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Network analysis is a method used to explore the structural relationships between people or organizations, and more recently between psychological constructs. Network analysis is a novel technique that can be used to model psychological constructs that influence language learning as complex systems, with longitudinal data, or cross-sectional data. The majority of complex dynamic systems theory (CDST) research in the field of second language acquisition (SLA) to date has been time-intensive, with a focus on analyzing intraindividual variation with dense longitudinal data collection. The question of how to model systems from a structural perspective using relation-intensive methods is an underexplored dimension of CDST research in applied linguistics. To expand our research agenda, we highlight the potential that psychological networks have for studying individual differences in language learning. We provide two empirical examples of network models using cross-sectional datasets that are publicly available online. We believe that this methodology can complement time-intensive approaches and that it has the potential to contribute to the development of new dimensions of CDST research in applied linguistics.
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METHODS FORUM
Network analysis for modeling complex systems
in SLA research
Lani Freeborn*, Sible Andringa , Gabriela Lunansky and Judith Rispens
University of Amsterdam, Amsterdam, The Netherlands
*Corresponding author. E-mail: l.j.v.freeborn@uva.nl
(Received 07 October 2021; Revised 29 July 2022; Accepted 15 August 2022)
Abstract
Network analysis is a method used to explore the structural relationships between people or
organizations, and more recently between psychological constructs. Network analysis is a
novel technique that can be used to model psychological constructs that influence language
learning as complex systems, with longitudinal data, or cross-sectional data. The majority of
complex dynamic systems theory (CDST) research in the field of second language acquisi-
tion (SLA) to date has been time-intensive, with a focus on analyzing intraindividual
variation with dense longitudinal data collection. The question of how to model systems
from a structural perspective using relation-intensive methods is an underexplored dimen-
sion of CDST research in applied linguistics. To expand our research agenda, we highlight
the potential that psychological networks have for studying individual differences in
language learning. We provide two empirical examples of network models using cross-
sectional datasets that are publicly available online. We believe that this methodology can
complement time-intensive approaches and that it has the potential to contribute to the
development of new dimensions of CDST research in applied linguistics.
Introduction
In the field of second language acquisition (SLA), complex dynamic systems theory
(CDST) is a theoretical paradigm used to study the complex and dynamic nature of
language, language use, and language development (Hulstijn, 2020). A complex system
is formed out of interactions between multiple internal and external system compo-
nents. For example, if conceptualizing language development as a complex system,
changes in development are dependent on interactions between a learners internal
resources like working memory, motivation, and personality, as well as external,
environmental resources like the teacher, learning materials, and language use (van
Geert, 1991). These internal and external resources are interrelated, whereby altering
one component could in turn alter other components of the system (de Bot et al., 2007).
In this way, a complex system is characterized by complete interconnectedness and
mutual causality (Larsen-Freeman & Cameron, 2008). Complex systems are inherently
© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms
of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted
re-use, distribution and reproduction, provided the original article is properly cited.
Studies in Second Language Acquisition (2022), 132
doi:10.1017/S0272263122000407
https://doi.org/10.1017/S0272263122000407 Published online by Cambridge University Press
dynamic; systems emerge over time through processes of self-organization and coad-
aptation between micro- and macro-level system components (Larsen-Freeman, 1997).
This means that complex systems are soft-assembled, whereby systems are more than
the sum of their parts, reflecting a multiplicative combination of attributes, experiences
and situational factors(American Psychological Association, 2017). CDST researchers
in SLA have acknowledged the impossibility of fully knowinga system, as complex
systems are characterized by unpredictability and nonlinearity, where changes in the
system can be disproportionate to the cause (Larsen-Freeman, 1997). Although a
complex system is, by definition, constantly in flux, the system can also demonstrate
periods of temporary stability. This is referred to as an attractor state; a self-sustaining
state in which interactions are actively reproduced over time(van Geert, 2019,
p. 168). An attractor state represents higher-order patterns of self-organization within
state space, from which the system moves toward or away from over time (Hiver, 2014).
To illustrate, an attractor state could refer to the tendency for learners not to participate
in class and remain silent (Hiver, 2014).
With the growing recognition that CDST approximates the reality of language
development (Hiver & Al-Hoorie, 2020a), more SLA researchers are adopting this
framework. However, there are many methodological considerations for conducting
empirical research within a CDST paradigm. Some of these include how to operatio-
nalize the system, how to assess the influence of contextual factors on the system, as well
as macro- and micro-structure considerations (Hiver & Al-Hoorie, 2016). Given the
inherent complexities of analyzing dynamic cause-effect relationships between systems
and their components, there has been much discussion about suitable methodologies
and suggestions of how to enhance our CDST toolbox (de Bot, 2011; Hiver &
Al-Hoorie, 2016,2020a; Hiver et al., 2022).
Hilpert and Marchand (2018) distinguish between three conceptual perspectives
to studying complex systems and their accompanying research designs: time-
intensive, relation-intensive, and time-relation intensive approaches. Firstly, time-
intensive approaches are used to make inferences about system behavior using
closely spaced observations over timeusing longitudinal data (Hilpert & Marchand,
2018, p. 192). The second approach, relation-intensive, focuses on identifying the
structure of the relationships among individuals or variables in a system using cross-
sectional data. Combining the first two approaches, time-relation intensive
approaches are used to make inferences about system behavior using closely spaced,
simultaneously collected observations of both within-element change and changing
between element relationships(Hilpert & Marchand, 2018).
The majority of CDST studies in the field of SLA to date have taken time-intensive
approaches, typically consisting of case studies characterized by dense data collection
with qualitative and descriptive data analyses (Hiver et al., 2022). For the last 30 years,
CDST researchers have focused on individual variability and the dynamics of pro-
cesses (van Geert & van Dijk, 2021). This is not surprising, given that CDST is
essentially a theory of change, concerned with how one state develops into another
state over time. However, as Hilpert and Marchand (2018) have pointed out, complex
systems can be studied from multiple perspectives. Besides analyzing change over
time, identifying the structure of a system is also a key aspect of CDST. Expanding our
line of inquiry to include relation-intensive approaches could contribute to the
development of new dimensions of CDST research and complement time-intensive
approaches. While researchers have a diverse selection of methods available for time-
intensive approaches, our methodological toolbox for relation-intensive methods is
lacking.
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In this article, we highlight network analysis as a potential methodology to model
complex systems from a relation-intensive perspective. While network analysis can
also be used for time- and time-relation intensive approaches, this article is focused on
network analysis for relation-intensive approaches only, due to the relative lack of
attention that this dimension has received by SLA researchers working within a CDST
paradigm. More specifically, we concentrate on psychological networks, as opposed to
social networks. SLA researchers have already explored social network analysis as a
suitable research methodology for CDST, for example to model relationships between
learners in a classroom and teacher networks as complex systems (Hiver & Al-Hoorie,
2016; Hiver & Al-Hoorie, 2020a; Mercer, 2014). SLA researchers have not yet
explored the potential of psychological networks to model psychological constructs
that influence language learning as complex systems. The network approach to
psychopathology has been used to reevaluate theories of mental disorders
(Borsboom et al., 2017; Borsboom & Cramer, 2013) and constructs such as intelli-
gence and cognitive development from a CDST perspective (Kievit, 2020;vander
Maas et al., 2006,2017). In this article we discuss how, similarly to psychology
research, individual differences in language learning can be modeled as nomological
networks, expanding our relation-intensive methods to include the study of phenom-
enological constructs. We begin with a brief review of CDST research designs used in
the field of SLA to date, in relation to the three different conceptual approaches to
studying complex systems as described by Hilpert and Marchand (2018). We then
expand discussion on relation-intensive approaches, the least researched dimension
in CDST. The remainder of the article discusses potential applications of network
analysis. To further aid discussion, we provide two examples of network models that
are estimated from publicly available data.
Research designs in CDST
Time-intensive methods
Most CDST research in applied linguistics is time-intensive, with longitudinal data
collection of a single variable (or multiple variables for a single case/participant) to
observe micro-level changes in the system over time (Hiver et al., 2022; Hiver & Larsen-
Freeman, 2019). Time-intensive studies tend to have dense data collection and small
sample sizes, with 40% of studies including a sample size of 10 participants or fewer
(Hiver et al., 2022). A particularly researched area is the development of L2 writing over
time using measures of complexity, fluency, and accuracy (CAF) (Evans & Larsen-
Freeman, 2020; Larsen-Freeman, 2006; Lowie et al., 2017; Lowie & Verspoor, 2019).
Some common CDST techniques used in these studies include assessing the degree of
variability in developmental trajectories and plotting longitudinal data on min-max
graphs for visual inspection. Several studies have used a time-series design based on the
view that frequent-enough measurements may be able to capture underlying develop-
mental processes (Van Geert & Steenbeek, 2005). For example, Waninge et al. (2014)
micro-mapped the motivational dynamics of four students during class time, taking
measurements at 5-minute intervals.
Another popular methodology for observing language development is retrodictive
modeling (Chan & Zhang, 2021; Evans & Larsen-Freeman, 2020; Nitta & Baba, 2018),
based on the idea that because what we observe has already changed, change can be
described retrospectively (Larsen-Freeman & Cameron, 2008). Retrodictive methods
such as process tracing have been used to study the development of language as well as
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individual differences over time. For example, Papi and Hiver (2020) used process
tracing of retrospective interviews to examine changes in six learnersmotivational
principles and Amerstorfer (2020) used process tracing with a combination of class-
room observations and retrodictive interviews to explore five learnersstrategic L2
development. Some time-intensive CDST studies have also used the idiodynamic
method,a mixed-methods approach to studying affective and cognitive states
(MacIntyre, 2012). These time-intensive approaches have provided insights into non-
linear L2 developmental processes and intraindividual variation over time.
Relation-intensive methods
In comparison to the number of studies that have taken a time-intensive approach, far
fewer CDST studies have taken a relation-intensive approach, which involves exploring
the structure of relationships between people or variables within a system with cross-
sectional data. As previously mentioned, SLA researchers have noted how social
network analysis is a suitable methodology for CDST, for example to analyze relation-
ships between learners in a classroom, teacher networks, or school networks (Mercer,
2014). However, this discussion has been mostly theoretical, with very few empirical
studies using social network analysis from a CDST perspective. For example, although
some applied linguistics researchers have used social network analysis to map the
distribution of conversational topics of bilinguals in different contexts (Tiv et al., 2020)
and to assess the impact of social networks in study abroad contexts (Gautier, 2019;
Paradowski et al., 2021; Zappa-Hollman & Duff, 2014), these studies are not typically
informed by CDST.
While relation-intensive approaches can focus on person-to-person interactions,
they can also be used to analyze relations among psychological variables (Marchand &
Hilpert, 2018). Taking a variable-centered relation-intensive approach necessitates
researchers to engage with psychological constructs on a phenomenological level,
and to carefully consider whether their methodology can effectively model complex
patterns of relationships among variables. Some SLA researchers have discussed how
psychological constructs such as the selfand L2 motivation can be conceptualized as
complex systems (Henry, 2014,2017; Mercer, 2011a). In one study, Mercer (2011a)
took a relation-intensive approach to explore how the self-construct could be conceived
of as a complex system. Using qualitative data of a single case study, Mercer (2011a)
created a three-dimensional network-based model of a students self-concepts that she
felt to be the most phenomenologically-realrepresentation of the data.
Besides this, few SLA researchers have attempted to model psychological constructs
as complex systems. There are a handful of CDST studies that are reminiscent of
relation-intensive approaches, which used quantitative methodologies often deemed
ill-suited for CDST. For example, conceptualizing L2 speech as a complex system, Saito
et al. (2020) investigated the effects that 30 different internal and external individual
differences had on the pronunciation of 110 L2 English speakers. Due to the large
number of variables included in their study, Saito et al. (2020) first conducted factor
analysis and then did regression analysis with the extracted factor scores on speech
ratings. In another study, Li et al. (2020) positioned themselves within a CDST
framework to explore the relationships between individual difference constructs
including foreign language classroom anxiety, foreign language enjoyment, self-
perceived achievement, and actual English achievement. To analyze data, Li et al.
(2020) conducted Pearson correlations to assess relationships between variables and
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used multiple regression analysis to assess the combined effect of anxiety and enjoy-
ment on language achievement. While these two studies are fine cross-sectional studies
in their own right, CDST scholars have argued that methods such as zero-order
correlations and linear regression oversimplify the complex realities of how individual
differences influence second language development and have questioned the use of
cross-sectional datasets in CDST research (Al-Hoorie & Hiver, 2022; Hiver, 2014).
Overall, very few SLA researchers to date have used relation-intensive approaches
within a CDST paradigm. There are also seemingly fewer methodologies available for
SLA researchers to explore relation-intensive approaches, with more conceptual dis-
cussion than empirical studies.
Time-relation intensive methods
Hilpert and Marchand (2018, p. 192) describe time-relation intensive research designs
as having closely spaced, simultaneously collected observations of both within-
element change and changing between element relationships.Only a few SLA studies
have analyzed interactions between variables and how these interactions change over
time. However, these studies cannot be strictly classified as time-relation intensive
approaches, as their data collection consisted of only a few time points. For example,
Serafini (2017) conducted longitudinal case studies to explore interactions between
cognitive and motivational individual differences at varying proficiency levels. Data
was collected twice from 87 university students learning L2 Spanish, at the beginning
and end of an academic semester. Serafini used Pearson correlations to analyze
associations between individual differences at each time point and created scatterplots
with regression and Loess lines to visualize relationships between variables and
compare differences across proficiency levels. Results showed that the relationship
between cognitive abilities and motivational constructs varied at each time point and
across learner proficiency levels, indicating that cognitive and motivational subsystems
are interdependent. In another study, Piniel and Csizér (2014) investigated changes in
21 studentsmotivation, anxiety, and self-efficacy at six time points throughout an
academic writing course. To analyze data, Piniel and Csizér used latent growth curve
modeling (LGCM) and cluster analysis to group together learners with similar trajec-
tories. Interactions between variables were also analyzed by comparing Pearson cor-
relations between IDs at each time point. Overall, results indicated that language
learning experience, ought-to L2 self, and writing anxiety showed a significant level
of nonlinear change over time. There was also a strong interrelationship between
motivation and anxiety, whereby more highly motivated learners had lower levels of
language learning anxiety.
Pfenninger and colleagues (Kleisch & Pfenninger, 2021; Pfenninger, 2020) have also
recently explored the use of generalized additive mixed modeling (GAMM) for a time-
relation intensive approach to SLA microdevelopment. GAMM is a type of analysis
used for time-series data that can consider nonlinear development, iterative processes,
and interdependency between variables (Pfenninger, 2020). Pfenninger (2020) used
GAMM to analyze the L2 developmental trajectories of four groups of children (N =
91) on different content and language integrated learning (CLIL) programs. The
children completed various language tasks four times a year for up to 8 years. Pfen-
ninger also combined GAMM with qualitative data to help identify what contributed to
developmental trajectories. Results showed that children had similar L2 trajectories
regardless of their age of onset, and that L2 growth was determined by various different
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external and internal states across time. In another study, Kliesch and Pfenninger
(2021) used GAMM to examine the L2 developmental trajectories of 28 adults (age
64þ) on a 7-month beginners Spanish course. Data was collected each week over 30
32 weeks, which included seven L2 measures, eight cognitive tasks, and measures of
well-being and motivation. GAMM revealed both linear and nonlinear increases in L2
proficiency over time, with considerable between-subject variability. While only a few
CDST studies have used time-relation intensive methods, findings indicate a complex
interplay between external and internal learner differences, which in turn interact with
language development in a nonlinear way over time.
Expanding our research agenda
CDST studies that have incorporated a relation-intensive element to their research
design are far less common compared to the number of studies that have taken time-
intensive approaches. Despite the fact that complexity theorists are interested in
understanding the relations [emphasis in original] that connect the components of a
complex system(Hiver & Larsen-Freeman, 2019, p. 287), to date there have been very
few attempts to empirically model these relations. One potential reason behind this is
relates to methodological challenges and the view that cross-sectional data, zero-order
correlations and linear regression are ill-suited to studying complex systems (Al-Hoorie
& Hiver, 2022). Another reason relates to the theoretical challenges of conceptualizing
abstract psychological constructs as complex systems. A number of individual differ-
ences constructs in language learning have been conceptualized as complex systems,
such as motivation (Papi & Hiver, 2020), strategy development (Amerstorfer, 2020),
anxiety (Gregersen, 2020), working memory (Jackson, 2020), and willingness to
communicate (MacIntyre, 2020). To examine these constructs from a relation-
intensive perspective, for example to model L2 motivation as a complex system, we
must consider the components that form the system, and how these components align
with our measurement instruments. Researchers must also confront the boundary
problem(Larsen-Freeman, 2017), accepting the theoretical impossibility of measuring
a complex system in its entirety, whereby the whole is greater than the sum of its parts
(Han, 2019, p. 156). Consideration should also be given to the phenomenological
validity of equating conceptual and theoretical concepts as systems, and the practical
implications this has for a chosen methodology (Hiver & Al-Hoorie, 2016). Mercer
(2011b, p. 59) discusses these issues in relation her network-based model of the self-
concept, acknowledging the theoretical and empirical difficulty of distinguishing the
blurred boundaries between different self-constructs. Despite the challenges of explor-
ing psychological constructs related to language learning from a relation-intensive
approach, and the inevitable reductionism this entails, focusing on system structures
can offer a perspective that is currently missing from CDST research in SLA.
Take the construct of L2 motivation, for example, which has been much discussed in
CDST research (e.g., Dörnyei, 2017, Dörnyei et al., 2015; Henry, 2014,2017; Hiver &
Papi, 2019; Hiver & Larsen-Freeman, 2019; Papi & Hiver, 2020). Most CDST research
on L2 motivation has been time-intensive with a focus on observing micro-level
changes in a small number of variables over time. Very few CDST researchers have
explored L2 motivation from a relation-intensive perspective, although there has been
some theoretical discussion of how to conceptualize the structural relationships
between motivational constructs as complex systems (Henry, 2014,2017). The L2
Motivational Self System (L2MSS) is a theoretical paradigm that was developed by
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Dörnyei (2005,2009) that conceptualizes L2 motivation from a self-perspective. The
L2MSS is comprised of three phenomenologically constructed concepts, each theorized
to be a primary source of motivation to learn an L2: the Ideal L2 Self, the Ought-to L2
Self, and L2 Learning Experience (Dörnyei 2005,2009). Although the L2MSS was not
originally conceptualized as a complex system, it has been conceptually extended to a
CDST paradigm (Henry, 2014,2017). For example, Henry (2017, p. 551) has described
the self-concept as a multifaced dynamic structure, which can be understood as the
product of constant interactions between different subsystems (such as, e.g., self-
efficacy and self-esteem).
Taking a relation-intensive approach to L2 motivation could provide insight into
the structural relationships between components of the L2 motivational system, and if
this were expanded to a time-relation intensive approach, could potentially identify
attractor states. SLA researchers have speculated about how the L2 self-system, in
particular the Ideal L2 self, can manifest as an attractor state (Henry, 2017;Hiver,
2014;Waningeetal.,2014), whereby changes in the vision of the Ideal L2 Self and
changes in the distance between it and the actual self, can be conceptualized as
changes in attractor state geometries(Henry, 2014, p. 87, emphasis in original).
Although longitudinal data is needed to show system self-organization and the
emergence of attractor states, cross-sectional data can provide a perspective that is
currently missing from CDST research in SLA. As Mercer (2011a) reflects in relation
to her network-based model of the self-concept:
Whilst the model out of necessity can only represent a snapshot of a fragment of
an individuals self-concept network in a specific context at a particular time,
the essence of the underlying form can be used to fundamentally understand
the structure and nature of self-concept. (p. 66)
Taking a relation-intensive CDST approach to the study of individual differences in
SLA can thus be viewed as complimentary of time-intensive approaches. Cross-
sectional data can provide insight into the structure of relationships between system
components, which, if combined with what we have learned from time-intensive CDST
studies, could enrich our understanding of the complex interplay between individual
differences and L2 development.
There is currently little guidance on how to analyze and model interactions between
system components from a relation-intensive perspective. As previously mentioned,
CDST researchers have questioned whether methods such as zero-order correlations
and linear regression are suitable for examining dynamic changes and interconnected
(Al-Hoorie & Hiver, 2022). Although scholars have emphasized the potential of
quantitative analyses for CDST research (Al-Hoorie & Hiver, 2022) for example to
identify network structure or nested phenomena, there appears to be an overall
reluctance to use cross-sectional data, with most CDST researchers preferring longi-
tudinal data. Until now, most studies that have taken a relation-intensive approach
have analyzed relationships between variables by correlations and multiple regression
analysis (Li et al., 2020; Piniel & Csizér, 2014; Saito et al., 2020; Serafini, 2017). However,
new advancements in statistics software and data analysis techniques such as GAMM
are enriching the CDST toolbox. Other techniques that have been proposed as appro-
priate methods to study complex systems with a relation-intensive element are latent
growth curve modeling (LGCM) and multilevel modeling (MLM) (Hiver & Al-Hoorie,
2020a; MacIntyre et al., 2017). To expand our CDST toolbox of relation-intensive
approaches, we could also utilize network analysis, an underexplored methodology in
SLA research.
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Network analysis
Network analysis has become a popular technique for studying complex systems in the
field of psychology. Readers should be aware that there are many different types of
network models; network analysis can be performed on cross-sectional data from a
relation-intensive perspective (Epskamp & Fried, 2019; Hevey, 2018), and also on
longitudinal time-series data from a time- or time-relation intensive perspective
(Bringmann et al., 2013). Although we outline some other variants of network analysis
later in the discussion section, it is beyond the scope of this article to describe each type
of network analysis in detail. We have opted to focus on psychological networks with
cross-sectional data for relation-intensive approaches, which is an underexplored
dimension of CDST research in applied linguistics.
As readers may be more familiar with social network analysis, we would also like to
briefly explain some differences between social networks and psychological networks.
Social networks show patterns of relationships among individuals or groups, whereas
psychological networks show patterns of relationships among variables (at item level or
composite level). It is important to note that with social networks, the relationships
between variables are known; social networks are created from an adjacency matrix,
whereby the relationships between variables are directly observed (OMalley & Onnela,
2019). In contrast, with psychological networks, relationships between variables are not
known but are estimated. Psychological networks are estimated from a variance-
covariance matrix, based on the strength of partial correlations between variables
(Epskamp & Fried, 2019).
Psychological network analysis has been used to model constructs such as intelli-
gence (van der Maas et al., 2006,2017) cognitive development (Kievit, 2020), and
mental disorders (Borsboom, 2017) from a CDST perspective, and has also been
applied to clinical research on psychological disorders such as depression and eating
disorders (Elliott et al., 2020; Lutz et al., 2018). In network models, variables (also
referred to as components) are represented as circles called nodes. In psychological
networks, nodes represent elements of a construct or an entire construct, such as
attitudes or symptoms of a mental disorder. Lines between nodes are called edges, which
represent the direct association between a pair of nodes. The strength of association
between nodes is called the edge weight; the thicker the edge, the stronger the associ-
ation. Edges in psychological networks are typically undirected, which reflect the
hypothesized multicausal relationships between system components. Positive relation-
ships are typically denoted using blue edges, while red edges are used to indicate
negative relationships. The layout of the network model can be selected by the
researcher. Psychological networks are often plotted (by default) using the
Fruchterman-Reingold algorithm (Fruchterman & Reingold, 1991), which places
nodes with stronger connections closer together, and nodes with weaker connections
further apart. Besides visual inspection, network models can be analyzed on several
different levels, depending on what the research questions are. For example, researchers
typically analyze the network density if the overall interest is the network structure or
focus on particular nodes and edges (Burger et al., 2022).
The most common models used to estimate psychological networks are pairwise
Markov random field (PMRF) models. Within PRMF models, Gaussian graphical
models (GGM) are used with continuous multivariate data to estimate partial corre-
lations between variables (Epskamp, 2014). Partial correlation networks are undirected
graphs, estimated by analyzing the strength of correlations between variables after
controlling for the effect of other measured variables in the network (Hevey, 2018). As
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such, a psychological network can be viewed as a nomological net, which functions as a
specification of the phenomenological concepts or theoretical constructs of interest in a
study, their observable manifestations, and the linkages between them(Hiver &
Al-Hoorie, 2016, p. 747). Psychological networks created using cross-sectional data
can therefore be viewed as a snapshot of the system at a given time.
Network analysis and CDST
Network analysis has some advantages over other relation-intensive methods used in
CDST research. One advantage is that network analysis is more conceptually aligned
with CDST compared to factor-based statistical techniques that are rooted in latent
variable theory (Fried, 2020). Originally developed by Spearman (1904), factor models
function under the theoretical assumption that a latent construct, such as intelligence or
personality, can be measured through observable indicators (e.g., behavioral tests or
questionnaire items). This means that there is a hypothesized unidirectional relation-
ship from the latent construct to the observable indicator, whereby answers to ques-
tionnaire items or tests are thought to reflectthe latent construct (Edward & Bagozzi,
2000). In contrast, from a network perspective, psychological constructs exist as
systems where components mutually influence each other without the need to call on
latent variables(Guyon et al., 2017, p. 2). Statistically, factor models and psychological
networks are closely related, as both analyze the covariance between observed variables.
The difference between each approach is their competing causal explanations (Fried,
2020). As van Bork et al. (2019, p. 1) explain, whereas latent variable approaches
introduce unobserved common causes to explain the relations among observed vari-
ables, network approaches posit direct causal relations between observed variables.
These two competing causal explanations are reflected in the choice of statistical
model selected by the researcher. For example, factor-based techniques such as SEM or
LGCM generate directed graphs, with edges from the latent construct to the observed
indicators and/or between latent constructs, which are determined by the researcher a
priori. Psychological network analysis is a more data-driven approach and produces an
undirected graph with edges estimated between all nodes, better reflecting key CDST
concepts such as multicausality and interconnectedness. This has already been noted in
the field of psychology, where researchers working from a CDST perspective are using
network analysis as an exploratory tool to better visualize the complex patterns of
relations between variables of interest (Hilpert & Marchand, 2018; Sachisthal et al.,
2019; van der Maas et al., 2017).
In this article, we explore how network analysis could be used to model psycholog-
ical constructs that influence language learning from a relation-intensive perspective.
We provide two examples of psychological networks created using the datasets of
existing studies that are publicly available online in support of Open Science practices.
As the nested nature of educational phenomena can be analyzed at multiple levels
(Marchand & Hilpert, 2018), our network models illustrate two different levels of
analysis; with nodes at item level and composite level. The first example is a network
model of L2 motivation made using the dataset from Hiver and Al-Hoories(2020b)
study on the role of vision in L2 motivation. This example explores how an individual
difference construct such as L2 motivation can be modeled as a complex system, by
analyzing relationships between the L2MSS at the item level. The second example is a
network model of individual differences in native language ultimate attainment, made
using the dataset from Dąbrowskas(2018) study. The second example takes a wider
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relation-intensive perspective by analyzing interactions between multiple individual
difference constructs at the composite level. Note that the authors of the original studies
(Dąbrowska, 2018; Hiver & Al-Hoorie, 2020b) did not position their research within a
CDST paradigm, and our reanalysis of their data is not a critique on their work.
We performed all statistical analyses using the open-source software R (R Core
Team, 2020) and the R-packages qgraph (Epskamp et al., 2012) and bootnet (Epskamp
et al., 2018a) in particular. The R code that we used to create these two examples is
available in the online Supplementary Materials on our Open Science Framework
(OSF) page. This article is not intended to serve as a tutorial in network analysis (for
tutorials, we refer readers to Burger et al., 2022; Epskamp et al., 2018a; and Hevey,
2018). Rather, our overall aim is to raise awareness of this methodology and illustrate
how it can be applied to model psychological constructs related to language learning
from a relation-intensive CDST perspective. Within each example, we evaluate (a) the
extent to which a network analysis of the datasets supports the same conclusions as the
original authors and (b) whether network analysis can offer any additional insights to
the original analyses.
Example 1
The first example was made using the dataset from Hiver and Al-Hoories(2020b)
study Reexamining the Role of Cision in Second Language Motivation: A Preregis-
tered Conceptual Replication of You, Dörnyei, and Csizér (2016).Both Hiver and
Al-Hoorie (2020b) and You et al. (2016) used SEM to explore interrelationships
between components of the L2 Motivational Self System (L2MSS). The L2MSS is a
theoretical paradigm that was developed by Dörnyei (2005,2009) based on Possible
Selves Theory (Markus & Nurius, 1986). The L2MSS is comprised of three components,
each theorized to be a primary source of motivation to learn an L2: the Ideal L2 Self, the
Ought-to L2 Self, and L2 Learning Experience. The ideal L2 self refers to learners
internal desires and wishes to learn the L2, while the ought-to L2 self refers to learners
perceived external duties and social pressures to learn the L2 (Dörnyei & Chan, 2013).
L2 experience concerns learnersattitudes toward learning, based on their experience of
the learning process and environment. In addition to these three components, vision
and imagery are also considered key aspects of the L2MSS, whereby motivation is
viewed as a function of the language learnersvision of their desired future language
selves(Dörnyei & Chan, 2013, p. 437). Vision can be considered as a combination of
imagery capacity and ideal selves and is typically measured by visual and auditory
learning style preferences, and vividness of imagery capacity (You et al., 2016). A
number of studies have used SEM to explore the interrelationships between these
motivational constructs and the extent to which the L2MSS can predict language
learning or intended effort (Dörnyei & Chan, 2013; Hiver & Al-Hoorie, 2020b; You
et al., 2016). However, as You et al. (2016, p. 97) have pointed out, because the L2
Motivational Self System was originally proposed as a framework with no directional
links among the three components, past empirical studies employing SEM have not
been uniform in specifying these interrelationships.For example, whereas some
studies have presented a directed pathway from the ideal L2 self to L2 learning
experience, other studies have reversed this relationship (for further details see You
et al., 2016).
Hiver and Al-Hoorie (2020b) conducted a conceptual replication and extension of
You et al. (2016) to evaluate the role of vision in L2 motivation and to assess whether
intended effort is an outcome or a predictor of motivation. They justified these aims in
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part due to the fact that You et al. did not test equivalent or competing models, which
could be considered a form of confirmation bias. Hiver and Al-Hoorie also stressed the
need for more robust research designs, and further replication of research on language
motivation. Hiver and Al-Hoorie (2020b) collected data from 1297 L2 learners of
English in secondary schools in South Korea. In addition to the same 10 scales of
motivation and vision used by You et al., Hiver and Al-Hoorie also included two
measures of L2 proficiency, midterm grades and final exam grades, which were
analyzed as one variable called L2 achievement. To determine the number of under-
lying factors, they submitted the dataset to Mokken scaling analysis, confirmatory
factor analysis, exploratory factor analysis, scree plot, optimal coordinates, and parallel
analysis (Hiver & Al-Hoorie, 2020b, p. 73). These analyses resulted in only four factors:
visual style, ideal L2 self, ought-to L2 self, and intended effort. With these four factors
and the measures of L2 achievement, Hiver and Al-Hoorie used SEM to test two
competing causal models of vision and L2 motivation, where intended effort was either
an antecedent or an outcome of motivation. Contrary to You et al. (2016), Hiver and
Al-Hoorie hypothesized intended effort to be an antecedent of the ideal L2 self and the
ought-to L2 self. In both competing models, vision (visual style) was considered a
predictor of motivation, which was the same as You et al. (2016). Results showed that
the model with intended effort as a predictor of motivation showed a better overall fit.
Although this was contrary to You et al.s model, Hiver and Al-Hoorie note that as their
dataset and analyses differed greatly from the initial study, their model cannot be used
to contradict You et al.s model and call for further replication of research on the
L2MSS.
In both studies (Hiver & Al-Hoorie, 2020b; You et al., 2016), the authors were
interested in the relationships between the L2MSS, vision, and intended effort. By using
SEM, they operationalized motivational constructs as latent variables, depicting
hypothesized causal relationships between latent constructs with unidirectional arrows.
However, in both studies, the authors note potential issues and limitations of using SEM
to model interactions between motivational constructs. One issue relates to the theo-
rized dynamic nature of the L2MSS and the multicausal relationships between moti-
vational constructs. Possible Selves Theory was originally proposed to have dynamic
qualities, whereby current and ideal selves are shaped by multiple ongoing processes
(Henry, 2014; Markus & Nurius, 1986). For example, Hiver and Al-Hoorie speculate
that once an L2 learner puts in the effort and engages in the L2 learning process, there
will be a dynamic interaction between motivation and task demands, leading to
continuous recalibration of that motivational construct(2020b, p. 86). One might
question the extent to which SEM can effectively model these dynamic interactions, as
SEM operationalizes motivational constructs as latent variables with a unidirectional
causal relationship. In fact, both studiesauthors acknowledge that a further limitation
of SEM is that it requires the researcher to specify the direction of the relationship
between latent constructs. SEM can only test the theoretical model that is selected by the
researcher, although equivalent or alternative models may likely exist. This issue was
illustrated by Hiver and Al-Hoories(2020b) two competing SEM models. As discussed
earlier, there has already been discussion of how the L2MSS could be conceptualized as
a complex system (Henry, 2014,2017) and manifest as an attractor state (Henry, 2017;
Hiver, 2014; Waninge et al., 2014). From a CDST perspective, causal relationships
between motivational constructs are not unidirectional, but reciprocal. To further
investigate the relationship between motivation and intended effort, Hiver and
Al-Hoorie (2020b) have encouraged researchers to consider using nonrecursive models
where causality is reciprocal. In this first example, we illustrate how network analysis
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can be used to model the L2MSS as a complex system, with hypothesized reciprocal
causation between motivational constructs with nodes at item level.
Network estimation and visualization
Figure 1 is a GGM of the L2MSS that we made with the dataset from Hiver and
Al-Hoories(2020b) study. In support of open science practices, they made their dataset
and analyses publicly available through the OSF website. To allow for ease of compar-
ison, we included the same variables in our network analysis as Hiver and Al-Hoories
SEM analyses, with the exception of visual style 2, which we explain in a later section.
The network model in Figure 1 has nodes at item level, to better explore the interre-
latedness of these motivational constructs, and the questionnaire items used to measure
them. Table 1 contains information about which items correspond to each node.
We chose GGM model selection (ggmModSelect function implemented in the
bootnet R-package; Epskamp et al., 2018a) as estimation method because of the large
size of the dataset. Model search works by setting edges to zero and using a stepwise
algorithm to continuously estimate the model until the optimal model is identified
(Epskamp, 2014). This technique uses Bayesian information criterion (BIC) obtained
through estimating the maximum likelihood of sparsity.
Figure 1. A network model of the L2MSS and L2 achievement.
Note: In this network model of the L2MSS, there are four motivational constructs: the ideal L2 self, the
ought-to L2 self, intended effort, and visual style. Each node represents a questionnaire item. Ought-to L2
self has been measured with six questionnaire items, and the other motivational constructs with five
questionnaire items. There are also two composite measures of L2 proficiency: L2_T1 (studentsmid-term
grades) and L2_T2 (studentsfinal grades).
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In their original analyses, Hiver and Al-Hoorie (2020b) tested for normality and
found that their data were not multivariate normal both in skewness and kurtosis. For
this reason, the network was estimated using Spearman correlations (Epskamp, 2014).
After estimating the model, we evaluated the stability of the network structure in
terms of edge-weight accuracy using bootstrapping (see Epskamp et al., 2018a for an
in-depth explanation of bootstrapping in psychological networks). We used 5,000
samples of the nonparametric bootstrap to assess the variability of the edge-weights.
This step should always be performed (Epskamp et al., 2018a) as any interpretation of
the network becomes limited if the network is unstable (Burger et al., 2022). The results
show a good overlap between the estimated model and the bootstrapped edge-weights,
indicating that the network of Figure 1 is stable. The results of the nonparametric
bootstrap can be viewed in the supplementary materials.
To assess the stability of the centrality coefficients, we again used bootstrapping. We
used the case-dropping bootstrap, specifically developed to this aim (Epskamp et al.,
2018a). The case-dropping bootstrap assesses the stability of the order of centrality in
subsets of the data, that is, after systematically dropping an increasing percentage of
participants from the dataset. The centrality stability for strengthcentrality was
Table 1. Legend of node labels
Node labels Items
L2 achievement
L2_T1 Mid-term grades
L2_T2 End of term grades
Ideal L2 self
IS1 I can imagine myself speaking English in the future with foreign friends at parties
IS2 I can imagine myself in the future giving an English speech successfully to the
public
IS3 I can imagine a situation in which I am doing business in foreigners by speaking
English
IS4 I can imagine myself speaking English in the future having a discussion with foreign
friends in English
IS5 I can imagine that in the future in a café with light music, a foreign friend and I will
be chatting in English casually over a cup of coffee
Ought-to L2 self
OS1 Studying English is important to me to gain the approval of my teachers
OS2 Studying English is important to me to gain the approval of my peers
OS3 Studying English is important to me to gain the approval of the society
OS4 I study English because close friends of mine think it is important
OS5 I consider learning English important because the people I respect think that I
should do it
OS6 My parents/family believe that I must study English to be an educated person
Visual style
VS1 I use color coding (e.g., highlighter pen) to help me as I learn
VS2 Charts, diagrams, and maps help me understand what someone says
VS3 When I listen to a teacher, I imagine pictures, numbers, or words
VS4 I highlight the text in different colors when I study English
VS5 I learn better by reading what the teacher writes on the board
Intended effort
IE1 I am prepared to expend a lot of effort in learning English
IE2 I find learning English really interesting
IE3 I would like to concentrate on studying English more than any other topic
IE4 Even if I failed in English learning, I would still learn English very hard
IE5 English would still be important to me in the future even if I failed in my English
course
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estimated on a sample of 5,000 bootstraps, which resulted in a correlation stability
coefficient (CS-coefficient) of 0.52 for the strengthcentrality. This is above the 0.5
(CS-coefficient) recommendation (Epskamp et al., 2018a), which is why we conclude
that the stability of node centrality in this network model is good. The results are
presented in the supplementary materials.
Based on the centrality indices (see Figure 2), ought-to L2 self 2 is the most central
component in the network model in Figure 1 in terms of node strength, followed by
intended effort 5. The questionnaire items that correspond to these components are
Studying English is important to me to gain the approval of my peersand English
would be still important to me in the future even if I failed in my English course.This
suggests that peer approval and perceived future importance of English play important
roles in L2 motivation, as they are the strongest direct relationships with other
motivational constructs in the system.
In addition to node strength, we also computed node centrality indices based on
closeness and betweenness. The closeness index indicates a short average distance of a
specific node to all other nodes(Hevey, 2018, p. 311). In the network model, the nodes
with the highest closeness are the five intended effort nodes. This is an interesting find
and indicates that intended effort may have an integral role in L2 motivation. Although
the role of central components is not yet fully understood, it is thought that central
nodes with high closeness are the most likely to both effect changes and be affected by
changes in the system (Hevey, 2018). The third measure of centrality, betweenness,
refers to how well one node connects other nodes together; nodes with high
Figure 2. Centrality plots for the L2MSS network.
Note: Centrality plots for the network model of the L2MSS. Centrality measures are shown as standardized
z-scores. The raw centrality indices can be found in the online Supplementary Materials.
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betweenness lie on the shortest path between pairs of nodes. As shown in Figure 2, the
node with the highest betweenness is intended effort 5, followed by ought-to L2 self
5 and ideal L2 self 1.
Interpreting the network model
Both You et al. (2016) and Hiver and Al-Hoorie (2020b) used SEM to evaluate the
relationships between the L2MSS, vision, and intended effort. The network model
contains four motivational constructs (ideal L2 self, ought-to L2 self, intended effort,
visual style) and the two measures of L2 achievement (midterm grades and final exam
grades). We can see the wider interconnectedness of components in the system, with
multiple interactions across different motivational constructs.
One of the first things we notice when looking at this network model is that,
although the motivational constructs are interrelated, there are only a few weak
edges between any of the motivational constructs and the two measures of L2
proficiency. For example, final grades have a weak partial correlation with ideal self
1 (0.10) and midterm grades have a weak negative partial correlation with ought-to
self 4 (0.09). The network analysis results are consistent with Hiver and Al-Hoories
(2020b) study, where the ideal L2 self was only a weak predictor of L2 achievement
(accounting for less than 1% of the variance), and the ought-to L2 self had almost no
predictive value.
Visual style
The visual style scale consists of five questionnaire items. As can be seen in Figure 1,
although each of the five measures of visual style are grouped together, the nodes are
not as closely grouped together compared to nodes measuring other constructs. In
Hiver and Al-Hoories SEM analyses, they excluded visual style 2 to improve
convergent validity, and also note that this scale had the lowest reliability in You
et al.s(2016) study. Removing items is typical with latent variable approaches, where
researchers drop variables that do not load onto factors or if there are cross-loadings
(Fried, 2020). With network analysis however, Fried (2020, p. 21) has pointed out that
items that load onto two factors simultaneously make for the potentially most
interesting items because they may build causal bridges between two communities
of items.Because of this, we decided to include visual style 2 in the network analysis.
The network model shows that visual style 2 is linked to three other nodes measuring
visual style, and also has a weak partial correlation with one measure of L2 achieve-
ment, on measure of the ideal L2 self, and one measure of intended effort. While visual
style 2 was left out of the SEM analyses, results of the network analysis tentatively
suggest that this questionnaire item may function as a bridge node between other
motivational constructs. In both You et al. (2016) and Hiver and Al-Hoories(2020b)
SEM analyses, they treated visual style as a predictor of the ideal L2 self and the ought-
to L2 self. The network model is an undirected graph, so our analyses cannot provide
additional insights into whether visual style is a predictor or outcome of motivation.
What the network analysis does provide, is a more complex pattern of relationships
between visual style and other system components than the original analyses. The
nodes that measure visual style are partially correlated with components from all
other motivational constructs in the network, as well as one measure of language
achievement.
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Intended effort
Besides the role of vision, You et al. and Hiver and Al-Hoorie were also interested in
the direction of the relationship between intended effort and the L2MSS. Hiver and
Al-Hoories analyses of two competing SEM models showed that intended effort was
a better predictor of the ideal L2 self and ought-to L2 self than an outcome. Previous
research has provided empirical evidence for reciprocal causal relationships between
motivation and academic achievement (Vu et al., 2021). The network model shows
that components of intended effort are related to components of all other subsystems,
as well as L2 achievement, indicating a complex pattern of relationships. The results
of the centrality indices highlight the overall importance of intended effort in L2
motivation, as the five intended effort variables have the highest closeness index in the
network. Overall, intended effort 5 emerges as the most central component of the
network. This item refers to the statement English would be still important to me in
the future even if I failed in my English course.Intended effort 5 also has the highest
centrality in terms of betweenness, and the second highest in terms of closeness and
strength. The question surrounding the role of central components will be further
discussed later in this article.
Example 2
The second example illustrates how network analysis can be used to explore the
relationships between multiple individual differences using the dataset from Dąbrows-
kas(2018) study Experience, aptitude and individual differences in native language
ultimate attainment. The dataset is publicly available online using the IRIS Database.
The network model made from Dąbrowskas(2018) dataset presents a different level of
analysis from the previous example. In contrast to the network model in example 1,
where each node represents a single questionnaire item, each node in the network
model in Figure 3 represents a distinct variable measured by aggregated task scores. In
the original study, Dąbrowska tested the assumption that adult native speakers tend to
converge on the same grammar. She addressed this question by considering two
opposing approaches to language acquisition: the usage-based perspective and the
modular perspective. From a usage-based perspective, language abilities are thought to
emerge out of interactions between general cognitive mechanisms and exposure to
linguistic input (Ellis & Wulff, 2018). From this perspective, causal mechanisms
interact iteratively to produce what appears to be structure(Bybee & Beckner, 2009,
p. 23). A usage-based approach is thus aligned with CDST, where linguistic knowledge
emerges as a network of interrelated and interacting components. In contrast, from a
modular perspective, language abilities are thought to stem from an innate universal
grammar, whereby different types of language knowledge rely on autonomous modules
within the mind (Tan & Shojamanesh, 2019).
Dąbrowska (2018) discusses the plausibility of these two theories in connection with
analyses of a dataset of 90 native English speakersperformance on different linguistic
and nonlinguistic tasks. She first analyzed the amount of individual variation on six
tasks that measured grammatical comprehension, receptive vocabulary, collocations,
nonverbal IQ, language analytic ability, and print exposure. Full details regarding which
tests were used to measure each construct can be found in the original study. Dąb-
rowska then conducted Pearson correlations to explore interactions between the six
aforementioned tasks as well as education (measured by number of years spent in
education). This revealed several significant correlations between the measures of
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language knowledge as well as between other variables. To determine potential causes
of individual differences in linguistic knowledge, Dąbrowska then conducted regression
analyses with the four predictor variables (nonverbal IQ, language analytic ability, print
exposure, and education) on each of the three measures of language knowledge.
Overall, results showed that nonverbal IQ was strongly related to grammar and
vocabulary, but not to collocations. Language analytic ability was also significantly
related to grammar and vocabulary, as well as several other variables. Print exposure
contributed more to vocabulary and collocations than to grammar, and education only
weakly predicted each measure of language knowledge. Based on the significant
correlations between the three measures of language knowledge and the fact that the
same nonlinguistic variables predicted different areas of language knowledge, Dąb-
rowska concluded that these findings support a usage-based approach.
Network estimation and visualization
Figure 3 is a GGM of partial correlations that includes the same seven variables used in
Dąbrowskas analyses. The network model was estimated using the least absolute
shrinkage and selection operator(LASSO), which is considered an appropriate
estimation method for smaller datasets (Epskamp et al., 2018a; Hevey, 2018). The
LASSO technique results in a sparser network, using only a relatively small number of
edges to explain the covariance in structure (Epskamp et al., 2018b). This makes the
estimated model more interpretable and accurate, as very small edges are removed from
Figure 3. A network model of individual differences in native language ultimate attainment.
Note: The nodes in this network are composite scores representing three measures of language proficiency
and four individual differences. The three proficiency measures are receptive vocabulary, collocations, and
grammatical comprehension. The four individual differences are nonverbal IQ, print exposure, language
analytic ability, and years of education.
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the estimated network (Epskamp et al., 2018a; Hevey, 2018). The LASSO applies a
regularization technique that is controlled by a tuning parameter. The tuning param-
eter was selected by minimizing the Extended Bayesian Information Criterion (EBIC),
for which we used the default setting of 0.5.
To assess network stability, we used a nonparametric bootstrap of 5,000 samples.
Bootstrapping results can be found in the supplementary materials. The bootstraps
show wide 95% confidence intervals, meaning that the estimated network structure is
not very stable and the found links should be interpreted with care. As such, our
discussion and interpretation of this network model is tentative, and a larger sample
size is needed to draw any strong conclusions. We did not compute centrality indices
for this dataset because the aim of this network analysis was to explore overall patterns
of relationships between variables, and also given the small number of variables in this
model.
Interpreting the network model
The network model in Figure 3 illustrates a complex system of interdependent
relationships between linguistic and nonlinguistic variables. Each node in the net-
work model in Figure 3 represents a composite variable. For example, the node
collocationsconsists of 40 multiple choice items on the Words That Go Together
test, and the node print exposureconsists of 130 items on the Author Recognition
Test. From a CDST perspective, the network model in Figure 3 provides a visualiza-
tion of how different aspects of language knowledge are related to both internal
resources (nonverbal IQ and language analytic ability) and external resources (print
exposure and education). When comparing to the results of the regression analyses in
the original study, the network model reflects the same overall patterns of relation-
ships between individual differences in language knowledge. For example, nonverbal
IQ is more strongly associated with grammar and vocabulary than with collocations,
and print exposure is more strongly associated with vocabulary and collocations than
with grammar. The fact that both analyses reveal the same overall patterns is not
surprising because partial correlations and multiple regression coefficients both
estimate of the strength of relationships between variables while controlling for the
effects of other measured variables (Hevey, 2018). The key difference is that regres-
sion analysis imposes unidirectional causal relationships between specific variables
selected by the researcher, whereas with network analysis there are no assumptions
regarding the direction of the relationships.
There are some subtle differences between the results of the network analyses and
Dąbrowskas analyses. For instance, whereas Dąbrowska found that language analytic
ability was significantly related to both grammar and vocabulary, the network
analysis shows that language analytic ability is only very weakly associated with
vocabulary. In the network model, the relationship between language analytic ability
and vocabulary knowledge appears to be altered by print exposure and nonverbal
IQ. Similarly, while Dąbrowskas analyses showed that education weakly predicted
each measure of language knowledge, the network analysis shows that the relation-
ship between education and language knowledge becomes weaker after controlling
for the effects of print exposure, nonverbal IQ, and language analytic ability. It is also
interesting to note that in the network model, the negative relationship between print
exposure and nonverbal IQ becomes stronger after controlling for other variables.
Another minor difference is that network analysis revealed a negative relationship
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between nonverbal IQ and print exposure whereas in Dąbrowskasanalysesthis
relationship was positive. The reason for this difference is that Dąbrowska trans-
formed the raw IQ scores into percentages, while we opted to conduct analyses with
the raw IQ scores. To confirm this, we conducted Pearson correlations between print
exposure and both the raw and transformed IQ scores that showed that print
exposure had a weak negative correlation with raw IQ scores (r(88) =.03, p =
.719) and a weak positive correlation with transformed IQ scores (r(88) =.08, p =
.440). However as these are very small differences, they cannot be interpreted as
meaningful. These slight differences revealed by the network analysis could be due to
the fact that we included all seven variables in the network analysis, whereas
Dąbrowska conducted three separate regression analyses for each measure of lan-
guage knowledge. By taking a more holistic approach including all variables within
the same analysis, additional patterns of relationships were revealed. This then raises
the question of how many variables should be included when working from a CDST
perspective.
Adding age to the network model
To explore this idea, we expanded on the original study by adding the variable age
to the network model. Participantsages were contained within the original dataset
that is available online, but Dąbrowska did not include this variable in her analyses. It
seemed particularly interesting to include this variable because Dąbrowska used the
dataset to evaluate the usage-based approach and the modular approach to language
acquisition. Age is an indirect measure of language experience. With first language
development, it is logical to assume that theolderapersonis,themoreexposureto
linguistic input they have. Thus, from a usage-based perspective, we might hypoth-
esize age to be significantly related to a number of other variables, including
measures of language knowledge. The 90 participants in Dąbrowskas study varied
greatly in age, with a range of 17 to 65 and a mean age of 38. The network model in
Figure 4 is a GGM of partial correlations between eight variables (the seven variables
from the original analyses plus age). The model was made following the same
procedures described for the network model in Figure 3. Similarly to the model
without age, the bootstraps show wide 95% confidence intervals, meaning that the
estimated network structure is also not very stable and the found links should be
interpreted with care.
The network model in Figure 4 shows that age is partially correlated with all other
variables. Out of the three measures of language knowledge (grammar, vocabulary, and
collocations), age is most strongly linked to vocabulary (0.35), which is the strongest
positive edge in the network.
1
This is in line with previous studies which have shown
that vocabulary is typically the only aspect of language knowledge that does not tend to
decline with age (Reifegerste, 2021). As could be expected, age is also related to print
exposure. Age has a negative association with nonverbal IQ and language analytic
ability, which is consistent with previous research on cognitive decline and aging
(Reifegerste, 2021). There is also a negative relationship between age and education,
1
We conducted the bootstrapped difference test to check whether the edges in the network significantly
differ from each other, in the supplementary materials. The edge Age-Vocabulary is significantly stronger
than the edge Age-Grammar, but not from the edge Age-Collocations knowledge. This means that the
difference between these edges has to be interpreted with care.
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which is logical considering that the percentage of university attendance has increased
over the years. Overall, the considerable effect that age has on the system provides
tentative support for the usage-based approach. Using network analysis, we can
visualize individual differences in language abilities as a complex system. While we
cannot draw conclusions about emergent processes from cross-sectional data, based on
this network model, we could speculate that vocabulary knowledge emerges out of
interactions between cognitive abilities (nonverbal IQ) and other language experience
(print exposure) throughout the lifespan (age).
In addition, controlling for age alters the partial correlations between other nodes.
For example, in the model that includes age, vocabulary knowledge has a weak positive
relationship with grammar (0.11) and language analytic ability (0.12), whereas in the
model without age, these relationships are weaker (0.06 and 0.05). This indicates that
age is a moderating variable. When controlling for age, the edge weight between
grammar and language analytic ability is stronger (more positive) because age has
negative partial correlations with grammar and language analytic ability. In a similar
way, age also moderates the relationship between IQ and print exposure; these variables
have an edge weight of 0.43 without age, and 0.26 when age is added to the model. In
this case, the edge weight between IQ and print exposure is weaker (less positive) when
controlling for age because age has negative partial correlations with IQ and print
exposure.
Although the network models estimated in example 2are not stable, and a larger
sample size is necessary to draw any firm conclusions, our examples serve to illustrate
how network analysis can be used to model multiple individual differences in language
learning from a CDST perspective. The network analyses support the same conclusions
Figure 4. A network model of individual differences in language knowledge, including age.
Note: In addition to the same variables as the network model in Figure 3, this model also has the variable
age. Blue edges denote positive partial correlations and red edges denote negative partial correlations.
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as the original study (Dąbrowska, 2018), but rather than analyzing unidirectional
relationships between individual difference constructs, the undirected network models
in example two depict hypothesized multicausal relationships between variables. By
estimating partial correlations between all variables, network analyses also reveal a
more complex network of relationships between variables than the original studys
regression analyses, offering additional insights into the data.
Discussion
We have provided two examples of how network analysis can be used to model complex
systems from a relation-intensive perspective. These examples serve to illustrate how a
network approach can offer new insights into which components form a system and the
nature of the relationships between components. Network analysis is conceptually
aligned with CDST, enabling us to model hypothesized multicausal relationships
between variables. We have shown how network analysis of cross-sectional data can
be used to model individual difference constructs as complex systems, viewing the
network as a snapshot of (part of ) a system in time. We illustrated this in example 1with
nodes at item level, to analyze motivational constructs on a micro level, and in example
2with node at composite level, to analyze the relationships between individual
differences and language knowledge on a more macro level. In both examples, network
analysis complements the original analyses by providing a more intricate pattern of
relationships between system components, and deeper understanding into the variables
of interest.
Besides the examples of psychological network analysis in this article, there are other
applications of network analysis that could also be beneficial to SLA researchers, such as
the network comparison test and dynamic network analysis. It is also important to
acknowledge that psychological network analysis is still a relatively new statistical
technique, and there are some unanswered questions regarding how certain aspects of
CDST fit with network analysis, for example regarding the question of how many
variables to include and the role of central components. In the following section, we
discuss some of these questions and highlight additional applications of network
analysis that could be applied to SLA research.
The network comparison test
The network comparison test is an application of network analysis that can be used to
compare group differences. The network comparison test statistically compares the
networks of two (or more) groups, such as in terms of node centrality and global
strength (van Borkulo et al., 2022). Networks can also be compared visually, which is
typicallydonebyconstrainingthelayout of the two models for ease of visual
comparison. Blanco et al. (2020) used a network comparison test to compare the
effects of two different interventions on treating depression. One group of patients
(n=45) received a 10-week Positive Psychology Intervention (PPI) while another
group (n=48) received a 10-week Cognitive-Behavioral Therapy (CBT) program.
Both groups completed clinical assessments of depression symptoms before and after
the intervention treatments. Blanco et al. (2020) used this data to create two network
models to compare before and after treatment. Results of the network comparison
test showed that only the PPI group showed significant changes in several edge
weights and global strength after intervention. In SLA research, the network
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comparison test could be used to statistically compare the networks of learners at
different proficiency levels, at different time points, or across learning conditions.
Both You et al. (2016) and Hiver and Al-Hoorie (2020b) conducted additional
analyses to compare the roles of vision and intended effort across male and female
L2 learners. This comparison could be also done using a network comparison test. As
such, the network comparison test could strengthen CDST inspired research by
providing a means for hypothesis testing and generalizations. Comparing networks
across groups could also help to ascertain the phenomenological validity of concep-
tualizing abstract psychological phenomena as complex systems. In addition, the
network comparison test could provide insight into how to influence systems
behavior, as illustrated by Blanco et al. (2020), and could a useful tool for SLA
researchers considering complex interventions (Hiver et al., 2022).
Dynamic network analysis
In examples 1and 2, we took a relation-intensive CDST approach by estimating GGMs
of cross-sectional data. The GGM can also be used with time-intensive and time-
relation intensive research designs, for single subjects and group data, respectively.
Dynamic network analysis requires intensive repeated measurements of variables, such
as with a time-series or panel design, typically obtained through Experience Sampling
Method (ESM), whereby participants provide self-reports at regular intervals during
the day (Bringmann et al., 2013). With single-subject data, auto regressive
(AR) modeling can model time dynamics within an individual by regressing one
variable on a previous measurement of the same variable (called a lagged variable).
The vector auto regressive (VAR) model is the multivariate extension of the AR model,
where a variable is regressed on all the lagged variables in the dynamic system(van
Bork et al., 2018, p. 18). The VAR model has two extensions: graphical VAR and
multilevel VAR. For single-subject data, graphical VAR can be used to create both
temporal and contemporaneous networks using the GGM (Epskamp et al., 2018).
Temporal networks have directed edges and show how the state of variables at one time
point influence the state of variables at the next time point. A contemporaneous
network model shows how variables predict each other at the same measurement
occasion, after accounting for temporal effects (Epskamp et al., 2018b), similarly to
GAMMs and LCGMs. Multilevel VAR modeling can be used to model both within-
group and between-group variance over time (Bringmann et al., 2013). For example,
Bringmann et al. (2013) combined VAR and multilevel VAR to follow 129 participants
changes in depressive symptoms during a treatment intervention, modeling time
dynamics at the individual and group level.
In the field of clinical psychology, researchers are exploring how dynamic network
modeling could provide insight into how people develop disorders over time, with the
aim of using this knowledge to target group and/or individual treatment interventions
(Bringmann et al., 2013; David et al., 2018; van Bork et al., 2018). Dynamic network
analysis could also prove to be a useful methodology for CDST researchers in applied
linguistics, and a few SLA studies have used ESM. For example, Waninge et al. (2014)
micro-mapped the motivational dynamics of four learners during their language
lessons. They took measurements at 5-minute intervals throughout lessons, resulting
in 10 observations per class. Similarly, Khajavy et al. (2021) used ESM to examine the
dynamic relationships between willingness to communicate (WTC), anxiety, and
enjoyment of 38 students throughout six language lessons. Students indicated their
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level of WTC, anxiety, and enjoyment on a scale of 1 to 10 at 5-minute intervals,
resulting in 10 observations per class. Gregersen et al. (2020) also used ESM to explore
the dynamics of language teacher well-being, where teachers used an app to respond to
a short survey 10 times a day for 7 days. Although smartphone technology has the
potential to use ESM more easily than in the past (Arndt et al., 2021; Gregersen et al.,
2020), it is still extremely challenging in most applied linguistics research settings to
obtain a large enough number of observations to conduct a dynamic network analysis.
For example, in the study by Bringmann et al. (2013), participants recorded depressive
symptoms 10 times a day for 12 days, which resulted in a total of 120 observations per
participant.
Nonlinearity
GGMs and VAR models are estimated based on assumptions of multivariate normality
that assume linear relationships between variables (Epskamp et al., 2018b). As such,
these models may not present a fully accurate view of the data if the relationships
between variables are nonlinear. For cross-sectional data, the Ising model is a nonlinear
model used for binary variables (Finnemann et al., 2021), but nonlinear models for
continuous variables have not yet been developed. For longitudinal data, while VAR
models fit linear effects, new types of network analysis have been developed that can
also capture nonlinear relationships between variables (Haslbeck et al., 2021). The
findings from several CDST studies with a time element have shown that language
development, and its relationship with individual differences, is nonlinear (Fogal, 2022;
Pfenninger, 2020; Piniel & Czisér, 2014). This is why some CDST research designs that
include a time element are using techniques such as GAMM instead of LGCM, as
GAMM can handle nonlinearity (Pfenninger, 2020). Researchers in the field of network
psychometrics have recently combined the VAR model with a Generalized Additive
Model (GAM) framework, to estimate time-varying VAR models (Haslbeck et al.,
2021). The field of network psychometrics is developing rapidly and is likely to produce
other useful techniques in the future that could further enrich our methodological
toolbox.
Latent network analysis
In the first example of the L2MSS, we compared Hiver and Al-Hoories(2020b) SEM
with our network analysis. For the past 100 years, psychological constructs have been
studied using latent variable approaches, which assume that observed variables corre-
late because they reflect the same underlying construct (van Bork et al., 2019). Network
analysis has been put forward as an alternative to latent variable approaches. From a
network perspective, correlations between observed variables may reflect mutual
interaction between psychological processes (van der Maas et al., 2006). Although
these two approaches have different competing causal explanations for the covariance
between observed variables, both create models for variance-covariance matrices and
are thus statistically equivalent (van Bork et al., 2019; van der Maas et al., 2006). Because
of this statistical equivalence, researchers have explored the idea that combining these
two approaches could be complementary, resulting in latent network analysis (Golino
& Epskamp, 2017; Guyon et al., 2017). Conceptually, a combined approach assumes
that manifestations of psychological attributes have a common cause (latent variables)
and that these latent variables interact (as a complex system) (Guyon et al., 2017). For
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example, Epskamp et al. (2017) have used latent network modeling to explore the
structure of interdependent relationships between latent variables. An advantage of
latent network analysis is that due to the incorporation of factor-based statistical
techniques, it is possible to test model fit against data, which is a limitation of
psychological network analysis (Epskamp et al., 2017; van der Maas et al., 2017). It
can also be considered a useful way of exploring latent variables within a dataset because
clusters in the network can tell us about the factor structures present, without having to
impose the direction of the relationship like SEM (Golino & Epskamp, 2017). Latent
network analysis can be used with cross-sectional data as well as time-series and
panel data.
The role of central components
In example 1, we computed centrality indices for the network model of the L2MSS,
which showed that the intended effort nodes have the highest centrality. Researchers
from different fields have questioned whether central components have predictive
ability and can be used to target interventions. The role of central components has so far
provided insights into the dynamic processes of genetic networks, cortical networks,
and ecosystems (for a detailed description see Rodrigues, 2019). In the field of clinical
psychology, findings from few studies indicate that central components could be used
to target treatment interventions and make predictions about diagnoses. For example,
in clinical research on eating disorders, central components have been predictive of
treatment dropout (Lutz et al., 2018) and treatment outcomes (Elliott et al., 2020). The
idea behind using central nodes to target interventions is that these nodes are more
likely to have bigger effects (either directly or indirectly) on the rest of the system
compared to targeting a less central node (Rouquette et al., 2018). Nodes with high
closeness in particular are more likely to be affected by changes in other components of
the system and are also more likely to trigger change.
From the first network model example in Figure 1, intended effort had the highest
node centrality in terms of closeness, suggesting that intended effort plays a key role in
triggering the dynamic processes involved in L2 motivation. This fits with Hiver and
Al-Hoories(2020b, p. 86) idea that putting in the effort to learn a language results in
dynamic interaction between motivational constructs and task demands.
However, readers should note that the use of centrality indices in psychological
networks is much debated (Bringmann et al., 2019). Centrality indices stem from
social network analysis, whereby the relationship between components/nodes is
known; the connections between nodes are observable. In comparison, in psycho-
logical networks the relationship between nodes is not directly observed, but is
estimated, based on the strength of partial correlations between our measurements
of psychological constructs. Bringmann and colleagues (Bringmann et al., 2019)
have advised researchers to interpret centrality measures with care, especially
betweenness and closeness centrality, as they are difficult to interpret and are
often unstable.
The number of variables to include
Complex systems are characterized by dynamic interaction between multiple internal
and external subsystems (de Bot et al., 2007; Larsen-Freeman & Cameron, 2008).
However, given the theoretical and practical impossibilities of analyzing the complete
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interconnected of a whole system, CDST researchers have to find a balance between
oversimplification and undersimplification. Larsen-Freeman et al. (2011) have pointed
out that a main methodological concern for CDST researchers is drawing boundaries
and defining what we conceptualize to be a functional whole.Yet, when conducting
network analysis, Hevey (2018, p. 307) has reasoned that it is critically important to
measure such potential confounding variables to ensure that their effects are controlled
for.The network model of Dąbrowskas(2018) dataset in example 2that includes age
illustrates Heveys reasoning, as age moderates the relationships between other system
components. It is highly likely that there are also other confounding variables that have
been omitted from the model, such as socioeconomic status, L2 knowledge and
experience, gender, and other cognitive abilities. As with other types of modeling,
adding further variables to the network model could have both predictable and
unpredictable effects on the rest of the system. Yet from a CDST perspective, it is
theoretically impossible to measure every component of a system. What network
analysis can do, is capture at least part of a system. Thus, while we acknowledge the
potential of a network approach to SLA and individual differences, it is important to be
mindful of its limitations.
Generalizability
Generalizability is another debated topic in CDST research. Several researchers have
pointed out the lack of generalizability of CDST studies and the lack of practical
implications that CDST can currently offer to the field of applied linguistics (Hiver
et al., 2022; Palloti, 2022). Generalizability is a complex topic and is related to the
distinction between idiographic and nomothetic methodological approaches. Idio-
graphic approaches focus on the individual level with within-subject designs, analyzing
intraindividual differences (Hamaker, 2012). Idiographic approaches use longitudinal
data and process-focused analyses. In contrast, nomothetic approaches focus on the
group level with between-subject designs, analyzing interindividual differences
(Hamaker, 2012). Nomothetic approaches use cross-sectional data and product-
focused analyses.
The majority of CDST studies to date have used idiographic approaches (Hiver et al.,
2022) because it is difficult to generalize from cross-sectional models to individual
dynamics. This concept is known as the ergodicity problem: The idea that group
statistics cannot be generalized to the individual and vice-versa (Lowie & Verspoor,
2019). As Molenaar (2004, p. 225) has pointed out, only under very strict conditions
which are hardly obtained in real psychological processescan a generalization be
made from a structure of interindividual variation to the analogous structure of
intraindividual variation.However, this does not mean that idiographic and nomo-
thetic approaches are in competition (Salvatore & Valsiner, 2010). In fact, they can be
viewed as complementary, or two sides of the same coin (Grice, 2004). When discussing
the idiographic-nomothetic debate in relation to research on personality, Grice (2004)
argued that:
Establishing the uniqueness of some persons developmental history, attitudes,
thoughts, behaviors etc., would require the negation of nomothetic principles.
Conversely, establishing the validity of a nomothetic principle that holds for all
people would require the study of individual persons, not simply aggregates of
Network analysis for modeling complex systems in SLA research 25
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persons. A true study of personality is therefore necessarily idiographic and
nomothetic. (p. 205)
Lowie and Verspoor (2019) have illustrated this point in relation to SLA, by investi-
gating the role of motivation and aptitude in both a group study and in 22 longitudinal
case studies. Their analyses showed that while learners showed different intraindividual
learning trajectories over time, there were overall similarities between learners in terms
of motivation and aptitude.
While some have argued that the idiographic approach undermines generalization
(Palloti, 2022; Spencer & Schönen, 2003), others have argued that idiography is a way to
pursue generalized knowledge (Salvatore & Valsiner, 2010). As Salvatore and Valsiner
(2010) have claimed, idiography is the pursuit of nomothetic knowledge through the
singularity of the psychological and social phenomena[emphasis in original] (p. 820). It
is also important to note that nomothetic refers to what can be generalized across a
sample population (e.g., from aggregated cross-sectional data), not what can be taken as
a general law across all populations (Hamaker, 2012). Hence, as with any other cross-
sectional data analysis, results of network analysis can only tell us about the population
from which the data was sampled and cannot be taken as a general law across all
populations or all individuals.
That said, a network approach offers a structural perspective that is currently
missing from CDST research in the field of SLA and enables us to expand our research
agenda beyond idiographic approaches, time-intensive approaches (Hiver et al., 2022).
Taking steps toward generalizable findings, network analysis provides a means to
quantitatively analyze the relationships between multiple variables and assess the
relative importance of each variable within the system. Compared to other statistical
techniques such as SEM, an advantage of network analysis is that it does not require a
priori assumptions about unidirectional causal relations, but instead it allows for
(hypothesized) bidirectional interactions between variables. As previously mentioned,
other applications of network analysis such as the network comparison test make it
possible for SLA researchers to test hypotheses and assess the extent to which systems
can be generalized across different learner populations. Although network analysis is
still relatively new, some researchers in clinical psychology have set out to examine its
methodological validity and to determine the most appropriate metrics for assessing
similarities between samples (Borsboom et al., 2017; Funkhouser et al., 2020).
Researchers have also begun to assess the extent to which network analytic tools can
inform the design of intervention studies. For example, Henry et al. (2020, p. 2) have
developed a statistical testing procedure to assess the efficacy of an intervention,
determining if the dynamical systems of different people have the same optimal
intervention studies.
Conclusion
In this article we provided a brief overview of research methods used by SLA researchers
working within a CDST paradigm. We put forward network analysis as a way to model
complex systems from a relation-intensive perspective and provided two examples of
how to apply network analysis to two different datasets. In the first example we
estimated a network model of L2 motivation, which provided a more fine-tuned picture
of the potential relationships between motivational constructs compared to the original
SEM analyses. In the second example we created a network model of individual
differences in native language knowledge, showing how network analysis can model
26 Lani Freeborn et al.
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the interconnectedness of individual difference constructs and different aspects of
language knowledge.
While CDST researchers have made considerable advances in describing language
development and changes in individual differences over time, the potential of relation-
intensive approaches has not yet been explored. Through our two examples of network
models, we hope to have illustrated that cross-sectional data does have a place in CDST
research, and that network analysis is a useful technique to add to the CDST toolbox.
Supplementary Materials. To view supplementary material for this article, please visit http://doi.org/
10.1017/S0272263122000407.
Acknowledgments. We would like to thank Han van der Maas, Wander Lowie, and the three anonymous
reviewers for their invaluable feedback and suggestions on an earlier draft of this manuscript.
Data Availability Statement. The experiment in this article earned an Open Materials badge for trans-
parent practices. The materials are available at https://osf.io/hjcvz/
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