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Developmental Methods for Emotion Dynamics



Emotions and their development are complex processes. Emotions are dynamic; involve multiple biological, psychological, and social systems; and can be idiosyncratic. However, much of the research on emotional development has used methods that do not capture the dynamic nature of emotions; focus only on one biological, psychological, or social system; and/or do not account for individual differences. I provide an overview of current methods for developmental studies on emotion dynamics. First, I introduce methods for examining emotions as dynamic processes. Then, I extend this discussion to multiple burst designs that capture emotion dynamics at multiple time scales (Ram & Diehl, 2015). Throughout, I discuss approaches for both individual and interpersonal emotion dynamics that are applicable across the lifespan. I conclude with a discussion of future directions in the study of emotion dynamics and their development.
This is a draft of a chapter that has been accepted for publication by
Oxford University Press in the forthcoming book The Oxford Handbook
of Emotional Development edited by Dr. Daniel Dukes, Prof. Andrea C.
Samson, and Prof. Eric A. Walle, and due for publication in 2021.
Developmental Methods for Emotion Dynamics
Jessica P. Lougheed
Purdue University
Author Note
Jessica P. Lougheed, Department of Psychology, University of British Columbia
Correspondence concerning this article should be addressed to Jessica P. Lougheed,
Department of Psychology, University of British Columbia Okanagan, 1147 Research Road,
Kelowna, BC, V1V 1V7. E-mail:
Emotions and their development are complex processes. Emotions are dynamic; involve multiple
biological, psychological, and social systems; and can be idiosyncratic. However, much of the
research on emotional development has used methods that do not capture the dynamic nature of
emotions; focus only on one biological, psychological, or social system; and/or do not account
for individual differences. I provide an overview of current methods for developmental studies
on emotion dynamics. First, I introduce methods for examining emotions as dynamic processes.
Then, I extend this discussion to multiple burst designs that capture emotion dynamics at
multiple time scales (Ram & Diehl, 2015). Throughout, I discuss approaches for both individual
and interpersonal emotion dynamics that are applicable across the lifespan. I conclude with a
discussion of future directions in the study of emotion dynamics and their development.
Keywords: Emotion dynamics; emotion regulation; emotional development; research
methods; developmental methods.
Developmental Methods for Emotion Dynamics
Emotions and their development have long been conceptualized as dynamic processes
that ebb and flow (Cole, Martin, & Dennis, 2004; Thompson, 1994). Therefore, the study of
emotion dynamics requires the use of methods that assess emotions across time and context. The
study of emotion dynamics is burgeoning in part to innovations in technology and statistics that
facilitate the uptake of dynamic approaches. However, dynamic approaches are still far from the
norm in the field. In this chapter, I introduce developmental methods for examining emotion
dynamics. I discuss methods for both individual and interpersonal processes, and the importance
of the time scale (e.g., moments, hours, days, years) of unfolding dynamics. First, I discuss
theoretical perspectives of time scales in emotion dynamics and their development. Then, I
review methods for examining emotion dynamics at different time scales. I end with a discussion
of future directions for developmental methods in emotion dynamics.
Several theoretical conceptualizations emphasize that emotions and their development are
dynamic processes unfolding at multiple time scales (Hollenstein, Lichtwarck-Aschoff, &
Potworowski, 2013; Lewis, 2000; Lougheed, 2019). In terms of individual processes, Lewis
(2000) conceptualized emotions as self-organizing at three time scales: micromomentary
emotions at the smallest time scale, longer-lasting moods, and an enduring structure of
personality. Lower-order system elements (i.e., momentary emotional states) coalesce into
moods, which, through repetition, can form the bases of individual differences such as
personality. These enduring personality structures in turn constrain the lower-order system
elements of emotional states and moods, creating a self-organizing structure over development
(Lewis, 2000). This perspective emphasizes that no conceptualization of emotional development
is complete unless it accounts for processes that occur at more than one time scale.
In terms of interpersonal processes, Butler (2011) put forth a conceptualization of
individuals in close relationships as temporal interpersonal emotion systems (TIES), in which
each partner’s emotion systems (physiological, behavioral, and subjective emotional responses)
interact with each other’s, creating a dyadic system in which emotions are regulated
interpersonally over time. With a focus on the developmental dynamics of the parent-adolescent
relationship, in my own work (Lougheed, 2019, 2020) I recast the dynamics of TIES within a
multiple time scale framework to illustrate how momentary interpersonal dynamics coalesce into
broader features of the relationship, which in turn constrain the momentary dynamics of TIES.
These two perspectives emphasize that emotional development occurs in the context of
relationships, which shape longer-term developmental processes. In turn, these developmental
processes (e.g., features of the relationship) constrain how emotions are regulated moment-by-
moment in the context of social interactions (see chapters MÉNDEZ LEAL & SILVERS;
RIEDIGER & BELLINGTIER; and STEPHENS ET AL., for more on the social regulation of
emotion, this volume).
Multiple time scale-perspectives on emotional development guide methods for examining
the development of emotion. First, these perspectives provide guidance in defining the dynamic
process of focus. Second, these perspectives are linked to methodological approaches that can
help match developmental theory to method. Finally, these perspectives help move
developmental science closer to uncovering the mechanisms of developmental change in
emotion. In the following sections, I review research that highlights the importance of dynamic
methods for the study of emotional development. I first discuss research methods for dynamics at
short, moment-to-moment time scales. Then, I discuss methods for emotion dynamics at longer
time scales across hours and days. I then discuss important future directions including the need to
incorporate multiple time scales into study designs to fully understand the mechanisms of
developing emotion dynamics.
Moment-to-Moment Dynamics
To date, the most common approaches used to examine the development of emotions has
been to observe individuals or dyads in emotion-eliciting tasks and have trained observers rate
the occurrence of positive and negative emotion expressions using a microanalytic coding
scheme (e.g., event-based or second-by-second). A few recent studies illustrate how innovative
analytical methods can leverage data that have been microanalytically coded to capture
individual and interpersonal emotion processes as they unfold over short time scales.
Individual dynamics. Several different analytical strategies have been used to examine
temporal relations among individuals’ emotion regulation strategy use and emotion expressions.
The aim of studies using such approaches is often to examine whether or not the use of strategies
is effective in down-regulating (i.e., resolving) negative emotions or up-regulating (i.e.,
enhancing) positive emotions. Building off of prior work that has used contingency analysis to
examine changes in children’s emotion expressions following the use of emotion regulation
strategies (Buss & Goldsmith, 1998), we recently used multilevel survival analysis (MSA) to
estimate the timing of children’s recurring anger expressions during a frustrating wait task from
children’s time-varying use of distraction and social bid strategies (Lougheed, Benson, Ram, &
Cole, 2019). MSA can be used to statistically test the timing of recurring behavioral events, and
time-varying influences of that timing. We found that the likelihood of children’s anger
expressions increased in the moments when they used bidding strategies that kept their attention
focused on a restricted item, but that the likelihood of anger expressions decreased in the
moments when they used a distraction strategy. One advantage of this analytical approach is that
it allows inferences about the effectiveness of strategy use—if the use of behavioral strategies
increases or decreases the likelihood of emotion expressions in time.
Dynamical systems approaches (Boker, 2001; Molenaar, 2004; Cole, Lougheed, Chow, &
Ram, 2020; Yang, Ram, Lougheed, Molenaar, & Hollenstein, 2019) are also gaining momentum
as methods for examining individual emotion dynamics. Statistical approaches using ordinary
differential equations are one example, and this method can be used to examine temporal
processes such as fluctuations, damping, and amplification of emotions (Boker, 2001). This
method was used in one study to examine children’s self-regulation during a frustrating wait task
(Cole, Bendezú, Ram, & Chow, 2017). It showed that 36-month-old children’s use of emotion
regulation strategies had a temporary damping effect on their negative emotion expressions, but
this damping effect was not long-lived during the task. This modeling strategy enabled the
authors to conclude that children’s use of emotion regulation strategies at this age is far from
Interpersonal dynamics. Increasing attention has been paid in recent years to the
development of emotion dynamics in interpersonal contexts, spurred forth by theoretical
conceptualizations of emotion regulation in the context of social interactions (Butler, 2011;
Campos, Walle, Dahl, & Main, 2011). Some studies have examined individuals as nested within
dyads to examine the development of interpersonal emotion dynamics (e.g., Morris et al., 2011;
see STEPHENS ET AL., this volume). Much of this work has examined physiological synchrony
in parent-infant dyads, which points to the importance of biobehavioral synchrony in the early
years to establishing the basis of an adaptive parent-child relationship and developing infant
regulation (Feldman, 2007, 2012). Caregivers and infants begin to develop a rhythm of
interaction that is reflected in synchrony at the physiological level through caregivers’ behavioral
attunement to infant cues (Feldman, 2007; Fogel, 1993). The basis of the relationship is
established through this process and infants learn the beginnings of physiological regulation.
Interpersonal synchrony has become a major topic in research on interpersonal emotion
dynamics and has been studied in a variety of relationship types, including parent-child and
romantic partners (see Harrist & Waugh, 2002; Timmons, Margolin, & Saxbe, 2015 for reviews).
There are a number of methods for examining synchrony (Helm, Miller, Kahle, Troxel, &
Hastings, 2018). Versions of actor-partner interdependence models (APIMs; Kenny & Kashy,
2011) including auto- and cross-lagged panel models allow researchers to test the influence of
Partner A’s emotions at time t on Partner B’s emotions at time t + n (and vice versa), while
controlling for the influence each partner’s own emotions at the previous time point. This type of
dynamic maps onto theoretical conceptualizations of synchrony and emotion transmission, in
which one partner’s emotions influence the other’s at a subsequent time point (Butler, 2011).
Across developmental periods, studies using these types of approaches have shown emotion
transmission in parent-child dyads in infancy and childhood (Feldman, 2007) and adolescence
(Papp, Pendry, & Adam, 2009). For example, mother-adolescent dyads show greater cortisol
synchrony when they are experiencing negative emotions (Papp et al., 2009). Emotion
transmission has been shown across different domains of the emotion system, including
sympathetic nervous system activity (Lougheed & Hollenstein, 2018; Lougheed, Koval, &
Hollenstein, 2016), and expressed and experienced emotions (Mancini, Luebbe, & Bell, 2016).
Innovative methods have also been used to examine temporal links between two
individuals’ observed behaviors. We used MSA to directly examine parental responses to
children’s and adolescent’s expressed emotions from observations of behaviors during parent-
child interactions (Lougheed, Craig, et al., 2016; Lougheed, Hollenstein, & Lewis, 2016;
Lougheed, Hollenstein, Lichtwarck-Aschoff, & Granic, 2015). Taking this approach showed that
parents of children with externalizing problems tended to be less contingent in responding
supportively to their children’s negative emotion expressions than parents of typically-
developing children (Lougheed et al., 2015), and that a similar lack of contingent responses may
also be related to adolescent internalizing symptoms (Lougheed, Craig, et al., 2016). Another
study examining age differences in parent-adolescent dynamics used recurrence quantification
analysis (Coco & Dale, 2014) together with growth curve modeling to examine lead-lag
associations in parent-adolescent emotion dynamics (Main, Paxton, & Dale, 2016). This study
showed that older adolescents supportively validate their mothers’ emotions more than younger
adolescents, but also that older adolescents tend more to be the “drivers” of negative emotional
exchanges than younger adolescents.
A number of research studies have examined socioemotional flexibility of the dyadic
parent-child system, which is the ability for a dyad to transition between emotions according to
situational demands of the interpersonal interaction (Hollenstein et al., 2013). This type of
flexibility is often examined by using microanalytically-coded observations of dyadic emotion
expressions during one or more interaction context and deriving measures such as the number of
observed transitions between dyadic emotion states, and the range of states expressed
(Hollenstein, 2013). Developmental research on dyadic flexibility has shown that in early
childhood, flexibility is negatively associated with the longitudinal development of children’s
externalizing behavioral problems (Hollenstein, Granic, Stoolmiller, & Snyder, 2004;
Lunkenheimer, Olson, Hollenstein, Sameroff, & Winter, 2011). In middle childhood, children
being treated for clinically-significant externalizing problems show increases in dyadic flexibility
over the course of successful therapeutic treatment (Granic, O’Hara, Pepler, & Lewis, 2007).
Flexibility increases during the adolescent transition before settling into a new, more established
dynamic, which suggests a reorganization of parent-adolescent interaction dynamics at the
microsocial level leading to longer-term developmental reorganization (Granic, Hollenstein,
Dishion, & Patterson, 2003). Flexibility in the parent-adolescent relationship is associated with
both parental and adolescent psychosocial adjustment (i.e., lower internalizing symptoms;
Lougheed & Hollenstein, 2016; van der Giessen, Branje, Frijns, & Meeus, 2013). Taken together,
microanalytic observations of behaviors enable researchers to examine the processes of how
emotions unfold both intra- and interpersonally. Researchers can examine important topics such
as the time course of emotions, how emotions are affected by behavioral strategies, and how
dyads respond to changing circumstances.
Hourly and Day-to-Day Dynamics
Emotion dynamics have also been examined at longer time scales during day-to-day life.
The experience sampling method (ESM; Bolger & Laurenceau, 2013) is the most common
approach and enables researchers to prompt participants to respond to questionnaires throughout
their day-to-day lives such as via participants’ mobile phones. This approach captures emotion
dynamics at a longer time scales than studies making use of behavioral observations, and the
specific time scale depends on the number of samples per day. This approach allows researchers
to examine individual and interpersonal emotion dynamics as they unfold in situ.
Individual dynamics. ESM has been used to investigate which emotion regulation
strategies individuals tend to use in their daily lives, and which strategies tend to be more
strongly associated with changes in emotional experiences (Brans, Koval, Verduyn, Lim, &
Kuppens, 2013). This approach has revealed valuable insights about the regulation of emotions
in daily life, such as that among adults, distraction strategies tend to be the most commonly used,
that multiple strategies tend to be used in combination, and that rumination and suppression
strategies are associated with decreases in positive and increases in negative emotional
experiences (Brans et al., 2013). Studies using similar approaches in adolescent samples have
also shown that rumination strategies tend not to be effective in resolving negative emotions, and
are also associated with greater psychosocial adjustment difficulties (Silk, Steinberg, & Morris,
The ESM method has yielded valuable information for developmental psychopathology.
Greater depressive symptoms have been associated with greater emotional variability in day-to-
day life in adolescence but not in late childhood (Larson, Raffaelli, Richards, Ham, & Jewell,
1990), which suggests developmental changes in the associations between emotion dynamics and
psychopathology. Emotion regulation dynamics also distinguish between youth experiencing
clinically significant anxiety disorders from typically-developing youth (Tan et al., 2012).
Specifically, adolescents experiencing anxiety do not differ from typically-developing
adolescents in their responses to typical day-to-day experiences, but rather show heightened
negative emotional reactivity to situations that are particularly emotionally challenging (Tan et
al., 2012).
Interpersonal dynamics. In terms of interpersonal dynamics, ESM has been used most
extensively in the context of adult romantic relationships (see Schoebi & Randall, 2015 for a
review; see also STEPHENS ET AL., this volume). When using ESM to collect data from
individuals nested in relationships, it is common to prompt participants to respond to
questionnaires at the same time. For example, by prompting participants to respond four times
per day corresponding to personally-relevant times of day (e.g., before work, when reuniting
with the partner at the end of the work day, before going to bed), researchers have examined the
extent to which individuals in couple relationships are influenced by each other’s emotional
experiences (Randall & Schoebi, 2015). A variation of multilevel APIMs was used to statistically
test if Partner A’s emotional experiences predicted change in Partner B’s subsequent emotional
experiences (and vice versa). Greater susceptibility to partner’s emotional experiences may be
protective against future distress, potentially because this susceptibility may indicate greater
opportunity for emotional co-regulation within the couple relationship (Randall & Schoebi,
2015). Another study using this method showed that humor may be an effective strategy for
interpersonal emotion regulation among romantic partners—a humorous remark by one partner
may increase both partner’s positive emotion and feelings of intimacy in the relationship (Horn,
Samson, Debrot, & Perrez, 2019).
One of the first examinations of parent-adolescent dynamics using ESM suggested that
parents’ and adolescents’ emotional experiences are linked (Larson & Richards, 1994). During
periods when family members were at home, adolescent girls’ emotional experiences predicted
parental experiences, i.e., their emotions were “transmitted” to their parents at an hour-to-hour
time scale (Larson & Richards, 1994). More recently, a new analytic method called grid-
sequence analysis (Brinberg, Fosco, & Ram, 2017; Brinberg, Ram, Hülür, Brick, & Gerstorf,
2018) has been applied to ESM data of parent-adolescent daily reports of connectedness
(feelings of closeness, trust, and support). This method converts categorical time series data into
sequences, and then interdyad differences in those sequences (i.e., how measured behaviors
unfold over time) can be examined. In one study, results showed interdyad differences in daily
reports of parent-adolescent connectedness with some dyads showing stable, high connectedness
over a few weeks and others showing variable connectedness and discrepant parent and
adolescent reports (Brinberg et al., 2017). Taken together, ESM methods enable researchers to
examine intra- and interpersonal emotion dynamics in day-to-day life outside of the lab setting.
Researchers can examine how individual’s own use of emotion regulation strategies, or their
partners’, effect subsequent emotional experiences, and innovative analytic approaches facilitate
the examination of complex patterns.
Discussion and Future Directions
Innovative methods are being used to examine emotion dynamics at different time scales,
ranging from micro-momentary to hourly and daily. The most common approach for examining
dynamics at the shortest time scales are recordings of behavioral expressions and physiological
responses. The most common approach for examining dynamics at hourly and daily time scales
is sampling individuals’ experiences with ESM. Developmental science is seeing increasing
uptake of such methods which is inspiring exciting new research directions. However, a
comprehensive developmental picture of emotion dynamics will not be available until we
combine innovative methods to assess dynamics across multiple time scales. The combination of
intensive observations of behavior (short time scale) over longer periods of development (long
time scale) are referred to as multiple burst designs (Ram & Diehl, 2015). In this final section, I
describe the few studies that have used multiple burst designs and point towards future directions
using such methods.
Multiple time scale designs. In one of the first explications of development at multiple
time scales, Nesselroade (1991) described short term change at the micro time scale (e.g.,
moments, seconds) as reflecting regulation and reinforcement processes, and changes at longer
time scales (e.g., months, years) as resulting from developmental processes. Incorporating
measurement “bursts” of intensive longitudinal data (micro time scale), repeatedly over
developmental time (i.e., longitudinally over months or years), enables researchers to examine
how longer-term development emerges from momentary dynamics, and in turn how momentary
dynamics are constrained by longer-lasting developmental structures (Nesselroade, 1991; Ram &
Diehl, 2015). The few studies to date that have used such multiple burst designs demonstrate its
value to the study of developing emotion dynamics.
To examine developmental changes in micro time scale dynamics, multiple burst designs
can involve repeating the same behavioral observation tasks at different ages (Cole, Lougheed, &
Ram, 2018). Examples include repeated observations of mothers soothing infants during
immunizations, a challenging emotional experience (Benson, Ram, & Stifter, 2018; Stifter &
Rovine, 2015); young children during laboratory tasks that challenge their self-regulation (Helm,
Ram, Cole, & Chow, 2016; Morales et al., 2018); children and parents during structured and
unstructured observations (Stoolmiller, 2016; Stoolmiller & Snyder, 2014); and of parents and
adolescents during conflict discussions (van der Giessen et al., 2013). It is critical to consider the
potential for practice effects when using behavioral tasks repeatedly in multiple burst designs.
The studies listed above are exemplars of designs that minimize this problem.
There are also a few examples that link dynamics at hourly and daily dynamics to
developmental change, although they are less common. In one study, adolescents’ emotion
dynamics were examined with ESM over the course of treatment for major depressive disorder
(Silk et al., 2011). Five bursts of experience sampling were conducted during an eight-week
treatment protocol. Results showed that differences in the intensity and lability of negative
emotional experiences between adolescents experiencing depression and typically-developing
adolescents decreased during the course of treatment, which provides strong evidence that the
effects of the treatment translated to the real-world context of daily emotional experiences (Silk
et al., 2011). Another study shows how multiple burst designs can incorporate features of several
research methods (Ram et al., 2014). Research on lifespan development has the particular
challenge of a “long” view of development spanning a broad range of ages. In this domain,
longitudinal studies can potentially span generations of researchers to examine development over
decades, which is not always feasible. An innovative approach combined cross-sectional
measurement over a broad range of ages with longitudinal panel design, daily diary, and
experience sampling protocols to capture age-related differences in daily emotion dynamics
between the ages of 18 and 89 years old (Ram et al., 2014). Participants completed three 21-day
measurement bursts and reported on emotional experiences and social interactions. Analyzing
these data with multilevel models enabled the examination of how dynamics within bursts and
individuals are related to age differences in emotions and social interactions (Ram et al., 2014).
Innovations in statistical analyses are facilitating the uptake of multiple time scale
designs by making new methods accessible to developmental scientists. A multiple time scale
multiphase latent basis growth model has recently been developed to allow the analysis of
behavioral change at multiple levels: (1) within task, and (2) longitudinal changes in these
within-task changes (Helm et al., 2016). The first application of this new method demonstrated
how young children’s self-regulatory behaviors change over the course of a frustrating laboratory
task, and how these within-task changes become less fragmented over age in line with children’s
developing executive control (Helm et al., 2016). An extension of MSA enables researchers to
model temporal contingencies between multiple streams of behaviors (i.e., dynamics at the micro
time scale) within a broader structural equation model to examine how momentary dynamics
mediate developmental processes over longer time periods (Stoolmiller & Snyder, 2014). The
first applications of this method showed how parent-child emotion dynamics are related to the
development of children’s antisocial behavior (Stoolmiller, 2016; Stoolmiller & Snyder, 2014).
Another new method integrates differential equations, which can describe momentary non-linear
dynamics, with multilevel growth modeling, which captures longitudinal change processes
(Benson et al., 2018). The first application of this method showed how caregiver-infant
interactions can be partitioned into self-and co-regulatory processes, and longitudinal changes in
those processes. It is clear that joint modeling approaches, which leverage combinations of
statistical approaches (e.g., micro time scale approaches cast within multilevel or growth
models), are the future of developmental methods for emotion dynamics.
Future directions. It is an exciting time for research on the development of emotion
dynamics. New study designs and statistical approaches are enabling a greater matching of data
analysis and hypothesis tests to complex developmental theories. At the same time, these new
approaches are facilitating the elaboration and refinement of theoretical conceptualizations of
emotion dynamics (Butler, 2011; Hollenstein et al., 2013; Lougheed, 2019). I propose several
goals for developmental scientists as we move the study of emotion dynamics forward in new
First, one goal is to explore and describe dynamics at the micro time scale. The analysis
of micro time scale dynamics is relatively new in the history of emotion science. Research that
focuses on exploring and describing emotion dynamics in different contexts (e.g., positive versus
negative emotional contexts; with different interaction partners such as parents and peers; in
different settings such as home versus school and work), at different ages, and among different
groups (e.g., gender, family structure, ethnicity, socioeconomic status) are crucial for forming a
foundation of knowledge on which to elaborate into multiple burst designs.
A second goal is to examine changes in short term dynamics longitudinally with multiple
burst designs. Many developmental scientists may already have access to such data sets, as it has
been a common practice to conduct longitudinal studies using laboratory observations of
behaviors. Now that statistical approaches for modeling dynamics at multiple time scales are
becoming more accessible, researchers could conduct secondary analyses of data that are already
available to accelerate the body of research on dynamics at multiple time scales. Such secondary
data analyses will benefit the design of new studies of dynamics at multiple time scales as we
develop and refine best practices for these designs.
Finally, mixed methods approaches will be a major asset to the study of developing
emotion dynamics. For example, studies could employ multiple complementary methods such as
behavioral observations at the micro time scale with approaches that capture hourly and daily
dynamics in situ with ESM. This approach would enable the observation of changes in dynamics
at three or more time scales and enable a detailed analysis of processes underlying
developmental change in emotion dynamics (see Hollenstein & Tsui, 2019 for more discussion
of this type of design).
The uptake of new methods also warrants some cautions. For example, sufficient
statistical power is a concern with all study designs and methods. As intensive longitudinal data,
and the statistical approaches to analyze them, become more common, it is important for
researchers to be well-trained in the more complex considerations for power (i.e., power at
multiple levels, such as within-person/dyad and between-person/dyad; Bolger & Laurenceau,
2013). Another concern involves the complexity of model specification—as a field we need to
beware the dangers of contributing to a scientific body of work built on a foundation of model
overspecification (e.g., inclusion of extraneous or too many predictors) and questionable research
practices (e.g., p-hacking) which may be easier to intentionally or unintentionally employ with
complex analyses where many analytic decisions must be made.
Conclusion. Development is nonlinear and proceeds at multiple time scales
(Nesselroade, 1991; Ram & Diehl, 2015). The complexity of emotional development is what
makes it such a rich area of study, but also what provides challenges to implementing research.
Between the increasing accessibility of advanced statistical methods, innovations in study
designs, and theoretical emphases on dynamic processes, developmental scientists are well-
positioned to take giant leaps forward in understanding the complex processes at play in
emotional development.
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