Early Child Language Mediates the Relation Between Home Environment
and School Readiness
and Ginette Dionne
Universite ´ Laval
Universite ´ de Sherbrooke
Daniel Pe ´russe and
Richard E. Tremblay
Universite ´ de Montre ´al
Universite ´ Laval
Home environment quality is a well-known predictor of school readiness (SR), although the underlying pro-
cesses are little known. This study tested two hypotheses: (a) child language mediates the association between
home characteristics (socioeconomic status and exposure to reading) and SR, and (b) genetic factors partly
explain the association between language and SR. Data were collected between 6 and 63 months in a large
sample of twins. Results showed that home characteristics had direct effects on SR and indirect effects
through child language. No genetic correlation was found between language and SR. These results suggest
that home characteristics affect SR in part through their effect on early language skills, and show that this pro-
cess is mainly environmental rather than genetic in nature.
School readiness (SR) is a multidimensional con-
struct that includes behavioral, emotional, cogni-
tive, and knowledge components that make the
child ‘‘ready to learn’’ at school entry (Blair, 2002;
Chew, 1981). As SR assessments are quite heteroge-
neous in content, their predictive validity regarding
future school achievement tends to vary greatly (for
a review, see LaParo & Pianta, 2000). Some have
argued that fluid cognitive skills, such as executive
functions and memory, are better predictors of
future school achievement (Blair, 2006). However,
more crystallized preacademic knowledge compo-
nents typically assessed in SR batteries, such as
number, letter, and color knowledge, have been
shown to predict early school achievement over
and above general cognitive ability (Forget-Dubois
et al., 2007; Hess, Holloway, Dickson, & Price, 1984;
Lemelin et al., 2007). Children who lack the under-
lying basic knowledge of the early curriculum may
experience difficulties in keeping up. Moreover,
preacademic knowledge likely reflects both the
underlying fluid cognitive skills needed for early
learning, as well as the exposure to basic knowl-
edge (Blair, 2006). Therefore, focusing on preaca-
demic knowledge is a valid way of assessing SR.
It is generally assumed that SR can be traced back
to prior influences in the home environment (Hess
et al., 1984; Melhuish et al., 2008; NICHD Early
Child Care Research Network, 2000). Indeed, many
characteristics of the home environment, including
attachment security and continuing sensitive care,
verbal stimulation, access to educational material in
the home, and specific parental practices such as
reading with the child, have been linked to SR (Bel-
sky & Fearon, 2002; Bradley & Caldwell, 1984; Britto,
Brooks-Gunn, & Griffin, 2006; Connell & Prinz,
2002; McLoyd, 1998; Reese, Cox, Harte, & McAnally,
2003; Stipek & Ryan, 1997). In addition, the gains in
SR by children of head start were shown to be
greater when parents were more involved in the
program (Parker, Boak, Griffin, Ripple, & Peay,
We are grateful to the parents and the twins participating in
the Quebec Newborn Twin Study. We also thank He ´le `ne Paradis
and Bei Feng for their assistance in data management and prepa-
ration, and Jocelyn Malo for coordinating the data collection.
This research was supported by grants awarded to Michel Boi-
vin, Daniel Pe ´russe, and Richard E. Tremblay from the National
Health Research Development Program (NHRDP, #6605-05-
2000⁄2590183), the Social Sciences and Humanities Research
Council of Canada (SSHRC), the Canadian Institutes of Health
Research (CIHR), the Fonds que ´be ´cois de la recherche sur la so-
cie ´te ´ et la culture (FQRSC), and the Fonds de la recherche en
sante ´ du Que ´bec (FRSQ).
Correspondence concerning this article should be addressed to
Ginette Dionne, E´cole de Psychologie, Universite ´ Laval, Pavillon
Fe ´lix-Antoine Savard, 2325 Rue des Bibliothe `ques, Quebec City,
QC, Canada G1V 0A6. Electronic mail may be sent to ginette.
Child Development, May/June 2009, Volume 80, Number 3, Pages 736–749
? 2009, Copyright the Author(s)
All rights reserved. 0009-3920/2009/8003-0010
1999; Ramey, 1999). Therefore, the association
between characteristics of the home environment
and SR is empirically documented.
In contrast, relatively few studies have tested
specific hypotheses regarding the processes leading
to the observed association (NICHD Early Child
Care Research Network, 2003). The process model
that has been tested most often is a mediation
model (Foster, Lambert, Abbott-Shim, McCarty, &
Franze, 2005; NICHD Early Child Care Research
Network, 2003). Mediation models can be thought
of as directional models specifying direct and indi-
rect effects of predictors on an outcome (Baron &
Kenny, 1986; Cole & Maxwell, 2003). Regarding the
effects of the home environment on SR, two types
of mediation models have been considered: A first
type of model posits that a distal measure of home
environment influences the outcome through an
ecologically proximal characteristic of the home
environment. The underlying principle of this
model is that the environment has specific rather
than global influences over developmental outcome
(Hoff, 2003). For example, socioeconomic status
(SES), a distal characteristic of the environment,
was shown to have an indirect effect on SR through
parental practices regarding home learning, a prox-
imal measure of the home environment quality
(Foster et al., 2005). A second type of mediation
model suggests that the environment could affect
the child outcome of interest through its effect on
another child outcome; for example, the influence
of home environment quality on SR was shown to
be mediated by sustained attention, a child charac-
Network, 2003). These two types of processes do
not have to be mutually exclusive; both allow the
testing of explanatory models involving global
effects of distal factors and specific effects of proxi-
mal factors, and the relation between these two
types of effects.
The first goal of this study was to test whether
the effect of home environment quality on SR can
be explained in part by its influence on early child
language. We considered SES as an indicator of the
general home environment quality and exposure to
reading as a specific feature of the family environ-
ment that is likely to promote SR both directly and
indirectly through its influence on early language.
Language as a Mediator Between Home Environment
Influence and SR
Before testing a mediation model, there must be
evidence that the putative predictor, outcome, and
mediator variables are associated (Baron & Kenny,
1986). Indeed, previous studies offer solid empirical
reasons to hypothesize that children’s language
skills may play a mediating role in the association
between the quality of the environment and SR.
First, many features of the home environment
are significantly predictive of later language skills
(Hart & Risley, 1992; Hoff & Tian, 2005; Peterson,
Jesso, & McCabe, 1999; Snow, Tabors, & Dickinson,
2001; Tabors, Roach, & Snow, 2001), especially of
skills associated with written language (Bus, van
IJzendoorn, & Pellegrini, 1995). Early vocabulary
has been associated with SES and the quality of
maternal speech; in fact, the effect of SES on early
vocabulary has been shown to be entirely mediated
by maternal speech in a sample of middle and high
SES children, a result consistent with the principle
of a specific (rather than global) effect of the family
environment on child language (Hoff, 2003). The
quality of the home literacy environment was asso-
ciated with vocabulary in first grade (Van Steensel,
2006). Sensitive parenting, maternal responsive-
ness, and the feedback children receive in their
interactions with adults have also been shown
to predict early language skills (Hirsh-Pasek &
Burchinal, 2006; Tamis-LeMonda & Bornstein, 2002;
Tomasello, 1992; Tomasello & Farrrar, 1986). Finally,
regular reading in the first 3 years of life in low-
income families was shown to predict later vocabu-
lary and general cognitive skills (Raikes et al.,
Second, the association between language skills
and SR is also well documented. In children from
low SES background, higher language competence
was correlated with SR ratings (Fiorentino & Howe,
2004). Narrative abilities were related to children’s
functioning in the classroom environment (Peterson,
1994). Similarly, children’s mean length of utter-
ances at age three was correlated with emerging lit-
eracy in kindergarten (Tabors et al., 2001). Language
skills at school entry were also found to be a protec-
tive factor moderating the association between
social risk and school achievement (Burchinal,
Roberts, Zeisel, Hennon, & Hooper, 2006). However,
the relation between language and SR remains to be
clarified because language has often been consid-
ered not only as a predictor of school achievement
(see, e.g., Agostin & Bain, 1997; Snow et al., 2001),
but also as a SR measure in itself (Dunn & Dunn,
1981; Majsterek & Ellenwood, 1995; Mantzicopoulos,
1999; Williams, Voelker, & Ricciardi, 1995). Given
the documented sequence of predictive associations
between home environment quality, early language
skills, and early school achievement, there is ground
Predictors of School Readiness737
to hypothesize that early language skills mediate the
association between home environment and SR.
Correlation Between Environmental and Genetic Factors
Even though previous studies have established
the importance of home environment quality for
the development of SR, the possibility that genetic
factors could play a role in this process needs to be
considered for two main reasons.
First, parents create the family environment
partly on the basis of their own genetic characteris-
tics. Thus, the family environment is probably asso-
consequence, to their children’s genes. Given the
likelihood of such a genotype–environment correla-
tion (rge; Plomin, DeFries, McClearn, & McGuffin,
2001), any model proposing a mediation of distal
environmental characteristics by a proximal envi-
ronmental characteristic (Foster et al., 2005) could
in fact reflect an association between two environ-
mental characteristics via the parental genotype.
This type of rgeis rarely taken into account because
measures of the environment are often not geneti-
cally informative (Turkheimer, D’Onofrio, Maes, &
Eaves, 2005). It is to be noted that in the context of
genetic studies, any source of variance other than
genes is considered as ‘‘the environment,’’ so the
definition of environment is much less specific than
in studies based on models of environmental influ-
Second, the association between two child char-
acteristics may stem in part from a shared genetic
basis, as most human traits and behaviors are
partly heritable (Turkheimer, 2000). This is espe-
cially true of cognitive development where many
cognitive skills aregenetically
Plomin & Spinath, 2004). The importance of these
genetic correlations led Plomin and Kovas (2005) to
conclude that the same ‘‘generalist genes’’ influence
many aspects of cognition. Therefore, a mediation
model positing child characteristics as mediator
and outcome, like the model tested by NICHD
Early Child Care Research Network (2003), could
partly reflect the effect of generalist genes.
Unfortunately, there is no simple solution for
including measures of the environment in geneti-
cally informative studies. The conclusions that can
be drawn remain limited by the difficulty to control
for rge(Purcell & Koenen, 2005; Turkheimer et al.,
2005), and the present study is no exception. How-
ever, it is possible to address the problem of poten-
tial genetic confounds in the association between
two child outcomes in this study by testing for a
genetic correlation between the mediator and the
outcome of our model, language and SR. Their
association could reflect a common genetic basis
rather than result from the indirect influence of
home environment quality and, at the very least,
this should be considered in the interpretation of
the mediation model.
There is indeed evidence that many language,
learning, and general cognitive abilities share a
common genetic basis, and that these may be more
important than environmental factors in explaining
the association and stability of these abilities
(Kovas, Haworth, Dale, & Plomin, 2007; Plomin &
Kovas, 2005). Yet, although heritability estimates of
fluid skills (e.g., IQ) range between 40% and 60%,
varying with age (McGue, Bouchard, Iacono, &
Lykken, 1993; Plomin et al., 2001) and show little
influence of the environment shared by children of
the same family, a different picture emerges when
SR is considered. Genetic studies of specific aspects
of SR show only modest heritability, ranging from
30% to 45% (Lemelin et al., 2007; Oliver, Dale, &
Plomin, 2005); a greater proportion of their variance
seems environmentally driven, more specifically
through factors shared by children in a family. Sim-
ilarly, genetic studies of early language have shown
that the environment shared by children of a family
is crucial to vocabulary development and, to a les-
ser extent, to grammar skills, although early lan-
guage skills are also moderately heritable (between
25% and 50%; Dale, Dionne, Eley, & Plomin, 2000;
Dionne, Dale, Boivin, & Plomin, 2003; Dionne,
Hohnen & Stevenson, 1999). To sum up, environ-
mental processes are important factors in the etiol-
ogy of both SR and early language skills, but both
are also heritable; whether their association is
partly accounted for by common genetic factors, as
the generalist genes model would suggest, remains
to be tested.
& Pe ´russe,2003;
Although child development research has moved
away from the Nature versus Nurture dichotomy
Bornstein, 2000; Maccoby, 2000), in practice, studies
of genetic factors and studies of environmental pro-
cesses generally have different goals and are con-
ducted independently of each other. The main goal
of this study was to test the hypothesis that the
contribution of home environment quality (SES and
exposure to reading) to SR is partly mediated
by the child’s language skills. Specifically, we
738Forget-Dubois et al.
hypothesized that a home environment offering a
variety of stimulating experiences and learning
opportunities during infancy would contribute to
SR, assessed just before school entry, partly through
its effect on early child language. We also tested
the hypothesis that the influence of SES, a global,
distal measure of the family environment, is medi-
ated by a more proximal measure, exposure to
reading. As language may share a common genetic
basis with SR, we also tested the hypothesis that
language skills and SR could be associated through
shared genes. This genetically informed analysis
provided the basis for the interpretation of the
mediation model: If language and SR were associ-
ated because they share a genetic basis, then the
process through which language mediates the rela-
tion between home characteristics and SR could not
be seen as strictly environmental. However, the
absence of a genetic correlation linking early lan-
guage and later SR would be consistent with a gen-
uine environmental process, or at least with the
absence of a direct genetic influence on the media-
The Quebec Newborn Twin Study (QNTS) is
based on a representative sample of twins born
between April 1995 and December 1998 in the
Greater Montreal Area, Canada. Names, addresses,
and phone numbers of all mothers of newborn
twins were collected from the birth records of the
Que ´bec Bureau of Statistics. The participating fami-
lies have been assessed yearly, starting when the
children were 5 months old (corrected for gesta-
tional age: they would have been 5 months old if
gestational duration had been 40 weeks, which is
rarely the case with twins. The uncorrected mean
age was 6.28 months (SD = .82). The participating
families were predominantly of European descent
(84%), whereas the remainder were of African (3%),
Asian (2%), and other (2%) descent. The remaining
9% did not provide information on ethnicity. The
maternal language of the parents was French for
71.9% of mothers and 72.3% of fathers, English for
9.9% of mothers and 8.8% of fathers, and another
language for 19.2% of mothers, and 18.9% of
fathers. The average household income was around
CAN $54,000, which is slightly above average for
similar households in this geographic area at that
time. Regarding education, 14% of the mothers did
not complete high school, 28% were high school
graduates, 48% had some college education or
graduated from college (9% did not provide any
information regarding education). For fathers, 11%
did not complete high school, 34% were high
school graduates, and 39% had some college educa-
tion or graduated from college (information was
unavailable for 16% of fathers). The analyses pre-
sented here are based on data gathered when the
twins were 6, 19, 32, and 63 months. At the first
assessment, 662 twin pairs were assessed. At the
63-month assessment, 446 twin pairs were still
enrolled in the study. Full data were available for
693 individual children in the present study (see
below for a detailed description of attrition and
treatment of missing data).
Predictors of SR
The predictors of SR were SES and a measure of
exposure to reading material. In an ecological per-
spective, SES represented a distal measure of home
environment quality, and exposure to reading a
proximal measure of stimulation in the home set-
ting. SES was computed from data on parental edu-
cation and household income obtained during a
home interview with the mothers when the twins
were 6 and 19 months. Education and income were
aggregated into 5-point scales (from 0 to 4): from
high school not completed to university diploma
obtained for the education scale, and from income
less than $20,000 to over $80,000 for the income
scale. Weaveraged 6-
income and paternal and maternal highest educa-
tion level to obtain a measure of family SES
(a = .80, M = 2.18, SE = .04, SD = 1.07).
The measure of exposure to reading material
was also based on data reported by the mother dur-
ing the home interviews. The items were initially
developed for Canada’s National Longitudinal
Study of Children and Youth and adapted for the
QNTS. We retained three items rated on a 8-point
Likert scale regarding exposure to reading at
19 months: The mothers reported on how often
they looked at books with the children, how often
they read to their children, and how often the chil-
dren looked at books by themselves at the time of
the interview. We computed a mean score from
these three items (a = .87) to reflect exposure to
reading. This score was reflected and log-trans-
formed to reduce negative skewness (M = .66,
SE = .01, SD = .32). The Exposure to Reading score
was available for 553 pairs of twins. The mothers
did not report on reading for each child individu-
ally, so exposure to reading is the same for both
and 19-month family
Predictors of School Readiness739
twins of a pair. The measure did not include the
mother’s own literacy practices.
expressive vocabulary short form checklist of the
Inventory (MCDI; Fenson et al., 1994) when the
twins were 19 and 32 months. The parents were
asked to indicate from a list of root words (77
words at 19 months and 100 words at 32 months)
those they had heard each of their twins say. To
reduce contamination of a twin’s assessment on the
cotwin, the parents completed the assessment for
one twin during the home visit and were asked to
complete and send in the assessment for the co-
twin 2 weeks later. Adapted and translated ver-
sions of the MCDI were used for French-speaking
families as described in Dionne, Tremblay, et al.
(2003). At the time of the measurement, no French
norms existed for the MCDI. For this reason, raw
scores were retained, regressed for age (taking into
account gestational duration) and standardized.
The correlation between the 19- and 32-month
scores is r = .44, p < .001.
At 19 months, 83.1% of children were assessed
in French and 16.9% in English. There was no dif-
ference between the means and variances of the
p = .27. The 32-month figures are very similar.
A mean score was computed from the 19- and
32-month scores allowing for one missing value
to maximize sample size. This measure of 19- and
32-month child expressive vocabulary was used in
the analysis as an index of early child language
skills (M = .01, SE = .04, SD = .91).
t(511) = 1.11,
SR and General Cognitive Ability
SR was assessed during a laboratory visit when
the twins were 63 months old on average, in the
summer prior to kindergarten entry. We used the
Lollipop test, a validated instrument assessing
knowledge of colors and shapes, spatial recognition,
numbers, and letters (Chew, 1981; Chew & Morris,
1984; Lemelin et al., 2007). A total score (with a max-
imum of 69) was calculated from the sum of the
items (a = .89, M = 42.16, SE = .59, SD = 12.89).
General cognitive ability was assessed during
the same visit using the Block Design subtest of the
Wechsler Preschool and Primary Scale of Intelli-
gence–Revised (WPPSI-R; Wechsler, 1989). The
scores were adjusted for age as instructed in the
test manual (M = 9.95, SE = .13, SD = 2.85). We
deemed it necessary to control for general cognitive
ability to capture the crystallized aspects of SR
independently of more fluid cognitive skills.
The analytic strategy combined a classic path
model and a genetically informative model. The
path model required independence of measures,
but the genetically informative model required twin
pairs, who cannot be considered independent.
Thus, we analyzed two data sets: Data Set 1 com-
prising one twin per pair selected randomly, and
Data Set 2 including all available twins of all pairs.
We first tested the hypothesis regarding the pre-
diction of SR using a path analysis model. Two indi-
rect paths were examined alongside the direct paths:
the mediation of contribution of SES and exposure
to reading to SR through child expressive vocabu-
lary and the mediation of SES by exposure to read-
ing. Full data were available for 351 children in Data
Set 1; the actual number of participants for each var-
iable varied from n = 450 (general cognitive ability)
to n = 658 (sex). Missing data were taken into
account using the full information maximum likeli-
hood (FIML) estimation procedure available in the
statistical package Amos 5 (Arbuckle, 2003).
We then examined the common genetic and
environmental etiology of language and SR using
classic twin modeling. This design is based on the
comparison of the covariance between monozygotic
(MZ) and dizygotic (DZ) twins for a given measure,
knowing that the genetic correlation is 1 between
MZ twins and .5 on average between DZ twins.
Formal analysis of twin data was done using the
Mx package (Neale, Boker, Xie, & Maes, 1999) to
partition the variance of measures between genetic
and environmental sources (Plomin et al., 2001).
The environmental variance was further decom-
posed to estimate the proportion of variance attrib-
augments the similarity between twins regardless
of zygosity, and to the unique environment, which
makes twins of a pair more different (Turkheimer
& Waldron, 2000). Moreover, twin studies allow
estimating the magnitude of genetic and environ-
mental correlations between traits or behaviors,
indicating to what extent they share the same
genetic and environmental sources of variance. Spe-
cifically, the genetic correlation refers to the correla-
tion between the genetic components of variance
associated with each measured behavior. Full data
for expressive vocabulary, SR, sex, age, general
740Forget-Dubois et al.
cognitive ability, and zygosity were available for
344 (148 MZ and 198 DZ) complete pairs in Data
Set 2. Missing data in covariables (definition vari-
ables) in the statistical package Mx results in the loss
of the whole case (Mx Script Library, n.d.), so we
conducted the analyses on the twins for which sex,
age, and general cognitive ability were available and
allowed for missing data on SR and expressive
vocabulary using the FIML estimation procedure
available in Mx (Neale et al., 1999). The model
included a total of 428 twin pairs with at least one
twin assessed on at least one of the two outcomes.
The correlation between language and other cog-
nitive abilities could be explained by general intelli-
gence g and its underlying genetic factors (Plomin &
Kovas, 2005; Plomin & Spinath, 2004). Accordingly,
we first conducted the genetic analysis using the
Block Design scores, a putative proxy of g, as a con-
trol variable, to assess the crystallized aspects of SR
rather than the fluid skills. However, putative ‘‘gen-
eralist genes’’ underlying the variance of g may also
influence other aspects of language and learning
(Plomin & Kovas, 2005), and partialing out the vari-
ance associated with the general cognitive ability
score (i.e. the Block Design score) may underesti-
mate the genetic correlation between early language
and SR. Therefore, the genetic analyses were per-
formed twice, with and without controlling for the
contribution of general cognitive ability to SR.
Attrition and Missing Data Treatment
A total of 989 families were asked to join the
QNTS; of these, 662 were assessed during the first
wave of data collection. The average attrition rate
from 6 to 63 months was 9.3% per year. The
remaining 441 families assessed at 63 months dif-
fered from those who left the study on socioeco-
nomic variables: At enrollment, those who stayed
until 63 months were more educated and had a lar-
ger family income; they were also more likely to
have an intact biological family and to have French
or English as their first language, and the mothers
were also slightly older (see Lemelin et al., 2007, for
details). We further analyzed the pattern of missing
data for the variables included in the study with
the MVA module in SPSS. The data were not miss-
ing completely at random (MCAR) according to
Little’s MCAR test (v2= 154.48, df = 73, p < .000).
Separate variance t tests obtained with the MVA
module showed that children who were evaluated
at 63 months for SR had higher mean 6⁄19 months
t(727.6) = 4.1,
p < .001,
exposed to reading at 19 months, t(464.7) = 2.4,
p = .02). Only 1% of SR assessments were rejected
as invalid so most of the missing assessments
resulted from participants lost to attrition.
These analyses revealed a pattern commonly
found in longitudinal studies, that attrition did not
happen entirely at random; low SES families were
more likely to dropout of the study before complet-
ing the 63 months assessment. As missing status in
participants was associated with measured sociode-
mographic characteristics included in the model
tested, we deemed it reasonable to consider them
missing at random (McKnight, McKnight, Sidani, &
Figueredo, 2007, p. 53) and treat them accordingly.
The FIML estimation procedure, contrary to multi-
ple imputation, is implemented directly in the pro-
cess of fitting a model; it treats missing data by
fitting the model to all nonmissing data for each
observation (McCartney, Burchinal, & Bub, 2006).
We chose FIML over multiple imputation because
there is evidence that FIML has more power unless a
great number of imputations are done (Graham,
Olchowski, & Gilreath, 2007). The downside of the
FIML procedure is that a single N is not representa-
tive of the study sample and thus cannot be
reported. All statistics reported in this article, includ-
ing the means and variances reported earlier, were
estimated in models fitted using FIML. It is to be
noted that the parameters estimated with the FIML
procedure did not differ from the parameters esti-
mated with a listwise deletion of missing cases in
any way likely to change the general conclusions.
Table 1 shows the correlations between SR, SES,
exposure to reading, and expressive vocabulary as
well as with the covariables. The correlation model
was fitted in Amos using Data Set 1 (one twin
selected per pair). Correlations were modest but
significant between the main variables, and the val-
ues of r were in the same range as those reported
in other studies that assessed the strength of the
relation between home environment and SR (e.g.,
NICHD Early Child Care Research Network, 2003).
Does Expressive Vocabulary Mediate the Relation
Between Home Environment and SR?
The path model including all the hypothesized
direct and indirect effects on SR was tested using
Amos 5. Covariables (sex, age when SR was mea-
sured, and general cognitive ability) were also
Predictors of School Readiness741
included. The full model with standardized esti-
mates of path coefficients is illustrated in Figure 1
and the unstandardized path coefficients with their
associated p values are reported in Table 2.
The overall model explained 33% of the variance
in SR; unsurprisingly, the most important predictor
was general cognitive ability, measured simulta-
neously with SR and entered as a covariable. The
hypothesized direct and indirect effects were signif-
icant but small as indicated by the small values of
the standardized path coefficients (which can be
interpreted like the Bs and bs in regression models,
as an indication of how the outcome varies as a
function of the predictors). Overall, the model was
consistent with the double mediation hypothesis:
Exposure to reading, a proximal measure of envi-
ronment, mediated the effect of SES, the more distal
measure, on SR but also on expressive vocabulary;
expressive vocabulary partially mediated the effect
of both SES and exposure to reading on SR. The
significance of the simple partial mediation can be
further investigated with the Sobel test (Preacher &
Leonardelli, 2001; Sobel, 1982). The partial media-
tion by expressive vocabulary of the effect of SES
on SR was near significance (z = 1.92, p = .05). The
direct path from SES to expressive vocabulary was
itself barely significant (p = .04), suggesting that the
most important indirect effect of SES was through
the more complex path of its effect on exposure to
reading and expressive vocabulary. The signifi-
cance of this indirect effect through two mediators
cannot be demonstrated with the Sobel test; how-
ever, it is accepted that the effect is significant if all
paths are significant for a given a level (Kline, 2005,
p. 162). All the paths involved in this indirect effect
are significant at the p < .001 level. The simple
mediation of the effect of SES on expressive vocab-
ulary by exposure to reading was significant
(z = 3.82, p < .001), as was the mediation of the
effect of exposure to reading on SR by expressive
vocabulary (z = 3.42, p < .001).
The total indirect effects of SES and exposure to
reading on SR were small (as reported in the lower
part of Table 2), but were considered significant
because all the path coefficients representing the
indirect effects were significant. The total effects
include the sum of direct and indirect effects of SES
and exposure to reading on SR. SES was the envi-
ronmental predictor that explained the biggest pro-
portion of variance in SR.
Is There a Genetic Correlation Between Expressive
Vocabulary and SR?
This analysis was based on Data Set 2 (all twins).
As indicated in Table 1, the phenotypic correlation
between early expressive vocabulary and 63-month
SR was r = .34, p < .001. The correlation in Data Set
2 was very similar (r = .33, p < .001). Descriptive
statistics and intraclass correlations for MZ and DZ
twins are reported in Table 3. Means and variances
of MZ and DZ twins are expected to be equal,
which was the case with these data. The correlation
differences for MZ and DZ twin pairs suggested a
modest heritability and a substantial effect of com-
mon environment for both measures.
A first bivariate correlated factors model (Loeh-
lin, 1996) was fitted to the data to estimate formally
the genetic and environmental components of the
variance for SR and expressive vocabulary, as well
as the genetic and environmental correlations
Correlations Between Variables
1. School readiness
3. Exposure to
7. General cognitive
Note. N = 711. Correlation model fitted using full information
maximum likelihood estimation to correct for missing data.
SES = socioeconomic status.
*p < .05. **p < .01. ***p < .001.
Figure 1. Direct and indirect influences on school readiness.
p = .18;
approximation = .03(90%
comparative fit index = .99; Akaike’s information criterion =
66.18. Nonsignificant (p > .05) paths are indicated by a dotted
line. Relevant correlations between exogenous variables are
omitted for simplicity.
interval = .00–.06);
742Forget-Dubois et al.
across measures. In this first model, we controlled
for the effect of sex on expressive vocabulary and
SR, and the effect of general cognitive ability and
age at testing on SR. This model showed a good
fit to the data compared with a phenotypic
saturated model ()2LL = 7786.68, df = 1569, p = .66,
Akaike’s information criterion (AIC) = 46487.68).
The best-fitting model is depicted in Figure 2 (the
covariables are not represented for clarity pur-
poses). Both child measures showed a small but
significant heritability, but the genetic correlation
between the two variables could be dropped from
the model without significantly affecting the fit, v2
difference = 2.10(1), p = .15. Common environment
was the most important source of variance, and the
common environmental correlation between vari-
ables was .55 (95% confidence interval (CI): .38–.76).
The unique environment factors, which include
measurement error, were also correlated at r = .21
(95% CI: .08–.34). Overall, the environmental corre-
lations suggest that expressive vocabulary and SR
were correlated because they shared the same
underlying environmental influences, not because
they shared the same underlying genetic influences.
We tested a second model without controlling
for the effect of general cognitive ability on SR.
Removing this covariable resulted in a significant
decrease of fit, )2LL = 7908.59, v2
119.81(1 df), p < .000. However, the estimates of A,
C, E, and the correlations between these factors
were very similar compared with the first model
Unstandardized Path Models and Effect Sizes
Exposure to reading ‹ SES
Exposure to reading ‹ sex
18⁄30-month expressive vocabulary ‹ exposure to reading
18⁄30-month expressive vocabulary ‹ SES
18⁄30-month expressive vocabulary ‹ sex
School readiness ‹ 18⁄30-month expressive vocabulary
School readiness ‹ exposure to reading
School readiness ‹ SES
School readiness ‹ general intelligence
School readiness ‹ sex
School readiness ‹ age
Indirect effectsTotal effects
Effects on school readiness
Exposure to reading
Comparison of MZ and DZ Twins Means, Variances and Intra-Class
Correlations (ICC) for Expressive Vocabulary and School Readiness
MZDZ MZ DZ
Note. The differences in means and variances between MZ and
DZ twins for school readiness and expressive vocabulary are not
significant. MZ = monozygotic; DZ = dizygotic; ICC = intraclass
Figure 2. Best-fitting bivariate genetic model.
Note. Age at testing and general cognitive ability entered as
covariate for school readiness, sex as covariate for both
)2LL = 7788.78,
Akaike’s information criterion = 4648.78.; A = additive genetic
factor, C = common environment, E = unique environment.
df = 1570,
p = .59,
Predictors of School Readiness743
and the genetic correlation was still nonsignificant.
These results suggest that the covariance of expres-
sive vocabulary and SR reflected shared common
and unique environmental influences but did not
reflect shared genetic influences; therefore, the con-
tribution of early child language to SR is not likely
to reflect an underlying genetic liability or ability.
The main objective of this article was to demon-
strate that the predictive association between char-
acteristics of the home environment and SR is
partly mediated by early child language skills. In
addition, the genetic–environmental etiology of the
association between early language and SR was
investigated to assess the extent to which this asso-
ciation stems from shared genetic and environmen-
tal factors. The results indicated that early child
language skills partly mediated the contribution of
SES and exposure to reading on SR. SES also
showed an indirect contribution to child expressive
vocabulary through its contribution to exposure to
reading. Second, although child expressive vocabu-
lary and SR were both moderately heritable, their
association was entirely explained by shared envi-
ronmental factors. The results of the mediation
model and the absence of a genetic correlation
between child language and SR are consistent with
the view that characteristics of the home environ-
ment, such as exposure to reading and SES, contrib-
ute to early language skills and that these, in turn,
contribute to SR. The specific conclusions of the
mediation model and genetic analysis are discussed
in detail in the following section.
The pattern of indirect contributions of SES to SR
was more complex than initially expected: The indi-
rect contribution of SES to SR was accounted for by
mediation through expressive vocabulary and also
by an indirect effect via both exposure to reading
and child expressive vocabulary. This result sug-
gests two processes by which SES affects early lan-
guage skills: a direct effect, and because higher SES
is predictive of greater exposure to reading, which
in turn has a positive effect on SR, an indirect
effect. Exposure to reading made a direct contribu-
tion to SR and an indirect contribution through
child expressive vocabulary.
These results are globally consistent with our
proposed model of the processes leading to the
association between home environment and SR:
children who benefit from presumably more stimu-
lating home environments, as indicated by SES and
exposure to reading, acquire better language skills,
and in turn acquire more of the competences and
knowledge associated with SR. The mediation of
the effect of SES (a distal measure of the quality of
the environment) by exposure to reading (a proxi-
mal measure) is consistent with the results reported
by Foster et al. (2005). These authors found a simi-
lar mediation process regarding SES, proximal mea-
sures of home environment, and emerging literacy,
which showed how differences in SES may become
associated with aspects of SR. Our results add
another component to that explanatory model, one
that takes into account early child language skills.
In other words, high SES parents tend to read more
to their toddlers which in turn contributes to their
children having more developed language skills. In
turn, better language skills help toddlers to profit
from the stimulating home environment, which
leads to better SR skills. These indirect effects are
added to the direct effects of SES and exposure to
reading on SR.
Prediction of the quality of early language was
also central to our model, because language had to
be predicted by SES and exposure to reading to
mediate the effect of these two environmental char-
acteristics on SR. Our results are consistent with pre-
vious findings showing that reading has a positive
impact on children’s vocabulary (Raikes et al., 2006;
Se ´ne ´chal, LeFevre, Hudson, & Lawson, 1996; Van
Steensel, 2006). Our results are also consistent with
Hoff’s studies of the association between SES and
vocabulary (Hoff, 2003; Hoff & Tian, 2005). Hoff
(2003) found a complete mediation of the effect of
SES by maternal speech abilities in a middle- to
high-SES sample that was interpreted as evidence
for specific rather than global effects of the home
environment on child language. We found a partial
mediation of the effect of SES by exposure to read-
ing, our proximal measure of home environment
quality and thus found evidence for specific effects,
but could not rule out a global effect of SES on
language development. As Hoff noted, it is not
known whether the environmental specificity princi-
ple applies across the whole range of SES. Moreover,
the remaining direct effect of SES in our study could
be mediated by unmeasured variables. Whether
early language skills could act as a protective factor
in preacademic and academic development, as Bur-
chinal et al. (2006) showed, also remains to be tested.
Although significant, the direct and indirect
effects of the environmental variables on SR were
744Forget-Dubois et al.
small. This is not surprising in the context of a lon-
gitudinal study with predictive measures taken
more than 30 months prior to the outcome mea-
sure. Yet, the initial correlations between the vari-
ables were similar in size to the correlations
reported in NICHD’s mediation model of the influ-
ences on SR (NICHD Early Child Care Research
Network, 2003), in which correlations between pre-
dictor, mediator, and outcome variables ranged
from nonsignificant to .34, with predictor and out-
come measures assessed between 6 and 54 months
of age. These results may reflect in part the diffi-
culty encountered in obtaining reliable measures of
the relevant aspects of home environment associ-
ated with preschool knowledge acquisition. It may
also indicate that the strongest environmental influ-
ences on SR are found later than 32 months.
Genetically Informative Analysis
Contrary to expectations derived from the theory
of ‘‘generalist genes,’’ the association between child
expressive vocabulary and SR was not explained by
a common genetic basis; it follows that the media-
tion linking children’s language skills and pre-
common genetic basis either. Previous genetically
informative studies (reviewed by Kovas et al., 2007;
Plomin & Kovas, 2005) point to a common genetic
basis for various cognitive abilities, language, and
learning processes. Considering that our main goal
was to test a model of environmental influences,
the concurrent test for a possible genetic mediation
is a significant contribution of this study.
The absenceof common
between language and SR is surprising. Plomin
and Kovas (2005) demonstrated convincingly that
finding a common genetic basis for different cogni-
tive abilities and disabilities is the norm rather
than the exception. They further propose that
genetic factors are responsible for the association
between language, general cognitive abilities, and
learning abilities, whereas environmental influ-
ences are responsible for change in the same abili-
ties over the span of childhood (Kovas et al.,
2007). On the contrary, our findings showed that
environment rather than genes is responsible for
the predictive association between early language
skills and SR. Furthermore, we found that common
environment is the most important source of sta-
bility between the two child outcomes. We pro-
discrepancy. First, the children in this study were
younger than those in the studies reviewed by
explanations for this
Kovas et al. It has been suggested that common
environment is a more important source of vari-
ance early in life (Plomin & Petrill, 1997). Second,
the child outcomes considered in the present study
required an important input from the environment
as children, regardless of their potential cognitive
abilities, must be taught preacademic notions.
Children learn vocabulary and basic knowledge
from competent adults in the context of specific
cultural groups; although all normally developing
children learn to talk, the specific familial, cultural,
economical, etc. context in which they grow up
induces variance in language skills (Hoff, 2006).
Third, it is possible that the association between
family environment, child language, and SR is
influenced by a genotype–environment correlation
that could not be detected in our model. For exam-
ple, children who inherit genetic risks associated
with poor SR skills could also be more likely to
receive less stimulation, if poorer environment is
correlated with the genes that they received from
their parents. Obviously, these explanations are
not mutually exclusive.
Implications of the Findings
Given that SR is one of the best predictors
of school success and school completion (Battin-
Pearson et al., 2000; LaParo & Pianta, 2000), having
empirical evidence that the early family environ-
ment matters gives even more support to family-
development. Indeed, the child language skills
assessed in this study were measured prior to
30 months. This indicates that environmental pro-
cesses contributing to the acquisition of basic
knowledge are not limited to the late preschool
years, but begin much earlier in infancy. This is
language acquisition showing that the verbal con-
tent of infant–mother interactions predicts the
rate of language development as early as age 2
Learning language may be one of the first manifes-
tations of the transactional processes involved
between children and their environment in any
form of learning. It follows that interventions aimed
at raising the quality of child language at a very
early age may have enduring results well into the
late preschool years and beyond.
Successful intervention programs with a focus
on the early years and improved child language
before school entry have shown positive contribu-
tions to early school success (Landry, Swank,
Predictors of School Readiness 745
Smith, Assel, & Gunnewig, 2006; Wasik, Bond, &
Hindman, 2006). These studies have shown that the
language development and literacy skills of chil-
increased by interventions targeting early language
skills. Successful programs have involved parental
training (Se ´ne ´chal & LeFevre, 2002), showing that
parents can be taught how to promote better learn-
ing. Such interventions would probably benefit
more children living in impoverished environ-
ments, thus reducing the gap between low-SES and
middle-class children at school entry.
Exposure of children to reading and reading
material is one early parental behavior that has
repeatedly been shown to contribute positively not
only to early reading skills (e.g., Se ´ne ´chal, 2006;
Se ´ne ´chal & LeFevre, 2002; Van Steensel, 2006) but
also to early child language (e.g., Debaryshe, 1993;
Foy & Mann, 2003; Se ´ne ´chal et al., 1996) and SR
(Britto et al., 2006; Reese et al., 2003). The child-
directed joint-attention episodes that joint reading
promotes have been repeatedly shown to provide
the best environment for early learning (Tomasello
& Farrrar, 1986). Finally, joint reading provides
age-appropriate material in an attractive format
and gives the child an introduction to the impor-
tance of words in our societies as vehicles of ideas
and feelings. For all of these reasons, early expo-
sure to reading may be one of the more potent
learning experiences of early childhood.
This study, as are most genetically informative
studies, is limited by the difficulty to control for
the effects of genotype–environment correlations.
Turkheimer et al. (2005) observed that the promises
to assess environmental influences while control-
ling genetic influences never really materialized. In
this study, we chose to use a genetic analysis to
provide a context for the interpretation of a nonge-
netic model. Even though our results strongly favor
an environmental process linking child language
and SR, we cannot claim that our analysis dis-
missed all genetic influences on the mediation pro-
cess; measures of home environment and child
outcomes may still be associated because of unde-
tected genotype–environment correlation. The con-
showing no evidence of a direct genetic effect at
work in the tested mediation process and that, at
the very least, environmental influences on this
process far outweigh any genetic influences. If a
genotype–environment correlation is present, chil-
analysis is limitedto
dren who inherited genes associated with better SR
may have been more likely to inherit favorable
environments and vice versa. This would not inval-
idate the idea that home environment has an
important effect on SR; it would rather imply that
children who are likely to inherit genetic risk fac-
tors are also likely to experience environmental
risk, while children likely to inherit protective
genetic factors would also be likely to grow up in a
protective environment. In such a case, children at
risk would be likely to benefit more from interven-
tion than children not at risk.
Another limitation concerns the variables used in
the longitudinal model. Although we chose vari-
ables measured prior to 30 months, we cannot
assume that their effect is limited to that period. In
fact, it is very likely that family SES assessed when
the children were 5 and 18 months was strongly
correlated with SES at 60 months. Thus, the model
presented in this article represents a plausible expla-
nation of the relation between various environmen-
tal influences and child outcomes, but does not
demonstrate causal relations. A third limitation is
the use of a single informant, the mother, for the
measure of expressive vocabulary in both twins.
Although there was a 2-week delay between the
assessments, they cannot be considered indepen-
dent. Furthermore, the mother was also the only
informant for the measures of SES and exposure to
reading, which cannot be considered independent
either. However, the questions from which these
measures were derived left little room for interpreta-
tion so it is unlikely that the results would have been
very different had our sources for the environmental
measures been independent. Finally, we measured
the occurrence and frequency of exposure to read-
ing, but the quality of interaction between parent
and child during book reading may be important to
consider (Bus, Belsky, van IJzendoorn, & Crnic,
1997). All these limitations underline the difficulty
of assessing the quality of the home setting and the
near impossibility to assess characteristics of the
home setting that may differ for each twin. This
study needs to be replicated with better, indepen-
dent measures of language and home environment.
The aim of this study was to assess the role of
early language skills in the process through which
the quality of home environment and academic
SR become associated. Concurrently, we assessed
the genetic and environmental etiology of the
association between early language skills and SR.
746Forget-Dubois et al.
We found that early language skills partly but sig-
nificantly mediated the influence of SES and expo-
sure to reading on SR. Moreover, the genetically
informative analysis suggested that this process
could represent a genuine influence of the home
environment on SR as there was no evidence of a
genetic correlation between early language skills
and SR. If a genetic effect was involved in the pro-
cess, it was necessarily indirect through undetected
rge. We conclude that a stimulating family environ-
ment has a positive effect on SR both directly and
through its effect on language development. These
results support early intervention in the family
environment, or through surrogate parenting, aim-
ing at improving child language and SR through
augmented exposure to reading.
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