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In this longitudinal multiinformant study negative emotionality and sensory processing sensitivity were compared as susceptibility markers among kindergartners. Participating children (N = 264, 52.9% boys) were Dutch kindergartners (Mage = 4.77, SD = 0.60), followed across three waves, spaced seven months apart. Results show that associations between parenting and child behavior did not depend on children’s negative emotionality. Sensory processing sensitivity, however, interacted with both (changes in) negative and (changes in) positive parenting in predicting externalizing, but not prosocial, behavior. Depending on the interaction, vantage sensitivity and differential susceptibility models were supported. The findings suggest that sensory processing sensitivity may be a more proximal correlate of individual differences in susceptibility, compared with negative emotionality.
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Sensory Processing Sensitivity as a Marker of Differential Susceptibility
to Parenting
Meike Slagt, Judith Semon Dubas,
and Marcel A. G. van Aken
Utrecht University, the Netherlands
Bruce J. Ellis
University of Utah
Maja Dekovi´
c
Utrecht University, the Netherlands
In this longitudinal multiinformant study negative emotionality and sensory processing sensitivity were
compared as susceptibility markers among kindergartners. Participating children (N264, 52.9% boys)
were Dutch kindergartners (M
age
4.77, SD 0.60), followed across three waves, spaced seven months
apart. Results show that associations between parenting and child behavior did not depend on children’s
negative emotionality. Sensory processing sensitivity, however, interacted with both (changes in)
negative and (changes in) positive parenting in predicting externalizing, but not prosocial, behavior.
Depending on the interaction, vantage sensitivity and differential susceptibility models were supported.
The findings suggest that sensory processing sensitivity may be a more proximal correlate of individual
differences in susceptibility, compared with negative emotionality.
Keywords: differential susceptibility, vantage sensitivity, negative emotionality, sensory processing
sensitivity
The differential susceptibility model postulates that children differ
in their general susceptibility to environmental influences, with some
being more strongly affected than others by both negative (risk-
promoting) and positive (development-enhancing) experiences (Bel-
sky, 1997a,2005;Belsky, Bakermans-Kranenburg, & van IJzen-
doorn, 2007;Boyce et al., 1995;Boyce & Ellis, 2005;Ellis, Boyce,
Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2011). To find
out which children are more and less susceptible, research has tar-
geted an array of potential susceptibility markers, ranging from ge-
notype to stress physiology to temperament. Reflecting a concern with
negative developmental outcomes, temperament studies have espe-
cially focused on negative emotionality (Belsky & Pluess, 2009)
whereas other traits have received less attention as potential suscep-
tibility markers. However, it may not be negative emotionality per se,
but rather a highly sensitive nervous system, manifested in the tem-
perament trait of sensory processing sensitivity (Aron & Aron, 1997;
Aron, Aron, & Jagiellowicz, 2012;Belsky & Pluess, 2013) that
constitutes a more proximal marker of susceptibility. In this study we
aimed to compare negative emotionality and sensory processing sen-
sitivity as markers of individual differences in susceptibility to par-
enting among children.
The differential susceptibility hypothesis differs from the tradi-
tional diathesis-stress model (Monroe & Simons, 1991;Zuckerman,
1999), in that the former highlights the disproportionate susceptibility
to both the negative effects of harsh environments and the beneficial
effects of supportive environments in the same individuals, whereas
the latter emphasizes the disproportionate vulnerability to negative
environments of some individuals. The differential susceptibility hy-
pothesis also differs from vantage sensitivity, which describes indi-
vidual differences in the tendency to benefit from positive features of
the environment only (Pluess & Belsky, 2013). According to this
model, some individuals show vantage sensitivity, meaning they
benefit disproportionately from enriched environments, whereas oth-
ers show vantage resistance, meaning they gain little to nothing from
enriched environments.
Negative Emotionality and Sensory
Processing Sensitivity
Early attempts to identify potential susceptibility markers called
attention to the temperament trait of negative emotionality (Bel-
sky, 1997b,2005;Belsky, Hsieh, & Crnic, 1998). Temperament
refers to constitutionally based individual differences in reactivity
and self-regulation, and negative emotionality is part of the reac-
This article was published Online First November 20, 2017.
Meike Slagt, Department of Clinical Child and Familiy Studies, Utrecht
University, the Netherlands; Judith Semon Dubas and Marcel A. G. van
Aken, Department of Developmental Psychology, Utrecht University, the
Netherlands; Bruce J. Ellis, Department of Psychology, University of Utah;
Maja Dekovi´
c, Department of Clinical Child and Familiy Studies, Utrecht
University, the Netherlands.
Support for this research was provided by the Netherlands Organisation
for Scientific Research (NWO Grant 406-11-030), the ISSBD-JF Mentored
Fellowship Program for Early Career Scholars, a Fulbright scholarship, and
an NWO Visitor Travel Grant (NWO Grant 040.11.494). We would like to
extend our sincere thanks and appreciation to the student assistants who
helped collect the data and the families that participated in this study.
Correspondence concerning this article should be addressed to Meike
Slagt, Department of Clinical Child and Family Studies, Utrecht Uni-
versity, P.O. Box 80.140, 3508 TC Utrecht, the Netherlands. E-mail:
m.i.slagt@uu.nl
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Developmental Psychology © 2017 American Psychological Association
2018, Vol. 54, No. 3, 543–558 0012-1649/18/$12.00 http://dx.doi.org/10.1037/dev0000431
543
tive dimension of temperament (which entails the arousability of
motor, affective, and sensory response systems; Rothbart & Bates,
2006). Negative emotionality itself can be described as the ten-
dency to be easily distressed (Putnam, Ellis, & Rothbart, 2001),
and it is a fairly general construct, which encompasses emotions of
fear, worry, sadness, discomfort and anger, frustration, and irrita-
bility. Negative emotionality has been associated with later inter-
nalizing as well as externalizing problems (e.g., Eisenberg et al.,
2009;Lemery, Essex, & Smider, 2002).
Research suggests children high on negative emotionality are
more susceptible to parenting and other environmental influences
(Belsky & Pluess, 2009;Pluess & Belsky, 2010a). Compared with
their less negatively emotional counterparts, these children showed
more internalizing and externalizing behavior problems and lower
social and academic adjustment when parenting quality was low,
and fewer behavior problems and better adjustment when parent-
ing quality was high (e.g., Belsky et al., 1998;Poehlmann et al.,
2012; for a meta-analysis, see Slagt, Dubas, Dekovi´
c, & van Aken,
2016). Children with a more difficult temperament have also been
found to be more susceptible to parenting in several studies (e.g.,
Bradley & Corwyn, 2008;Roisman et al., 2012). In these studies,
difficult temperament was measured using a composite of several
temperament traits, combining negative emotionality with traits
such as surgency or low effortful control.
More recently, sensory processing sensitivity (SPS) has been
advanced as a potential susceptibility marker (Aron & Aron, 1997;
Aron et al., 2012), although compared with negative emotionality
it has been studied less extensively and mainly among adults (Ellis
et al., 2011). SPS is a personality trait that entails a low sensory
threshold and high sensitivity to subtle stimuli, deep cognitive
processing of stimuli, behavioral inhibition in novel situations or in
situations generating conflicting responses, and high emotional
and physiological reactivity (Aron & Aron, 1997;Aron et al.,
2012). Individuals high on SPS tend to be more aware of infor-
mation in their environment, and tend to process this information
on a deeper and more complex level than other people, which
affects the way they plan, think, and learn. Because they process
experiences more thoroughly, their development is believed to be
more strongly affected by, or susceptible to, their environment.
The trait of SPS is measurable by means of the Highly Sensitive
Person scale (Aron & Aron, 1997) and its short child-version, the
Highly Sensitive Child scale (HSC; Pluess et al., 2017). Based on
five studies featuring four samples, the HSC has been shown to be
a valid and reliable measure that performs well in children and
adolescents, and is relatively independent from other traits. About
20% of the population is thought to be characterized by a highly
sensitive personality (Aron & Aron, 1997;Aron et al., 2012),
although some contend that SPS in children is best considered as
a continuous rather than a dichotomous variable (Benham, 2006;
Boterberg & Warreyn. 2016). In previous research SPS has been
associated with, among other things, neuroticism among adults
(e.g., Aron & Aron, 1997;Smolewska, McCabe, & Woody, 2006).
When linking SPS to the model of adult temperament developed
by Rothbart and colleagues (Evans & Rothbart, 2008), SPS was
associated with negative affect and its subscale sensory discomfort
(unpleasant affect resulting from the sensory qualities of stimula-
tion), as well as with orienting sensitivity and its subscale percep-
tual sensitivity (awareness of slight, low intensity stimulation
arising from within the body and the environment). Among two
independent samples of children, SPS was positively correlated
with behavioral inhibition, behavioral activation, effortful control,
negative emotionality, and positive emotionality (Pluess et al.,
2017). Among a sample of adolescents, SPS was positively asso-
ciated with neuroticism and openness, and negatively with extra-
version. Little is known about the development of SPS across the
life course, although recent findings do suggest that in middle
childhood children are more sensitive than in adolescence (Pluess
et al., 2017).
Empirical evidence for SPS as a susceptibility marker is still
scarce. Most studies on SPS have been conducted on adults. Work
by Aron, Aron, and Davies (2005) revealed that a stressful child
rearing history predicted high levels of shyness and negative
affectivity within a sample of undergraduate students, but only for
those scoring high on SPS. An fMRI study suggested that adults
higher on SPS processed information about both sad and happy
faces more thoroughly compared to those who scored lower on
SPS, as indicated by increased activation of brain regions involved
in awareness, empathy, and self-other processing (Acevedo et al.,
2014). SPS was also related to increased brain activation in re-
sponse to subtle changes in stimuli, specifically in regions associ-
ated with higher order visual processing (Jagiellowicz et al., 2011).
The first study examining SPS among children tested a school-
based depression prevention program among an at-risk population
of 11-year-old girls in England. Girls higher on SPS, compared
with those low on this trait, benefitted more from the intervention,
as reflected in reduced depression scores (Pluess & Boniwell,
2015).
Negative Emotionality as a Susceptibility Marker
Although negative emotionality has been studied more fre-
quently in relation to differential susceptibility compared to SPS,
it faces two issues. First, there is no clear theoretical rationale for
why negative emotionality would make individuals more suscep-
tible. When the differential susceptibility model was developed, it
did not include predictions about which individual characteristics
would distinguish more and less susceptible individuals. It was
empirical results that, somewhat surprisingly, called attention to
negative emotional reactivity as a potential susceptibility marker
(Belsky, 1997b,2005;Belsky et al., 1998). Yet because negative
emotionality entails the expression of negative emotions, and is
oftentimes related to negative environmental circumstances (Kiff,
Lengua, & Zalewski, 2011;Rothbart & Bates, 2006), it is not
immediately evident why this trait would indicate a general sus-
ceptibility to both negative and positive environments. Perhaps
more susceptible children, compared with less susceptible chil-
dren, are more easily overwhelmed by environmental stimuli;
especially when they are infants, negative emotionality may be a
common reaction to such overstimulation (Aron & Aron, 1997;
Aron et al., 2012;Boterberg & Warreyn, 2016). Accordingly,
negative emotionality among infants may, in some cases, be an
outcome of high SPS. By contrast, SPS is theoretically linked to a
broader sensory awareness and processing of information in the
environment, regardless of valence (Aron et al., 2012). In total,
although it is not entirely clear how high levels of negative
emotionality contribute to making some individuals more suscep-
tible to the environment than others, it is conceivable that SPS,
because of its very characteristics, potentiates susceptibility. SPS,
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544 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
therefore, may be a more proximal marker of differential suscep-
tibility.
Second, and related, many of the studies that found support for
differential susceptibility depending on children’s negative emo-
tionality, measured negative emotionality in infancy (e.g., Pluess
& Belsky, 2010b;Poehlmann et al., 2012;Roisman et al., 2012).
Researchers have observed that when differential susceptibility
with negative emotionality as a marker is studied beyond toddler-
hood, frequently no interaction effects or diathesis-stress effects
are obtained (Kiff et al., 2011;Slagt, Dubas, & van Aken, 2016;
Slagt, Dubas, Dekovi´
c, & van Aken, 2016). Negative emotionality
may operate as a susceptibility marker early in life because the
immaturity of neurobiological systems amplifies emotional and
behavioral responses (Calkins, Graziano, & Keane, 2007). As
these systems mature, and increased cortical development exerts
greater control over neural pathways, negative emotionality may
become more specifically linked to vulnerability to negative affect
and experiences, as per diathesis-stress. At later ages, negative
emotionality may therefore be a less accurate marker of individual
differences in susceptibility. By contrast, how SPS and its relation
to susceptibility change across age is unknown at present, but there
is no reason to expect this relation to be age-specific. Thus,
although susceptible children’s negative emotionality may de-
crease as they get older, these children may continue to score high
on SPS. Building on this idea, the current study aimed to compare
negative emotionality and SPS as susceptibility markers among 4-
to 6-year-old children.
We focused on 4- to 6-year-old children in kindergarten, which
marks a period of change in the developmental agenda (Rimm-
Kaufman & Pianta, 2000). It is a period in which many important
skills develop, such as prosocial behavior (Eisenberg & Fabes,
1998). At the same time, individual differences in disruptive and
antisocial behaviors can start to be reliably identified (Campbell,
Shaw, & Gilliom, 2000). Parenting, temperament, and above all,
interactions between parenting and temperament can have a pow-
erful impact on the development of children’s prosocial and ex-
ternalizing behaviors during this developmental period (Grusec,
2011;Rothbart & Bates, 2006). Moreover, this may be an age
during which potential differences in the extent to which negative
emotionality and SPS indicate susceptibility have become visible
(Slagt et al., 2016).
The Current Study
In this study we tested negative emotionality and sensory pro-
cessing sensitivity as potential susceptibility (or vulnerability)
markers among kindergartners. We focused on negative as well as
positive parenting behaviors and child outcomes. The differential
susceptibility model reflects the combination of diathesis-stress
and vantage sensitivity models, and selection of a restricted range
of environments and outcomes, such as a harsh environment and
child problem behavior, would make the models hard to distin-
guish.
In the current research negative parenting entailed behaviors
reflecting negative control and hostility, and positive parenting
entailed behaviors reflecting positive control and warmth (Mac-
coby & Martin, 1983). As to child adjustment, on the negative side
we focused on child externalizing problem behaviors and on the
positive side we focused on prosocial behavior. Externalizing
behaviors can be described as outer-directed, generating discom-
fort and conflict in the surrounding environment. They include
hyperactive, oppositional, and aggressive behavior (Achenbach &
Edelbrock, 1978). Prosocial behaviors are voluntary behaviors
intended to benefit others (Eisenberg & Fabes, 1998).
Finally, differential susceptibility predicts that more susceptible
individuals are more likely to experience sustained developmental
change in response to their environment. Whether this change
occurs in response to the state of the environment at the beginning
of a developmental trajectory, or to changes in the environment
over time, or to both, is an open question. Using three waves of
longitudinal data spanning over a year, we estimated latent growth
curve models for parenting as well as child behaviors. Next, using
latent interactions (Klein & Moosbrugger, 2000;Maslowsky,
Jager, & Hemken, 2015) we examined whether temperament in-
teracts with both parenting behavior at the beginning of the study
and with changes in parenting during the study, in predicting
changes in child behavior during the study and child behavior at
the end of the study.
In sum, in this study we compared negative emotionality and
sensory processing sensitivity as susceptibility markers among
kindergartners. Given the age of the children in our sample and
results from a recent meta-analysis (Slagt, Dubas, Dekovi´
c, & van
Aken, 2016), we expected either no interactions with negative
emotionality, or interactions reflecting a diathesis-stress pattern.
Further, we expected sensory processing sensitivity to moderate
associations between (changes in) parenting and (changes in) child
behavior, with interactions reflecting a differential susceptibility
pattern.
Method
Participants
Information about the study was distributed to parents of chil-
dren in kindergarten, at 49 regular elementary schools in the
province of Utrecht, the Netherlands. Parents could voluntarily
sign up their children for the study at a website, where they gave
active informed consent, filled out their contact information, and
completed a short screening questionnaire inquiring after chil-
dren’s negative emotionality and surgency. In this way, 280 chil-
dren signed up for the study. Analyses included only the 264
children who participated with their mothers; 14 children who
participated with their fathers only were excluded from analyses.
Participating children were boys (52.9%) and girls (47.1%)
between the ages of 3.67 and 7.20 years at the start of the study
(M4.77, SD 0.60). Most of the children (97.6%) were born
in the Netherlands, as were their mothers (92.3%). Mothers of
participating children were between the ages of 21.55 and 47.88
years (M37.51, SD 4.40) at the start of the study, and were
mostly married (76.2%) or cohabiting (17.6%). Mothers were
highly educated, with 6.5% having no high school diploma or
having finished lower vocational education, 27.7% having finished
intermediate vocational education, and 65.8% having finished
higher vocational education or university. Gross annual household
income was less than the national mode (35,000,-) for 8.5% of
families, between one and 1.5 times national mode for 15.1% of
families, 1.5 to 2 times national mode for 34.4% of families, and
more than two times the national mode for 42.0% of families.
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545
SPS AS MARKER OF DIFFERENTIAL SUSCEPTIBILITY
Teachers who provided data on child behavior were mostly women
(99.5% at T1, 97.3% at T2, and 96.3% at T3).
Most families continued to participate throughout the study:
94% of the mothers who provided data at the screening also
provided data at Time 1 (T1), 86% at Time 2 (T2) and 83% at
Time 3 (T3); teachers reported on 84% of the children at Time 1,
75% at Time 2, and 67% at Time 3. Complete data on study
variables (child temperament traits, mother-reported parenting,
and teacher-reported child behavior across three times) were pro-
vided by 95% of the participating families at screening, and by
70% of the participating families at T1, 74% at T2, and 72% at T3.
Children with complete data across waves generally did not differ
from children with missing data on demographic variables or on
study variables, as indicated by
2
- and independent samples t
tests. The one exception was that children with missing values had
mothers who reported higher levels of negative parenting at each
of the three waves, ps.05, ds0.40, 0.31, and 0.28 at T1, T2,
and T3, respectively. Missing values were handled in Mplus 7.2
using Full Information Maximum Likelihood (Enders & Bandalos,
2001).
Procedure
After the screening, three more waves of data collection took
place, spaced 7 months apart. At each of these three waves mothers
reported on their parenting behaviors, whereas teachers reported
on children’s externalizing and pro-social behavior. In addition,
mothers provided information on children’s negative emotionality
during the screening, and on children’s SPS at T1. Families were
given a gift certificate after completing T1 and T3, and a lottery
was organized in which two families who had participated in all
three waves could win tickets to a theme park. Finally, regular
newsletters were sent to the participating families and schools,
keeping them informed on the progress of the study. The research
ethics committee of the faculty of social sciences at Utrecht
University declared this study exempt; it was deemed to be non-
invasive for participants.
Measures
Child externalizing and prosocial behavior. Teachers re-
ported on externalizing behaviors and prosocial behaviors at each
wave using the Dutch version of the Strengths and Difficulties
Questionnaire (Goodman, 2001;van Widenfelt, Goedhart, Tref-
fers, & Goodman, 2003). Each subscale consists of five items,
measured on a 3-point scale (0 not true to 2 definitely true).
The conduct problems subscale (“Often has temper tantrums or hot
temper”) and attention problems subscale (“Easily distracted, has
trouble concentrating”) were summed to an externalizing behavior
score; the items in the prosocial subscale (“Considerate of other
people’s feelings”) were summed to a prosocial behavior score.
Cronbach’s s for externalizing behavior and prosocial behavior
were .82 and .79 at T1, .84 and .82 at T2, and .82 and .81 at T3.
Negative parenting. Negative parenting was measured using
four scales. The overreactivity scale from the Parenting Scale
(Prinzie, Onghena, & Hellinckx, 2007) contains nine items (e.g.,
“When my child misbehaves...Ispeak calmly to my child vs. I
raise my voice or yell”) answered on a 7-point Likert scale,
ranging from a high probability to use an effective discipline
strategy to a high probability of making a discipline mistake (sat
T1, T2 and T3 were .82, .83, and .80, respectively). The power
assertion scale from the Parenting Dimensions Inventory (Power,
1993) contains 12 items, consisting of short scenarios to which the
parent is asked to indicate the likelihood of responding in a
specific way (e.g., “After an argument about toys your child hits
his/her play mate. . . . How likely is it that you will use physical
punishment?”). Answers can range from 1 (very unlikely)to5
(very likely;s at T1, T2 and T3 were .77, .80, and .77, respec-
tively). The ignoring scale from the Nijmegen Parenting Question-
naire (Gerrits, Dekovi´
c, Groenedaal, & Noom, 1996) consists of
five items (e.g., “When my child does something that is not
allowed, I oftentimes look angry and pretend like he/she is not
there”), with answers ranging from 1 (totally disagree)to6(totally
agree;s at T1, T2 and T3 were .79, .84, and .85, respectively).
Finally, the inconsistent discipline from the Parenting Dimensions
Inventory (Power, 1993) contains eight items (e.g., “My child
oftentimes manages to convince me to punish him/her lighter than
I intended”), with answers ranging from 1 (totally disagree)to6
(totally agree;s at T1, T2 and T3 were .87, .88, and .87,
respectively). Confirmatory factor analysis (CFA) in Mplus 7.2
showed that these four scales can be combined into one construct,
with factor loadings ranging between .46 and .90. Following van
de Schoot, Lugtig, and Hox (2012) and Widaman, Ferrer, and
Conger (2010), we established partial strict measurement invari-
ance across waves,
2
(318) 443.66, p.001, CFI .962,
TLI .958, RMSEA .040, which entailed invariant factor
loadings, measurement intercepts, and unique factor variances.
Factor scores of the final CFA model were saved and used for
further analyses. Cronbach’s s of the total scale were .87, .88, and
.87 at T1, T2, and T3, respectively.
Positive parenting. Positive parenting was measured using
five scales. The responsiveness scale from the Nijmegen Parenting
Questionnaire (Gerrits et al., 1996) contains eight items (e.g., “I
help my child when he/she has difficulties”) with answers ranging
from 1 (totally disagree)to6(totally agree;s at T1, T2 and T3
were .79, .80, and .82, respectively). The autonomy granting scale
from the Nijmegen Parenting Questionnaire (Gerrits et al., 1996)
consists of four items (e.g., “I regularly encourage my child to
explore things”), answers ranging from 1 (totally disagree)to6
(totally agree;s at T1, T2 and T3 were .72, .70, and .70,
respectively). The positive interactions scale from the Parenting
Practices Scale (Strayhorn & Weidman, 1988) contains five items
(e.g., “How often do you and your child laugh together”), with
answers ranging from 1 (never)to5(several times a day;satT1,
T2 and T3 were .76, .74, and .75, respectively). The positive
parenting scale from the Alabama Parenting Questionnaire (Essau,
Sasagawa, & Frick, 2006) contains five items (e.g., “You praise
your child when he/she behaves well”), answers ranging from 1
(never)to5(always;s at T1, T2 and T3 were .78, .70, and .72,
respectively). Finally, the inductive discipline scale from the Par-
enting Dimensions Inventory (Power, 1993) contains 12 items
consisting of short scenarios to which the parent is asked to
indicate the likelihood of responding in a specific way (e.g., “After
an argument about toys your child hits his/her play mate....How
likely is it that you will point out the consequences of your child’s
behavior to your child?”). Answers are given ranging from 1 (very
unlikely)to5(very likely;s at T1, T2 and T3 were .84, .82, and
.85, respectively). Confirmatory factor analysis in Mplus 7.2
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
546 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
showed that these five scales can be combined into one construct,
with factor loadings ranging between .34 and .67. We established
partial strict measurement invariance across wave,
2
(318)
443.66, p.001, CFI .962, TLI .958, RMSEA .040.
Factor scores were saved and used for further analyses. Cronbach’s
s of the total scale were .85, .85, and .87, at T1, T2, and T3,
respectively.
Negative emotionality. Children’s negative emotionality was
assessed using the Dutch version of the Children’s Behavior Ques-
tionnaire—Short From (Putnam & Rothbart, 2006;Rothbart,
Ahadi, Hershey, & Fisher, 2001). Mothers reported children’s
anger/frustration (“Has temper tantrums when s/he doesn’t get
what s/he wants”), soothability (“Is very difficult to soothe when
s/he has become upset”), fear (“Is afraid of burglars or the “boogie
man.””), and sadness (“Cries sadly when a favorite toy gets lost or
broken”). Items could be answered on a 7-point scale ranging from
1(extremely untrue of your child)to7(extremely true of your
child).Anot applicable response option was also available, for
when the child had not been observed in the situation described.
Scale scores were created by averaging applicable item scores.
Following previous research (Rothbart et al., 2001), the anger/
frustration, reversed soothability, fear, and sadness scales were
subsequently averaged into a negative emotionality score (␣⫽
.81).
Sensory processing sensitivity. Children’s SPS was assessed
using a Dutch parent-report version of the HSC (adapted from
Pluess et al., 2017;Pluess & Boniwell, 2015), which was back-
translated together with the second author. Items (“My child finds
it unpleasant to have a lot going on at once,” “My child notices
when small things have changed in his/her environment”) could be
answered on a 7-point scale ranging from 1 (not at all)to7
(extremely), with higher scores indicating higher SPS. Internal
consistency was satisfactory (␣⫽.77).
Results
Descriptive Results
Descriptive statistics and correlations for measures of child
behavior, parenting, and temperament traits are presented in Table
1. Children’s externalizing and prosocial behavior as well as
parents’ positive and negative parenting all displayed high rank-
order stability, both from T1 to T2 and from T2 to T3. Mean levels
of externalizing behavior were stable throughout the study, T1 to
T2: t(155) ⫽⫺0.13 p.90, d⫽⫺0.01, and T2 to T3:
t(139) ⫽⫺0.41, p.97, d⫽⫺0.00; whereas prosocial behavior
increased slightly, t(154) 2.14, p.03, d0.18, and T2 to T3:
t(138 1.65, p.10, d0.11). Levels of negative parenting
increased slightly from T1 to T2, t(244) 3.66, p.001, d
0.08 and remained stable after that, t(244) ⫽⫺0.81, p.42,
d⫽⫺0.02. Positive parenting decreased between T1 and T2,
t(244) ⫽⫺12.33, p.001, d⫽⫺0.37, and then increased again,
t(244) 5.90, p.001, d0.15.
Change in Parenting Behavior and Child Behavior
To answer our research questions, we used latent growth curve
modeling (LGM; Duncan, Duncan, & Strycker, 2006) and the
latent moderated structural equation technique (LMS; Klein &
Table 1
Descriptive Statistics and Correlations Among Child Behaviors, Parenting, and Temperament Traits
Measure 1234567891011121314
1. Externalizing behavior T1
2. Externalizing behavior T2 .66
ⴱⴱⴱ
3. Externalizing behavior T3 .55
ⴱⴱⴱ
.77
ⴱⴱⴱ
4. Prosocial behavior T1 .53
ⴱⴱⴱ
.39
ⴱⴱ
.33
ⴱⴱⴱ
5. Prosocial behavior T2 .30
ⴱⴱⴱ
.43
ⴱⴱⴱ
.39
ⴱⴱⴱ
.46
ⴱⴱⴱ
6. Prosocial behavior T3 .22
ⴱⴱⴱ
.37
ⴱⴱⴱ
.44
ⴱⴱⴱ
.45
ⴱⴱⴱ
.69
ⴱⴱⴱ
7. Negative parenting T1 .11 .23
ⴱⴱ
.15 .15
.14 .17
8. Negative parenting T2 .14 .23
ⴱⴱ
.13 .15
.18
.19
.94
ⴱⴱⴱ
9. Negative parenting T3 .15
.24
ⴱⴱ
.14 .13 .16
.13 .91
ⴱⴱⴱ
.94
ⴱⴱⴱ
10. Positive parenting T1 .04 .10 .01 .09 .10 .06 .60
ⴱⴱⴱ
.54
ⴱⴱⴱ
.61
ⴱⴱⴱ
11. Positive parenting T2 .08 .11 .02 .12 .14 .13 .65
ⴱⴱⴱ
.70
ⴱⴱⴱ
.76
ⴱⴱⴱ
.89
ⴱⴱⴱ
12. Positive parenting T3 .05 .07 .01 .05 .09 .04 .56
ⴱⴱⴱ
.55
ⴱⴱⴱ
.67
ⴱⴱⴱ
.90
ⴱⴱⴱ
.92
ⴱⴱⴱ
13. Negative emotionality .06 .12 .05 .12 .10 .02 .21
ⴱⴱ
.24
ⴱⴱⴱ
.22
ⴱⴱⴱ
.03 .08 .06
14. SPS .02 .02 .04 .05 .04 .06 .08 .05 .05 .12 .11 .10 .43
ⴱⴱⴱ
N210 177 163 210 176 163 245 245 245 245 245 245 252 209
M3.02 3.20 3.07 7.58 7.83 7.84 2.61 2.67 2.65 5.17 5.07 5.11 3.33 4.28
SD 3.39 3.52 3.39 2.08 2.12 2.16 .66 .67 .64 .26 .28 .27 .71 .80
Note.T1Time 1; T2 Time 2; T3 Time 3; SPS sensory processing sensitivity.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
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547
SPS AS MARKER OF DIFFERENTIAL SUSCEPTIBILITY
Moosbrugger, 2000) in Mplus 7.2 (Muthén & Muthén, 1998
2012). LGM provides mean levels (i.e., intercepts) and change
rates (i.e., slopes) that represent the developmental trajectories of
variables. Variances of these growth factors reflect interindividual
variation in the level or rate of change (Duncan et al., 2006). LMS
allows for testing interactions between observed (i.e., tempera-
ment) and latent (i.e., intercept and slope of parenting) variables.
All models were estimated using robust maximum likelihood
estimation (MLR), which yields standard errors that are robust to
non-normality. To control for inflation of Type I error rates we
applied a false discovery rate (FDR) procedure, which takes into
account the proportion of expected false positive results among a
set of significant findings (Benjamini & Hochberg, 1995).
We started by fitting four univariate growth curves to model
changes in positive parenting, negative parenting, prosocial behav-
ior, and externalizing behavior (see Figure 1, panel A). These
models also allowed us to examine whether participants would
vary in both their initial score as well as in how much they change.
Significant variability is a prerequisite that must be met before
intercepts and slopes can be used as predictors or outcomes in
subsequent models (Duncan et al., 2006).
At least two slope factor loadings must be fixed to two different
values to identify the model (Duncan et al., 2006). For positive and
negative parenting, we specified a change trajectory by fixing the
slope factor loadings for T1 and T3 to 0 and 1, with the factor
loading for T2 freely estimated. Freely estimating the second
factor loading enabled us to model an unspecified trajectory in
which the shape of the trajectory is determined by the data. For
prosocial and externalizing behavior, we specified a change tra-
jectory by fixing the slope factor loadings for T1 and T3 to 1 and
0. By fixing the slope loadings in this way, the slope represents the
rate of change (increase or decrease) from T1 to T3. Further, the
intercept now corresponds to the initial level, T1, in the case of
parenting, and to the level at the end of the study, T3, for child
behavior, in line with our hypotheses. To retain enough degrees of
freedom, error variances were set equal.
When the nonlinear model did not provide significant incremen-
tal fit, a more parsimonious, linear, model was selected, in which
the T2 slope loading was constrained to 0.5. A linear growth model
was preferred for negative parenting:
2
(3) 14.61, p.01,
CFI 0.99, RMSEA 0.13, compared with a nonlinear model,
⌬␹
2
(1) 2.98, p.08. Nonlinear growth models were preferred
for positive parenting,
2
(2) 0.75, p.23, CFI 1.00,
RMSEA 0.00, compared to a linear model, ⌬␹
2
(1) 106.95,
p.001, externalizing behavior,
2
(2) 1.03, p.60, CFI
1.00, RMSEA 0.00, compared with a linear model, ⌬␹
2
(1)
5.61, p.02, and prosocial behavior,
2
(2) 0.73, p.69,
CFI 1.00, RMSEA 0.00, compared with a linear model,
⌬␹
2
(1) 9.82, p.002.
Parameter estimates of the final univariate models are shown in
Table 2. On average, parents decreased in positive parenting across
the study, although this decrease took place between T1 and T2,
followed by a slight increase between T2 and T3 (see slope
loading). Further, parents linearly increased in negative parenting.
Parents varied in their level of positive and negative parenting at
the beginning of the study and in the extent to which their parent-
ing behaviors changed across the study, indicating that variation in
parenting behavior can be used as a predictor of child behavior.
Children’s level of externalizing behavior remained stable
throughout the study, but their pro-social behavior increased, with
most of the change taking place between T1 and T2. Like their
parents, children varied in their level of externalizing and prosocial
behavior at the beginning of the study and in how much their
behavior changed over the course of the study.
1
Associations Between Parenting Behavior and Child
Behavior
Next, we examined associations between parenting behavior and
child behavior in four bivariate LGM models. (see Figure 1, panel
B). Given concerns about the large number of parameters being
estimated if all constructs had been included in the same model,
four separate models were estimated: (a) positive parenting with
prosocial behavior, (b) positive parenting with externalizing be-
havior, (c) negative parenting with prosocial behavior, (d) negative
parenting with externalizing behavior.
Results are presented in Table 3. All models showed good fit:
negative parenting ⫽⬎externalizing behavior:
2
(10) 13.60,
p.19, CFI 1.00, RMSEA 0.04; positive parenting ⫽⬎
externalizing behavior:
2
(9) 5.59, p.78, CFI 1.00,
RMSEA 0.00; negative parenting ⫽⬎prosocial behavior:
2
(10) 15.71, p.11, CFI 0.99, RMSEA 0.05; positive
parenting ⫽⬎prosocial behavior:
2
(9) 9.61, p.38, CFI
1.00, RMSEA 0.02. Higher levels of negative parenting at the
beginning of the study predicted higher levels of externalizing
behavior at the end of the study, but not changes in externalizing
behavior. Positive parenting did not predict externalizing behavior
at the end of the study nor changes in externalizing behavior over
the course of the study. Finally, none of the (changes in) parenting
behaviors predicted (changes in) prosocial behavior.
Associations Between Temperament and Child
Behavior
Next, we examined associations between temperament (negative
emotionality and SPS) and child behavior in four multivariate
models. These models also included the associations between
parenting behaviors and child behaviors estimated in the previous
step. Based on correlations (see Table 1), negative emotionality
and SPS were allowed to covary in all models. The intercepts of
negative parenting and negative emotionality were also allowed to
covary based on modification indices, which significantly im-
proved model fit, ⌬␹
2
(1) 9.22, p.002.
All models showed good fit: negative parenting ⫽⬎external-
izing behavior:
2
(17) 22.61, p.16, CFI 0.99, RMSEA
0.04; positive parenting ⫽⬎externalizing behavior:
2
(17)
17.36 p.43, CFI 1.00, RMSEA 0.01; negative parent-
ing ⫽⬎prosocial behavior:
2
(17) 25.15, p.09, CFI 0.99,
RMSEA 0.04; positive parenting ⫽⬎prosocial behavior:
2
(17) 24.56, p.11, CFI 0.99, RMSEA 0.04. Neither
1
Forty-seven percent of children changed teachers between T1 and T2
(a new school year started), and 26% of children changed teachers between
T2 and T3. In total, 57% of children changed teachers at some point during
the study. Additional analyses showed that whether or not children
switched teachers over the course of the study did not predict changes in
child behavior across the study, nor did it reduce slope variance. Thus,
having different teachers does not seem to be confounded with actual
changes in child behavior.
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548 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
Figure 1. Schematic display of all estimated models and parameters. In each model, one parenting behavior
(either negative or positive) and one child behavior (either externalizing or prosocial) was modeled. In
multivariate models and latent moderated structural equation (LMS) models, main effects of both temperament
traits were included. In LMS models, interactions between one temperament trait and one parenting behavior
predicting one child behavior were modeled. T1 Time 1; T3 Time 3; ␭⫽slope loading; ␮⫽mean;
2
variance; εerror variance; ␳⫽correlation; ␤⫽path coefficient. In subscripts: i intercept; s slope; p
parenting; c child behavior; t temperament trait; ne negative emotionality; sps SPS; np negative
parenting. Parameters estimated in each step are: Step 1, univariate growth models: panel A; Step 2, bivariate
growth models: panel B, only
is_p
is_c
ii
is
si
ss
; Step 3, multivariate growth models: panel B, only
is_p
is_c
ne_sps
ne_np
ii
is
si
ss
ne_i
sps_i
ne_i
sps_i
; Step 4, LMS models: panel B.
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549
SPS AS MARKER OF DIFFERENTIAL SUSCEPTIBILITY
negative emotionality nor SPS were related to externalizing or
prosocial behavior (see Table 3). Negative emotionality and SPS
were strongly associated with each other (B0.24, SE 0.04,
␤⫽0.43, p.001), and negative emotionality was related to
negative parenting at T1 (B0.10, SE 0.03, ␤⫽0.21, p.01).
Interactions Between Temperament and Parenting
Finally, we tested interactions between temperament traits and
parenting behaviors using LMS. Eight models were estimated: The
four models described in the previous section with interactions of
parenting behaviors with negative emotionality added, and the four
models described in the previous section with interactions of
parenting behaviors with SPS added. Typical fit indices are not
available with models that use latent variable interactions due to
adjustments made during estimation (Muthén & Muthén, 1998
2012). As such, measures of relative fit (e.g., Bayesian information
criteria) were used to compare models with and without interac-
tions, as well as log likelihood ratio difference tests using an
appropriate correction for the MLR estimator (Satorra & Bentler,
2001).
Table 4 shows that each of these interaction models demon-
strated better fit compared with their corresponding model without
interactions. That is, BIC indices were lower and loglikelihoods
significantly closer to zero in models with interactions (estimated
in this step) compared with models without interactions (estimated
in the previous step). Parameter estimates in Table 5 indicate that
SPS interacted with both negative and positive parenting in pre-
dicting externalizing, but not prosocial, behavior. Specifically,
changes in positive parenting interacted with SPS in predicting
changes in externalizing behavior across the study as well as levels
of externalizing behavior at the end of the study. Likewise,
changes in negative parenting interacted with SPS in predicting
changes in externalizing behavior across the study as well as levels
of externalizing behavior at the end of the study. Parenting at the
beginning of the study did not interact with SPS in predicting
externalizing behavior, except for one interaction between nega-
tive parenting at T1 and SPS predicting changes in externalizing
behavior. Negative emotionality did not interact with either neg-
ative or positive parenting in predicting child behavior.
2
To follow-up on the five significant interactions, we estimated
the relation between the predictor and the outcome at temperament
values plus, exactly at, or minus one SD from the sample mean
(i.e., simple slopes; Cohen, Cohen, West, & Aiken, 2003). Fur-
thermore, to demonstrate a differential susceptibility effect, we
calculated the region of significance with respect to the predictor
(i.e., parenting) in case of a significant interaction (Preacher,
Curran, & Bauer, 2006;Roisman et al., 2012). This region iden-
tifies the range of predictor values for which regression lines
estimated at different temperament values significantly differ from
each other. When differential susceptibility is warranted, these
lines should differ significantly both at low values (M2SD)of
the predictor (“for worse”) and at high values (M2SD)ofthe
predictor (“for better”; Roisman et al., 2012). If diathesis-stress is
warranted, these lines should differ only at the “for worse” side of
the predictor. If vantage sensitivity is warranted, these lines should
differ only at the “for better” side of the predictor.
Figure 2, panel A shows that the more negative parenting
parents reported at the beginning of the study, the more children
increased in externalizing behavior, but only if children were high
on SPS. The interaction was most consistent with a vantage sen-
sitivity pattern: When they received low levels of negative parent-
ing (lower than M0.11 SD), children high on SPS decreased
more in externalizing behavior than children low on SPS. When
they received high levels of negative parenting (higher than M
0.11 SD), children high on SPS did not increase more in external-
izing behavior than children low in SPS.
Next, Figure 2, panel B shows that the more parents increased in
negative parenting, the higher children scored on externalizing
behavior at the end of the study, but only if children were average
or high on SPS. The interaction was most consistent with a vantage
sensitivity pattern: When parents did not increase in negative
parenting (change rate lower than M1.64 SD), children high on
SPS showed lower levels of externalizing behavior at T3 than
children low on SPS. Only when parents very strongly increased in
negative parenting (change rate higher than M2.50 SD), did
children high on SPS show higher levels of externalizing behavior
at T3. Likewise, Figure 2, panel C shows that the less parents
decreased in positive parenting, the lower children scored on
externalizing behavior at the end, but only if children were average
or high on SPS. The interaction was most consistent with vantage
sensitivity: When parents did not decrease in positive parenting
(change rate higher than M1.68 SD), children high on SPS
showed lower levels of externalizing behavior at T3 than children
low on SPS. Only when parents very strongly decreased in positive
2
Additional analyses were run including child age and gender as cova-
riates and these analyses demonstrated that results were similar when
including these variables as covariates. That is, the same main effects and
the same interactions emerged between parenting behaviors and tempera-
ment traits.
Table 2
Parameter Estimates of the Univariate Latent Growth Models
Latent variable
Intercept Slope Intercept NSlope
Slope loading T2 M
2
M
2
r
Positive parenting 1.69 5.17
ⴱⴱ
.06
ⴱⴱⴱ
.06
ⴱⴱⴱ
.001
.22
Negative parenting .50 2.62
ⴱⴱⴱ
.42
ⴱⴱⴱ
.04
.03
ⴱⴱⴱ
.23
Externalizing behavior .24 3.18
ⴱⴱⴱ
10.15
ⴱⴱⴱ
.13 5.93
ⴱⴱⴱ
.44
ⴱⴱ
Prosocial behavior .08 7.93
ⴱⴱⴱ
3.11
ⴱⴱⴱ
.35
2.31
ⴱⴱ
.47
ⴱⴱ
Note.T2Time 2.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
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550 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
parenting (change rate lower than M2.82 SD), did children high
on SPS show higher levels of externalizing behavior at T3. How-
ever, it should be noted that in both cases, the simple slopes of
children scoring average or high on SPS did not actually cross the
region of significance at the “vantage sensitivity side” of the
figures, and instead fell outside the observed range of externalizing
behavior in this region. To rule out that these findings are an
artifact of using small extreme groups, we tested simple slopes at
less extreme levels of SPS. Instead of using M1SD and M
1SD (reflecting approximately the highest and lowest 16%), we
used the top and bottom 30%. The results obtained this way were
similar to the results presented here. The findings therefore do not
seem to be due to estimating simple slopes for small or extreme
groups.
Finally, the interactions with changes in parenting behavior
predicting changes in externalizing behavior were most consistent
with the differential susceptibility model. Figure 2, panel D shows
that the more parents increased in negative parenting, the more
children increased in externalizing behavior, but only if children
were high on SPS. Compared with average children, sensitive
children decreased the most in externalizing behavior when par-
ents decreased in negative parenting (change rate lower than M
1.49 SD), but increased the most in externalizing behavior when
parents increased in negative parenting (change rate lower than
M2.11 SD). Likewise, Figure 2, panel E indicates that the less
parents decreased in positive parenting, the more children de-
creased in externalizing behavior, but only if children were high on
SPS. Compared with average children, sensitive children de-
creased the most in externalizing behavior when parents main-
tained high levels of positive parenting (change rate lower than
M1.55 SD), but increased the most in externalizing behavior
when parents’ parenting became less positive (change rate lower
Table 3
Parameter Estimates of the Bivariate and Multivariate Latent Growth Models
Model
Negative
parenting ⫽⬎
Externalizing behavior
Positive parenting ⫽⬎
Externalizing behavior
Negative
parenting ⫽⬎
Prosocial behavior
Positive parenting ⫽⬎
Prosocial behavior
Parameter b(SE)b(SE)b(SE)b(SE)
Bivariate latent growth models
I parenting T1 ⫽⬎ I child behavior T3 1.09 (.49) .22
.68 (1.32) .05 .46 (.25) .17 .03 (.75) .00
I parenting T1 ⫽⬎ S child behavior .25 (.53) .07 .77 (1.27) .08 .02 (.28) .01 .41 (.84) .07
S parenting ⫽⬎ I child behavior T3 .01 (2.07) .00 1.99 (12.62) .02 .28 (1.17) .03 10.51 (9.34) .23
S parenting ⫽⬎ S child behavior 3.40 (2.71) .24 18.53 (16.59) .29 .27 (1.35) .03 3.70 (9.02) .09
Multivariate latent growth models
Negative emotionality ⫽⬎ I child behavior T3 .10 (.43) .02 .32 (.44) .07 .09 (.25) .04 .05 (.25) .02
Negative emotionality ⫽⬎ S child behavior .09 (.45) .03 .03 (.45) .01 .37 (.27) .17 .34 (.27) .16
SPS ⫽⬎ I child behavior T3 .04 (.43) .01 .09 (.44) .02 .14 (.21) .06 .17 (.21) .08
SPS ⫽⬎ S child behavior .27 (.56) .09 .34 (.57) .11 .12 (.30) .06 .11 (.30) .06
Negative emotionality NSPS .24 (.04) .42
ⴱⴱⴱ
.24 (.04) .43
ⴱⴱⴱ
.24 (.04) .42
ⴱⴱⴱ
.24 (.04) .43
ⴱⴱⴱ
Note.T1Time 1; T3 Time 3; I intercept; S slope; SPS sensory processing sensitivity. For brevity, only the structural parameters of the models
are displayed. Slope loadings, intercept and slope means and variances, and intercept-slope covariances can be requested from Meike Slagt.
p.05.
ⴱⴱⴱ
p.001.
Table 4
Fit Statistics Comparing Models With and Without Interactions
Model
Negative parenting ⫽⬎
Externalizing behavior
Positive parenting ⫽⬎
Externalizing behavior
Negative parenting ⫽⬎
Prosocial behavior
Positive parenting ⫽⬎
Prosocial behavior
Fit statistic
Baseline
model
With
interaction
Baseline
model
With
interaction
Baseline
model
With
interaction
Baseline
model
With
interaction
Interactions with negative emotionality
BIC 4227.48 4131.68 3083.91 3035.79 3782.35 3693.76 2633.65 2605.66
2 log likelihood 2038.57 1982.90 1466.78 1434.95 1816.00 1763.94 1241.65 1219.89
No. of parameters
a
27 30 27 30 27 30 27 30
Null model—Model with interaction
2
(3) 111.34,
p.001
2
(3) 63.66,
p.001
2
(3) 200.00,
p.001
2
(43) 43.52,
p.001
Interactions with sensory processing sensitivity
BIC 4227.48 3532.69 3083.91 2615.12 3782.35 3188.31 2633.65 2262.65
2 log likelihood 2038.57 1686.21 1466.78 1227.43 1816.00 1514.02 1241.65 1051.19
No. of parameters
a
27 30 27 30 27 30 27 30
Null model—Model with interaction
2
(3) 704.72,
p.001
2
(3) 478.70,
p.001
2
(3) 603.96,
p.001
2
(3) 380.92,
p.001
Note. BIC Bayesian Information Criterion.
a
Intercepts of parenting were constrained to zero for centering in the models testing interactions.
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551
SPS AS MARKER OF DIFFERENTIAL SUSCEPTIBILITY
than M1.66 SD). The pattern for children low on SPS was
exactly opposite to the pattern for children high on SPS, for both
negative and positive parenting. Compared with average sensitive
children, low sensitive children increased the most in externalizing
behavior when negative parenting decreased, but decreased the
most in externalizing behavior when negative parenting increased.
Likewise, they increased the most in externalizing behavior when
positive parenting was maintained, but decreased the most in
externalizing behavior when positive parenting decreased.
To rule out that these findings are an artifact of using small
extreme groups, we tested simple slopes at less extreme levels of
SPS, namely the top and bottom 30%. The results obtained this
way were similar to the results presented here. The exception was
that in Figure 2D the simple slope estimated at low levels of SPS
was no longer significant, making this interaction resemble differ-
ential susceptibility rather than contrastive effects. In general, we
found that if we made the SPS groups larger and less extreme, the
low SPS slope became nonsignificant sooner than the high SPS
slope. Thus, at some point we would end up with what looks like
straightforward support for differential susceptibility, also for the
interaction involving changes in positive parenting (Figure 2E).
Discussion
Recently sensory processing sensitivity (SPS) has been postu-
lated as a potential susceptibility marker, along with the more often
studied temperament trait of negative emotionality (Aron et al.,
2012;Belsky & Pluess, 2013). In this study we compared Negative
emotionality and SPS as susceptibility markers among kindergart-
ners. We found that associations between (changes in) parenting
and (changes in) child behavior did not depend on children’s
negative emotionality. SPS however, interacted with both negative
and positive parenting in predicting externalizing, but not proso-
cial, behavior. Depending on the interaction, vantage sensitivity,
differential susceptibility, and contrastive effects models were
supported.
Negative Emotionality and Sensory Processing
Sensitivity as Potential Susceptibility Markers
When accounting for negative emotionality and SPS simultane-
ously, only SPS moderated associations between changes in par-
enting and changes in child behavior (Figures 2D and 2E). Com-
pared with average sensitive children, sensitive children decreased
the most in externalizing behavior when negative parenting de-
creased, but increased the most in externalizing behavior when
negative parenting increased. Likewise, sensitive children de-
creased the most in externalizing behavior when high levels of
positive parenting were maintained, but increased the most in
externalizing behavior when positive parenting decreased. These
findings seem to support the differential susceptibility model.
That negative emotionality moderated associations between par-
enting and child behavior “for better and for worse” in previous
studies (for a review, see Belsky & Pluess, 2009), could potentially
have been driven by SPS, a concept that partly overlaps with
negative emotionality (Aron & Aron, 1997;Pluess, 2016;
Smolewska et al., 2006). That is, although high levels of negative
emotionality may coincide with high levels of susceptibility in
certain studies—and negative emotionality therefore serves as a
marker of differential susceptibility in these studies—it does not
follow that negative emotionality also enhances differential sus-
ceptibility in children. Negative emotionality may only serve as a
marker of differences in susceptibility due to its association with
SPS. However, to conclude that SPS is then the actual cause of
Table 5
Parameter Estimates of the Latent Moderated Structural Equation Models
Model
Negative parenting ⫽⬎
Externalizing behavior
Positive parenting ⫽⬎
Externalizing behavior
Negative
parenting ⫽⬎
Prosocial behavior
Positive
parenting ⫽⬎
Prosocial behavior
Parameter b(SE)b(SE)b(SE)b(SE)
Interactions with negative emotionality
Intercept Parenting T1 NE ⫽⬎ Intercept
child behavior T3 .77 (.89) .11 8.75 (4.53) .40 .31 (.82) .09 .11 (1.76) .00
Intercept Parenting T1 NE ⫽⬎ Slope child
behavior .12 (.71) .03 1.42 (2.91) .04 .10 (.35) .03 .05 (1.23) .00
Slope Parenting NE ⫽⬎ Intercept child
behavior T3 9.65 (15.61) .32 135.79 (70.66) .60 5.50 (17.97) .29 9.98 (26.28) .12
Slope Parenting NE ⫽⬎ Slope child
behavior 4.77 (7.74) .21 63.14 (36.52) .34 2.94 (4.74) .21 1.23 (15.23) .03
Interactions with SPS
Intercept Parenting T1 SPS ⫽⬎ Intercept
child behavior T3 3.48 (1.83) .33 5.67 (4.59) .29 .26 (.38) .07 .51 (1.08) .05
Intercept Parenting T1 SPS ⫽⬎ Slope child
behavior 4.51 (1.95) .68
7.37 (5.35) .56 .25 (.34) .08 .22 (1.27) .03
Slope Parenting SPS ⫽⬎ Intercept child
behavior T3 94.22 (35.79) .47
ⴱⴱ
138.56 (59.82) .49
.24 (2.97) .01 4.58 (10.85) .07
Slope Parenting SPS ⫽⬎ Slope child
behavior 111.98 (36.58) .85
ⴱⴱ
185.79 (65.96) .98
ⴱⴱ
.99 (3.05) .07 1.34 (13.37) .02
Note.T1Time 1; T3 Time 3; NE negative emotionality; SPS sensory processing sensitivity. For brevity, only the parameters involving latent
interactions are displayed. Other parameters can be requested from the Meike Slagt.
p.05.
ⴱⴱ
p.01.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
552 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
increased susceptibility instead, would be premature. First, a direct
test of the potential causal role of SPS in increasing susceptibility
is still lacking; our nonexperimental study can do no more than
support its role as a correlate. Our findings do suggest, however,
that SPS may be a more proximal correlate of individual differ-
ences in susceptibility compared with negative emotionality, par-
ticularly among kindergartners. Second, our measure of SPS was
based on parent-reported responses to a questionnaire, instead of a
Figure 2. Linear relation between (A) negative parenting at T1 and changes in externalizing behavior; (B)
changes in negative parenting and externalizing behavior at T3; (C) changes in positive parenting and
externalizing behavior at T3; (D) changes in negative parenting and changes in externalizing behavior; (E)
changes in positive parenting and changes in externalizing behavior. Computed at one standard deviation below
the mean (low), the mean (average), and one standard deviation above the mean (high) of sensory processing
sensitivity (SPS). The added explanation beneath the x-axis of panels B–E (decrease/increase) is derived from
the parameter estimates of the slopes reported in Table 2. The shaded areas indicate the predictor values at which
differences among (point estimates on) slopes for different SPS values become significant. When the differential
susceptibility account is warranted, these lines—reflecting different temperament values—should differ both at
low values of or decreases in negative parenting/high values of or maintenance of positive parenting (“for
better”) and at high values of or increases in negative parenting/low values of or decreases in positive parenting
(“for worse”).
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
553
SPS AS MARKER OF DIFFERENTIAL SUSCEPTIBILITY
direct assessment of the sensitivity of the nervous system. None-
theless, the first links have been established between questionnaire
measures of SPS and activation in brain regions involved in
attention and action planning, awareness, self-other processing,
and higher order visual processing (Acevedo et al., 2014;Jagiel-
lowicz et al., 2011), suggesting that this questionnaire taps into
sensitivity of at least some areas of the central nervous system. The
concept of a highly sensitive central nervous system is itself
complicated, because the nervous system is composed of many
opposing and counterregulatory circuits (Boyce & Ellis, 2005).
Whether the nervous system can actually be more sensitive as a
whole, or whether it is more likely that the sensitivity of certain
circuits to certain stimuli (as in the studies by Acevedo et al., 2014
and Jagiellowicz et al., 2011) gives rise to the personality trait of
SPS, is something that future research would have to clarify.
Associations between parenting and child behavior did not
depend on children’s negative emotionality in our study. Several
studies did find interactions with negative emotionality that sup-
ported a diathesis-stress model (e.g., Leve, Kim, & Pears, 2005;
Slagt et al., 2016) or a differential susceptibility model (e.g.,
Belsky & Pluess, 2009;Poehlmann et al., 2012). However, a
meta-analysis (see Table 2,inSlagt, Dubas, Dekovi´
c, & van Aken,
2016) showed that approximately 27% of tested interactions be-
tween parenting and negative emotionality/difficult temperament
predicting externalizing behavior were nonsignificant. Moreover,
after infancy no overall interaction effect was found, indicating
that associations between negative parenting and negative child
adjustment were similar for children higher and lower on negative
emotionality.
Susceptibility Markers and Age
The age at which temperament traits are assessed seems to be an
important consideration in studying differential susceptibility.
Among the current sample of kindergartners, SPS acted as an
indicator of differences in susceptibility “for better and for worse,”
whereas negative emotionality did not. If susceptible individuals
remain susceptible throughout their lives, it could be that negative
emotionality no longer picks up on differences in susceptibility if
measured after infancy (Slagt, Dubas, Dekovi´
c, & van Aken,
2016). For susceptible children, levels of negative emotionality
may change after infancy, through maturation and socialization.
Susceptible children may learn to regulate their negative emotion-
ality as they get older (Eisenberg et al., 1996;Eisenberg, Spinrad,
& Eggum, 2010;Rothbart & Bates, 2006), and their displays of
negative emotionality may become less pronounced if they are
frequently exposed to a supportive environment (Blandon, Calkins,
Keane, & O’Brien, 2010). SPS may therefore be a better marker of
increased susceptibility to environmental influences at later ages. One
of the assumptions underlying this rationale is that the stability of
SPS, and of the relation between SPS and “susceptibility,” is higher
than that of negative emotionality and its relation to “susceptibility.”
This is, in fact, still unknown, and it would be important for future
research to test this assumption.
Vantage Sensitivity
In addition to differential susceptibility, modest support for
vantage sensitivity was also found in this study. When they re-
ceived low levels of negative parenting at the start of the study,
sensitive children decreased more in externalizing behavior than
less sensitive children. Further, compared with less sensitive chil-
dren, sensitive children had the lowest levels of externalizing
behavior when negative parenting decreased, but similar levels of
externalizing behavior when negative parenting increased. Like-
wise, sensitive children had the lowest levels of externalizing
behavior when high levels of positive parenting were maintained,
but similar levels of externalizing behavior when positive parent-
ing decreased.
Two explanations are provided for these findings. First, our
sample was a relatively well-functioning, high-SES sample, in
which the parents generally showed low levels of negative parent-
ing. Children’s susceptibility at any given point in time may
depend on their previously experienced (parenting) environment
(Boyce & Ellis, 2005;Ellis, Del Giudice, & Shirtcliff, 2013).
Specifically, an initial propensity for susceptibility “for better and
for worse” early in life may, for some children, develop into a
biased susceptibility toward contextual adversity (i.e., vulnerabil-
ity) or contextual support (i.e., vantage sensitivity) depending on
specific environments encountered early in life (i.e., stress or
support; Pluess, 2015, see Cleveland et al., 2015 for an example).
Translated to our study, repeated exposure to low levels of nega-
tive parenting may have made sensitive children more susceptible
to supportive cues in their environment, and as such more likely to
respond to low levels of negative parenting. This would have
resulted in support for the vantage sensitivity model for associa-
tions involving levels of negative parenting (Figure 2A).
Second, the simple slopes of children scoring average or high on
SPS did not actually cross the region of significance at the “van-
tage sensitivity side” of interactions involving changes in negative
and positive parenting (Figures 2B and 2C). Instead, they fell
outside the observed range of externalizing behavior in this region.
This could indicate a restriction of range problem, although what
outcome would qualify as being “more positive” than very low
levels of externalizing behavior is not a straightforward issue. For
instance, prosocial behavior and antisocial behavior have been
viewed as two separate dimensions, on which individuals can score
low simultaneously (Hawley, Little, & Pasupathi, 2002). All in all,
these interactions do not seem to provide support for vantage
sensitivity at present.
Low Sensory Processing Sensitivity
Surprisingly, the pattern we found for children low on SPS was
exactly opposite to the pattern we found for children high on SPS.
Compared with average sensitive children, low sensitive children
increased the most in externalizing behavior when negative par-
enting decreased, but decreased the most in externalizing behavior
when negative parenting increased. Likewise, low sensitive chil-
dren increased the most in externalizing behavior when high levels
of positive parenting were maintained, but decreased the most in
externalizing behavior when positive parenting decreased. These
findings seem counterintuitive and are in fact indicative of con-
trastive effects, where associations between parenting and child
adjustment are significantly different from zero for children low as
well as children high on a given susceptibility marker, but in
opposite directions. Such findings have been reported in the liter-
ature occasionally (e.g., see Leerkes, Nayena Blankson, & O’Brien,
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
554 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
´
2009), but are not regularly found (Slagt, Dubas, Dekovi´
c, & van
Aken, 2016). Using additional analyses we found that if we made the
SPS groups larger and less extreme, the low SPS slope became
nonsignificant sooner than the high SPS slope. Thus, at some point we
would end up with what looks like straightforward support for dif-
ferential susceptibility in this study. However, choosing specific val-
ues for estimating simple slopes seems an arbitrary process. These
analyses do illustrate that it is important to be aware of how choosing
specific moderator values at which to estimate simple slopes, can
affect the interpretation of interaction effects. Future research would
have to bear out the robustness of these findings and their potential
explanations.
Prosocial Behavior
Several possible reasons exist for why findings for prosocial
behavior were not significant. First, the variance in prosocial
behavior was slightly lower compared with the variance in exter-
nalizing behavior, although both were significant. Second, it may
be that the range of variation in self-reported parenting captured in
our high-SES sample did not relate to the range of variation in
prosocial behavior captured in this study. Third, other studies that
found support for differential susceptibility among kindergartners
in predicting positive outcomes have tended to focus on composite
measures of social competence that included prosocial behavior on
teacher– child relationship quality, peer status (e.g., Stright et al.,
2008;Roisman et al., 2012), or conscience, moral sense, or rule-
compatible conduct (Kochanska, 1997;Kochanska, Aksan, & Joy,
2007). Still, it is hard to say whether the discrepancy between our
findings and theirs is due to predicting slightly different positive
behaviors, or to other differences between the studies; future
research would have to bear this out.
Limitations
The results of this study should be considered in the light of four
limitations. First, the children with complete data, compared with
children with incomplete data, had mothers who reported lower
levels of negative parenting. However, because data on negative
parenting are virtually complete across the three waves (93%
available at each wave), it is reasonable to assume that although
missingness is related to observed data (i.e., negative parenting), it
was not related to unobserved data. That is, data were missing at
random instead of missing not at random, and assumptions of
analyses were met (Enders & Bandalos, 2001). Second, all par-
enting measures were based on parent report. Differences in self-
reported parenting might not only reflect differences in actual
parenting behaviors, but also differences in the extent to which
parents were affected by biases such as current mood and social
desirability at the time of their reporting (Schwarz, 1999). This
might compromise the power of self-reported parenting as a pre-
dictor of child behavior, as variation in self-reported parenting
does not perfectly map onto variation in actual parenting (Whis-
man & McClelland, 2005). Third, most children were born in the
Netherlands and came from families with a fairly high socioeco-
nomic status. The results may thus be limited to Dutch samples
with middle- to high-SES, and it remains to be seen whether they
can be generalized to more at-risk or ethnically diverse samples.
Finally, due to the relatively low number of fathers reporting on
their parenting behavior, this study focused on maternal parenting
behavior. Future research could examine the different impact
mothers and fathers may have on the development of their chil-
dren’s adjustment.
Conclusions
Ultimately, research into differential susceptibility and how it
relates to temperament can have applied value as well. The dif-
ferential susceptibility model demands a positive relabeling of
children not as vulnerable to harsh circumstances, but as suscep-
tible to both harsh and supportive circumstances. In particular,
infants who are high on negative emotionality, might, at least in
some instances, be better conceptualized as susceptible in general.
Traits like SPS might be used to recognize which children are
likely to be more and less susceptible. Still, research on differential
susceptibility has a long way to go before its findings are robust
enough to be used in practice.
In conclusion, in this longitudinal, multiinformant study we
examined whether associations between parenting and child be-
havior would depend on children’s negative emotionality and
sensory processing sensitivity, and to what extent this would
support differential susceptibility, diathesis-stress, and vantage
sensitivity models. Sensory processing sensitivity interacted with
changes in negative and positive parenting, predicting changes in
externalizing behavior, in a manner consistent with differential
susceptibility. Thus, among the current sample of kindergartners,
sensory processing sensitivity was able to mark differences in
susceptibility “for better and for worse,” whereas negative emo-
tionality was not. The findings suggest that sensory processing
sensitivity may be a more proximal correlate of individual differ-
ences in susceptibility, compared with negative emotionality. Al-
though interactions involving changes in parenting predicting
changes in child behavior were most in line with the differential
susceptibility model, associations involving levels of negative
parenting supported the vantage sensitivity model. These findings
draw attention to the importance of considering multiple related
susceptibility markers simultaneously, age in relation to differen-
tial susceptibility, and considering changes versus stable levels of
the environment and developmental outcomes.
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Received October 22, 2016
Revision received July 22, 2017
Accepted August 15, 2017
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
558 SLAGT, DUBAS, VAN AKEN, ELLIS, AND DEKOVIC
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... In order to examine children and adolescents, the Highly Sensitive Child (HSC) Scale was developed. The basis for its layout was the HSP scale [2] and the tool itself was also used to evaluate the sensitivity of preschool children [30]. A factor analysis of the scale showed that the HSC scale has sufficient internal consistency and favourable psychometric properties for independent samples [2]. ...
... In addition, research conducted using the HSC scale confirmed that children who achieved high scores on this scale are more sensitive and respond positively to psychological intervention [31]. Furthermore, the longitudinal multi-informant study indicates that SPS may correlate with individual differences in susceptibility to both positive and negative quality parenting [30]. Moreover, it is widely known from the studies conducted to date that although the trait itself is not a disorder, the coexistence of high sensitivity with psychological and psychiatric problems has been demonstrated among highly sensitive people growing up in unfavourable conditions (cf., [16,32,46,47]). ...
... In the analysis of psychological features in general, one should take into account both the conditions of the environment in which a person develops, and the cultural context of Poland [37] and of Europe as a whole [35,36,38,39]. The trait of SPS is associated with the occurrence of special benefits, especially if the person develops in favourable conditions (cf., [6,30,31]) alternatively difficulties may occur if the person develops in particularly difficult conditions [50,51]. Creating a supportive environment for highly sensitive children and youth may be likened to creating an organizational culture, especially from the perspective of creating a more proenvironmental culture (cf., [37]). ...
Article
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The research described herein is based on the assumptions of the sensory processing sensitivity concept and the meta framework for the concept of environmental sensitivity. The adopted theoretical framework shows that individuals differ in their sensitivity to the environment, with some being more sensitive than others. From the evolutionary perspective, it has also been assumed that sensory processing sensitivity follows a normal distribution in the population, with a minority being exceptionally or highly sensitive to environmental stimuli. We explored data from a sample of 928 young adolescents in two studies. The tool used to evaluate their sensitivity was the Highly Sensitive Child Scale, which in studies 1 and 2 had a three-factor structure. Latent class analysis was used for the interpretation of the data of the studied groups. The obtained results indicate the existence of three groups which differ significantly from each other according to the HSC result. Based on the obtained results, it may be assumed that young adolescents are divided into three groups characterized by different sensitivities and their percentage distribution is not in agreement with the research conducted to date. The acquired information has both a theoretical value and a practical applicability, prompting reflection about the different aspects of the study, such as cultural differences, changes related to the development stage and the characteristics of the evaluation tool itself. From the perspective of possible applications, the obtained results may provide important information (1) to decision-makers who plan support or intervention programs at various levels of prevention, (2) for practitioners to provide them with the means with which to consider sensitivity as an important factor in coping with difficulties through diversified and adequate support (3) that is broadly applicable in the face of an environmental crisis (pandemic, the changing structure of class groups which is related to the number of refugees).
... Internalizing problems during childhood and adolescence are predictive of multiple negative developmental outcomes such as peer victimization (Reijntjes et al., 2010), internalizing disorders (e.g., anxiety, depression), and poor health later in life (Essex et al., 2014;Essex et al., 2009;Herrenkohl et al., 2010). Given the severity of the possible negative developmental outcomes of internalizing problems, it is important to determine risk factors predictive of internalizing problems during childhood and adolescence, particularly in times of Research has identified the personality trait sensory processing sensitivity (SPS) as a risk factor for developing internalizing problems (Aron et al., 2012;Slagt et al., 2018). SPS is a relatively stable trait that reflects an individual's sensitivity to environmental influences such as other people's expressed emotions, loud noises and pain, and the intensity of the individuals reaction in response. ...
... According to this theory, individuals scoring high on the SPS personality trait are more susceptible to environmental influences in a for-betterand-for-worse manner, resulting in worse developmental outcomes under negative circumstances but also better developmental outcomes in a supportive environment (Belsky & Pluess, 2016). With regards to parenting, this theory was supported by highly sensitive children showing stronger positive outcomes in relation to positive parenting practices while also showing more negative outcomes as a result of negative parenting practices in earlier research (Liss et al., 2005;Slagt et al., 2018). There are two possible explanations why we did not find the interaction between parenting and sensitivity. ...
... The second explanation is that SPS may interact with parenting practices, yet not with parenting style as such. Earlier research has established the moderating effect of parenting on SPS for certain aspects of parenting, like parental care, responsiveness, autonomy granting, positive interactions, and inductive discipline (Liss et al., 2005;Slagt et al., 2018). For other aspects of parenting, like parental overprotection, the interaction effect was not found (Liss et al., 2005). ...
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The personality trait sensory processing sensitivity (SPS) is an established risk factor for the development of internalizing problems. Highly sensitive adolescents react stronger to environmental cues including parenting environment and stressful life events. The aim of the current study was to examine if the perceived impact of COVID-19, mediates the link between SPS and internalizing problems. In addition, it was tested if parenting style moderates the mediating effect of perceived COVID-19 impact between SPS and internalizing problems among adolescents. The study had a cross- sectional design and data were collected between April-July 2020 during the first lockdown in the Netherlands. Participants were 404 adolescents aged 9–18 years (M age = 13.49). Questionnaires were administered online to assess SPS (Highly Sensitive Child Scale), parenting style (Parenting Style Inventory-II), internalizing problems (Patient Health Questionnaire-4) and COVID-19 pandemic impact (COVID-19 impact scale). The SPSS macro PROCESS was used to test the mediation model of perceived COVID-19 impact and the moderated mediation model with parenting style as a moderator. A relationship was found between SPS and internalizing problems which is partly mediated by the COVID-19 impact. The moderating effect of parenting style was not found. These findings provide insight into the effect the pandemic has had on highly sensitive adolescents. Further research is needed to develop and test interventions to support sensitive youth and thus possibly prevent the development of internalizing problems.
... SPS has been regarded as a more proximal marker of susceptibility [8,9,51]. Research on the Differential Susceptibility Model [39] demonstrates that greater sensitivity leads to more vulnerability to negative environments and greater vantage sensitivity in positive environments, and postulates that children differ in their general susceptibility to environmental influences with some being more affected than others by both adverse and supportive environments [43,52]. ...
... For instance, Pluess and Boniwell [53] found that girls with high SPS benefited more from depression intervention and performed a decreasing score on depression when compared with girls with low SPS. Slagt et al. [51] also proposed that SPS played a moderating role in the association between parenting styles and changes in child behaviors. However, it is unclear whether SPS moderate the interactive effect of gene (i.e., CRHBP gene polymorphism) and family environment (i.e.,authoritative parenting and authoritarian parenting) on children's internalizing problems. ...
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Full-text available
The present study aimed to examine how CRHBP rs10062367 polymorphism interacted with parenting styles and sensory processing sensitivity (SPS) to impact on preschoolers’ internalizing problems. A total of 446 preschoolers (Mage = 4.55, SD = 1.07) participated in the study and their saliva were extracted to genotype the CRHBP rs10062367 polymorphism, and their parents were invited to complete a battery of questionnaires to assess parenting styles, preschoolers’ SPS, and internalizing problems. Results indicated that high SPS preschoolers with A allele exhibited fewer internalizing problems under the condition of positive parenting while they exhibited more internalizing problems under the condition of negative parenting. The findings provide support for the Differential Susceptibility Model/Biological Sensitivity to Context Theory that A allele of rs10062367 and high SPS might be the “susceptibility markers” of children to environments.
... While specific neurobiological factors are involved in the physiological and behavioural responses of highly sensitive individuals, other factors such as the early environment would also seem to play a role (Aron & Aron, 1997). For example, a positive early environment (e.g., good parenting practices or intervention programmes) means people with high sensitivity display greater social-emotional well-being (Nocentini et al., 2018;Pluess & Boniwell, 2015;Slagt et al., 2018). On the contrary, an emotionally fragile early parenting environment (e.g., alcoholism or psychological disorders in one of the parents, abusive behaviours, or poor parental care) leads highly sensitive individuals to express lower life satisfaction (Booth et al., 2015). ...
Article
Introduction The Highly Sensitive Person Scale based on the sensory-processing sensitivity is a self-assessment questionnaire consisting of 27 items. The scale is designed in order to identify individuals with high sensitivity. Objective The objective was to develop a French version of the scale. We tested its internal consistency and test-retest reliability on a French population sample. Another aim of this study was also to question the multidimensionality of the scale, for which several different models are suggested within the literature. Method After translation and back-translation, a validation study was conducted on 814 adults. They were invited to complete an online questionnaire during the lockdown implemented due to COVID 19, between March 31st and May 11th (2020). Results The internal reliability of the French version of HSPS was very good, with a Cronbach's alpha of .90, as was that of the factors. Correlations between factors were significant (p < .001). The intra-class correlation (ICC) for test-retest was .889 (0.874–0.903; 95% confidence interval). Factor analyses suggested a 4-factors structure, mixing the models found in the literature. Conclusions This study focused on a French adaptation of the HSPS scale. The results showed good psychometric qualities and stayed true to the original HSPS scale. The scale could be useful both to practitioners in their clinical practice and to researchers in fundamental research.
... 2) Emotional reactivity refers to more intense emotional responses to environmental stimuli (Aron, 2013). Several studies showed that high-SPS individuals are more sensitive to their environment than others: beneficial in positive environments (Pluess & Boniwell, 2015;Slagt et al., 2018), adversely in negative environments (Booth et al., 2015;Boterberg & Warreyn, 2016;Liss et al., 2005) and both (e.g., Scrimin et al., 2018). Empathy is also a feature of SPS (Acevedo et al., 2014). ...
Article
Sensory processing sensitivity (SPS) is a trait characterized by stronger sensitivity to the environment, both for better and for worse. The present study used the Self-Determination Theory (SDT) to judge this environment in terms of basic psychological needs met in a school context. This study aimed to gain insights into the moderating role of SPS in the relationship between students' need satisfaction, motivation and behavioral engagement. A total of 1253 primary school students aged 8 to 13 participated. All students completed questionnaires assessing the degree of SPS, need satisfaction (autonomy, competence and relatedness), motivation (intrinsic, extrinsic and amotivation) and behavioral engagement. Results revealed that SPS did not moderate the relationship between need satisfaction and motivation or the relationship between need satisfaction and behavioral engagement. Our findings show that SPS does not appear to influence the positive or negative effects of the degree of need satisfaction. All students benefit from higher need satisfaction, including those with stronger SPS.
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Very few studies have investigated the relationship between sensory processing sensitivity (SPS) and interpersonal variables; none has particularly focused on romantic relationship satisfaction. In the context of romantic relationships, this study aimed to identify whether SPS is a risk factor (hypothesizing that traits make individuals more vulnerable to the effects of adverse environments) or a susceptibility marker (hypothesizing that traits make individuals more susceptible to the effects of both nourishing and adverse environments). To understand this, we tested whether an increased level of SPS is associated with a decreased level of romantic relationship satisfaction through negative affectivity and conflict resolution styles. Furthermore, we tested whether these proposed relationships intensified when the childhood environment was negative. A total of 206 unmarried young adults who had been in a romantic relationship for at least two years completed the measures of SPS, childhood environment, negative affectivity, conflict resolution styles, and relationship satisfaction. The results indicated that negative affectivity and negative conflict resolution styles mediated the association between SPS and satisfaction in a relationship; however, childhood environment did not moderate these relationships. These findings suggest that beyond childhood factors, SPS is an independent risk factor for developing negative outcomes in romantic relationships. This study also significantly contributes to the literature by revealing the possible mechanisms between SPS and romantic relationship satisfaction.
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Sensory processing sensitivity is an individual difference that captures the extent to which people show heightened emotional reactivity to, and increased cognitive processing of, their environment. Although central to its definition, there has been no research examining whether highly sensitive individuals display stronger reactivity to naturally occurring negative and positive events in everyday life. We addressed this gap by carrying out a 21-day online diary study with 239 participants, varying in sensory processing sensitivity, who reported their daily life-satisfaction, affective experiences, and self-esteem along with appraisals of the most negative and positive events of the day. Multilevel analyses demonstrated that individuals higher in sensory processing sensitivity showed greater reactivity to more subjectively intense negative events, but no difference in their reactivity to positive events. These findings provide initial insights into how sensory processing sensitivity manifests in daily emotional reactivity with greater reactivity to negative events in our study.
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Background There is a growing interest in exploring the neurocognitive mechanisms that may underlie psychological resilience. However, how the bottom-up automatic information processing relates to trait resilience has received less attention. We aimed to explore the relationship between trait resilience and trait-like automatic information processing in healthy adults. Methods Eighty-four healthy adults were recruited to explore whether and how resilience was related to sensory sensitivity by event-related potentials (ERPs). Resilience was measured by Connor-Davidson Resilience Scale (CD-RISC). Sensory sensitivity, more specifically, sensitivity of automatic mismatch detection was measured by two ERPs components, i.e., the mismatch negativity (MMN) with a passive auditory oddball paradigm and the error-related negativity (ERN) with an auditory Go/NoGo task. Using the multiple linear regression analyses, the relationship between self-reported resilience and the sensitivity of automatic mismatch detection (MMN/ERN amplitude/latency) was explored. Results The results showed that psychological resilience was positively correlated with both MMN and ERN latencies, i.e., higher resilience scores were associated with delayed MMN and ERN latencies. However, resilience was not significantly correlated with MMN and ERN amplitudes. Conclusions Our results suggested that relatively higher resilience might link with less sensory sensitivity as reflected by slower automatic detection to mismatch information in the environment.
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Sensory processing sensitivity (SPS) can be defined as a personality characteristic that includes the individual characteristics of sensitivity towards endogenous and exogenous stimuli. The differences in environmental sensitivity can play a crucial role in the academic context of health professionals, thus defining it as an area of research that must be addressed. The reduced scale for highly sensitive people (HSP) is a short (16 items) and adapted version of the original scale for highly sensitive people (HSP). This study aims to analyze the psychometric properties of reduced versions of the Highly Sensitive Person Scale (r-HSP Scale) in Spanish nursing students. Once the questionnaire was translated, its psychometric characteristics were analyzed. The Spanish version of the r-HSP scale was administered to 284 university students enrolled in the Nursing Degree. The results from the factorial analysis confirmed the structure of sensitiveness of six factors in our sample. This structure included the following dimensions: (1) Instability, (2) Surroundings, (3) Interaction with others, (4) Sensoperception, (5) Sensitivity, and (6) Insecurity. Additionally, the Cronbach’s α values indicated that the Spanish version of the r-HSP scale had an adequate reliability (α = 0.702). The r-HSP scale is defined as a reliable, valid, and agile replica of the original structure of sensitivity in Spanish university students.
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A large number of studies document that children differ in the degree they are shaped by their developmental context with some being more sensitive to environmental influences than others. Multiple theories suggest that Environmental Sensitivity is a common trait predicting the response to negative as well as positive exposures. However, most research to date has relied on more or less proximal markers of Environmental Sensitivity. In this paper we introduce a new questionnaire-the Highly Sensitive Child (HSC) scale-as a promising self-report measure of Environmental Sensitivity. After describing the development of the short 12-item HSC scale for children and adolescents, we report on the psychometric properties of the scale, including confirmatory factor analysis and test-retest reliability. After considering bivariate and multivariate associations with well-established temperament and personality traits, we apply Latent Class Analysis to test for the existence of hypothesized sensitivity groups. Analyses are conducted across 5 studies featuring 4 different U.K.-based samples ranging in age from 8-19 years and with a total sample size of N = 3,581. Results suggest the 12-item HSC scale is a psychometrically robust measure that performs well in both children and adolescents. Besides being relatively independent from other common traits, the Latent Class Analysis suggests that there are 3 distinct groups with different levels of Environmental Sensitivity-low (approx. 25-35%), medium (approx. 41-47%), and high (20-35%). Finally, we provide exploratory cut-off scores for the categorization of children into these different groups which may be useful for both researchers and practitioners. (PsycINFO Database Record
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Previous research on sensory processing sensitivity and related concepts showed an association with internalizing problems. The current explorative study investigated the underlying factor structure of the parent-report Highly Sensitive Person Scale (HSPS) and its association with problems in daily functioning. Caregivers of 235 children (3–16 years) completed the HSPS as well as questions on daily functioning. First, the factor structure of the HSPS was explored and evaluated. Second, both differences in reported problems between a high SPS and a control group, and in SPS factors between children with few versus many problems, were examined. Results suggested that the scores of the HSPS have good internal consistency and supported a two-factor structure which distinguishes Overreaction to Stimuli (OS) and Depth of Processing (DP). Children with high SPS were reported to have more internalizing problems. High OS was more common in children who cried excessively as a baby, children with medically unexplained physical symptoms (MUPS), sleeping, eating and drinking problems while high DP was more common in children with MUPS and sleeping problems. This study provides the first empirical evidence that the parent-report HSPS may add valuable information to the assessment of children with problems in daily functioning.
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In this longitudinal study, we examined whether children differ in their susceptibility to harsh and responsive parenting as reflected in their externalizing and prosocial behaviour two years later. We focused on three potential susceptibility markers assessed during middle childhood: Negative emotionality, impulsivity, and effortful control. Participants were 120 Dutch children (6–11 years old; 54% girls). Parenting was assessed using both observations and self-report questionnaires. Parental responsiveness predicted decreased externalizing behaviour two years later among children high on impulsivity (in case of observed responsiveness) or low on effortful control (in case of observed and self-reported responsiveness) but not among children low on impulsivity or high on effortful control. Observed harsh parenting predicted decreased prosocial behaviour, especially among children with average or high negative emotionality. The findings support a diathesis–stress model more than they do a differential susceptibility model. High impulsivity seemed to be a vulnerability factor, predicting increased externalizing behaviour when parents lacked responsiveness. Also, high negative emotionality served as a vulnerability factor, predicting decreased prosocial behaviour when parents were harsh, while low negative emotionality served as a protective factor, buffering against decreased prosocial behaviour. Finally, low effortful control might operate as a vantage-sensitivity factor, predicting decreased externalizing behaviour when parents were responsive. Copyright © 2015 John Wiley & Sons, Ltd.
Data
Chapter Data, Program Inputs and Outputs for all LGM Examples in the textbook "An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications, Second Edition". Model specifications are included providing program syntax for Amos, EQS, LISREL, and Mplus software programs. The files are arranged by chapter and include syntax, data, and output files for all examples a particular software program is capable of estimating. The first three chapters (specification of the LGM, LGM and repeated measures ANOVA, and multivariate representations of growth and development) cover the development of the LGM. These are followed by three chapters involving multiple group issues and extensions (analyzing growth in multiple populations, accelerated designs, and multilevel longitudinal approaches), and followed by the chapter on growth mixture modeling, which addresses multiple-group issues from a latent class perspective. The remainder of the book covers 'special topics' (chapters on interrupted time series approaches to LGM analyses, growth modeling with ordered categorical outcomes, Missing data models, a latent variable framework for LGM power analyses and Monte Carlo estimation, and latent growth interaction models). The zipfile is quite large (1MB) since it contains all files for the various software programs.
Article
Several models of individual differences in environmental sensitivity postulate increased sensitivity of some individuals to either stressful (diathesis-stress), supportive (vantage sensitivity), or both environments (differential susceptibility). In this meta-analysis we examine whether children vary in sensitivity to parenting depending on their temperament, and if so, which model can best be used to describe this sensitivity pattern. We tested whether associations between negative parenting and negative or positive child adjustment as well as between positive parenting and positive or negative child adjustment would be stronger among children higher on putative sensitivity markers (difficult temperament, negative emotionality, surgency, and effortful control). Longitudinal studies with children up to 18 years ( = 105 samples from 84 studies, mean = 6,153) that reported on a parenting-by-temperament interaction predicting child adjustment were included. We found 235 independent effect sizes for associations between parenting and child adjustment. Results showed that children with a more difficult temperament (compared with those with a more easy temperament) were more vulnerable to negative parenting, but also profited more from positive parenting, supporting the differential susceptibility model. Differences in susceptibility were expressed in externalizing and internalizing problems and in social and cognitive competence. Support for differential susceptibility for negative emotionality was, however, only present when this trait was assessed during infancy. Surgency and effortful control did not consistently moderate associations between parenting and child adjustment, providing little support for differential susceptibility, diathesis-stress, or vantage sensitivity models. Finally, parenting-by-temperament interactions were more pronounced when parenting was assessed using observations compared to questionnaires. (PsycINFO Database Record