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Relations of Growth in Effortful Control to Family Income,
Cumulative Risk, and Adjustment in Preschool-age Children
Liliana J. Lengua &Lyndsey Moran &Maureen Zalewski &
Erika Ruberry &Cara Kiff &Stephanie Thompson
#Springer Science+Business Media New York 2014
Abstract The study examined growth in effortful control
(executive control, delay ability) in relation to income, cumu-
lative risk (aggregate of demographic and psychosocial risk
factors), and adjustment in 306 preschool-age children (50 %
girls, 50 % boys) from families representing a range of income
(29 % at- or near-poverty; 28 % lower-income; 25 % middle-
income; 18 % upper-income), with 4 assessments starting at
36–40 month. Income was directly related to levels of execu-
tive control and delay ability. Cumulative risk accounted for
the effects of income on delay ability but not executive con-
trol. Higher initial executive control and slope of executive
control and delay ability predicted academic readiness, where-
as levels, but not growth, of executive control and delay ability
predicted social competence and adjustment problems. Low
income is a marker for lower effortful control, which demon-
strates additive or mediating effects in the relation of income
to children’s preschool adjustment.
Keywords Effortful control .Income .Cumulative risk .
Academic readiness .Social competence .Adjustment
problems
Poverty and low income have pervasive adverse effects on
children’s developmental outcomes (Duncan et al. 2010)
which might be accounted for by disruptions to children’s
developing self-regulation associated with low income
(Raver et al. 2011). Children growing up in economically
disadvantaged households tend to demonstrate lower self-
regulation (Raver et al. 1999). In turn, self-regulation is a
critical predictor of social, emotional and academic compe-
tence and adjustment problems. Thus, the development of
self-regulation might represent a pathway through which in-
come impacts childhood problems. To understand the extent
to which income-related disruptions to the development of
self-regulation account for adjustment problems in children
growing up in low-income contexts, it is critical to examine
growth trajectories of self-regulation and explicitly test wheth-
er deviations in growth trajectories account for the effects of
income on children’s adjustment. A core aspect of self-
regulation is effortful control, which was investigated in this
study. Specifically, this study examined the relations of in-
come and cumulative risk to the development of effortful
control across the preschool period. Further, the relation of
growth in effortful control to adjustment was examined, test-
ing the hypotheses that cumulative risk accounts for the effects
of income on growth in effortful control, which in turn medi-
ates the effects of low income on academic readiness, social
competence and adjustment problems.
Effortful control is a temperament construct conceptualized
as the executive-based core of self-regulation that includes
executive attention and inhibitory control (Rothbart and Bates
2006). It includes the ability to shift attention from irrelevant
or distracting stimuli, focus on relevant stimuli, and inhibit an
L. J. Lengua (*):L. Moran :E. Ruberry :S. Thompson
Department of Psychology, University of Washington, Box 351525,
Seattle, WA 98195, USA
e-mail: liliana@u.washington.edu
L. Moran
e-mail: lmoran@uw.edu
E. Ruberry
e-mail: eruberry@uw.edu
S. Thompson
e-mail: sfengler@uw.edu
M. Zalewski
University of Oregon, Eugene, USA
e-mail: zalewski@uoregon.edu
C. Kiff
University of California, Los Angeles, USA
e-mail: ckiff@mednet.ucla.edu
J Abnorm Child Psychol
DOI 10.1007/s10802-014-9941-2
undesired or dominant response to produce a preferred or
correct non-dominant response, facilitating the regulation of
attention, emotions and behavior (Rothbart et al. 2000).
Effortful control abilities are present as early as 6- to 7-
months of age and increase modestly through toddlerhood
(Sheese et al. 2008). The most marked increase in effortful
control occurs in the period from 3 to 6 years (Carlson 2005;
Kochanska et al. 1996;Reedetal.1984). Given this period of
rapid development, it is important to understand the role of
contextual experiences in shaping effortful control, which can
shed light on processes that promote or divert the development
of effortful control and adjustment problems.
Effortful control is a consistent predictor of a range of
indicators of children’s functioning. It predicts academic com-
petence and readiness (Blair and Razza 2007; Buckner et al.
2009; McClelland et al. 2007; Obradovic et al. 2010; Raver
et al. 2011; Razza et al. 2010; Valiente et al. 2007), social-
emotional competence (e.g., Eisenberg et al. 2003; Raver et al.
1999), externalizing (Hughes and Ensor 2009; Kochanska and
Knaack 2003; Lavigne et al. 2012;Lengua2003), and inter-
nalizing problems (deBoo and Kolk 2007; Eisenberg et al.
2001a, 2b; Hopkins et al. 2013;Lengua2003;2006;Muris
et al. 2008. Recent research suggests that rates of growth in
effortful control, along with individual differences in levels,
are important in explaining outcomes. In one study, greater
increases in effortful control predicted fewer problems and
better social competence above the effects of initial levels of
effortful control in pre-adolescent children (King et al. 2013).
Similar patterns were found in the relation of growth in
executive function to adjustment problems (Hughes et al.
2010) and inhibitory control to aggression (Bridgett and
Mayes 2011). A lower rate of growth in effortful control might
interfere with children’s ability to navigate increasingly com-
plex demands, contexts and relationships. However, this has
not been examined in preschool-age children. Further, identi-
fying the factors that contribute to the development of effortful
control is critical for understanding its role in children’s
adjustment.
Children from lower income families tend to demonstrate
lower effortful control or executive function compared to
children from higher income families (e.g., Eisenberg et al.
2001a,b; Evans and English 2002; Hughes et al. 2010;Li-
Grining 2007;Mezzacappa2004;Mistryetal.2010). These
differences are present in preschool-age (Lengua et al. 2007;
Wanl es s e t al. 2010) and school-age children (e.g., Lengua
2006). However, few studies have examined the relation of
income to growth trajectories of effortful control or character-
ized potential differences in rates of growth across income
levels. Evidence suggests that income is related to initial levels
of effortful control or executive function, but not to rates of
changes during middle childhood (Hughes et al. 2010;King
et al. 2013). More evidence is needed on the relation of
income to developmental trajectories of effortful control in
early childhood to understand the role of income in diverting
the development of effortful control. This study was designed
to test the relation of family income and its related risk factors
to growth in effortful control, with equal representation of
families across income levels and assessments of effortful
control across 4 time points.
In addition, studies examining income-related risk factors
that predict developmental trajectories of effortful control are
needed to elucidate the processes that account for the effects of
income, identify families at elevated risk, and clarify potential
targets for intervention. Low income is associated with a
number of risk factors, including stress, residential instability,
neighborhood problems, family conflict, parental mental
health problems, and many other factors that often co-occur
and have cumulative effects on children’s adjustment
(Ackerman et al. 2004; Evans 2003;Linveretal.2002;
Mistry et al. 2002), predicting children’s academic achieve-
ment, social competence, externalizing and internalizing prob-
lems, among other developmental outcomes (Evans et al.
2013). As cumulative risk has been shown to mediate the
effects of poverty on a range of health and developmental
outcomes (e.g., Evans and Cassells 2014; Evans and Kim
2012; Wells et al. 2010), it is possible that children’sexperi-
ences of risk, particularly the burden of stress associated with
an accumulation of risk, might account for the effects of low
income on children’s effortful control (Lavigne et al. 2012;
Raver et al. 2013; Vernon-Feagans and Cox 2013). A growing
body of evidence indicates that the accumulation of poverty-
related risk factors partially accounts for the effects of poverty
on children’s self-regulation (Buckner et al. 2003; Evans and
English 2002; Evans et al. 2013; Hughes and Ensor 2009;
Lengua et al. 2007;Mistryetal.2010; Raver et al. 2013;
Sektnan et al. 2010). Cumulative risk measures have been
shown to correlate with lower effortful or executive control
(Lengua et al. 2007;Mistryetal.2010) and delay of gratifi-
cation (Evans 2003; Evans and English 2002), but to date,
there is little evidence of the effect of cumulative risk on
developmental changes in effortful control. It is possible that
cumulative risk might account for the effects of income, or
that variations in risk exposure over time might clarify time-
specific variations in children’s effortful control above the
effects of income. Both of these possibilities were tested in
the current study.
It should be noted that there has not been a standard
approach to creating cumulative risk indices (Evans et al.
2013). There are a variety of risk factors included in cumula-
tive risk scores and numerous approaches to aggregating
factors. Decisions about formulating cumulative risk scores
should be guided by the research question. In the present
study, we were interested in understanding whether the accu-
mulation of risk factors that are associated with income ac-
count for the relation between income and growth in effortful
control. Therefore, the risk factors included in the cumulative
JAbnormChildPsychol
risk score represented demographic (mother education, single
parent status), contextual (household density, residential in-
stability) and psychosocial factors (negative life events, ma-
ternal depression) that often co-occur with low income and
have previously been shown to relate to children’sdevelop-
mental outcomes. Although each of these factors might indi-
vidually relate to children’s adjustment, we are proposing that
the burden of stress associated with the co-occurrence of risk
is relevant in accounting for the effects of income on the
development of effortful control. A cumulative risk score
captures the burden of risk experienced by children in low-
income families (Vernon-Feagans and Cox 2013).
Further, comprehensive models that test whether growth in
effortful control accounts for the effects of income on chil-
dren’s adjustment can clarify this potential mechanism of the
effect of income. Few prior studies have tested whether ef-
fortful control accounts for the effects of low income on
children’s adjustment. One study showed that sustained atten-
tion, a component of effortful control, partially mediated the
relation of family risk to receptive language, an indicator of
school readiness (Razza et al. 2010). Another study demon-
strated thateffortful control mediated the effects of cumulative
risk on academic achievement (Swanson et al. 2012).
However, the question of whether the effects of income and
cumulative risk on children’s adjustment are accounted for by
their effect on the development of effortful control has not
been addressed. In this study, income and cumulative risk
were examined as predictors of growth in effortful control,
with the hypothesis that changes in effortful control would
partially account for the effects of income and cumulative risk
on adjustment.
It is important to comment on some issues related to the
conceptualization and measurement of effortful control. First,
there is considerable overlap in the conceptualization and
operationalization of effortful control and executive function
(Bridgett et al. 2013;Zhouetal.2012). Although executive
functions include attention regulation and inhibitory control,
they also include higher-order functions such as planning,
decision making, and problem solving. The term effortful
control emerges from the temperament literature and includes
basic attentional, inhibitory, and delay abilities, perhaps
representing the core of executive functions. We focus on
these more basic executive functions in this study.
As to measurement, measures of effortful control often
combine attention regulation and inhibitory control dimen-
sions with reward delay, and there is empirical support for a
single latent factor underlying these (Allan and Lonigan 2011;
Sulik et al. 2010). However, evidence suggests that these
aspects of effortful control may differ in their developmental
course, predictors and relations with social-emotional and
academic outcomes (Brock, Rimm-Kaufman, Nathanson,
and Grimm 2009; Carlson 2005; Kim et al. 2013;Kingetal.
2013; Li-Grining 2007; Razza et al. 2010). Further, executive
attention and inhibitory control may reflect more directly
activity in the prefrontal cortex, whereas delay ability in
reward contexts may reflect an additional motivational com-
ponent related to the mesolimbic dopaminergic pathway
(Dixon 2010), sometimes characterized as the hot vs. cool
distinction (e.g., Brock et al. 2009; Hongwanishkul et al.
2005). In this study, consistent with previous studies of effort-
ful control (Kim et al. 2013;Li-Grining2007), we examine
separate delay ability and executive control dimensions to
explore the possibility of differential relations with income,
cumulative risk, and children’s adjustment.
This study utilized data from a community sample that
oversampled lower income families to test the effects of
income on developmental trajectories of effortful control and
characterize differences in trajectories across income levels.
The study tested the hypotheses that cumulative risk would
account for the effects of income on the development of
effortful control, and that developmental changes in effortful
control would mediate the effects of income and cumulative
risk on children’s academic readiness, social competence and
adjustment problems.
Method
Participants
Participants were 306 mothers and their 36–40 month-old
children (M=37, SD=0.84) who were recruited from a
university-hospital birth register, daycares, preschools, health
clinics, and charitable agencies. Families at these sites re-
ceived information forms about the study and could indicate
their interest in participating in the study on the forms.
Recruitment sites, other than the birth register, received an
honorarium of $100 when 90 % or more of their families
returned forms, regardless of the number of families indicating
interest in participating. If a site returned 75 % or 50 % of the
forms, the site received $75 or $50, respectively.
Families were recruited to obtain equal representation
across income levels. The 2009/2010 Federal HHS Poverty
Guidelines (U.S. Department of Health & Human Services,
2010), in place at T1, which is an income-to-needs ratio based
on the family’s income from all sources and the number of
people in the home, was used to recruit families and to
describe the income levels represented in the sample. The
distribution included 29 % of the sample at or near poverty
(N=90 at or below 150 % of the federal poverty threshold),
28 % lower income (N=84 between 150 % poverty and the
local median income of $58 K), 25 % middle-income (N=77
between the median income and $100 K), and 18 % upper-
income (N=54 above $100 K). To participate, families were
required to have reasonable proficiency in English (self-
determined) to comprehend the assessment procedures, and
J Abnorm Child Psychol
children diagnosed with a developmental disability were ex-
cluded. Participants included 50 % girls. The racial and ethnic
composition of the sample of children included 64 %
European American, 10 % Latino or Hispanic, 9 % African
American, 3 % Asian American, 2 % Native or American
Indian, and 12 % multiple racial and ethnic backgrounds.
Mothers’educational distribution included 3 % mothers with
some high school attainment, 6 % completed high school,
34 % with some college, technical school or professional
school, 30 % college graduates, and 27 % with post-
graduate education. Eighty-one percent of mothers were mar-
ried or had stable live-in partners, 12 % were never married,
7 % were separated, divorced or widowed and were single
heads-of-household.
Analyses suggested that minimal bias was introduced
as a result of missing data. Complete data were available
on 222 cases (73 %), with 53 cases missing 1 variable
(17 %), 13 cases missing 2 variables (4 %), and 18 cases
(6 %) missing 3 or more. All participants had complete
T1 income and cumulative risk data. Complete effortful
control data were available for 88 % of participants at
T1, 95 % at T2, 94 % at T3, and 94 % at T4. T4 Teacher
reports of child adjustment were available for 77 % of
participants. Missingness was related to lower income
and lower effortful control. However, the effect sizes of
the associations of missingness were modest, M= 0.16,
Range=0.01–0.28, and did not reach suggested thresh-
olds for introducing substantial bias (i.e., r>0.40, Collins
et al. 2001). Thus, all analyses were conducted using
missing data estimation and were based on the complete
sample of 306.
Procedures
Families were assessed in offices on a university campus.
They were assessed at 4 time points separated by 9 months
each when children were 36–40, 45–49, 54–58, and 63–67
months. With approval by the Human Subjects Institutional
Review Board, both active parental consent and child as-
sent were secured prior to data collection. Assessments
included, behavioral, neuropsychological, and question-
naire measures administered by trained experimenters.
Children completed neuropsychological and behavioral
measures of effortful control while mothers completed
questionnaire measures in a separate room. Families re-
ceived $70 for their first assessment and compensation
increased by $20 for each of the 3 subsequent assessments.
With parental consent, children’s teachers were mailed a
questionnaire and asked to complete measures about chil-
dren’s adjustment once they had the children in their class-
rooms for at least a month. Teachers received $15 for
returning the questionnaires.
Measures
Income
Mothers reported on household income from all sources on a
14-point Likert scale that provided a fine-grained breakdown
of income at the lower levels facilitating identification of
families at the federal poverty cutoff (e.g., 1=$14,570 or less,
2=$14,571-$18,310, 3= $18,311-22,050, etc.). However, the
14-point variable representing the full range of income was
used for analyses (M=8.75, SD =3.93, Range=1.00–14.00).
Correlations among T1-T4 income ranged from 0.80 to 0.88.
Given the high stability in income, only T1 income was
analyzed.
Ethnic or Racial Minority Status
Mothers reported on their children’s racial and ethnic back-
ground. Although 31 % of the children were from ethnic or
racial minority groups, the numbers of children in each
group were small, precluding comparisons across ethnic
or racial groups. However, a variable representing a child’s
status as a racial or ethnic minority was created. If parents
reported their children to be Latino/Hispanic, African
American, Asian American, Native American or Alaskan
Native, Pacific Islander, multiple or other, their minority
status was coded as 1. European-American children’s status
was coded as 0.
Adolescent Parent
Mothers reported their age at the time of the study child’
s
birth, and 3 % were adolescent parents (≤19 years) when the
child was born.
Cumulative Risk
Cumulative risk was assessed at all 4 time points and
included 7 risk factors: low education, single parent, resi-
dential instability, family structure transitions, household
density, negative events, and maternal depression, which
represent risk factors commonly included in cumulative
risk indices. There are numerous approaches to calculating
acumulativeriskindex,including efforts to avoid artifi-
cially dichotomizing continuous variables (Evans et al.
2013). Dichotomous risk factors (education, single parent,
residential instability, divorce) were scored as 0=not pres-
ent, 1=present. Continuous risk factor scores (household
density, negative events, depression) were converted into
proportions of the total possible score so that each score
ranged from 0 to 1, and thus, were on a similar scale as the
dichotomous variables. The total cumulative risk score was
the sum of all component factors.
JAbnormChildPsychol
Mothers reported on their education. Risk was indicated
by mothers’not graduating from high school (3 % of the
sample at T1). Mothers reported on marital status and were
identified as single parents if they indicated being never
married, currently widowed, separated or divorced, or hav-
ing a live-in partner for <1 year (19 % at T1). Residential
instability was indicated by the family changing house-
holds ≥3 times in the previous 3 years at T1 (10 %) and
any move in the 9 months between assessments at T2-T4.
Family structure transitions were indicated by mothers
reporting being divorced in the child’s lifetime at T1
(3 %) or during the 9 months between assessments at T2-
T4. Household density was calculated as the number of
individuals living in the home divided by the number of
rooms in the home. At T1 the ratio ranged from 0.18–1.75,
with a mean of 0.52, i.e., on average, there were twice as
many rooms as individuals in the home. The score was
converted to a proportion of the highest score in the sample.
Negative life events were assessed with parent report on the
General Life Events Schedule for Children (Sandler et al.
1986, August). The 29 events include moderate to major
negative events including changing schools, death of a family
member or friend, parental arrest, and loss of friends. Parents
reported whether events occurred in the previous 9 months,
and total scores were the number of events. The average
number of life events at T1 was 5.3, SD=4.0, range 0–26.
The total score was converted into a proportion of the possible
29 events.
Mothers reported on their depressive symptoms over the
previous month using the 20-item Center for Epidemiological
Studies–Depression Scale (CES-D; Radloff 1977), designed
to measure depressive symptoms in the general population.
Participants indicated whether each symptom was present on a
scale of 0 (rarely/never) to 3 (most of the time), and the items
were summed for a total score. Internal consistency was 0.88.
The T1 average score was 10.01, SD=8.38, range 0–46.67.
The total score was converted into a proportion of the total
possible score of 60. Correlations among T1-T4 cumulative
risk scores ranged from 0.49 to 0.80.
Effortful Control
Effortful control was assessed at all 4 times with identical
measures of attention, inhibitory control, and delay ability.
Modeled after traditional cognitive tests, measures were se-
lected to be of varying difficulty for children across childhood
so that identical measures could be used over time. Although
some of the measures were normed for children older than
those in the sample, there was variability in performance even
at these early ages. Conversely, some measures were devel-
oped for younger children, and as a result showed less vari-
ability at the later time points. Averaging across these test
scores resulted in adequate variability at each time point.
Proportion scores were used so that scores were on a compa-
rable scale. The mean, standard deviation and range for each
task across time-points are reported in Table 1. Given growing
evidence that delay ability operates differently than the atten-
tion and inhibitory control aspects of effortful control, separate
variables were created for executive control and delay.
Executive control was assessed using 6 tasks. The
Inhibition and Auditory Attention subscales of the NEPSY
developmental neuropsychological assessment battery
(Korkman, Kirk, and Kemp 1998) were designed for use with
children 5 and older. However, the scales were administered to
the children in this study to allow for use of identical measures
of effortful control longitudinally. Thus, these tasks were
difficult for children at the start of the study, but age-
appropriate by the end. The Inhibition subtest assesses the
ability to inhibit a dominant response to enact a novel re-
sponse. Children are shown an array of circles and squares
and asked to label each shape in an opposite manner (e.g., say
circle when shown a square). Auditory Attention is a
Tabl e 1 Descriptive statistics for executive control subscales and delay
ability
M SD MIN MAX
Inhibition T1 0.18 0.32 0.00 1.00
Inhibition T2 0.49 0.40 0.00 1.00
Inhibition T3 0.74 0.32 0.00 1.00
Inhibition T4 0.86 0.23 0.00 1.00
Auditory Attention T1 0.09 0.24 0.00 0.93
Auditory Attention T2 0.26 0.34 0.00 0.98
Auditory Attention T3 0.49 0.35 0.00 1.00
Auditory Attention T4 0.62 0.35 0.00 1.00
Bear/Dragon T1 0.62 0.20 0.33 1.00
Bear/Dragon T2 0.87 0.20 0.33 1.00
Bear/Dragon T3 0.95 0.12 0.47 1.00
Bear/Dragon T4 0.98 0.07 0.43 1.00
Day/Night T1 0.44 0.33 0.00 1.00
Day/Night T2 0.62 0.30 0.00 1.00
Day/Night T3 0.71 0.28 0.00 1.00
Day/Night T4 0.83 0.22 0.00 1.00
Card Sort T1 0.42 0.20 0.00 0.89
Card Sort T2 0.61 0.26 0.00 1.00
Card Sort T3 0.78 0.16 0.25 1.00
Card Sort T4 0.83 0.14 0.03 1.00
HTKS T1 0.01 0.07 0.00 0.65
HTKS T2 0.19 0.27 0.00 0.85
HTKS T3 0.42 0.32 0.00 0.95
HTKS T4 0.62 0.28 0.00 0.98
Gift Delay T1 0.62 0.25 0.09 1.00
Gift Delay T2 0.76 0.23 0.08 1.00
Gift Delay T3 0.78 0.19 0.17 1.00
Gift Delay T4 0.75 0.21 0.17 1.00
J Abnorm Child Psychol
continuous performance test that assesses the ability to be
vigilant and to maintain and shift selective sets. Children are
required to listen to a series of words and respond only when
they hear a target word, refraining from responses to other
words. Scores on the Inhibition and Auditory Attention sub-
scales were the proportion of correct responses.
Behavioral inhibitory control was assessed using Bear-
Dragon (an appealing monkey puppet was substituted in this
study; Kochanska, et al. 1996), which requires the child to
perform actions when the directive is given by the monkey
puppet, but not when given by a dragon puppet. Children’s
actions were scored as performing no movement, wrong
movement, partial movement, or complete movement, with
scores ranging from 0–3 on 10 trials. Trial scores were
summed across both monkey and dragon trials, and the total
scores were converted into the proportion of the sum of trials
to the total possible score.
Cognitive inhibitory control was assessed using Day-
Night (Gerstadt et al. 1994), which requires the child to say
“day”when shown a picture of moon/stars and “night”
when shown a picture of the sun. Responses were scored
1=correct non-dominant response or 0=dominant re-
sponse. Total scores were the proportion of correct re-
sponses out of 16 trials.
The Dimensional Change Card Sort (DCCS; Zelazo et al.
2003) assesses cognitive inhibitory control, attention focusing
and set shifting. In this task, children were introduced to two
boxes with slots in the top. Target cards were attached to the
front of each box. The target cards included a silhouetted
figure on a colored background (star on blue, truck on red).
Children were instructed to sort cards first according to shape
(6 trials) then according to color (6 trials). The experimenter
stated sorting rules before each trial and presented a card
labeled according to the current dimension (e.g., on a shape
trial, “Here’s a truck. Where does it go?”). If children correctly
sorted ≥50 % of cards, they advanced to the next level in
which the target cards integrated the sorting properties. Target
cards consisted of a colored figure on a white background
(blue star, red truck), and children were again instructed to sort
according to shape (6 trials), then color (6 trials). If they
correctly sorted ≥50 % of the cards, children advanced to the
next level in which they were instructed to sort by color if the
card had a border on it and by shape if the card lacked the
border (12 trials). The score was the proportion of correct
trials out of the total 36 possible trials.
Head-Toes-Knees-Shoulders (HTKS) integrates attention
and inhibitory control (Ponitz et al. 2008). Children are asked
to follow the experimenter’s instructions, but to enact the
opposite of the direction (e.g. touch toes when asked to touch
head). Behaviors were coded as 0=touched the directed body
part, 1=self-corrected, or 2=correctly touched the opposite.
Scores were the proportion of the sum of the item scores
across 20 trials to the total possible score.
Delay ability was assessed using a gift delay task
(Kochanska et al. 1996) in which children were told that they
would receive a present, but that it needed to be wrapped.
Children were instructed to sit facing the opposite direction
and not peek while the experimenter noisily wrapped the gift.
Children’s peeking behavior (frequency, degree, latency to
peek, latency to turn) was rated. Also, behaviors indicating
difficulty delaying (fidgeting, tensing, out of seat, grimacing)
were rated as 0=not present, 1=moderate, 2=strong, and
summed for a difficulty delaying score. Peeking frequency,
degree, latencies, and difficulty delaying (reversed) were con-
verted to proportions of the total possible score for each and
averaged. Twenty percent of all tasks were independently
recoded to assess inter-rater reliability with ICC’s=0.72 to
0.98.
An executive control score was computed at each time
point as the mean of the proportion scores of the individual
tasks and was considered missing if ≥50 % of the component
scores were missing (α=0.67, ICC= 0.83). A delay ability
score was computed at each time point as the mean of the
proportion scores for the individual delay indicators and was
considered missing if ≥50 % of the component scores were
missing (α=0.77, ICC=0.91).
Cognitive Ability
An estimate of cognitive ability was obtained using verbal and
nonverbal NEPSY subtests and included as covariates. An
estimate of verbal ability was obtained using the
Comprehension of Instructions subtest which is designed to
assess the ability to receive, process, and execute oral instruc-
tions of increasing syntactic complexity. An estimate of non-
verbal ability was assessed with the Block Construction sub-
test designed to assess the visual-spatial and visual-motor
ability to reproduce three-dimensional constructions from
models or from two-dimensional drawings. Comprehension
and Block Construction scores were correlated 0.48 and were
combined for an overall estimate of cognitive ability (Sattler
2001).
Child Adjustment
At T4, teachers rated children’s academic readiness, social
competence, and total adjustment problems. Teachers rated
children’s academic readiness using the School Readiness
Survey (National Household Education Survey, 2007) in
which teachers report on 9 items indicating children’s ability
to identify colors and letters, count, write their names, hold a
pencil correctly, produce intelligible speech, and recognize
letter sounds. Teachers rated children’s social competence
and total problems using the preschool teacher report form
of the Social Skills Rating Scale (SSRS: Gresham and Elliot
1990). Teachers rated children’s cooperation (e.g., puts away
JAbnormChildPsychol
toys, helps with tasks; 12 items), assertiveness (e.g., self-
confident, introduces self; 8 items) and self-control (e.g.,
controls temper, attends to instructions; 10 items) for a social
competence score (30 items). Teachers rated children’sexter-
nalizing problems (7 items), internalizing problems (6 items)
and hyperactivity (6 items) for a total adjustment problems
score (19 items). In this study, alphas for the composite SSRS
scales were 0.91 for social competence and 0.87 for total
adjustment problems.
Results
Analytic Plan
Analyses were conducted to examine the patterns of growth in
effortful control, compare patterns of growth across income
levels, test the effects of income and cumulative risk on
growth in effortful control, and test whether growth in effort-
ful control mediated the effects of income and cumulative risk
on adjustment. First, correlations were examined to determine
the plausibility of the proposed relations. Second, uncondi-
tional growth models of executive control and delay ability
were tested to determine whether there was significant vari-
ability in latent growth parameters. Third, unconditional
growth models were compared across income levels to test
for differences in rates and variability of growth. Fourth, time-
varying effects of cumulative risk on effortful control were
examined to test an alternative model for the effects of cumu-
lative risk on effortful control. Fifth, controlling for gender,
cognitive ability and ethnic/minority status, growth of execu-
tive control and delay ability was conditioned on income and
cumulative risk to test whether they predicted effortful control
growth and whether cumulative risk accounted for the effects
of income. In turn, effortful control growth factors were tested
as predictors of academic readiness, social competence and
adjustment problems to test the hypothesis that growth in
effortful control would account for the effects of income and
cumulative risk on adjustment (see Fig. 1). Tests of indirect
effects of income on effortful control through cumulative risk
and on adjustment through effortful control were used to
examine whether effortful control mediated the effects of
income and cumulative risk on adjustment. All analyses were
conducted in Mplus 6.0 (Muthen and Muthen 2010)using
Full Information Maximum Likelihood Estimation (FIMLE)
which uses all the data available simultaneously to calculate
parameter estimates. Our examination of the pattern of miss-
ing data suggested that missing data introduced minimal bias
and aligned with the assumptions of FIMLE. Therefore, data
from all families were included in analyses (N=306). To test
indirect effects, Mplus produces the Sobel test which is a
conservative test of indirect effects (MacKinnon et al. 1995).
Preliminary Analyses
Descriptive statistics for the study predictors, the correlations
among them, and their relations with adjustment are presented
in Table 2. The correlations show that child gender was related
to effortful control and adjustment, with boys demonstrating
lower effortful control and social competence and higher
adjustment problems. Ethnic or racial minority and adolescent
parent status were related to lower income, higher cumulative
risk and lower effortful control. Therefore, gender, minority
status, and adolescent parent status were included as covari-
ates in subsequent analyses. Income and cumulative risk were
related to executive control, delay ability (except for T4),
academic readiness, social competence and adjustment prob-
lems. Also, executive control and delay ability were related to
children’s academic readiness, social competence and adjust-
ment problems, indicating the plausibility of the hypotheses
that income and cumulative risk predict effortful control,
which in turn, accounts for the association of income and
cumulative risk to children’s adjustment.
Income and Cumulative Risk Predicting Growth in Effortful
Control
Unconditional Growth
Unconditional growth models of executive control and delay
ability were specified with the intercept reflecting T1 levels,
and linear and quadratic growth factors indicated by the T1-T4
measures. Both executive control, M=0.28, p<0.05, SD
2
=
0.014, p<0.05, and delay, M=0.62, p<05, SD
2
=0.04,
p<0.05, demonstrated intercepts significantly different than
0 with significant variability in initial levels. In addition, both
executive control, M= 0.25, p<0.05, SD
2
=0.01, p< 0.05, and
delay ability, M= 0.17, p<0.05, SD
2
=0.01, p< 0.05, demon-
strated significant linear growth and significant variance in the
linear growth factor, indicating individual differences in chil-
dren’s rates of linear growth. Finally, both executive control,
M=−0.03, p<0.05, SD
2
=0.001, p<0.10, and delay ability,
M=−0.04, p<0.05,SD
2
=0.000, n.s., demonstrated significant
quadratic growth. However, in both cases, the variance of the
quadratic factor was non-significant. This indicates that
growth in executive control and delay ability included a
curvilinear pattern, that is, the rate of change decelerated, but
that the pattern of deceleration was essentially invariant across
children. Models estimating the quadratic growth factor were
compared to those excluding the quadratic growth factor. For
both executive control, χ
2
-difference [1]=100.77, p≤0.01,
and delay ability, χ
2
-difference [1]= 72.99, p≤0.01, the model
including the quadratic growth factor fit the data better than
the model excluding the quadratic growth factor despite the
nonsignificant variance of the quadratic factor. Therefore, all
subsequent analyses included both linear and quadratic
J Abnorm Child Psychol
growth factors for effortful control. However, the variances
for the quadratic factors were set to 0, and as such, quadratic
growth was not examined in relation to income, cumulative
risk or children’s adjustment.
Cross-Income Comparisons
To rule out the possibility that effortful control growth rate or
variability might differ at different levels of income, particu-
larly for families at or near the poverty cutoff, growth rates of
executive control and delay ability were compared across
income categories representing families who were at- or
near-poverty, lower income (below the county median), mid-
dle income (median to $100 K), and upper income (≥$100 K)
(Fig. 2). Models with all growth parameters set to be equal
across groups were compared to those with all growth param-
eters free to differ across groups. Significant χ
2
-difference
tests across these models indicated that growth parameters
were not equivalent across the groups. To identify the source
of the differences, first the intercept growth factors were
constrained across groups, and then the linear slope factors
were constrained across groups. Chi-square difference tests
across these models indicated that intercept factors for execu-
tive control, χ
2
-difference [3]= 18.47, p≤0.01, and delay abil-
ity, χ
2
-difference [3]=12.51, p≤0.01, differed significantly
across income groups, whereas slope factors did not (execu-
tive control: χ
2
-difference [3]= 3.58, ns.; delay ability: χ
2
-
difference [3]= 2.77, ns.). This means that the income groups
differed in initial levels of executive control and delay ability
but not in the rate of linear growth, and thus, initial income-
related differences in levels of effortful control were main-
tained across the 4 time points of the study. Analyses were
also conducted testing for differences across the quadratic
growth factors, even though there was not significant variabil-
ity in this factor overall, to ensure that there were no differ-
ences in rates of deceleration of growth at different income
cutoffs. No differences in quadratic growth patterns were
found across income groups.
Time-Varying Effects of Cumulative Risk
It was possible that varying levels of cumulative risk at dif-
ferent time points accounted for variations in levels of exec-
utive control and delay ability, rather than the hypothesized
model in which cumulative risk was expected to account for
the effects of income on the intercept and slope of the effortful
control dimensions. To test this, we tested latent growth
models in which the intercept and slope factors of either
executive control or delay ability were conditioned on the
covariates (child gender, ethnic minority status, cognitive
ability, adolescent parent status) and family income, and
time-specific effects of cumulative risk were tested by
regressing the residual variance of the observed indicators of
executive control or delay ability at each time point on the
time-corresponding indicator of cumulative risk. These
models demonstrated good fit to the data (executive control
RMSEA=0.05, CFI=0.95; delay ability RMSEA = 0.04,
CFI= 0.93). The results indicated that there were no time-
specific effects of cumulative risk on executive control. T2
cumulative risk predicted unique variance in T2 delay ability
above the effects of covariates and income on the growth
factors, β=−0.18, p=0.001. However, none of the other
time-specific effects were significant. Given minimal evi-
dence of time-specific effects, subsequent models were tested
excluding them.
Conditional Growth Models
Models in which growth in executive control and delay ability
were conditioned on covariates, income and cumulative risk
were tested (Table 3). Intercept, linear growth and quadratic
growth factors were specified, with the variance of the
Fig. 1 Model of effortful control growth factors as mediators of the effects of income and cumulative risk on children’s adjustment. The model was
tested separately for executive control and delay ability
JAbnormChildPsychol
Tab l e 2 Descriptive statistics and correlations among study variables
M SD INC CR EC1 EC2 EC3 EC4 DA1 DA2 DA3 DA4 Academic Readiness Social Comp. Total Problems
Child Gender
a,c
––−0.06 −0.01 −0.09 −0.13* −0.11* −0.10 −0.14* −0.11 −0.21** −0.20** −0.10 −0.19* 0.18*
Eth/Racial Min
b,c
––−0.22* 0.21* −0.12* −0.20* −0.11* −0.18* −0.16* −0.13* −0.16* −0.17* −0.10 −0.11 0.11
Adolescent Par. ––−0.24* 0.33* −0.09 −0.12* −0.14* −0.10 −0.13* −0.24* −0.13* −0.11 −0.09 −0.20* 0.20*
Cognitive Ability 0.23 0.07 0.23* −0.22* 0.52* 0.57* 0.57* 0.51* 0.33* 0.37* 0.25* 0.14* 0.39* 0.31* −0.26*
Family Income 8.75 3.93 –−0.60* 0.19* 0.24* 0.24* 0.24* 0.24* 0.14* 0.13* 0.08 0.22* 0.17* −0.27*
Cum. Risk T1 0.86 0.73 –−0.15* −0.21* −0.25* −0.21* −0.19* −0.23* −0.10 −0.01 −0.22* −0.21* 0.27*
Exec. Control T1 0.29 0.15 –0.50* 0.53* 0.42* 0.26* 0.27* 0.12* 0.19* 0.29* 0.18* −0.10
Exec. Control T2 0.49 0.20 –0.61* 0.46* 0.31* 0.36* 0.25* 0.15* 0.41* 0.21* −0.22*
Exec. Control T3 0.68 0.17 –0.65* 0.29* 0.40* 0.26* 0.17* 0.52* 0.25* −0.24*
Exec. Control T4 0.78 0.15 –0.29* 0.35* 0.24* 0.19* 0.54* 0.30* −0.25*
Delay Ability T1 0.62 0.25 –0.49* 0.40* 0.22* 0.26* 0.21* −0.25*
Delay Ability T2 0.76 0.23 –0.45* 0.34* 0.20* 0.30* −0.35*
Delay Ability T3 0.78 0.19 –0.40* 0.18* 0.24* −0.29*
Delay Ability T4 0.75 0.21 –0.04 0.15* −0.14*
INC, Income; CR, Cumulative Risk; EC, Executive Control; DA, Delay Ability
*p≤0.05
a
Child gender coded 0= girl,1 = boy
b
Ethnic/Racial Minority coded 0= not minority,1= minority
c
Point biserial correlations are reported for dichotomous variables
J Abnorm Child Psychol
quadratic growth factors set to 0. The models for executive
control, RMSEA=0.04, CFI=0.94, and delay ability,
RMSEA=0.03, CFI= 0.95, demonstrated adequate fit to the
data. Child gender, ethnic or racial minority status, cognitive
ability, and mothers’adolescent parent status were included as
covariates. Boys and children whose mothers were
Child A
g
e in Months
Ave. Proportion Correct
(a) (b)
Fig. 2 Growth Patterns for a)
executive control and b)delay
ability across income levels
Tabl e 3 Standardized coefficients of the effects of income and cumulative risk on executive control, delay ability, and T4 adjustment
Executive Control Delay Ability Academic
Readiness
Soc.
Comp.
Tot.
Probs.
Intercept T1 Linear Slope Intercept T1 Linear Slope
At Entry Step 2 At Entry Step 2 At Entry Step 2 At Entry Step 2
Step 1
Child Gender
a
−0.03 −0.03 −0.03 −0.03 −0.18** −0.18** −0.05 −0.04
Ethnic/Racial Min
b
−0.03 −0.01 −0.08 −0.07 −0.10 −0.08 −0.10 −0.13
Cognitive Ability 0.76** 0.75** −0.04 −0.06 0.59** 0.54** −0.69** −0.66**
Adolescent Par. −0.10 −0.07 0.03 0.06 −0.19** −0.16** 0.06 0.02
Income
c
0.12* 0.10 0.05 −0.02 0.20** 0.05 −0.35* −0.06 0.07 0.01 −0.14
t
Step 2
Cumulative Risk
b
−−0.01 –−0.13 –−0.16 –0.41* 0.01 −0.12
t
0.12
t
Step 3
Executive Contr. 0.48** 0.33** −0.22**
Intercept T1
Slope 0.47** 0.11 −0.15
Or
Delay Ability 0.14 0.34** −0.39**
Intercept T1
Slope 0.38** −0.14
t
−0.01
t
p≤0.10
*p≤0.05
**p≤0.01
a
Child gender coded 0= girl,1= boy
b
Ethnic/Racial Minority coded 0=not minority,1=minority
c
The effectsof income and cumulative risk on adjustment differ slightly depending on whether executive control or delay ability is included in the model,
but the magnitude of the effects and pattern of significance remain the same
JAbnormChildPsychol
adolescents when they were born demonstrated lower initial
levels of delay ability. Minority status was not related to
effortful control growth factors. Cognitive ability was related
to higher initial executive control, but it was unrelated to the
slope of executive control. Cognitive ability was related to
higher initial delay ability and also predicted smaller increases
in delay across the study. That is, cognitive ability was related
to higher initial levels of delay ability that remained higher but
grew at a slower rate compared to the children who started
with lower levels of cognitive ability. Comparing children
below the mean of cognitive ability with those at or above
the mean, children who had lower cognitive ability started and
ended the study with significantly lower delay ability.
The effects of income on the intercept and linear slope
were tested next. Income was related significantly to higher
initial levels of executive control, but unrelated to growth in
executive control. Income was related significantly to
higher initial levels of delay ability and less growth in delay
ability. That is, children from families with higher income
had higher initial delay ability that remained higher but
grew less compared to children from lower income fami-
lies, whose levels of delay ability grew at a greater rate but
remained lower. To characterize this, the mean level of
delay ability of children whose family income was at or
below 200 % of the poverty threshold was compared with
children whose family income was above that threshold at
each time point, T1 M low income=0.57, M high income=
0.66, t (266)=3.07, p=0.002; T2 M low income= 0.72, M
high income= 0.78, t (272) =1.99, p=0.05, T3 M low in-
come= 0.75, M high income=0.80, t (282)=2.21, p=0.03,
T4 M low income=0.74, M high income = 0.76, t (278) =
0.75, indicating that lower-income children started the study
with lower delay ability but demonstrated greater gains.
When cumulative risk was added to the model as a
mediator of the effects of income, the effects of income
on the intercepts of executive control and delay ability
became non-significant. Cumulative risk was significantly
related to lower initial delay ability and a greater rate of
increase in delay ability across the study. Children with
higher cumulative risk started the study with lower delay
ability that grew more rapidly across time. To characterize
this, we compared level of delay ability in children with
cumulative risk scores at or below the mean of cumulative
risk with those of children above the mean of cumulative
risk at each time point, T1 M low risk=0.65, M high risk=
0.57, t (266)= 2.27, p=0.02; T2 M low risk= 0.80, M high
risk=0.67, t (272)=4.47, p<0.00, T3 M low risk=0.80, M
high risk=0.74, t (281)=2.40, p=0.02; T4 M low risk=
0.75, M high risk=0.75, t (276)=0.28, ns., showing that
children with lower cumulative risk had higher levels of
delay ability, whereas children with higher cumulative risk
started the study significantly lower, but made greater gains
over time.
Indirect Effect of Income Through Cumulative Risk
Income demonstrated a significant total effect on the intercept
of executive control, β=0.12, p=0.05, but the indirect effect
of income through cumulative risk was not significant.
Income demonstrated a trend toward an indirect effect on the
intercept of delay ability, β=0.10, p=0.10, and a significant
indirect effect on the slope of delay ability, β=−0.25, p=0.05,
through cumulative risk.
Income, Cumulative Risk and Effortful Control Growth
Predicting Adjustment
Measures of T4 academic readiness, social competence and
adjustment problems were included in the models to test
whether growth in effortful control accounted for the effects
of income on children’s adjustment (Fig. 1and Table 3).
Separate models were tested for executive control and delay
ability. The intercept and linear slope of either executive
control or delay ability were specified as predictors of the
T4 adjustment indicators. Income and cumulative risk were
included as predictors of the intercept and slope of the
effortful control growth factors, as well as of the adjustment
indicators.
After accounting for effortful control (executive control or
delay ability), neither income nor cumulative risk were signif-
icantly related to children’s adjustment, although there were
trends toward effects of income on lower total problems and
cumulative risk on lower social competence and higher total
problems. Above the effects of income and cumulative risk,
initial levels of executive control predicted higher academic
readiness and social competence and lower total problems.
Initial levels of delay ability also predicted higher social
competence and lower total problems, but not academic read-
iness. Neither the executive control nor delay ability slope was
related to social competence or total problems, whereas both
were related to academic readiness. Greater gains in executive
control and delay ability were related to higher T4 academic
readiness. Tests of indirect effects indicated that there were
significant indirect effects of cumulative risk on social com-
petence, β=−0.08, p=0.05, and total problems, β=0.07,
p=0.04, through initial levels of delay ability. There was also
a significant indirect effect of cumulative risk on academic
readiness through the slope of delay ability, β=−0.20, p=0.03.
There were no significant indirect effects of income on ad-
justment through effortful control.
Discussion
This study sought to add to our understanding of the relation
of income to the development of effortful control in young
J Abnorm Child Psychol
children and the role it plays in children’s adjustment.
Effortful control has been posited to account for the effects
of income on adjustment, and the results of this study only
partially support that premise. The findings demonstrated that
lower income was related to lower effortful control and that
the effects of income on effortful control were accounted for
by cumulative risk, but only for delay ability, not for executive
control. Further, better effortful control was related to better
adjustment, with effortful control predicting children’sadjust-
ment above the effects of income and cumulative risk.
However, the hypothesis that developmental changes in ef-
fortful control during the preschool period would account for
the effects of low income on children’s adjustment was only
partially supported.
Income was related to differences in levels but not trajec-
tories of effortful control. That is, initial observed income
differences in preschool-age children’s levels of effortful con-
trol were maintained throughout the study. Also, the rate and
variability of growth was equivalent at all levels of income,
including for children at- or near-poverty. This is consistent
with the results of another study that examined the relation of
income to growth in executive function across early- to
middle-childhood (Hughes et al. 2010). The findings suggest
that income might exert its influence on the development of
effortful control earlier in childhood or onother factors that, in
turn, impact the development of effortful control. During the
preschool period, low income can be used as a marker to
identify children who have elevated risk exposure, are at risk
for lower effortful control, and are in need of additional
support to enhance their effortful control. However, more
proximal contextual or socialization factors that account for
individual variation in developmental trajectories of effortful
control should be identified. There is a growing body of
evidence that classroom-based interventions in preschool set-
tings can promote the development of effortful control
(Bierman et al. 2008; Raver et al. 2011). In addition, parenting
has been shown to be a key predictor of executive function in
preschool-age children relative to other income-related family
risk factors (Rhoades et al. 2011) and to predict changes in
effortful control over time (Lengua et al. 2007; 2014). Future
studies should examine whether parenting and other sociali-
zation experiences predict growth in effortful control, mediate
the effects of income, and can promote effortful control when
targeted in an intervention for low-income or other high-risk
families.
A key aim of this study was to test the hypothesis that
growth in effortful control would account for the effects of
income on children’s adjustment. The findings are consistent
with the results of prior research indicating that effortful
control mediated the effects of income (Razza et al. 2010)
andcumulativerisk(Swansonetal.2012)onacademic
achievement. However, this study provided little evidence of
effortful control, either executive control or delay ability,
accounting for the effects of income on adjustment. Rather,
we found that delay ability accounted for the effects of cumu-
lative risk on adjustment. There is extensive prior evidence
that delay ability, similar to impulsivity, is a critical factor in
adjustment (Beauchaine, Hinshaw, and Pang 2010;Lengua
2003; Mischel et al. 1988). The results of the present study
suggest that cumulative risk plays a role in children’sdevel-
opment of delay ability, which in turn, partially accounts for
the effects of risk on adjustment.
However, the pattern of findings, particularly for executive
control, was more consistent with additive effects, such that
effortful control is an additional, relevant factor in understand-
ing children’s developmental outcomes along with low in-
come. Higher initial levels of both executive control and delay
ability predicted greater academic readiness and social com-
petence and lower adjustment problems above the effects of
income and cumulative risk, pointing to relevant independent
effects of effortful control on children’s adjustment. The fact
that effortful control tends to be lower in low-income children
highlights a cascade effect of low income. In addition to the
risk conferred on children’s adjustment by low income, chil-
dren in low income contexts have lower effortful control that
independently predicts more problematic outcomes.
Contrary to our hypotheses, growth in effortful control was
unrelated to adjustment except for academic readiness. These
findings are inconsistent with prior evidence demonstrating
that initial levels as well as growth of effortful control were
relevant to children’s behavioral, social and emotional adjust-
ment (Bridgett and Mayes 2011;Hughesetal.2010;King
et al. 2013). It should be noted that these prior studies exam-
ined the effects of growth in effortful control or executive
functioning in older children. Thus, it is possible that the rate
of developmental change is more relevant when children are
older and required to navigate contexts more independently
than during the preschool period, in which case, a more
accelerated rate of growth would be beneficial. Alternatively,
it is possible that the importance of the rate of growth of
effortful control, relative to its level, might depend on the
stage-salient developmental tasks or outcomes relevant to
the developmental period in question. That is, individual
differences in the rate of growth of effortful control might
have greater relevance to emerging developmental outcomes,
whereas individual differences in levels of effortful control
differentiate levels of adjustment regardless of developmental
period. The latter possibility is consistent with the results of
the current study, in which growth in effortful control predict-
ed children’s academic readiness, an outcome specific to the
preschool period. Children’s gains in effortful control across
the preschool period are likely to support their learning be-
haviors, such as focused and sustained attention, task persis-
tence, and perhaps, frustration tolerance when learning new
information and skills. However, this pattern of findings
should be replicated prior to drawing conclusions.
JAbnormChildPsychol
It is notable that the pattern of development of executive
control and delay ability and their relations to income, cumula-
tive risk, and adjustment differed, pointing to the value of
examining delay in reward contexts separately from the more
purely “cool”cognitive executive control construct. Cumulative
risk predicted initial levels and changes in delay but not in
executive control and is perhaps more relevant to the develop-
ment of delay ability. Cumulative risk may represent children’s
more proximal experiences of low income or poverty. Higher
levels of cumulative risk may be experienced as a chaotic home
environment, which could be a common experience for children
living in poverty and low income (Evans and Wachs 2010). The
unpredictability inherent in chaotic or high cumulative risk
environments may make the experience of rewarding conditions
unpredictable, rendering children less able to tolerate the dis-
comfort associated with waiting for a reward, or making it more
adaptive to pursue a reward when it is available. In addition,
such environments might activate children’s physiological stress
response systems (Evans and Kim 2007; Zalewski, Lengua,
Kiff, and Fisher 2012) that might be differentially related to
executive control and delay ability (Davis et al. 2002;Lengua
et al. in press), hinting at the possibility that stress has differing
effects on the underlying biological systems associated with
executive control and delay ability.
The differential relations of executive control and delay
ability to adjustment outcomes also highlight the value of
examining these separately, with patterns consistent with the
findings in other studies. For example, previous research
showed that executive control, but not delay ability, predicted
academic readiness (e.g., Kim et al. 2013), similar to our
findings for levels of executive control and delay ability.
However, by examining growth in effortful control factors,
we found that growth in both executive control and delay
ability predicted academic readiness, even if the initial levels
of delay ability did not. Gains in the ability to delay reward-
motivated approach might facilitate greater persistence and
compliance in a classroom or learning context. Future studies
can continue to clarify the potential differences in develop-
mental trajectories, predictors and outcomes of executive con-
trol and reward delay components of effortful control.
Strengths of this study include the use of a relatively large
sample that was recruited to have equal representation of a range
of income categories, including over-representation of lower
income families, thus providing a rigorous test of the effects of
income. In addition, the use of growth modeling and cross-
group analyses clarified the association of income and cumula-
tive risk to developmental changes in effortful control. Also, the
multi-method assessments reduce the likelihood that method
variance or reporter bias accounts for the observed effects.
However, the use of teacher-reported outcomes might limit the
findings to children’s behavior in classrooms. It is important to
note that the sample was recruited to represent the full range of
income, and consequently a range of risk. The pattern of
associations could be different in a high-risk sample, such as a
sample including only families living at or near poverty.
A potential limitation of the study was the measurement
of delay ability. Although executive control was assessed
using multiple tasks, the delay ability indicators were all
drawn from one task, which might have impacted the pat-
tern of findings. However, previous research has shown the
longitudinal predictive value of children’s delay of gratifi-
cationevenwhenassessedwithasingletask(e.g.,Mischel
et al. 1988). A larger question that arises is whether delay
should be examined separately from executive control.
Although some evidence suggests that a single factor un-
derlies both the executive and delay components of effort-
ful control (Allan and Lonigan 2011;Wiebeetal.2011), the
findings of this study suggest value in examining delay
ability separately. It is acknowledged that the pattern of
growth of delay ability that was observed might have re-
sulted from the delay task being insufficiently challenging
for the children in the later time points. A more pronounced
pattern of linear increase might have been observed if the
delay period were lengthened or if the demands of the task
were increased. However, other studies have also shown a
distinct growth pattern across delay and executive tasks
(Carlson 2005). Future research should address the ques-
tion of the relation of executive control and delay ability
using both theoretical and empirical approaches and devel-
opmental models.
The findings of this study clarify the relation of income to
developmental patterns of executive control and delay ability,
highlighting potential additive effects of executive control and
the mediating effect of delay ability in the relation of income
to children’s academic readiness, social-emotional and behav-
ioral adjustment. These findings suggest that, although low
income is a marker for lower effortful control, and conse-
quently for greater adjustment problems, researchers need to
identify contextual and socialization factors that predict de-
velopmental changes in effortful control that can be targets of
interventions aimed at promoting effortful control in early
childhood.
Acknowledgements Support for this research was provided by NICHD
grant R01HD054465 awarded to the first author. The authors thank the
families who participated in this study.
Conflict of Interest There are no conflicts of interests.
References
Ackerman, B., Brown, E., & Izard, C. (2004). The relations between
contextual risk, earned income and the schooladjustment of children
from economically disadvantaged families. Developmental
Psychology, 40,204–216.
J Abnorm Child Psychol
Allan, N., & Lonigan, C. (2011). Examining the dimensionality of
effortful control in preschool children and its relation to academic
and socioemotional indicators. Developmental Psychology, 47,
905–915.
Beauchaine, T. P., Hinshaw, S. P., & Pang, K. L. (2010). Comorbidity of
attention-deficit/hyperactivity disorder and early-onset conduct dis-
order: biological, environmental, and developmental mechanisms.
Clinical Psychology: Science and Practice, 17,327–336.
Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C., & Domitrovich,
C. E. (2008). Executive functions and school readiness intervention:
Impact, moderation, and mediation in the Head Start REDI program.
Development and Psychopathology, 20,821–843.
Blair, C., & Razza, R. (2007). Relating effortful control, executive func-
tion, and false belief understanding to emerging math and literacy
abilityinkindergarten.Child Development, 78,647–663.
Bridgett, D.J., & Mayes, L. C. (2011). Development of inhibitory control
among prenatally cocaine exposed and non-cocaine exposed youths
from late childhood to early adolescence: The effects of gender and
risk and subsequent aggressive behavior. Neurotoxicology and
Teratology, 33,47–60.
Bridgett, D.J., Oddi, K. B., Laake, L. M., Murdock, K. W., & Bachmann,
M. N. (2013). Integrating and Differentiating Aspects of Self-
Regulation: Effortful Control, Executive Functioning, and Links to
Negative Affectivity. Emotion, 13,47–63. doi:10.1037/a0029536.
Brock, L., Rimm-Kaufman, S., Nathanson, L., & Grimm, K. (2009). The
contributions of ‘hot’and ‘cool’executive function to children's
academic achievement, learning-related behaviors, and engagement
in kindergarten. Early Childhood Research Quarterly, 24,337–349.
Buckner, J., Mezzacappa, E., & Beardslee, W. (2003). Characteristics of
resilient youths living in poverty: The role of self-regulatory pro-
cesses. Development and Psychopathology, 15,139–162.
Buckner, J., Mezzacappa, E., & Beardslee, W. (2009). Self-regulation and
its relations to adaptive functioningin low income youths. American
JournalofOrthopsychiatry,79,19–30.
Carlson, S. (2005). Developmentally sensitive measures of executive
function in preschool children. Developmental Neuropsychology,
28,595–616.
Collins, L., Schafer, J., & Kam, C. (2001). A comparison of inclusive and
restrictive strategies in modern missing data procedures.
Psychological Methods, 6,330–351.
Davis, E. P., Bruce, J., & Gunnar, M. R. (2002). The anterior attention
network: Associations with temperament and neuroendocrine activ-
ityin6-year-oldchildren.DevelopmentalPsychobiology, 40,43–56.
doi:10.1002/dev.10012.
deBoo, G., & Kolk, A. (2007). Ethnic and gender differences in temper-
ament, and the relationship between temperament and Depressive
and Aggressive mood. Personality and Individual Differences, 43,
1756–1766.
Dixon, M. (2010). Uncovering the neural basis of resisting immediate
gratification while pursuing long-term goals. The Journal of
Neuroscience, 30,6178–6179.
Duncan, G. J., Ziol-Guest, K. M., & Kalil, A. (2010). Early-childhood
poverty and adult attainment, behavior and health. Child
Development, 81,306–325.
Eisenberg, N., Cumberland, A., Spinrad, T., Fabes, R., Shepard, S.,
Reiser, M., Murphy, B. C., Losoya, S., & Guthrie, I. (2001a). The
relations of regulation and emotionality to children’s externalizing
and internalizing problem behavior. Child Development, 72, 1112–
1134.
Eisenberg, N., Gershoff, E., Fabes, R., Shepard, S., Cumberland, A.,
Losoya, S., et al. (2001b). Mother’s emotional expressivity and
children’s behavior problems and social competence: Mediation
through children’s regulation. Developmental Psychology, 37,
475–490.
Eisenberg, N., Valiente, C., Fabes, R., Smith, C., Reiser, M., Shepard, S.,
Losoya, A., Guthrie, I., Murphy, B., & Cumberland, A. (2003). The
relations of effortful control and ego control to children's resiliency
and social functioning. Developmental Psychology, 39,761–776.
Evans, G. (2003). A multimethodological analysis of cumulative risk and
allostatic load among rural children. Developmental Psychology, 39,
924–933.
Evans, G. W., & Cassells, R. C. (2014). Childhood poverty, cumulative risk
exposure, and mental health in emerging adults. Clinical
Psychological Science, 2, 287–296. doi:10.1177/2167702613501496.
Evans, G., & English, K. (2002). The environment of poverty: Multiple
stressor exposure, psychophysiological stress, and socioemotional
adjustment. Child Development, 73,1238–1248.
Evans, G. W., & Kim, P. (2007). Childhood poverty and health:
Cumulative risk exposure and stress dysregulation. Psychological
Science, 18,953–957. doi:10.1111/j.1467-9280.2007.02008.x.
Evans, G. W., & Kim, P. (2012). Childhood poverty and young adults’
allostatic load: The mediating role of childhood cumulative risk
exposure. Psychological Science, 23,979–983.
Evans, G. W., & Wachs, T. D. (2010). Chaos and its influence on
children’s development: An ecological perspective. (G. W. Evans &
T. D. Wachs, Eds.). Washington, DC US: American Psychological
Association.
Evans, G. W., Li, D., & Whipple, S. S. (2013). Cumulative risk and child
development. Psychological Bulletin, 139,1342–1396. doi:10.
1037/a0031808.
Gerstadt, C., Hong, Y., & Diamond, A. (1994). The relationship between
cognition and action: Performance of children 3.5-7 years old on a
Stroop-like day-night test. Cognition, 53,129–153.
Gresham, F. M., & Elliot, S. N. (1990). Social Skills Rating System. Circle
Pines, MN: American Guidance Service.
Hongwanishkul, D., Happaney, K., Lee, W., & Zelazo, P. (2005).
Assessment of hot and cool executive function in young children:
Age-related changes and individual differences. Developmental
Neuropsychology, 28,617–644.
Hopkins, J., Lavigne, J. V., Gouze, K. R., LeBailly, S.A., & Bryant, F. B.
(2013). Multi-domain models of risk factors for depression and
anxiety symptoms in preschoolers: Evidence for common and spe-
cific factors. Journal of Abnormal Child Psychology, 41,705–722.
Hughes, C., & Ensor, R. (2009). Independence and interplay between
maternal and child risk factors for preschool problem behaviors?
International Journal of Behavioral Development, 33,312–322.
Hughes, C., Ensor, R., Wilson, A., & Graham, A. (2010). Tracking
executive function across the transition to school: A latent variable
approach. Developmental Neuropsychology, 35,20–36.
Kim, S., Nordling, J. K., Yoon, J. E., Boldt, L. J., & Kochanska, G.
(2013). Effortful control in “hot”and “cool”tasks differentially
predicts children’s behavior problems and academic performance.
Journal of Abnormal Child Psychology, 41,43–56.
King, K., Lengua, L., & Monahan, K. (2013). Differentiating executive
and motivational components of self-regulation: Differences in tra-
jectories, predictors and adjustment. Journal of Abnormal Child
Psychology, 41,57–69. doi:10.1007/s10802-012-9665-0.
Kochanska, G., & Knaack, A. (2003). Effortful control as a personality
characteristic of young children: Antecedents, correlates, and con-
sequences. Journal of Personality, 71,1087–1112.
Kochanska, G., Murray, K., Jacques, T., Koenig, A., & Vandegeest, K.
(1996). Inhibitory control in young children and its role in emerging
internalization. Child Development, 67,490–507.
Korkman,M.,Kirk,U.,&Kemp,S.(1998).NEPSY: A Developmental
Neuropsychological Assessment. Manual: Psychological Corporation.
Lavigne, J. V., Gouze, K. R., Hopkins, J., Bryant, F. B., & LeBailly, S. A.
(2012). A multi-domain model of risk factors for ODD symptoms in
a community sample of 4-year olds. Journal of Abnormal Child
Psychology, 40,741–757.
Lengua, L. (2003). Associations among emotionality, self-regulation,
adjustment problems and positive adjustment in middle childhood.
Journal of Applied Developmental Psychology, 24,595
–618.
JAbnormChildPsychol
Lengua, L. (2006). Growthin temperament and parenting as predictors of
adjustment during children’s transition to adolescence.
Developmental Psychology, 42,819–832.
Lengua, L., Honorado, E., & Bush, N. (2007). Cumulative risk and
parenting as predictors of effortful control and social competence
in preschool children. Journal of Applied Developmental
Psychology, 28,40–55.
Lengua, L. J., Zalewski, M., Fisher, P., Moran, L. (in press). Does HPA-Axis
dysregulation account for the effects of income on effortful control and
adjustment in preschool? Infant and Child Development.
Li-Grining, C. (2007). Effortful control among low-income preschoolers
in three cities: Stability, change, and individual differences.
Developmental Psychology, 43,208–221.
Linver, M., Brooks-Gunn, J., & Kohen, D. (2002). Family processes as
pathways from income to young children’s development.
Developmental Psychology, 38,719–734.
MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study
of mediated effect measures. Multivariate Behavioral Research, 30,
41–62.
McClelland, M., Cameron, C., Connor, C., Farris, C., Jewkes, A., &
Morrison, F. (2007). Links between behavioral regulation and pre-
schoolers’literacy, vocabulary, and math skills. Developmental
Psychology, 43,947–959.
Mezzacappa, E. (2004). Alerting, orienting, and executive attention:
Developmental properties and sociodemographic correlates in an
epidemiological sample of young, urban children. Child
Development, 75,1373–1386.
Mischel, W., Shoda, Y., & Peake, K. (1988). The nature of adolescent
competencies predicted by preschool delay of gratification. Journal
of Personality and Social Psychology, 54,687–696.
Mistry, R., Vandewater, E., Huston, A., & McLoyd, V. (2002). Economic
well-being and children’s social adjustment: The role of family
process in an ethnically diverse low-income sample. Child
Development, 73,935–951.
Mistry, R., Benner, A., Biesanz, J., Clark, S., & Howes, C. (2010). Family
and social risk, and parental investments during the early childhood
years as predictors of low-income children’s school readiness out-
comes. Early Childhood Research Quarterly, 25,432–449.
Muris, P., van der Pennen, E., Sigmond, R., & Mayer, B. (2008).
Symptoms of anxiety, depression, and aggression in non-clinical
children: Relationships with self-report and performance-based
measures of attention and effortful control. Child Psychiatry and
Human Development, 39,455–467.
Muthen, L., & Muthen, B. (2010). Mplus User’s Guide (6th ed.). Los
Angeles, CA: Muthen & Muthen.
Obradovic, J., Bush, N., Stamperdahl, J., Adler, N., & Boyce, W. (2010).
Biological sensitivity to context: The interactive effects of stress
reactivity and family adversity on socio-emotional behaviour and
school readiness. Child Development, 81,270–289.
Ponitz, C., McClelland, M., Jewkes, A., Connor, C., Farris, C., &
Morrison, F. (2008). Touch your toes! Developing a direct measure
of behavioral regulation in early childhood. Early Childhood
Research Quarterly, 23,141–158.
Radloff, L. (1977). The CES-D Scale: A self-report depression scale for
research in the general population. Applied Psychological
Measurement, 1,385–401.
Raver, C. C., Blackburn, E. K., Bancroft, M., & Torp, N. (1999).
Relations between effective emotional self-regulation, attentional
control, and low-income preschoolers’social competence with
peers. Early Education and Development, 10,333–350.
Raver, C., Jones, S., Li Grining, C., Zhai, F., Bub, K., & Pressler, E.
(2011). CRSP’s impact on low-income preschoolers’preacademic
skills: Self-regulation as a mediating mechanism. Child
Development, 82,362–378.
Raver, C. C., Blair, C. W., & Willoughby, M. (2013). Poverty as a
predictor of 4-year-olds’executive function: New Perspectives on
models of differential susceptibility. Developmental Psychology, 49,
292–304.
Razza, R. A., Martin, A., & Brooks-Gunn, J.(2010). Associations among
family environment, sustained attention, and school readiness for
low-income children. Developmental Psychology, 46,1528–1542.
Reed, M., Pien, D., & Rothbart, M. K. (1984). Inhibitory self-control in
preschool children. Merrill-Palmer Quarterly, 30,131–148.
Rhoades, B. L., Greenberg, M. T., Lanza, S. T., & Blair, C. (2011).
Demographic and familial predictors of early executive function
development: Contribution of a person-centered perspective.
Journal of Experimental Child Psychology, 108,638–662.
Rothbart, M., & Bates, J. (2006). Temperament. In N. Eisenberg, N.
Damon, & R. Lerner (Eds.), Handbook of child psychology: Vol.
3, Social, emotional, and personality development (6th Ed.) (pp. 99–
166). Hoboken, NJ: Wiley.
Rothbart, M., Ahadi, S., & Evans, D. (2000). Temperament and person-
ality: Origins and outcomes. Journal of Personality and Social
Psychology, 78,122–135.
Sandler, I., Ramirez, R., & Reynolds, K. (1986, August). Life stress for
children of divorce, bereavedc and asthmatic children. Paper pre-
sented at the annual meeting of the American Psychological
Association. D.C.: Washington.
Sattler, J. M. (2001). Assessment of Children: Cognitive Applications, 4th
Edition. San Diego, CA: Sattler Publishing, Inc.
Sektnan, M., McClelland, M., Acock, A., & Morrison, F. (2010).
Relations between early family risk, children's behavioral regula-
tion, and academic achievement. Early Childhood Research
Quarterly, 25,464–479.
Sheese, B. E., Rothbart, M. K., Posner, M. I., White, L. K., & Fraundorf,
S. H. (2008). Executive attention and self-regulation in infancy.
Infant Behavior & Development, 31,501–510.
Sulik,M.J.,Huera,S.,Zerr,A.A.,Eisenberg,N.,Spinrad,T.L.,Valiente,
C.,DiGiunta,L.,Pina,A.A.,Eggum,N.D.,Sallquist,J.,Edwards,
A.,Kupfer,A.,Lonigan,C.J.,Phillips,B.M.,Wilson,S.B.,Clancy-
Menchetti, J., Landry, S. H., Swank, P. R., Assel, M. A., & Taylor, H.
B. (2010). The Factor Structure of Effortful Control and Measurement
Invariance Across Ethnicity and Sex in a High-Risk Sample. Journal
of Psychopathology and Behavioral Assessment, 32,8–22.
Swanson, J., Valiente, C., & Lemery-Chalfant, K. (2012). Predicting
academic achievement from cumulative home risk: The mediating
roles of effortful control, academic relationships, and school avoid-
ance. Merrill-Palmer Quarterly, 58,375–408.
Valiente, C., Lemery-Calfant, K., & Castro, K. (2007). Children’seffort-
ful control and academic competence: Mediation through school
liking. Merrill-Palmer Quarterly, 53,1–25.
Vernon-Feagans, L., & Cox, M. (2013). The family life project: And
epidemiological and developmental study of young children living
in poor rural communities: I. Poverty, rurality, parenting and risk: An
introduction. Monographs of the Society for Research in Child
Development, 78,1–23.
Wanless, S. B., McClelland, M. M., Tominey, S. L., & Acock, A. C.
(2010). The influence of demographic risk factors on children’s
behavioral regulation in prekindergarten and kindergarten. Early
Education and Development, 22,461–488.
Wells, N. M., Evans, G. W., Beavis, A., & Ong, A. D. (2010). Early
childhood poverty, cumulative risk exposure, and body mass index
trajectories through young adulthood. American Journal of Public
Health, 100,2507–2512.
Wiebe, S., Sheffield, T., Nelson, J., Clark, C., Chevalier, N., & Espy, K.
(2011). The structure of executive function in 3-year-olds. Journal
of Experimental Child Psychology, 108,436–452.
Zalewski, M., Lengua, L. J., Kiff, C. J., & Fisher, P. A. (2012).
Understanding the relation of low income to HPA-axis functioning
in preschool children: Cumulative family risk and parenting as
pathways to disruptions in cortisol. Child Psychiatry and Human
Development, 43,924–942. doi:10.1007/s10578-012-0304-3.
J Abnorm Child Psychol
Zelazo, P., Muller, U., Frye, D., & Marcovitch, S. (2003). The
development of executive function in early childhood.
Monographs of the Society for Research in Child Development,
68, 274.
Zhou, Q., Chen, S., & Main, A. (2012). Commonalities and differences in
the research onchildren’s effortful control and executive function: A
call for an integrated model of self-regulation. Child Development
Perspectives, 6,112–121.
JAbnormChildPsychol
