, the
findings in the few studies in which lags in nonverbal abilities were examined
are less consistent. Snowling et al. (2003) found that children who later faced
dyslexia performed slightly lower at age 4 and evidently lower at age 6. In
contrast, the children with dyslexia in the study of Torppa et al. (2010) had
normal nonverbal IQ before reading onset.
In the current study we compared at-risk dyslexic, at-risk non-dyslexic,
and non-dyslexic control children without familial risk, on several subtests
of verbal and nonverbal IQ when they were 4 years of age. In addition, we
addressed two more issues. Firstly, whereas various abilities within the verbal
domain have been considered, a differentiation among nonverbal abilities has
not been made. In the current study different aspects of nonverbal IQ were
examined. As a second issue, we considered whether the possibly lower IQ of
children who go on to develop dyslexia specifically relates to their later reading
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difficulties, or whether their lower IQ is due to (subclinical) problems that are
comorbid to dyslexia. We hypothesized that this could especially be true for
a possible lower nonverbal IQ: in models of decoding, verbal abilities (e.g.,
the phonological lexicon in dual-route models or the semantic component in
connectionist models) do explicitly play a part, whereas nonverbal abilities do
not. Because dyslexia and dyscalculia often co-occur (Landerl & moll, 2010),
we chose to include arithmetic ability in the present study. In addition, basic
arithmetic skills are, besides basic reading skills, arguably the most important
foundational skills acquired during the early school years.
5.2 Method
5.2.1 ParticipantsThree groups of children of the Dutch Dyslexia Programme (see van Bergen,
de Jong, Plakas, maassen, & van der Leij, 2012) were involved in this study. The
at-risk dyslexic group consisted of 44 dyslexic children with a familial risk for
dyslexia. The at-risk non-dyslexic group comprised 100 children with a familial
risk but without dyslexia. Finally, the control group included 68 children without
a familial risk and without dyslexia. Two children without a familial risk were
categorized as dyslexic and were omitted from the study. All at-risk children
had at least one parent and one close relative with dyslexia. On average, the
scores of the dyslexic parents belonged to the bottom 5% on reading fluency.
A detailed description of the assessment of familial risk is given in van Bergen
et al. (2012). Informed consent was obtained from all parents. All but one child
attended mainstream education.
At the end of Grade 2, children were considered dyslexic when their
score on the word-reading fluency task described below corresponded to the
weakest 10% in the population. In an earlier paper we reported that these dyslexic
children were impaired in reading accuracy and fluency, spelling, phonological
awareness, and rapid naming (van Bergen et al., 2012). In the present paper we
report new data of the same sample. Table 5.1 presents characteristics of the three
groups. Percentages of boys were similar across groups and age differences were
not significant. however, parental education (averaged over both parents) was
lower in the at-risk than in the control group. Furthermore, the at-risk dyslexic
group lagged behind on arithmetic (bottom Table 5.2).
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Table 5.1 Children’s Characteristics by Group
At-risk
Dyslexic Non-dyslexic Control p
Sample size 44 100 68
No (%) of boys 27a (61%) 57
a (57%) 41
a (60%) .855
Age in months
At nonverbal ability assessment 48.20a (2.74) 47.68
a (3.02) 47.07
a (2.25) .095
At verbal ability assessment 53.19a (1.60) 53.01
a (1.45) 52.69
a (1.07) .144
Parental education 3.33a (0.86) 3.53
a (0.78) 4.17
b (0.72) <.001
Note. The group means are given with standard deviations in parentheses. Numbers and means in the same row that do not share subscripts differ at p < .05 on the χ2-test or Tukey’s test. No = number.
5.2.2 Measuresmeasures were selected for nonverbal IQ (at age 4), verbal IQ (at age 4½),
and school achievement (at age 8; end second grade). The reliabilities of the
measures are listed in Table 5.2.
Nonverbal IQ
The nonverbal-IQ test battery of Tellegen, Winkel, Wijnberg-Williams, and
Laros (1998) is widely used in the Netherlands and Flanders because of its high
psychometric quality (Evers, van Vliet-mulder, & Groot, 2000). It was originally
developed for deaf children; hence, measures are genuinely nonverbal. For
hearing children spoken language is used during assessment to create a natural
situation, but the verbal instructions do not provide additional information. The
test battery is composed of six subtests. The performance tasks (block design,
patterns, and object assembly) tap visual-spatial processing, visual-motor
coordination, and persistence. Solutions can be obtained by trial-and-error. The
reasoning tasks (picture completion, categories, and analogies) measure fluid
reasoning about concrete and abstract categories. From the presented material
a principle has to be detected and subsequently applied to new material.
Each subtest consists of two parts, with in total 14 to 17 items
of increasing difficulty. In Part 1 of each subtest, the first few items are first
demonstrated by the tester to show the requirements of the items. Part
2 of each subtest starts with an item for practice. The items in Part 2 of the
Performance tests are stopped after 2’30 minutes for practical reasons. All test
items are scored as either correct or incorrect. Following an incorrect response,
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the tester shows the correct solution so the child understands the purpose of
the test without verbal explanation. Each subtest contains a discontinuation
rule. Scaled subtest scores were derived from the manual. The scores combine
to form a nonverbal-IQ score with a reliability of .90. The six subtests are
described below.
Performance tests.
Block design. The child arranges coloured squares according to a
design depicted in a booklet. The difficulty increases from designs with only
red coloured squares to designs with red, yellow, and red/yellow squares.
Patterns. The test involves copying abstract line patterns from a
booklet. The child has to draw the patterns by connecting dots. It starts with
simple patterns (e.g., connecting 2 dots) and increases to patterns that require
connecting 4, 9, or 16 dots. Drawings are scored according to whether the right
dots are connected, rather than manual dexterity.
Object assembly. The test requires the child to do jigsaw puzzles of
three to six pieces. The puzzle images include familiar objects or animals. In
Part 1 the puzzles come with the help of a picture and a frame to fit the puzzle
in. In later items no frame and picture are provided.
Reasoning tests.
Picture completion. The child completes a set of pictures of which parts
are missing. In the items in Part 1 four pictures are shown. Of each picture a part
is missing (e.g., four different animals are depicted without legs). The missing
parts are depicted on four cards. Part 2 consists of items depicting a scene (e.g.,
a boy in a kitchen) with one or two elements missing. The child completes the
picture with the appropriate cards.
Categories. In this test the child has to detect the underlying concept
of a set of pictures. The first part requires the child to sort cards in two semantic
categories, like dolls and teddy bears. In the second part three pictures are
shown that belong to the same semantic category (e.g., fruit). Out of five cards
two have to be picked that also belong to this category.
Analogies. In Part 1, pieces have to be sorted in two small trays. The
pieces differ on three dimensions: form (circles, squares, and triangles), colour
(blue and red), and size (small and large). The booklet shows the sorting
principle above the trays. In Part 2 the same changing principle as in the
example analogy has to be applied. For instance, the example analogy depicts
a small blue triangle that is transformed into a big blue triangle, meaning a
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change in the dimension size.
Verbal IQ
We selected the adapted and standardized Dutch version of the Reynell
Developmental Language Scales (van Eldik, Schlichting, lutje Spelberg, van der
meulen, & van der meulen, 2001) and three tests from a language-skill battery
(Schlichting, van Eldik, lutje Spelberg, van der meulen, & van der meulen,
2003). All items are scored as either correct or incorrect. Each subtest contains
a discontinuation rule. Standard scores were derived from the manuals.
Language comprehension. The test assesses sentence comprehension.
The sentences concern relations between objects and increase in complexity.
The child has to show the meaning of the sentence using the objects provided,
for example “Put the knife on the plate”. There are 87 items. The test’s reliability
is .90.
Expressive vocabulary. The child is required to name pictured objects
or to complete the description of a picture (e.g., “That are trains. This one is
long and this one is…” [short]). In this way the knowledge of 62 nouns, verbs,
adjectives, adverbs, and prepositions was assessed. The reliability is .90.
Expressive syntax. In this test syntactic structures are elicited using
pictures and objects. In the first 12 items the child has to repeat a sentence
literally. In later items a sentence needs to be repeated with a variation, or a
sentence needs to be completed. The sentences are embedded in a short story.
For example, the experimenter shows a picture of a man and a woman and
two apples, and says “They are both going to eat an apple. This one is for him
and …” after which the child says: “this one is for her”. There are 40 items. The
reliability is .86.
Verbal short-term memory. Children repeat series of one to six words.
Words are one-syllabic concrete nouns. The test starts with a practice and a
test item of one word. Subsequently, blocks of items increasing from two to
five words are given. Each block consists of one practice and three test items.
Finally, there are two 6-word items, adding up to 15 items. The reliability is .79.
School Achievement
Reading. Word-reading fluency was assessed using a list comprising 150
monosyllabic words (Verhoeven, 1995). All words contain at least one consonant
cluster. The score is the number of words read correctly within one minute. This
test is widely used in Dutch education. Its reliability for this age is .96.
Arithmetic. Two subtests of the arithmetic tempo test (de Vos, 1992)
23629 Bergen, Elsje van V.indd 124 19-12-12 12:24
IQ of four-year-olds who go on to develop dyslexIa
125
5
were administered: addition and subtraction. Each subtest includes 40
problems of increasing difficulty. Per problem two operands have to be added
or subtracted. All operands and outcomes are below 100. The score was the
number of correctly solved problems within one minute. The reliability of each
subtest is .93 (Stock, Desoete, & Roeyers, 2010). A total score was computed as
the sum of the standard scores.
5.2.3 ProcedureTesting lasted one hour at age 4, two hours at age 4½, and two hours at age
8. The tests at age 8 reported in this paper are part of a larger test battery (see
van Bergen et al., 2012, for an overview and results). Tests were administered
individually in a quiet room at university.
5.3 Results
Group means on the IQ subtests are depicted in Figure 5.1 and 5.2. Table 5.2 gives
the descriptive statistics of the reading and arithmetic measures. Correlations
among all measures can be found in the online appendix. Distributions were
approximately normal and missingness varied from 0 to 4%. The results are
presented in three sections. First, we consider the factor structure of the
various verbal and nonverbal IQ tests. Next, we relate IQ at the age of 4 to
reading and arithmetic achievement at the end of second grade. Finally, we
examined differences in verbal and nonverbal IQ among at-risk dyslexic, at-risk
non-dyslexic, and non-dyslexic control children without familial risk.
5.3.1 Structure of the IQ TestsConfirmatory factor analysis was used to test the hypothesised structure of the 10
IQ-subtests in the full sample. The analyses were conducted with mplus (version
2.21, muthén & muthén, 2007) using maximum likelihood estimation. One latent
variable was specified for verbal IQ (four indicators) and two for nonverbal IQ: a
performance factor and a reasoning factor, each with three indicators. Allowing
a correlated error between object assembly and categories, this model had an
excellent fit: χ2 (31, N=212) = 34.24, p = .315, RmSEA = .022 [90% CI: .000-.058],
SRmR = .037, CFI = .995. A two-group model with an at-risk and a non at-risk
group also had an excellent fit and yielded virtually identical parameter estimates.
23629 Bergen, Elsje van V.indd 125 19-12-12 12:24
Chapter 5
126
9
10
11
12
13
Blockdesign
Patterns Objectassembly
Picturecompletion
Categories Analogies
At-risk dyslexicAt-risk non-dyslexicControl
100
105
110
115
Languagecomprehension
Expressivevocabulary
Expressive syntax Verbal short-termmemory
At-risk dyslexicAt-risk non-dyslexicControl
Figure 5.1. Children’s Performance on Nonverbal Ability measures by Group9
10
11
12
13
Blockdesign
Patterns Objectassembly
Picturecompletion
Categories Analogies
At-risk dyslexicAt-risk non-dyslexicControl
100
105
110
115
Languagecomprehension
Expressivevocabulary
Expressive syntax Verbal short-termmemory
At-risk dyslexicAt-risk non-dyslexicControl
Figure 5.2. Children’s Performance on Verbal Ability measures by Group
23629 Bergen, Elsje van V.indd 126 19-12-12 12:24
IQ of four-year-olds who go on to develop dyslexIa
127
5
Tab
le 5
.2 C
hild
ren’
s Pe
rfor
man
ce b
y G
roup
on
IQ F
acto
r Sco
res
and
mea
sure
s of
Sch
ool A
chie
vem
ent
At-
risk
Dys
lexi
cN
on-d
ysle
xic
Con
trol
Effec
t siz
e (C
ohen
’s d)
MSD
MSD
MSD
RD v
s. R
ND
RD v
s. C
RND
vs.
C
Non
verb
al a
bili
ty (4
yr)
Perf
orm
ance
-0.4
2 a0.
820.
12b
0.86
0.10
b0.
930.
580.
57-0
.01
Reas
onin
g-0
.38 a
0.91
0.04
b0.
840.
18b
0.82
0.52
0.69
0.17
Verb
al a
bili
ty (4
½ y
r)-0
.45 a
0.93
-0.0
1 b0.
930.
30b
0.80
0.55
0.94
0.39
Scho
ol a
chie
vem
ent (
end
Gra
de 2
)
Read
ing
19.2
1 a7.
1657
.01 b
19.4
967
.50 c
18.9
02.
002.
560.
56
Arit
hmet
ic
Add
ition
12.1
8 a4.
2816
.12 b
4.36
17.2
1b4.
250.
931.
180.
26
Sub
trac
tion
9.91
a4.
6513
.85 b
4.91
14.8
5 b3.
901.
011.
270.
26
Not
e. N
umb
ers
and
mea
ns in
the
sam
e ro
w th
at d
o no
t sha
re s
ubsc
ripts
diff
er a
t p <
.05
on T
ukey
’s p
ost-
hoc
test
. Coh
en’s
d is
cal
cula
ted
usin
g th
e SD
s of
the
cont
rols
. RD
= a
t-ris
k dy
slex
ic, R
ND
= a
t-ris
k no
n-dy
slex
ic, C
= c
ontr
ol.
23629 Bergen, Elsje van V.indd 127 19-12-12 12:24
Chapter 5
128
5.3.2 The Relationship of IQ at Age 4 and Second-Grade School Achievement Structural equation modelling was used to examine a model of the relationship
of the three IQ factors with later reading and arithmetic achievement. In this
model, the three IQ factors were specified to load on one second-order factor,
labelled IQ. Note that with three factors this second-order model is equivalent
to the three-factor model that was examined in the previous section. Reading
and arithmetic achievement were also specified to load (with equal factor
loadings) on a common factor, labelled School Achievement. Finally, School
Achievement was regressed on IQ.
The fit of this model was adequate: χ2 (50, N=212) = 69.57, p = .035,
RmSEA = .043 [.012-.066], SRmR = .047, CFI = .974. however, the model fit was
improved significantly by adding a direct effect from verbal IQ to reading (Δχ2(1)
= 7.20, p = .007). The final model (presented in Figure 5.3) provided a good fit to
the data: χ2 (49, N=212) = 62.37, p = .095, RmSEA = .036 [.000-.060], SRmR = .043,
CFI = .982. Note that the factor School Achievement reflects the variance that
arithmetic and reading have in common. The significant extra path from verbal
IQ to reading indicates that verbal abilities predict unique variance in reading
after differences in arithmetic have been controlled.
5.3.3 Differences Among Reading GroupsThe at-risk dyslexic, at-risk non-dyslexic, and control children were compared
on the three IQ factors (the verbal, performance and reasoning factors). To
this end factor scores were derived on the basis of the three-factor model that
adequately described the 10 IQ subtests (see above). Descriptive statistics for
the scores on the three factors are presented in Table 5.2. Differences among
the groups on the three IQ factors were evaluated using one-way ANOVAs
followed by two planned contrasts: at-risk dyslexic versus at-risk non-dyslexic,
and at-risk non-dyslexic versus controls. Subsequently, the analyses were
performed with arithmetic or parental education as covariate.
Nonverbal Abilities
Nonverbal abilities were subdivided into a performance and a reasoning factor,
but the results were very similar. For both, the group effect was significant
(performance factor: F(2, 209) = 6.54, p = .002; reasoning factor: F(2, 209) =
6.22, p = .002). The at-risk non-dyslexic group outperformed the at-risk dyslexic
group (performance: F(1, 209) = 11.64, p = .001; reasoning: F(1, 209) = 7.67,
23629 Bergen, Elsje van V.indd 128 19-12-12 12:24
iQ oF Four-year-oldS wHo go on to develoP dySleXia
129
5nvIQ
re
ason
ing
.66
.68
.50
.72 .6
4
.82
.69
.14
.20 .4
2
.69 .9
3 .73
.82
Scho
ol
nvIQ
p
erfo
rman
ce
vIQ
IQ
.14
.93.73
.20
.69
.66
.68 66
.46
.52
.64
Patt
erns
Ob
ject
ass
emb
ly
Cat
egor
ies
Pict
ure
com
pl.
Bloc
k de
sign
Ana
logi
es
Lang
uage
com
pr.
Exp
r. vo
cab
Exp
r. sy
ntax
verb
al S
TM
68
Arit
hmet
ic
66
Read
ing
.82
69.69
.69 .5
5 .5
5
.50
.72
.72
.72
Lang
uage
com
pr.
.81
.74 .8
0
.60
Fig
ure
5.3.
Str
uctu
ral E
quat
ion
mod
el o
f the
Eff
ects
of I
Q a
t Age
4 o
n Ba
sic
Scho
ol A
chie
vem
ent a
t Age
8.
23629 Bergen, Elsje van V.indd 129 19-12-12 12:24
Chapter 5
130
p = .006), but did not significantly differ from the control group, (performance:
F(1, 209) < 1; reasoning: F(1, 209) = 1.15, p = .285).
Next, we examined whether the lower nonverbal ability of the at-risk
dyslexics was related to their later arithmetic difficulties (see bottom Table 5.2).
Arithmetic as covariate controls for the variance in IQ that is related to arithmetic
and that arithmetic and reading have in common (similar to the model in Figure
5.3). In ANCOVAs, the group differences on both performance (F(2, 208) = 2.11,
p = .124) and reasoning (F(2, 208) = 2.18, p = .115) disappeared, after controlling
for arithmetic (performance: F(1, 208) = 13.46, p < .001; reasoning: F(1, 208) =
8.75, p = .003).
We also conducted ANCOVAs with parental education as covariate
to test whether the lower performance of the at-risk dyslexic group could be
due to a lower SES. The results for the two ANCOVAs were similar: Although
the effect of parental education was significant (performance: F(1, 203) = 5.57,
p = .019; reasoning: F(1, 203) = 11.38, p = .001), the group effect remained
significant (performance: F(2, 203) = 5.39, p = .005; reasoning: F(2, 203) = 3.64,
p = .028). moreover, the follow-up contrast comparing the at-risk children with
and without dyslexia remained significant (performance: F(1, 203) = 10.78, p =
.001; reasoning: F(1, 203) = 7.14, p = .008).
Verbal Abilities
The ANOVA on the verbal-IQ factor revealed a group difference, F(2, 209) = 9.56,
p < .001. The contrasts confirmed a stepwise pattern: the at-risk dyslexics scored
lower than the at-risk non-dyslexics, F(1, 209) = 7.57, p = .006, who scored lower
than the controls F(1, 209) = 4.90, p = .028.
In contrast to the nonverbal-IQ findings, the ANCOVA on verbal IQ with
arithmetic as covariate showed that the effect of arithmetic was not significant,
F(1, 208) = 1.85, p = .175, and that the effect of group (F(2, 208) = 6.17, p = .003)
and the two contrasts remained significant.
Finally, in an ANCOVA the effect of parental education was significant,
F(1, 203) = 19.47, p < .001. Nevertheless, the overall group effect remained
significant, F(2, 195) = 4.10, p = .018, as did the difference between the two
at-risk groups, F(1, 203) = 6.45, p = .012. however, after controlling for parental
education the significant difference between the at-risk non-dyslexic and the
control group disappeared, F(1, 195) < 1.
23629 Bergen, Elsje van V.indd 130 19-12-12 12:24
IQ of four-year-olds who go on to develop dyslexIa
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5
5.4 Discussion
The current study examined the relationship between intelligence in four-year-
olds and their reading status four years later in a sample of children with and
without a familial background of dyslexia. We found that at-risk children who
went on to be classified as dyslexic were impaired relative to control children
(i.e., not at-risk and non-dyslexic) on both verbal and nonverbal IQ. The at-risk
children who became normal readers showed slight but significantly poorer
performance in the verbal, but not in the nonverbal domain. All reported
deficits of the at-risk dyslexic children are relative to the other two groups rather
than relative to norm scores. Our sample scored in general above average on
IQ, probably due to the overrepresentation of parents with a high educational
level (see van Bergen et al., 2012, p.33).
Given that reading and IQ are correlated, it might be regarded as
obvious that a group of poor readers has a somewhat lower IQ than a group
of normal readers. however, within an IQ-matched subsample of children with
below-average full-scale IQ and with (n = 75) and without (n = 26) familial risk,
37% versus 8% of the children developed dyslexia. This difference demonstrates
once more that low reading cannot be explained by low IQ alone.
The at-risk dyslexic children lagged behind across tests of verbal IQ. At 4
years old, the at-risk dyslexia group was characterized by relatively poor sentence
comprehension, poor expressive syntax, weak vocabulary, and poor verbal short-
term memory (d = .94 on the factor score). This confirms previous reports of early
oral-language deficits in dyslexic children (Scarborough, 1990; Snowling et al.,
2003; Torppa et al., 2010). These studies as well as the current one reported large
effect sizes, suggesting that these findings are robust across orthographies.
The verbal IQ of the at-risk children who did not develop dyslexia were
somewhat lower (d = .39) relative to the controls. Previous studies (Snowling
et al., 2003; Torppa et al., 2010) found similar effect sizes, although none of
these effects reached significance. Generally, the at-risk non-dyslexic children
perform slightly lower in the verbal domain, which may only be detectable in
large samples.
Controlling for differences in parental education suggests that, relative
to their peers without familial risk, the lower language skills of children at-risk
might be attributable to their lower socioeconomic background. It could well be
that the at-risk families offer a less rich language environment. however, Neiss
23629 Bergen, Elsje van V.indd 131 19-12-12 12:24
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and Rowe (2000) disentangled the environmental and genetic components
underlying the correlation between parental education and children’s verbal
IQ. They estimated that the environmental effect of parental education explains
no more than 4% of children’s verbal IQ. As both children’s IQ (Walker, Petrill, &
Plomin, 2005) and parental education (Rowe, Vesterdal, & Rodgers, 1998) are
for about two-thirds genetic in origin, the disappearance of the group effect
in the ANCOVA might well be through ‘controlling for’ genetic rather than
environmental differences between the groups. Indeed, the parents of the
control children were somewhat better in verbal reasoning (van Bergen et al.,
2012), a proxy for verbal IQ. however, an extra ANCOVA with parental verbal-
reasoning as covariate1 showed no significant effect of verbal reasoning, which
favours the genetic explanation of SES’ effect.
Interestingly, the at-risk dyslexic group also lagged behind on
nonverbal IQ, although somewhat less than in the language domain. In line
with Snowling et al. (2003), we found that around 4 years of age these children
performed on average over half a standard deviation lower on nonverbal IQ
than the controls. however, this difference reached significance only in the
present study due to the larger sample size. In contrast, Torppa et al. (2010)
did not find differences on nonverbal IQ at age 5. Nevertheless, the present
findings agree with both studies in that at-risk children who did not develop
dyslexia had age-adequate nonverbal IQ.
At a more general level, we were interested in the nature of the link
between IQ at age 4 and reading at age 8. Are the lower IQ’s shown by the future
dyslexics specifically related to reading or are they also related to another basic
academic skill, arithmetic? Our results from structural equation modelling as
well as ANCOVAs indicate that verbal IQ is specifically related to reading, not
to arithmetic, which is expected in view of the linkage between the language
and reading systems (hayiou-Thomas, harlaar, Dale, & Plomin, 2010). Yet, our
observed unique relation between verbal IQ and reading does not elucidate
whether they are independent consequences of shared aetiological factors
or, alternatively, whether there is a causal link, with the developing reading
system building on the language system.
In contrast to verbal IQ, we found that nonverbal IQ is related to both
reading and arithmetic. This is in line with the generalist-genes hypothesis put
1 Only scores of the weakest-reading parent were available. Details of this analysis can be obtained from the first author.
23629 Bergen, Elsje van V.indd 132 19-12-12 12:24
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5
forward by Kovas and Plomin (2007). Kovas, haworth, Dale, and Plomin (2007)
found that one-third of the genes that influence one academic domain are
specific to that domain, one-third overlaps with other academic domains, and
one-third overlaps with genes associated with IQ. For dyslexia, the identified
candidate susceptibility genes are associated with anomalies in neuronal
migration and axon growth (Galaburda, LoTurco, Ramus, Fitch, & Rosen, 2006).
In light of the deficiency in these fundamental processes, pleiotropic cognitive
deficits are to be expected. This suggests that the poor nonverbal IQ of the
future dyslexics might well be epiphenomenal to poor reading, rather than
causally linked.
In the current study we only assessed fairly basic school skills. Even
higher predictive relations are expected between preschool IQ and later
reading comprehension and mathematics. A limitation of the study is the lack of
measures of attention or motor problems for example, since the groups might
differ in this respect. Our study did, however, include measures of arithmetic
problems, of which the findings highlighted the value of studying comorbidity.
Finally, it should be noted that verbal IQ batteries (e.g., the WISC, Wechsler,
2004) usually also include verbal reasoning, besides vocabulary and verbal
short-term memory measures. It is not entirely clear if and how the absence of
a measure of verbal reasoning has affected our findings. If differences among
the groups in verbal reasoning are relatively smaller, then inclusion of verbal
reasoning would have resulted in somewhat smaller differences in verbal IQ.
however, given the robust differences on the other verbal tests it is unlikely that
this would have changed the overall pattern of differences among the groups.
Nevertheless, it is important that our results can be fully compared with those
of other familial risk studies (see Introduction) in which verbal reasoning was
not included either.
Our study is of interest for the debate on the diagnosis of dyslexia. The
children with dyslexia already had a lower full-scale IQ before reading onset,
arguably as part of their disorder. According to the aptitude-achievement
discrepancy model these children need to have even more severe reading
difficulties in order to be diagnosed as dyslexic. Since it is now more or less
accepted that children with reading difficulties might have additional language
difficulties, some scholars have proposed leaving verbal IQ out of the definition
and instead using a discrepancy with nonverbal IQ. however, the future dyslexic
readers in our study also had lower nonverbal IQ. Our findings, together with
23629 Bergen, Elsje van V.indd 133 19-12-12 12:24
Chapter 5
134
evidence from earlier mentioned behavioural and molecular genetic studies,
raise the possibility that dyslexia in itself affects brain circuits not related to
reading. If so, then it seems undesirable to tap these circuits by IQ-tests and to
take these IQ-scores into account in the diagnosis of dyslexia.
Our findings show that at the group level the children who went on to
develop dyslexia performed relatively poorly in both verbal and nonverbal IQ
at 4 years of age. Even though the mechanisms that might link these abilities
to reading acquisition are not fully understood, our findings demonstrate that
the cognitive development of the group of children who will fail to learn to
read is atypical.
23629 Bergen, Elsje van V.indd 134 19-12-12 12:24
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5
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van Eldik, m., Schlichting, J., lutje Spelberg, h., van der meulen, B., & van der meulen, S. (2001). Reynell test voor taalbegrip [Rey-nell test for language comprehension]. Lis-se: Swets & Zeitlinger B.V.
Verhoeven, L. (1995). Drie-minuten-toets. handleiding [Three-minutes-test. manual]. Arnhem: Cito.
Walker, S. O., Petrill, S. A., & Plomin, R. (2005). A genetically sensitive investigation of the effects of the school environment and so-cio-economic status on academic achie-vement in seven-year-olds. Educational Psychology, 25(1), 55-73. doi:10.1080/0144341042000294895
Wechsler, D. (2004). The Wechsler intelligence scale for children - Fourth edition. London: Pearson Assessment.
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Don’t become a mere recorder of facts, but try to penetrate the mystery of
their origin.
- Ivan Pavlov -
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Who will develop dyslexia? The series of studies reported in this thesis study
cognitive precursors of dyslexia in parents and children. We investigated, firstly,
the cognitive profile characteristic of children who go on to develop dyslexia.
As a related issue we examined whether children’s skills assessed prior to
Grade 1 are specifically predictive of later reading development or are also
involved in later arithmetic development. The second goal was to examine
the impact of the cognitive profile of parents and the literacy environment
parents create on children’s reading outcome. The studies employed a
prospective design, in which the progress of children at high and low family
risk was followed. Children at high risk had a family history of dyslexia. After
some years of reading instruction they were categorized as either dyslexic
or non-dyslexic (below or above the 10th percentile cut-off on word-reading
fluency, respectively). Subsequently, they were compared concurrently and
retrospectively with each other and with typically developing children without
such a family background. The studies reported in the current thesis include
two independent samples of the Dutch Dyslexia Programme (DDP), labelled
the Amsterdam and the national sample (see also the General Introduction,
Section 1.5.1). The Amsterdam sample (N = 79) covers children’s development
from kindergarten to Grade 5 (11 years old). Findings are described in Chapter
2. Chapters 3, 4, and 5 report findings on the national study (N = 212), including
the development from 3½ to 9 years of age (Grade 3).
In what follows I will review the findings regarding precursors and
literacy skills in children and precursors in families. Each topic will be concluded
by linking the findings to Pennington’s (2006) multiple cognitive deficit model,
as outlined in the General Introduction (Section 1.2.1 and Figure 1.1). Family-
risk studies provide the opportunity to test implications of the multiple deficit
model and to add more details for the model of dyslexia. Do the findings of the
current set of studies lend support for this model and can the findings allow
us to start on specifying the model for the case of dyslexia? Thereafter I will
extend Pennington’s model to the intergenerational multiple deficit model,
which includes an extra level to account for parental effects. This chapter will
be closed by suggestions for future research and concluding remarks.
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6
6.1 Precursors in Children
Two categories of child precursors of dyslexia were studied: general cognitive
ability (IQ) and preliteracy skills. The first were studied at kindergarten entry
(age 4) in Chapter 5 (the national sample). Preliteracy skills were assessed
during the last year in kindergarten (age 5 or 6) and reported in Chapter 2 for
the Amsterdam sample and Chapter 4 for the national sample.
6.1.1 IQThe relation between verbal and nonverbal IQ around the age of four, and
reading outcome at the end of Grade 2 was the focus of Chapter 5. It was found
that at-risk children who go on to become dyslexic were impaired relative to
controls on both verbal and nonverbal IQ, with the gap being larger for verbal
IQ. The at-risk children who do not become dyslexic showed good nonverbal
abilities, but their verbal IQ was slightly but significantly lower than that of
controls. Furthermore, it appeared that nonverbal IQ was equally strongly
related to later reading achievement (e.g., word-reading fluency) as to later
arithmetic achievement (e.g., mental-arithmetic fluency), while verbal IQ was
specifically predictive of reading.
An unresolved issue is the nature of the link between early IQ
and subsequent reading ability. Three possibilities are 1) the inclusion of
reading-related tasks in the IQ battery, 2) IQ and reading being independent
consequences of the same aetiological factors, and 3) a causal link from IQ to
reading. Each of these possibilities will be discussed below for verbal and for
nonverbal IQ.
According to the first possibility, verbal IQ and reading are related
because verbal IQ tests include subtests that assess component skills that are
known to be related to reading ability and known to be deficient in individuals
with dyslexia (cf. Siegel, 1999). Note that this first account is compatible with
both a shared aetiology account and a causal account. That is, the component
skill that is supposedly involved in both verbal IQ and reading could be
associated with reading through partly shared aetiology or could causally
influence reading ability. A component skill shared by verbal IQ and reading
might be verbal short-term memory (deficient in dyslexia, see e.g. de Jong,
1998), which was indeed part of the verbal IQ battery of the study presented
in Chapter 5. Nevertheless, group differences on this subtest did not exceed
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group differences on the other verbal IQ subtests (see Chapter 5, Figure 2), a
finding that is not in agreement with this first account. Concerning nonverbal
IQ, this first account cannot explain the relation between early nonverbal IQ
and subsequent reading as there is no common component skill known.
Second, the literature suggests that early language and later reading
ability indeed share aetiological factors, which causes the phenotypic
correlation between them. hayiou-Thomas, harlaar, Dale, and Plomin (2010),
for example, reported that language (or verbal IQ) at age 4 and subsequent
reading performance are largely influenced by common genetic and shared
environmental influences. Knowing that the genetic and shared environmental
correlations are strong does not, however, reveal whether early verbal IQ
and later reading are causally linked, with language supporting reading
development, or whether they independently draw upon common aetiological
influences.
Regarding nonverbal IQ and a shared-aetiology account, we found that
nonverbal IQ was related to later reading as well as later arithmetic skills. This
finding fits with the generalist genes hypothesis (Kovas & Plomin, 2007; Plomin
& Kovas, 2005 - see also the General Introduction, Section 1.2.2). The generalist
genes hypothesis states that genes influencing different academic domains
and IQ partly overlap (Kovas, haworth, Dale, & Plomin, 2007). Evidence from
molecular genetic studies more clearly points to the possibility that reading,
arithmetic, and nonverbal IQ are related because of partly shared aetiological
factors. The candidate genes that have been linked to dyslexia all play a role in
early brain development: they can be responsible for anomalies in neuronal
migration and axon growth (Galaburda, LoTurco, Ramus, Fitch, & Rosen, 2006).
The resulting subtle cortical malformations are probably more widespread than
just cortical areas involved in reading. Therefore, it is expected that cognitive
deficiencies are not solely restricted to the reading process.
Lastly, I will consider a causal account. Verbal IQ was found to be
specific for reading development, which is consistent with a partly causal link,
with the developing reading system building on the language system. Indeed,
models for word decoding generally include aspects of language skills, like the
phonological lexicon in the dual-route cascaded model (Coltheart, Rastle, Perry,
Langdon, & Ziegler, 2001) and phonological and semantic units in the triangle
connectionist model (Seidenberg & mcClelland, 1989). moreover, a causal
link for the phonological aspects of language is often proposed (e.g., hulme,
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6
Bowyer-Crane, Carroll, Duff, & Snowling, in press; Vellutino, Fletcher, Snowling,
& Scanlon, 2004), although the relation between phonological skills and
reading could also be reciprocal (e.g., Burgess & Lonigan, 1998). Interventions
to promote broader language skills in kindergarten children that would result
in a subsequently faster reading development, would provide evidence for a
causal interpretation. In the absence of such evidence, explaining the relation
between verbal IQ and reading in terms of shared underlying factors is just as
plausible. In sum, our study showed that early verbal IQ is a specific predictor of
word-reading fluency, but the nature of this association remains elusive.
moving on to nonverbal IQ, one could defend a causal link by arguing
that nonverbal IQ indicates an individual’s ability to acquire new insights and
skills. however, this is at best an explanation for early literacy development,
like grasping the alphabetic principle. It is unclear how an individual’s ability
to acquire new insights and skills would translate into developing such a
specific skill as word-level reading fluency, and hence, such an unspecified
explanation is difficult to defend. Therefore, explaining the relation between
early nonverbal IQ and subsequent reading in terms of common aetiological
factors seems most plausible.
Although our data do not reveal the nature of the link between IQ
and reading, they do show that children who go on to develop dyslexia have
a lower IQ than typically developing children. Because at the time their IQ was
assessed they could not yet have failed to learn letters or word reading, their
lower IQ at this age cannot be a consequence of reading failure and reduced
print exposure. In previous prospective studies IQ differences were either
neglected or were not possible to investigate (e.g., due to matching on IQ as in
de Jong & van der Leij, 2003). For that reason, the relative low IQ of four-year-
olds who go on to be dyslexic is in itself a unique finding.
6.1.2 Preliteracy SkillsAs mentioned above, preliteracy skills were assessed during the last year in
kindergarten (age 5 or 6) for both samples. Chapter 2 reports the results for
the Amsterdam sample and Chapter 4 for the national sample. The studies
included the most important preliteracy skills: phonological awareness, rapid
naming, and letter knowledge. Again, the longitudinal character ascertains
that these cognitive deficiencies are due to aetiological factors rather than due
to poor reading and less print exposure.
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In the Amsterdam sample, children were categorised as dyslexic or
non-dyslexic in Grade 5. The at-risk dyslexic group read less fluently than the
at-risk non-dyslexics and the controls across reading tasks and throughout
primary school. Retrospectively, in kindergarten the at-risk group with dyslexia
was slower on rapid naming and knew fewer letters than the other two groups.
however, group differences on phonological-awareness measures did not
reach significance. The absence of a difference could be due to low levels of
phonological awareness in Dutch children of this age (see also de Jong & van
der Leij, 1999). For many children the tasks were too difficult, which might have
obscured existing group differences in the latent ability. Another factor that
might play a role is the timing of identifying dyslexia. Reading outcome was
not assessed until fifth grade, when word-reading fluency in Dutch is more
strongly related to rapid naming than to phonological awareness (Vaessen &
Blomert, 2010). hence, a group categorized as dyslexic at this age is expected
to show larger deficiencies in rapid naming than phonological awareness.
In line with this, the children with dyslexia were impaired on rapidly naming
familiar pictures and colours during kindergarten.
moving on to the national sample, Chapter 4 presents the findings
regarding preliteracy skills in kindergarten. In Chapter 4 reading status was
assessed in third grade and preliteracy skills at the end of kindergarten. The
at-risk dyslexic group was impaired on all preliteracy skills. The at-risk children
without later dyslexia showed normal letter knowledge and rapid naming,
but performed below controls on phonological awareness. Note that the two
independent samples converge on the conclusion that at-risk dyslexic children
are impaired, compared to the other two groups, on rapid naming and letter
knowledge in kindergarten. The findings across the samples differ, however,
for phonological awareness: no group differences in the Amsterdam sample
and a step-wise pattern (at-risk dyslexia < at-risk no-dyslexia < controls) in
the national sample. Although this may seem contradictory, it should be
mentioned that in the national sample the phonological-awareness tasks were
more appropriate for the children’s ability level. For this reason, we attach more
importance to the group differences in the national sample.
With regard to the multiple deficit model (Pennington, 2006), the
current set of studies shows that the cognitive processes associated with
dyslexia are phonological awareness, rapid naming, and verbal and nonverbal
IQ. Note that these associations are not necessarily causal (see the discussion
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6
on IQ in Section 6.1.1). Letter knowledge might be added to the list of cognitive
processes as an underlying skill of reading. however, it could also be regarded
as belonging to the symptom level, being a forerunner or autoregressor of
reading.
The multiple deficit model predicts that normal reading children with
a family risk do slightly poorer on reading-related skills than normal reading
children without such risk. This is because there is evidence that the underlying
cognitive processes of reading are also complex traits, influenced by multiple
genetic and environmental factors (Naples, Chang, Katz, & Grigorenko, 2009;
Petrill, Deater-Deckard, Thompson, De Thorne, & Schatschneider, 2006), so
the at-risk no-dyslexia children are expected to receive some of the involved
risk factors. A difference between the two normal reading groups was indeed
found for verbal IQ and phonological awareness, but not for rapid naming. how
their surprisingly good rapid naming can be explained aetiologically remains
puzzling. It seems that verbal IQ and phonological awareness are related to both
reading status and family-risk status, whereas rapid naming only differentiates
between children differing in reading status but not between children differing
in risk status. The at-risk children without dyslexia are good at rapid naming
and, in anticipation of Section 6.3.2, their affected parents are relatively good at
rapid naming. This suggests that good rapid naming somehow decreases the
probability of becoming dyslexic, but how remains unclear.
The findings regarding preliteracy skills of the at-risk children who
went on to become dyslexic confirmed that phonological awareness, rapid
naming, and letter knowledge are cognitive risk factors for dyslexia. Chapter
4 additionally investigated comorbidity with dyscalculia and whether the
investigated preliteracy skills are also cognitive risk factors for dyscalculia.
Dyslexia and dyscalculia (defined as scoring in the bottom 10% on word-
reading fluency or arithmetic fluency, respectively) were indeed comorbid,
as evidenced by the 42% rate of dyscalculia in the at-risk dyslexic group. In
comparison, these figures were 20% and 8% in the at-risk and not at-risk
normal readers, respectively. The trio of preliteracy skills was shown to be
predictive of later arithmetic achievement as well. Rapid naming was equally
strongly related to reading and arithmetic, but phonological awareness and
letter knowledge were more specific precursors of reading. After controlling
for rapid naming and letter knowledge in a regression analysis, phonological
awareness dropped out as a predictor of arithmetic. Therefore, our data did not
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provide unequivocal support for the hypothesis of De Smedt and colleagues
(Boets & De Smedt, 2010; De Smedt & Boets, 2010; De Smedt, Taylor, Archibald,
& Ansari, 2010) that individuals with dyslexia have difficulties with retrieving
arithmetic facts from long-term memory because these facts are phonological
in nature. Our data suggest that the speed of processing or speed of retrieving
phonological codes might be more important in predicting later achievement
in arithmetic fluency.
The conclusion that some of the cognitive processes of importance
to reading are also important for arithmetic, whereas others are distinct to
reading, is in line with the multiple deficit model and the generalist genes
hypothesis (Kovas & Plomin, 2007; Plomin & Kovas, 2005 - see also the hybrid
model in the General Introduction, Section 1.2.3). In sum, nonverbal IQ and
rapid naming are shared and therefore contribute to the correlation between
arithmetic and reading. Likewise, at the lower end of the distribution, they
contribute to the comorbidity between dyscalculia and dyslexia. Verbal IQ,
phonological awareness, and letter knowledge were found to be skill-specific
cognitive processes, contributing to the dissociation between arithmetic and
reading. The inclusion of arithmetic and its associated disorder dyscalculia
in the study of the specificity of dyslexia precursors underscore the value of
studying comorbidity. Other comorbidities are also of interest, but were not
dealt with in the current studies.
6.2 Literacy Skills in Children
Chapter 3 compares for the national sample the three groups of children at
the end of Grade 2 on literacy skills and their main cognitive underpinnings:
phonological awareness and rapid naming. On all five reading tasks, which
measure accuracy and fluency of word and pseudoword reading, the at-
risk children with dyslexia were severely impaired compared to the control
children, as indicated by large effect sizes (1.74 to 2.83). In addition, they made
many errors in spelling words and in phoneme deletion, and were slow in serial
naming of familiar items.
Although the at-risk group without dyslexia had literacy skills within
the normal range for their age, they read significantly less accurately and
fluently than controls on all of these reading measures (with effect sizes in the
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6
range of 0.43 to 0.74). The same step-wise pattern was found for spelling skills
and phonological awareness. A salient finding was the good performance of
the at-risk children without dyslexia on rapid naming. Despite the fact that they
fared significantly worse than controls on literacy and phonological awareness,
they were equally good on naming digits and colours rapidly.
The group differences for phonological awareness and rapid naming
in second grade mirror those for kindergarten, so the picture did not change.
Apparently, phonological awareness is associated with both reading and risk
status, while rapid naming is only related to reading status. The at-risk children
who go on to develop normal reading skills might do well despite their family
risk because the efficiency of the processes that rapid naming tap might protect
them against dysfluent reading. On the other hand, one could argue that they
still have mild literacy problems due to their mild phonological awareness
deficit.
The multiple deficit model states that developmental disorders,
like dyslexia, are influenced by many genetic and environmental risk factors
that each increase the liability for the disorder without being necessary or
sufficient for causing it. From this it follows that the multiple deficit model
predicts a liability or risk function that is continuously distributed in the normal
population. For our three groups this would mean a high liability for the at-
risk dyslexia group, moderate liability for the at-risk no-dyslexia group, and
low liability for the control group. At the behavioural level these differences
in liability should be observable as differences in literacy skills, with the at-
risk no-dyslexia group taking up an intermediate position. This hypothesis
was confirmed: on all literacy tasks we found that the at-risk children who
did not meet the dyslexia criterion still performed significantly weaker than
controls. Nonetheless, as described in the previous section, this pattern was
not systematically found at the cognitive level.
6.3 Precursors in Families
In addition to predictors of dyslexia residing in children we examined possible
predictors in their families. more specifically, we studied effects of home literacy
environment and parental literacy skills on children’s reading outcome.
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6.3.1 Home Literacy EnvironmentChildren’s socio-economic status, as indexed by parental level of education, can
be seen as a distal family background characteristic. The literacy environment
that parents provide at home might act as a mediating proximal process
between socio-economic status and children’s reading development (cf.
Leseman, Scheele, mayo, & messer, 2007). Both these distal and proximal family
characteristics were investigated.
In the Amsterdam as well as in the national sample the families with
a parent with dyslexia (i.e., at-risk families) had lower levels of education
compared to the families with average to good reading parents (i.e., control
families). Within the at-risk sample, the parents of the affected children had
slightly lower educational levels than those of the unaffected children.
Parental level of education could assert an influence on children’s
reading development via the proximal family environment. hence, it was
investigated whether there is a relation between the literacy environment that
parents provide at home and children’s family history of dyslexia and reading
outcome. In the Amsterdam sample (Chapter 2) home literacy environment
was assessed during the second (and final) year of kindergarten. The three
groups were compared on library memberships, frequency of shared reading,
and on the number of books in the home, but they did not differ significantly
on any of these measures. In the national sample (Chapter 4) the groups did
not differ either on cognitive stimulation by parents, but there was a tendency
for parents of control children to own more magazines, newspapers and books.
The two at-risk groups did not differ in any of the measures of home literacy
environment.
Although behavioural genetic studies point to substantial heritability
of reading, they also estimate that roughly 30% of individual differences is due
to environmental factors (Byrne et al., 2009; Petrill et al., 2006; Taylor, Roehrig,
hensler, Connor, & Schatschneider, 2010). The moderate environmental
influences do not leave much room to find effects of home literacy environment,
especially because our volunteer sample probably does not represent the
full environmental range. Indeed, our data about children’s home literacy
environment did not reveal significant group differences. It should be admitted
however, that home literacy environment was only measured superficially and
subjective. When the home literacy environment is assessed more thoroughly,
preferably with objective observations, the question whether children’s literacy
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6
environment at home influences their reading development can be answered
more reliably. Also other environmental factors warrant further investigation,
such as pre- and perinatal factors and school and classroom characteristics.
Despite the mentioned limitations, our findings are in agreement with findings
from other family-risk studies, which also failed to show effects of home literacy
environment on children’s reading outcome (Elbro, Borstrøm, & Petersen, 1998;
Snowling, muter, & Carroll, 2007; Torppa et al., 2007). Thus, no environmental
risk factors of substantial effect have been identified that would have been
easy targets for intervention.
To summarize, we did find an effect of parent’s educational level, but
this did not translate into detectable effects of literacy practices at home. This
conclusion might seem paradoxical. however, the lower educational level of
the parents of the at-risk sample might be partly a consequence of their reading
difficulties. This could be the main reason for a relation between parental
education and offspring’s reading outcome, rather than via environmental
differences in literacy stimulation. moreover, environmental effects of parental
education are smaller than what might be expected intuitively. Genetic factors
account for about two-thirds of individual differences in parental education
(Rowe, Vesterdal, & Rodgers, 1998) as well as in reading attainment (Byrne et
al., 2009; Petrill et al., 2006; Taylor et al., 2010). Therefore, the intergenerational
relation between parental education and their offspring’s reading ability might
well be largely genetically mediated. In line with the given explanations, Walker,
Petrill, and Plomin (2005) found that school achievement shows substantial
genetic influence (i.e., 69%). Shared environmental influences accounted for
only 12% of the variance. While controlling for genetics, most of these shared
environmental influences were due to effects of social-economic status
(parental education and occupation) rather than school characteristics.
It should be borne in mind, though, that the high heritability of
reading performance does not imply at all that educational improvements
are pointless. Instead, they positively impact on almost all children’s reading
achievement and raise the average of standardized scores of a class receiving
effective reading intervention. Nonetheless, it is likely that individual differences
among children remain largely genetically driven (Olson, Byrne, & Samuelsson,
2009). This suggests that children with a genetic constraint on their reading
development need increased reading instruction, a notion that I will come
back to in Section 6.5.
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6.3.2 Parental SkillsThe key innovating factor of the present family-risk studies is probably the
inclusion of cognitive abilities of the parents. We went beyond using parental
literacy for the sole purpose of dichotomizing children into high and low
family-risk samples by examining the relation between reading and reading-
related skills of the parents and reading skills of their children. We had
objective measures of the parents with dyslexia. Although all children in the at-
risk sample have a parent with dyslexia, they might still vary in their degree of
family risk for dyslexia. We tested this in both the Amsterdam and the national
sample by comparing the groups of at-risk children with and without dyslexia
on the reading skills of their affected parent. Since parents pass on their genes
to their offspring and shape their environment, parental reading skills might be
taken as an indicator of the offspring’s liability to dyslexia.
In the Amsterdam sample (Chapter 2) the dyslexia of the parents of
the affected children was more severe than the dyslexia of the parents of the
unaffected children. This was indicated by group differences on reading fluency
of words and pseudowords, with effect sizes of 0.78 and 1.71, respectively. This
is an intriguing finding, because the affected parents read on average at the
fifth percentile compared to national norms. Yet in this restricted range group
large differences were observable.
Employing data of the national sample (Chapter 3), we had the
opportunity to replicate the above findings, and in addition, to extend them by
investigating parental spelling, nonword repetition, and rapid naming. As we
lacked a direct measure of phonological awareness, nonword repetition was
used as a proxy. The difference between the at-risk children with and without
dyslexia was replicated for the affected parent’s word-reading fluency, although
with a smaller effect size (Cohen’s d was 0.48). The two at-risk groups did not
differ in parental pseudoword reading, spelling, and nonword repetition, though
both groups were impaired compared to controls. Interestingly, however, the
parents of the at-risk dyslexia children were slower on alpha-numeric rapid
naming than those of the at-risk no-dyslexia children. This underscores the
special role of rapid naming, at least in transparent orthographies. In regression
analyses parental word-reading fluency and rapid naming also predicted at-risk
children’s word-reading fluency, even after controlling for children’s concurrent
phonological awareness and rapid naming.
In the two above mentioned studies data were reported of the
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6
parent with dyslexia. Chapter 4 (about the national sample) completes this
by examining the influence of the parent without dyslexia for the first time.
It was unfortunate that we did not have objective measures of the literacy
skills of the parent without dyslexia; we only had their report on whether they
experienced literacy difficulties. Nevertheless, this measure appeared to be
a very good indicator of reading fluency: in a subsample we found a strong
correlation (.85) between assessed and self-reported literacy skills. In addition,
this measure yielded interesting findings. As hypothesised, also for the non-
dyslexic parents there was a difference between the two at-risk groups: the
parents of the affected children reported more literacy difficulties compared to
those of the unaffected children. moreover, the literacy difficulties of the non-
dyslexic parent explained additional variance in children’s reading fluency after
controlling for literacy difficulties of the dyslexic parents.
Thus, the findings in the Amsterdam sample regarding the affected
parent were only partly replicated in the national sample. however, the results
concerning the unaffected parent further support the conclusion that children
at family risk for dyslexia differ in their liability, as indicated by differences in
parental reading skills between at-risk children with and without dyslexia.
moreover, differences between the two family-risk groups in the severity
of the dyslexia of the affected parent have now been replicated in Finnish,
in the Jyväskylä Longitudinal Study of Dyslexia. Torppa, Eklund, van Bergen,
and Lyytinen (2011) showed differences in parental reading fluency, accuracy,
and spelling. Regression analyses showed that these measures also predicted
children’s literacy skills in Grade 3; parental literacy skills even predicted
children’s reading accuracy over and above children’s preliteracy skills.
Do the findings regarding precursors in families lend support for the
multiple deficit model? According to this model, the aetiology of dyslexia (and
other developmental disorders) is multifactorial and probabilistic. multiple
genetic risk variants interact with each other and with multiple environmental
risks to ultimately produce the disorder at the behavioural level. Regarding
environmental risks, we investigated aspects of one environmental factor:
home literacy environment. however, we did not find effects on children’s
reading outcome. Children’s social-economic status (as indexed by parental
level of education) did differ among groups, arguably asserting influence on
children’s reading development via environmental as well as genetic pathways
(see Section 6.3.1).
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Genetic risk factors were not measured directly. Although there is
now a huge body of evidence indicating that genes contribute importantly to
individual differences in reading ability (e.g., Byrne et al., 2009; hayiou-Thomas
et al., 2010), the specific gene variants found thus far only explain a tiny
part of these differences (see Bishop, 2009, for the example of the KIAA0319
gene), despite substantive work in the field of molecular genetics. This
phenomenon also applies to other common traits and is called the mystery
of the missing heritability (see e.g., manolio et al., 2009). Genetic screening
is therefore not (yet) informative about a child’s genetic liability to dyslexia
(Bishop, 2009). Nevertheless, we proposed (see General Introduction, Section
1.4.2) that something can be said about children’s liability. Since parents pass
on their genetic material to their offspring and shape their environment,
cognitive abilities of parents could be used as an indicator of the genetic and
environmental risk and protective factors in the multiple deficit model.
The current studies provide two kinds of support for parental skills
being an indicator for children’s liability. First, as in other family-risk studies,
two samples of children were recruited based on having or not having a parent
with dyslexia. The current and previous family-risk studies found a large effect
of having a family history on children’s risk of becoming dyslexic. For example,
in Chapter 3 it was found that the rate of dyslexia was 30% in the high-risk
group and only 3% in the low-risk groups, whereas by definition 10% of an
unselected sample would meet the employed dyslexia criterion. Thus, having
a parent with dyslexia increases the risk (and having a second parent with
literacy difficulties increases the risk even more, see Chapter 4). Conversely,
having average to good reading parents decreases the risk.
Secondly, within the at-risk sample it was found that affected and
unaffected parents of the affected children had more literacy problems than
those of the unaffected children. moreover, when considering at-risk children’s
reading fluency on a continuous scale (rather than having or not having
dyslexia), parental reading skills were significant predictors of children’s reading
skills. The current investigation of the impact of parental skills on children’s skills
are more fine-grained than in previous family-risk studies: Previous family-risk
studies only assessed reading skills of the parents (or even relied on self-report
of dyslexia) for the purpose of dichotomising their sample into children with
and without family risk (Boets et al., 2010; de Bree, Wijnen, & Gerrits, 2010;
Pennington & Lefly, 2001; Scarborough, 1990; Snowling, Gallagher, & Frith,
23629 Bergen, Elsje van V.indd 152 19-12-12 12:25
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6
2003; Torppa, Lyytinen, Erskine, Eklund, & Lyytinen, 2010 - but see Snowling,
muter, & Carroll, 2007, for an exception). In contrast, we studied parent-child
resemblance for both parents for reading and reading-related skills.
Our findings support the view that skills of parents indicate their
offspring’s liability, which in itself is the combination of all genetic and
environmental factors that affect reading development. Thus, parental skills
might shed light on the aetiological level in the multiple deficit model. It is
not possible to disentangle the genetic and environmental contribution to the
intergenerational transmission of skills. however, according to our data the
transmission of risk seems to be mainly via genes. It is important to note that
genes are inherited, not phenotypic traits. Thus, although our data reinforce
the view that parental skills are indicative of their offspring’s liability, parental
skills will never completely specify it.
6.4 The Intergenerational Multiple Deficit Model
In the discussion of Chapter 4 an extension of the multiple deficit model was
proposed: the intergenerational multiple deficit model. Below I will elaborate
on this model. The model is depicted in Figure 6.1. In the figure it can be seen
that a top layer is added to Pennington’s multiple deficit model (see Chapter
1, Section 1.2.1), which represents characteristics of parents. Note that again
influences between layers are omitted from the figure.
Cognitive abilities of parents, for instance reading ability, form part
of their phenotype (PT in Figure 6.1). Their phenotype is the result of their
genotype (GT) in interaction with their environment. Genes do not code
for cognitive and behavioural traits but for the structure of proteins and the
regulation of gene expression, which in highly complex ways and in interaction
with the environment guides the building and maintenance of the brain (Fisher
& Francks, 2006; Fisher, 2006). Despite this gap between genes and cognition,
for traits that show genetic influences in behavioural genetic studies there
must be a relationship between genotypic and phenotypic variation. In other
words, for heritable traits parental phenotype is a proxy for their genotype.
As both parents pass on half of their genes to their offspring, the genotype
of both of them determines the genotype of their offspring. It follows that
the phenotype of parents must be related to some extent to the genotype
23629 Bergen, Elsje van V.indd 153 19-12-12 12:25
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154
of children, which includes children’s genetic risk and protective factors for a
particular developmental disorder.
In addition to transmission of parental skills via genetic pathways,
parental skills could be passed on via environmental pathways. Parents largely
shape their children’s childhood environment, which creates a relation between
parents’ characteristics and children’s environment. For example, good reading
parents are more likely to spend a lot of time reading (Chapter 4), thereby
providing a role model to their children. moreover, they appear to be better
educated (Chapter 2 and 3), and as a result, might live in better neighbourhoods
and might send their children to better achieving schools. hence, the cognitive
phenotype of parents could be one of the factors that determines children’s
environmental risk and protective factors. however, other aspects of the
phenotype of parents might also influence children’s reading development.
For instance, the behaviour of parents and the interaction between them
determines how structured or chaotic the household is, something that has
been shown to be related to children’s school performance (hanscombe,
haworth, Davis, Jaffee, & Plomin, 2011). In conclusion, the phenotype of
parents must also be related to a certain degree to children’s environmental
risk and protective factors.
Given the above two lines of reasoning, the phenotype of parents is
informative about children’s genetic and environmental factors. Focussing on
developmental disorders, this suggests that certain aspects of the phenotype
of parents can inform us about a child’s liability to a particular developmental
disorder. Regarding dyslexia, the aspects of the phenotype of parents that are
expected to shed light on children’s liability to dyslexia are skills in accurate and
fluent reading, spelling, and their cognitive underpinnings like phonological
awareness and rapid naming. Related skills (such as language and arithmetic)
and their underlying cognitive abilities might also play a role. The ability of
parents on each of the relevant continua can be conceptualised as a position
in multivariate space. The position of father and mother in multivariate space
is proposed to be indicative of a child’s predisposition towards dyslexia. The
intergenerational transfer of skills from parents to children goes through
genes, environment, and their interaction. Family studies can point to the
skills that show intergenerational correlations; genetically sensitive studies can
ultimately disentangle the contributions of genetic and environmental effects
to such an intergenerational correlation.
23629 Bergen, Elsje van V.indd 154 19-12-12 12:25
general diScuSSion
155
6
Behavioural disorders
Cognitive processes
Neural systems
Aetiological risk and protective factors
Level of analysis
N1 N
2 N
3
C1 C
2 C
3
Figure 6.1. The intergenerational multiple deficit model (an extension of Pennington’s multiple deficit model). Double headed arrows indicate interactions. Causal connections between levels of analyses are omitted. GT
p1 = genotype of parent 1; PT
p1 = phenotype of parent 1
G = genetic risk or protective factor; E = environmental risk or protective factor N = neural system C = cognitive process
D = disorder
G1 E
1 G
2
D1 D
2 D
3
Parental influences GTp1
PTp1
PTp2
GTp2
Ch
ild
Figure 6.1. The intergenerational multiple defi cit model (an extension of Pennington’s multiple defi cit model). Double headed arrows indicate interactions. Causal connections between levels of analyses are omitted.
GTp1 = genotype of parent 1; PTp1 = phenotype of parent 1G = genetic risk or protective factor; E = environmental risk or protective factorN = neural systemC = cognitive processD = disorder
23629 Bergen, Elsje van V.indd 155 19-12-12 12:25
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156
The intergenerational multiple deficit model is inspired by the
current studies on dyslexia, but is generally applicable to other multifactorial
developmental disorders with a genetic component. Examples of such disorders
include attention-deficit/hyperactivity disorder, developmental coordination
disorder, dyscalculia, specific language disorder, and autism spectrum disorder.
With respect to autism spectrum disorder, a number of studies (e.g., Bölte &
Poustka, 2006; happé, Briskman, & Frith, 2001; Losh et al., 2009) have studied
the cognitive phenotype of parents of probands (as opposed to children of
probands, as in family-risk studies of dyslexia) and found in parents similar but
milder impairments as in their children, indicating parent-child resemblance.
A second example concerns specific language impairment. In a recent study
(Bishop, hardiman, & Barry, 2012), language skills of probands and their parents
were found to correlate. In another recent study (Bishop et al., 2012) it was
found that a parent’s non-word repetition ability was a predictor of whether the
child would develop specific language impairment. These examples provide
evidence of intergenerational transfer of cognitive skills other than reading.
6.5 Future Directions
The present collection of studies has yielded some answers, but meanwhile has
raised new questions. To start with, our and previous family-risk studies did not
show significant effects of the home literacy environment on children’s reading
outcome, but behavioural genetic studies do find shared and non-shared
environmental influences. Is the home literacy environment important but did
we fail to find this because of other reasons (see Section 6.3.1)? Or are other
environmental factors more important, like pre- and perinatal factors, peer
influences, or characteristics of the school and classroom? Genetic sensitive
studies can estimate how ‘environmental’ these factors actually are.
A second interesting area for future studies regards comorbidity. From
the (intergenerational) multiple deficit model it follows that comorbidities
between developmental disorders are to be expected. This points to the
importance of studying developmental disorders that commonly co-occur.
By doing so, one can uncover shared and distinct risk factors at each of the
levels of explanation. This not only helps to understand the origin of the
comorbidity, but also the developmental paths leading to each of the disorders.
N2
N3
E1
G2
D2
D3
23629 Bergen, Elsje van V.indd 156 19-12-12 12:25
General Discussion
157
6
The intergenerational multiple deficit model is inspired by the
current studies on dyslexia, but is generally applicable to other multifactorial
developmental disorders with a genetic component. Examples of such disorders
include attention-deficit/hyperactivity disorder, developmental coordination
disorder, dyscalculia, specific language disorder, and autism spectrum disorder.
With respect to autism spectrum disorder, a number of studies (e.g., Bölte &
Poustka, 2006; happé, Briskman, & Frith, 2001; Losh et al., 2009) have studied
the cognitive phenotype of parents of probands (as opposed to children of
probands, as in family-risk studies of dyslexia) and found in parents similar but
milder impairments as in their children, indicating parent-child resemblance.
A second example concerns specific language impairment. In a recent study
(Bishop, hardiman, & Barry, 2012), language skills of probands and their parents
were found to correlate. In another recent study (Bishop et al., 2012) it was
found that a parent’s non-word repetition ability was a predictor of whether the
child would develop specific language impairment. These examples provide
evidence of intergenerational transfer of cognitive skills other than reading.
6.5 Future Directions
The present collection of studies has yielded some answers, but meanwhile has
raised new questions. To start with, our and previous family-risk studies did not
show significant effects of the home literacy environment on children’s reading
outcome, but behavioural genetic studies do find shared and non-shared
environmental influences. Is the home literacy environment important but did
we fail to find this because of other reasons (see Section 6.3.1)? Or are other
environmental factors more important, like pre- and perinatal factors, peer
influences, or characteristics of the school and classroom? Genetic sensitive
studies can estimate how ‘environmental’ these factors actually are.
A second interesting area for future studies regards comorbidity. From
the (intergenerational) multiple deficit model it follows that comorbidities
between developmental disorders are to be expected. This points to the
importance of studying developmental disorders that commonly co-occur.
By doing so, one can uncover shared and distinct risk factors at each of the
levels of explanation. This not only helps to understand the origin of the
comorbidity, but also the developmental paths leading to each of the disorders.
N2
N3
E1
G2
D2
D3
In examining the specificity of precursors for dyslexia we included arithmetic
and dyscalculia, but comorbidities with other developmental disorders were
not considered. Including more than just a single (dis)ability in future work will
enhance our understanding. The same applies for studying intergenerational
transfer: parents might confer risks in different domains, like reading, language,
and attention. Therefore, future studies are needed to test whether a more
complete picture of parents’ cognitive and behavioural profile gives a more
reliable indication of their offspring’s liability to a particular disorder.
The third future direction concerns the relation between parental
and children’s skills in an unselected sample. The findings reported in this
thesis on parent-child resemblance might not be generalizable to the normal
population since we had selected samples of high and low family risk. Building
on the reported studies, we are currently conducting a study that aims to
investigate relations between parents’ and children’s skills across the entire
range of reading abilities. For that purpose we test whole families in NEmO
– the science museum in Amsterdam – on reading, arithmetic, rapid naming
and phonological awareness, using the same tests for all family members. In
addition, environmental factors are assessed via questionnaires and genetic
factors via saliva samples. Over 600 participants have been tested so far. Their
data allows us to examine intergenerational transfer of skills and to study the
cognitive, environmental, and genetic factors that influence reading ability
and disability.
Finally, our studies have practical implications which warrant further
investigation. The current and previous family-risk studies suggest that children
who have a combination of weak-reading parents and weak preliteracy skills in
kindergarten run the highest risk of developing dyslexia. This group of children
is likely to have a high genetic endowment for dyslexia. This suggests that for
this group classroom instruction in reading is not sufficient. We are currently
investigating whether these children can be assisted by offering a reading
intervention, consisting of regular extra practice from mid-kindergarten till
mid-Grade 2. Preliminary results (Zijlstra, van Bergen, Koomen, & van der Leij,
2012) confirm that these children are most susceptible for dyslexia, but that
early intervention can indeed ameliorate the impact of their high liability,
provided that the intervention is intensive and individually tailored.
23629 Bergen, Elsje van V.indd 157 19-12-12 12:25
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6.6 Concluding Remarks
The present thesis has characterized the cognitive profile of children with a
family background of dyslexia that do and do not become dyslexic readers.
This has revealed precursors of dyslexia, and by including arithmetic outcome,
the specificity of these precursors. In addition, it has shed light on the
cognitive profile of their parents, which provided a start in understanding
the intergenerational transfer of reading ability and disability. Together, the
findings on child and parental level have advanced our understanding of the
factors influencing the liability and the development of dyslexia. however,
more research is needed to move the science of reading towards elucidating
the causal chain that leads to the behavioural manifestation of dyslexia.
23629 Bergen, Elsje van V.indd 158 19-12-12 12:25
General Discussion
159
6
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The greater becomes the volume of our sphere of knowledge,
the greater also becomes its surface of contact with the unknown.
- Jules Sageret -
Summary / Samenvatting
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Summary / Samenvatting
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164
Who will develop dyslexia? Cognitive precursors in parents and children
Children with dyslexia are characterized by slow and laborious reading. Dyslexia
negatively affects educational and occupational attainment. Therefore, it is
important to increase our understanding of the causes of dyslexia.
Studies comparing mono- and dizygotic twins have revealed that
dyslexia is moderately to highly heritable. This also fits with the observation
that dyslexia tends to run in families. Family-risk studies employ this fact. Such
studies follow the development of at-risk children: children with a family history
of dyslexia. After a few years of reading instruction, the at-risk children who
have developed dyslexia can be identified. Subsequently, these children can
be compared in retrospect to at-risk children without dyslexia and to typically
developing children without family risk. Did the children with later dyslexia
already demonstrate cognitive deficits before they started to learn to read? In
this way family-risk studies enable the investigation of precursors of dyslexia.
Several models have been formulated in which dyslexia is attributed
to a single cognitive deficit, but none of these models can readily explain
the range of deficits associated with dyslexia. Pennington has developed
a model in which developmental disorders (like dyslexia) are caused by a
combination of underlying cognitive factors, which can be affected to different
degrees. his model –the multiple deficit model– is depicted in Figure 1.1 in
Chapter 1. Some of these cognitive deficits are specific and some are shared
with other developmental disorders. The current thesis studied the specificity
of precursors of reading ability by investigating whether they also predict
arithmetic ability.
Inspired by the results of aetiological studies, the multiple deficit model
includes genetic and environmental factors that influence the probability of
developing a disorder; in this case, dyslexia. From this multifactorial aetiology
if follows that dyslexia is not an all-or-none condition, but instead that the
liability or risk of developing dyslexia is continuously distributed. In the present
thesis it is argued that a family-risk study offers the opportunity to test this
implication. Given that parents pass on their genes to their offspring and create
their childhood environment, it is expected that reading skills of parents can be
seen as indicators for children’s liability to dyslexia.
23629 Bergen, Elsje van V.indd 164 19-12-12 12:25
165
Summary
In short, the aims of the current thesis are threefold:
1. To study children’s cognitive precursors of dyslexia (and their specificity)
before reading instruction begins;
2. To test whether the cognitive profile of parents is indicative of a child’s
liability or predisposition to dyslexia;
3. To contribute to testing, specifying, and extending the multiple deficit
model.
As mentioned above, we employed a family-risk design to pursue these aims.
The reported studies are part of the Dutch Dyslexia Programme. Children
were considered at high risk if they had at least one parent and another family
member with dyslexia. The parents of the low-risk children were both average
to good readers. Two independent samples were followed, referred to as the
Amsterdam (N = 79) and the national (N = 212) sample. In both samples the
analyses consisted of, among others, comparing the following three groups: 1)
at-risk children with dyslexia, 2) at-risk children without dyslexia, and 3) control
children (i.e., low-risk children without dyslexia). Roughly one-third of the at-
risk children developed dyslexia.
In the Amsterdam sample (Chapter 2) children were followed from
kindergarten through Grade 5. The at-risk children who went on to have
dyslexia had poor letter knowledge in kindergarten. In addition, they were
impaired on a cognitive skill called rapid naming. Rapid naming is the ability
to rapidly retrieve the name of a symbol from long-term memory, measured
by naming a list of familiar items, like colours or pictures. The at-risk children
without dyslexia performed better on letter knowledge and rapid naming,
but still slightly below the level of controls. The same pattern was found
for subsequent reading development. A key finding related to the reading
difficulties of the parents with dyslexia: those of the affected children were
more severely impaired than those of the unaffected children.
Chapters 3 to 5 are devoted to the national sample, reporting the
development of 4 to 9 years of age. IQ was assessed at the age of 4 and
preliteracy skills at the end of kindergarten. The investigated preliteracy
skills included letter knowledge, rapid naming, and phonological awareness.
Phonological awareness is the ability to detect and manipulate sounds in
spoken words. In kindergarten this can be measured with items like “What are
the sounds in ‘cat’?” Answer: “/k/ /a/ /t/”. Compared to controls, at-risk children
23629 Bergen, Elsje van V.indd 165 19-12-12 12:25
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with later dyslexia were impaired across IQ and preliteracy tasks. The other at-
risk children showed mild deficits in verbal IQ and phonological awareness, but
not in nonverbal IQ, letter knowledge, and rapid naming. Relations between
these precursors and later reading and arithmetic revealed that nonverbal IQ
and rapid naming were related to both outcome measures, whereas the other
precursors were more specific for reading. At-risk dyslexia children continued
to show deficits in phonological awareness and rapid naming in second and
third grade, alongside impairments in literacy and arithmetic. At this age, the
at-risk no-dyslexia children had weaknesses in phonological awareness and
literacy, although they scored within normal limits. Importantly, the finding
in the Amsterdam sample regarding differences between the two at-risk
groups in parental reading could be replicated and extended: also parental
rapid naming differentiated the groups, as did literacy difficulties of the parent
without dyslexia. In both the Amsterdam and the national sample there was
no clear evidence of effects of home literacy environment on children’s reading
outcome.
Chapter 6 gives an extensive summary of the findings and discusses
theoretical implications. To return to the multiple deficit model, one of
the predictions of this model is that the liability distribution for a given
developmental disorder is continuous. Both the findings at the child-level and
parent-level support this prediction. It is argued that cognitive skills of parents
are an indicator of their offspring’s position on the liability continuum. Chapter
6 works towards specifying the multiple deficit model for the case of dyslexia
and concludes by presenting an extended model: the intergenerational
multiple deficit model (see Figure 6.1), which includes the effect of parental
characteristics. Finally, recommendations for future research are given.
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Samenvatting
Wie wordt dyslectisch? Cognitieve voorlopers in ouders en kinderen
Bij kinderen met dyslexie gaat het lezen moeizaam en traag. Dyslexie heeft
een negatieve invloed op zowel schoolprestaties als het beroepsniveau. het is
daarom van belang om de oorzaken van dyslexie beter te begrijpen.
Onderzoek met mono- en dizygote tweelingen heeft uitgewezen dat
dyslexie in sterkte mate erfelijk is. Dit past ook bij de waarneming dat dyslexie
binnen bepaalde families veel voorkomt. Familiaire risico studies maken gebruik van
dit feit. Zulke studies volgen de ontwikkeling van risicokinderen: kinderen bij wie
dyslexie in de familie voorkomt. Wie van deze risicokinderen dyslexie ontwikkeld
hebben kan worden vastgesteld nadat ze een paar jaar leesonderwijs hebben
gehad. Vervolgens kunnen deze kinderen retrospectief worden vergeleken met
risicokinderen zonder dyslexie en met zich normaal ontwikkelende kinderen die
geen familiair risico dragen. Toonden de kinderen met latere dyslexie al cognitieve
tekorten voordat ze leerden lezen? Op deze manier maken familiaire risico studies
het mogelijk voorlopers van dyslexie op te sporen.
Er zijn verschillende modellen geformuleerd waarin dyslexie
toegeschreven wordt aan een enkelvoudig cognitief tekort, maar geen
van deze modellen kan het brede scala van met dyslexie geassocieerde
kenmerken verklaren. Pennington heeft een model ontwikkeld waarin
ontwikkelingsstoornissen (zoals dyslexie) verklaard worden uit een combinatie
van onderliggende cognitieve factoren waarop een individu in meer of
mindere mate uitvalt. Zijn model –het multiple deficit model– is afgebeeld
in Figuur 1.1 in hoofdstuk 1. Sommige van die cognitieve tekorten zijn
specifiek voor één bepaalde stoornis en sommige worden gedeeld met
andere ontwikkelingsstoornissen. In dit proefschrift wordt de specificiteit van
voorlopers van leesvaardigheid bestudeerd door te onderzoeken in hoeverre
zij ook rekenvaardigheid voorspellen.
Gebaseerd op bevindingen van etiologische studies bevat het multiple
deficit model genetische en omgevingsfactoren die de kans op het ontwikkelen
van een stoornis beïnvloeden, in dit geval dyslexie. Uit deze multifactoriële
etiologie volgt dat dyslexie niet een alles-of-niets conditie is, maar dat de
vatbaarheid voor, of het risico op het ontwikkelen van dyslexie continu verdeeld
is. In het huidige proefschrift wordt beargumenteerd dat een familiaire risico
studie de mogelijkheid biedt om deze implicatie te toetsen. Aangezien ouders
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hun genen doorgeven aan hun kinderen en de omgeving van hun kinderen
creëren, valt het te verwachten dat de leesvaardigheid van ouders kan worden
gezien als een indicator voor het risico op dyslexie dat kinderen lopen.
In het kort zijn de doelen van dit proefschrift:
1. het onderzoeken van cognitieve voorlopers van dyslexie (en hun
specificiteit) voordat het leesonderwijs begint.
2. Toetsen of het cognitieve profiel van ouders indicatief is voor het risico
dat een kind loopt op dyslexie.
3. Bijdragen aan het toetsen, specificeren en uitbreiden van het multiple
deficit model.
Zoals hierboven genoemd maakten we gebruik van een familiair risico design
om deze doelen na te streven. De gerapporteerde studies zijn onderdeel van het
Dutch Dyslexia Programme1. Als kinderen ten minste één dyslectische ouder en
een ander dyslectisch familielid hadden werden ze als hoog risico beschouwd.
De ouders van de laag-risico kinderen waren beiden gemiddelde tot goede
lezers. Twee onafhankelijke steekproeven werden gevolgd: de Amsterdamse (N
= 79) en de nationale steekproef (N = 212). In beide steekproeven werden drie
groepen vergeleken: 1) risicokinderen met dyslexie, 2) risicokinderen zonder
dyslexie, en 3) controle kinderen (d.w.z., kinderen met een laag risico en zonder
dyslexie). Ongeveer een derde van de risicokinderen ontwikkelde dyslexie.
In de Amsterdamse steekproef (hoofdstuk 2) werden de kinderen van
groep 2 tot en met groep 7 gevolgd. De risicokinderen die later dyslectisch
werden hadden als kleuter een zwakke letterkennis. Daarnaast scoorden ze
zwak op een cognitieve maat genaamd snelbenoemen. Snelbenoemen is de
vaardigheid om snel namen van symbolen uit het lange termijn geheugen op
te halen. Dit wordt gemeten met het hardop benoemen van een lijst bekende
items, zoals kleuren of plaatjes. De risicokinderen zonder dyslexie presteerden
beter op letterkennis en snelbenoemen, maar wel net iets onder het niveau van
de controle kinderen. Voor de latere leesontwikkeling werd hetzelfde patroon
gevonden. Een belangrijke bevinding had betrekking op de dyslectische
ouders: de ouders van wie de kinderen dyslectisch werden waren zwaarder
dyslectisch dan de ouders van wie de kinderen niet dyslectisch werden.
1 Zie http://www.nwo.nl/nwohome.nsf/pages/NWOA_6ZJGAJ
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Samenvatting
De hoofdstukken 3, 4 en 5 zijn gericht op de nationale steekproef
en rapporteren de ontwikkeling van 4 tot 9 jaar. Op 4-jarige leeftijd werd het
IQ bepaald en aan het eind van de kleuterschool werden de voorschoolse
vaardigheden in kaart gebracht. De onderzochte voorschoolse vaardigheden
bestonden uit letterkennis, snelbenoemen en fonologisch bewustzijn.
Fonologisch bewustzijn is de vaardigheid om klanken in gesproken woorden
te herkennen en te manipuleren. In de kleuterklas kun je dit meten met vragen
als: “Welke klanken zitten er in ‘kat’?” met als antwoord: “/k/ /a/ /t/”. Vergeleken
met de controle kinderen scoorden de risicokinderen bij wie later dyslexie
werd vastgesteld zwak op alle IQ taken en voorschoolse vaardigheden. De
andere risicokinderen lieten milde tekorten zien op verbaal IQ en fonologisch
bewustzijn, maar niet op nonverbaal IQ, letterkennis en snelbenoemen.
Analyse van de relaties tussen deze voorlopers en het latere lezen en rekenen
wees uit dat nonverbaal IQ en snelbenoemen gerelateerd zijn aan beide
schoolvaardigheden, terwijl de andere voorlopers meer specifiek zijn voor het
lezen. Risicokinderen met dyslexie bleven in groep 4 en 5 problemen houden
met fonologisch bewustzijn en snelbenoemen, en liepen daarnaast achter
met lezen, spellen en rekenen. De risicokinderen zonder dyslexie waren op
die leeftijd matig (maar niet onvoldoende) in fonologisch bewustzijn, lezen
en spellen. Een belangrijk resultaat was dat de bevinding in de Amsterdamse
steekproef van verschillen in leesvaardigheid tussen de dyslectische ouders
van de twee risicogroepen kon worden gerepliceerd en uitgebreid: ook
snelbenoemen van de ouders maakte onderscheid tussen deze groepen.
Daarnaast was een opvallend resultaat dat de ouder zonder dyslexie ook
zwakker las wanneer het kind dyslectisch werd. Wat betreft de invloed van de
omgeving was er echter in zowel de Amsterdamse als de nationale steekproef
geen duidelijk bewijs voor effecten van geletterdheid van de thuisomgeving
op de leesuitkomst van kinderen.
In hoofdstuk 6 worden de resultaten van de verschillende studies
samengevat en worden theoretische implicaties besproken. Om terug te
komen op het multiple deficit model, één van de voorspellingen van dit
model is dat de risicoverdeling voor een gegeven stoornis continu is. Zowel
de bevindingen op kind- als ouderniveau ondersteunen deze voorspelling.
Beargumenteerd wordt dat de cognitieve vaardigheden van ouders indicatief
zijn voor de positie van hun kinderen op het risicocontinuüm. In hoofdstuk
6 wordt toegewerkt naar de specificatie van het multiple deficit model voor
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dyslexie. Afgesloten wordt met de introductie van een uitgebreider model: het
intergenerationele multiple deficit model (zie Figuur 6.1), waarin de effecten van
ouderkenmerken zijn toegevoegd. Tot slot worden enige aanbevelingen voor
verder onderzoek gegeven.
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23629 Bergen, Elsje van V.indd 171 19-12-12 12:25
Wetenschap vergt offers.
- Meneer Hoop, mijn leraar scheikunde –
[Science requires sacrifices. Mr Hoop, my chemistry teacher]
Acknowledgements / Dankwoord
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Acknowledgements / Dankwoord
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Acknowledgements
The most read section of my thesis; I hope it doesn’t disappoint you. There is but
one name on the cover, but many contributed scientifically or non-scientifically
to its culmination.
First Peter. I couldn’t have wished for a better mentor. I appreciate
your approachability, involvement, and critical approach. Thank you for the
stimulating scientific conversations and for your enthusiasm for your area of
research, that has become mine as well. Aryan, thank you for setting up this
unique and large-scale study, for having faith in me, and for assigning me to
your beautiful project. Frans, my thesis would have never come about without
all your statistics teaching. All three of you, thank you for your supervision and
the freedom you gave me to work from Zürich, to follow courses, and to do extra
research projects. maggie, Bart, Ludo, Frank, and Judith, thank you very much
for taking the time out of your busy schedules to review my thesis and be my
opponents. To all parents and children who participate in the Dutch Dyslexia
Programme (DDP): thank you so much for your dedication. You taught us a
great deal about dyslexia. I have recognized your participation by representing
you and your child on the cover.
The research group Learning Problems (OLP) changes in composition,
but remains characterized by diverse personalities that together form
a warm nest and a close team. I miss you guys. I reminisce about the good
time in G0.05, the Coffee Company (our other office), the conferences and
accompanying holidays, and the writing retreats. Eva and Judith acted as my
big OLP-sisters. Judith, thank you for taking care of me. Eva, thanks for being a
role model. We grew up in neighbouring villages and shared as kids the same
recorder teacher, but only really got to know each other in OLP. Unfortunately
we don’t live in neighbouring villages any more, but nowadays you Skype me
weekly from Sydney, no matter whether I am in Amsterdam, York, or Oxford.
marleen and Debora, once we formed the new OLP trio and shared our joys
and sorrows. Debora, many thanks for your support, smile, and working out-
of-hours company. haytske, you are open, full of energy and special, and I like
that. Titia, so much that we share: passion for science, the DDP, and of course
our self-developed NEmO-project. madelon and marloes, my fellow Peter-PhD-
students, it’s great to have you by my side during this nerve wrecking hour and
the preparations. madelon, I enjoy discussing analyses and reading research
23629 Bergen, Elsje van V.indd 174 19-12-12 12:25
175
acknowledgementS
with you. During one of our Coffee Company sessions even a new project was
born, the Em-project, which will shine in your thesis. marloes: great energy.
Although we only had a short period together in OLP, I got to know you well
because of the writing retreat and the Canada holiday. Britt: great chef, fresh
DDP-blood, and NEmO help; super! Anna, Ben, and Evelien, many thanks for
all your DDP work and for answering my questions. Other colleagues that I like
to mention: Dominik and Julia (we started together in the C-building), maaike
(adoptive sister), Anne and Cristina (fellow reading researchers), Suzanne
(juggling mate), and helma, Jantine, Francine and Bettina (social-emotional
bulwark).
moving on to the researchers outside the University of Amsterdam
that contributed to my scientific development. Faculty of human movement
Sciences, thank you for providing good education and the challenges beyond,
and for fuelling scientific passion. mark, Lisa, and Andrew, thanks to you I had
an amazing internship in Aberdeen. mark, thank you for your confidence in
me as a researcher, especially when I had lost mine. minna and JLD, thank you
for initiating our fruitful international collaboration; kiitos. maggie and CRL, I
had a nice and valuable study visit in York. many thanks for your help with my
research proposal. Excellent that we are now both based in Oxford. Robin, we
never run out of things to talk about, research and stats in particular. I have
good memories of your stay with us in Amsterdam and I love being together
in Oxford. Simon, thank you for the exciting multidisciplinary collaboration.
Dorothy and OSCCI, thanks a lot for welcoming me in the research group and
for offering me this excellent opportunity to expand my (scientific) horizon.
Kate and LCD, thank you for the ‘honorary membership’. Oriel College and
Orielenses, I feel privileged to be part of you. It is a sometimes slightly bizarre
but brilliant experience.
For the highly needed mental distraction and physical outlet I was
lucky to have session for 10 hours/week at gymnastics club Olympia and
sport acrobatics club CCO. It was good fun! Thanks to, among others, map,
Guido, our flyers Simone, Tamara and miranda, and of course my base and
sister Anke. Anke, we have been through a lot together and I am proud of you.
Oxford Sirens, so cool to be part of the cheerleading competitive squad. Yet
again somersaults and throwing girls… Professor Van der Werf and his team:
amazing. my life is a present. my family in law, Zoia, Adi, Pierre and Bernadette,
thanks for your support and interest. Brothers Aart and Geert, and sisters Anke
23629 Bergen, Elsje van V.indd 175 19-12-12 12:25
176
and Janneke, I am so happy to have you! Rita, Bart, and Suzanne, it’s great to
have you in the family. mommy and daddy, you always being there for me gives
me such a secure feeling. I love coming home. Yves, you are my soul mate, my
rock; even though it’s again at distance. I can’t live without you.
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DankwoorD
Dankwoord
Het meest gelezen onderdeel van mijn proefschrift; Ik hoop dat het niet
tegenvalt… Eén naam op de kaft, maar velen die wetenschappelijk – of juist
niet wetenschappelijk – hebben bijgedragen.
Allereerst Peter. Een betere leermeester had ik me niet kunnen wensen.
Ik waardeer je toegankelijkheid, betrokkenheid en kritische benadering.
Bedankt voor de stimulerende wetenschappelijke gesprekken en voor je
enthousiasme voor je onderzoeksgebied, dat nu ook het mijne geworden is.
Aryan, bedankt voor het opzetten van dit unieke en gigantische onderzoek en
voor het vertrouwen dat je in me stelde door mij op je mooie project te zetten.
Frans, zonder al het statistiekonderwijs van jou was dit proefschrift er nooit
gekomen. Alledrie bedankt voor jullie begeleiding en de ruimte en vrijheid
die jullie me hebben gegeven voor het werken vanuit Zürich, het volgen van
cursussen, en het doen van extra onderzoeksprojecten. Maggie, Bart, Ludo,
Frank en Judith, hartelijk bedankt voor de tijd en energie die jullie hebben
gestoken in het beoordelen van mijn proefschrift en in het opponeren. Alle
ouders en kinderen die meedoen aan het Dutch Dyslexia Programme (DDP):
hartelijk bedankt voor jullie inzet. Zonder jullie waren we geen moer wijzer
geworden over dyslexie. Als dank heb ik u en uw kind op de kaft gezet.
De onderzoeksgroep Onderwijsleerproblemen (OLP) wisselt van
samenstelling, maar blijft gekenmerkt door uiteenlopende karakters die samen
een warm nest en een hecht team vormen. Ik mis jullie. Met weemoed denk ik
terug aan de leuke tijd in G0.05, de Coffee Company (ons andere kantoor), de
conferenties met de vakanties eromheen, en de schrijfweken. Eva en Judith
fungeerden als mijn grote OLP-zussen. Judith, bedankt voor je goede zorgen.
Eva, bedankt voor je voorbeeldfunctie. We groeiden op in buurdorpen en hadden
als kind dezelfde blokfluitjuf, maar leerden elkaar bij OLP pas echt kennen.
Helaas wonen we niet meer in buurdorpen, maar nu Skype je me wekelijks
vanuit Sydney, of ik nu in Amsterdam, York, of Oxford ben. Marleen en Debora,
wij vormden ooit het nieuwe trio bij OLP en deelden lief en leed. Debora, dank
je wel voor je steun, glimlach en overwerkgezelligheid. Haytske, je bent open,
energiek en bijzonder en dat mag ik graag. Titia, zoveel dat we delen: passie
voor wetenschap, het DDP, en natuurlijk ons zelf opgezette NEMO-project.
Madelon en Marloes, mijn mede Peter-promovenda’s, heel fijn dat jullie mij
terzijde willen staan tijdens dat zenuwslopende uur en de aanloop ernaartoe.
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178
madelon, heerlijk om me jouw analyses en leesonderzoek te bediscussiëren.
Tijdens één van onze gesprekken in de Coffee Company werd zelfs een nieuw
onderzoek geboren, het Em-project, dat straks in jouw proefschrift schittert.
marloes: heerlijk energiek. In de korte tijd samen in OLP heb ik je toch goed
leren kennen dankzij de schrijfweek en Canadavakantie. Britt: topkok, vers
DDP-bloed en NEmO-hulp; super! Anna, Ben en Evelien, bedankt voor al jullie
werk aan het DDP en het beantwoorden van al mijn vragen. Andere collega’s
die ik graag wil noemen: Dominik en Julia (gedrie gestart in het C-gebouw),
maaike (adoptiezusje), Anne en Cristina (medeleesonderzoekers), Suzanne
(jongleermaatje), en helma, Jantine, Francine en Bettina (sociaal-emotioneel
bolwerk).
Dan de onderzoekers buiten de UvA die hebben bijgedragen aan mijn
wetenschappelijke ontwikkeling. Faculteit der Bewegingswetenschappen
aan de VU, bedankt voor de gedegen opleiding, de uitdagingen daarbuiten,
en jullie aanstekelijke enthousiasme voor onderzoek. mark, Lisa en Andrew,
dankzij jullie had ik een geweldige stage in Aberdeen. mark, bedankt voor je
vertrouwen in mij als onderzoeker, met name toen ik het mijne kwijt was. minna
en JLD, dank je wel voor het opstarten van onze vruchtbare internationale
samenwerking; kiitos. maggie en CRL, ik had een leuke en waardevolle periode
in York. Bedankt voor je hulp met mijn onderzoeksvoorstel. Geweldig dat we
nu allebei in Oxford zitten. Robin, over onderzoek en vooral statistiek raken
wij nooit uitgepraat. Ik heb goede herinneringen aan je verblijf bij ons in
Amsterdam en vind het super dat ik nu bij jou in Oxford ben. Simon, bedankt
voor de spannende multidisciplinaire samenwerking. Dorothy en OSCCI,
bedankt voor het opnemen van mij in de onderzoeksgroep en het bieden van
deze geweldige kans om mijn (wetenschappelijke) horizon te verruimen. Kate
en LCD, bedankt voor het ‘erelidmaatschap’. Oriel College en Orielenses, ik voel
me bevoorrecht dat jullie me hebben opgenomen. het is een soms ietwat
bizarre maar prachtige ervaring.
Voor de broodnodige geestelijke afleiding en fysieke uitlaatklep had ik
gelukkig 10 uur/week trainen bij turnvereniging Olympia en acrogymvereniging
CCO. het was super leuk! O.a. met dank aan juf map, Guido, onze bovenpartners
Simone, Tamara en miranda en natuurlijk mijn onder en zusje Anke. Anke, we
hebben samen heel wat meegemaakt en ik ben trots op je. Oxford Sirens, heel
tof om deel uit te maken van het cheerleading wedstrijdteam. Toch weer salto’s
en meisjes gooien... Prof. Van der Werf en zijn team: geweldig. mijn leven is een
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dankwoord
cadeautje. mijn schoonfamilie, Zoia, Adi, Pierre en Bernadette, bedankt voor
jullie steun en interesse. Broers Aart en Geert, en zusjes Anke en Janneke, wat
ben ik blij met jullie! Rita, Bart en Suzanne, fijn dat jullie erbij zijn gekomen.
Papa en mama, wat een veilig gevoel dat jullie altijd voor me klaar staan. het is
altijd heerlijk thuis te komen. Yves, mijn rots in de branding, ook al is het weer
op afstand. Ik kan niet zonder je.
23629 Bergen, Elsje van V.indd 179 19-12-12 12:25
Learn from yesterday, live for today, hope for tomorrow. The important thing is
not to stop questioning.
- Albert Einstein -
Publications & Curriculum Vitae
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Publications & Curriculum Vitae
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List of Publications
Litt, R.A., de Jong, P.F., van Bergen, E., & Nation, K. (in press). Dissociating
crossmodal and verbal demands in paired associate learning (PAL):
What drives the PAL-reading relationship? Journal of Experimental Child
Psychology.
van der Leij, A., van Bergen, E., & de Jong, P.F (2013). Longitudinal family-risk
studies of dyslexia: Why some children develop dyslexia and others
don’t. In A.J. Fawcett (Ed.), British Dyslexia Association Handbook 2013.
Bracknell: British Dyslexia Association.
van Bergen, E., de Jong, P.F., Plakas, A., maassen, B., & van der Leij, A. (2012). Child
and parental literacy levels within families with a history of dyslexia.
Journal of Child Psychology and Psychiatry, 53(1), 28-36. Chapter 3 of
the present thesis
Torppa, m., Eklund, K., van Bergen, E., & Lyytinen, h. (2011). Parental literacy
predicts children’s literacy: A longitudinal family-risk study. Dyslexia,
17(4), 339-355.
van Bergen, E., de Jong, P.F., Regtvoort, A., Oort, F., S. van Otterloo, & van der Leij,
A. (2011). Dutch children at family risk of dyslexia: Precursors, reading
development, and parental effects. Dyslexia, 17(1), 2-18. Chapter 2 of
the present thesis
van Swieten, L.m., van Bergen, E., Williams, J.h.G., Wilson, A.D., Plumb, m.S.,
Kent, S.W., & mon-Williams, m.A. (2010). A test of motor (not executive)
planning in developmental coordination disorder and autism. Journal
of Experimental Psychology: Human Perception and Performance, 36(2),
493-499.
van Bergen, E., van Swieten, L.m., Williams, J.h.G., & mon-Williams, m. (2007).
The effect of orientation on prehension movement time. Experimental
Brain Research, 178(2), 180-193.
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183
Publications & curriculum Vitae
Submitted or in Revision
van Bergen, E., de Jong, P.F., maassen, B., Krikhaar, E., Plakas, A., & van der Leij,
A. (in revision). IQ of four-year-olds who go on to develop dyslexia.
Chapter 5 of the present thesis
Torppa, m., Eklund, K., van Bergen, E., & Lyytinen, h. (submitted). Late-emerging
and resolving dyslexia: A follow-up study from kindergarten to Grade
8.
van Bergen, E., de Jong, P.F., maassen, B., & van der Leij, A. (submitted). The
effect of parents’ literacy skills and children’s preliteracy skills on the
risk of dyslexia. Chapter 4 of the present thesis
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184
Curriculum Vitae
Elsje van Bergen was born in Landsmeer, the
Netherlands, on 18th December 1981. After
completing secondary education at Pascal
College in Zaandam in 2000, she lived for a year in
Zaķuimuiža, Latvia, and attended the Rīgas Doma
Kora Skola. Back in the Netherlands, she studied
Sciences and Innovation management for a
year at the University of Utrecht (Propaedeutic
diploma, 2002), after which she proceeded to
study human movement Sciences at the VU University Amsterdam (Bachelors,
2005, cum laude; masters, 2006, cum laude) and a minor in Romanian at the
University of Amsterdam (2008, cum laude). In 2006 Elsje carried out her six-
month master’s project at the School of Psychology, University of Aberdeen,
Scotland, working with Professor mark mon-Williams. She investigated
reach-to-grasp movements in children (with and without developmental
coordination disorder), healthy adults, and stroke patients. From 2004 to 2008
she was employed by the Faculty of human movement Sciences, VU University
Amsterdam: first as a teaching- and research-assistant and the final year as a
PhD-student, studying sources of interlimb interactions. Subsequently she
switched to the field of reading research. From 2008 to 2012 she carried out her
PhD project at the Research Institute of Child Development and Education at the
University of Amsterdam. her research focused on the development of reading
and its cognitive underpinnings in children born into families with and without
a history of dyslexia. During her PhD Elsje obtained the BKO, the certificate for
teaching at universities. In 2012 she moved to England to join Professor Dorothy
Bishop’s group at the Department of Experimental Psychology, University
of Oxford. her postdoctoral research project is called ‘Reading fluency: Like
parent like child?’. In this project she studies the cognitive, environmental, and
genetic factors that influence reading ability and disability. moreover, she aims
to identify parental skills that can predict their offspring’s reading skills. Elsje
is funded by a Rubicon grant from the Netherlands Organisation for Scientific
Research. She has also been awarded a Junior Research Fellowship at Oriel
College, Oxford.
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