Deconstructing the construct: A network perspective on psychological phenomena

Abstract and Figures

In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like 'depression' is not seen as a latent variable that underlies symptoms like 'lack of sleep' or 'fatigue', and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep -> fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real.
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Deconstructing the construct: A network perspective
on psychological phenomena
Verena D. Schmittmann, Angélique O.J. Cramer, Lourens J. Waldorp, Sacha Epskamp,
Rogier A. Kievit, Denny Borsboom
Department of Psychology, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands
Psychological construct
Dynamical systems
Latent variable model
Formative model
Reective model
In psychological measurement, two interpretations of measurement systems have been
developed: the reective interpretation, in which the measured attribute is conceptualized
as the common cause of the observables, and the formative interpretation, in which the
measured attribute is seen as the common effect of the observables. We advocate a third
interpretation, in which attributes are conceptualized as systems of causally coupled
(observable) variables. In such a view, a construct like depressionis not seen as a latent
variable that underlies symptoms like lack of sleepor fatigue, and neither as a composite
constructed out of these symptoms, but as a system of causal relations between the
symptoms themselves (e.g., lack of sleep /fatigue, etc.). We discuss methodological
strategies to investigate such systems as well as theoretical consequences that bear on the
question in which sense such a construct could be interpreted as real.
Ó2011 Elsevier Ltd. All rights reserved.
Current theorizing and research in psychology is
dominated by two conceptualizations of the relationship
between psychological attributes (e.g., neuroticism) and
observable variables (e.g., worries about things going
wrong;Edwards & Bagozzi, 2000). In the rst of these
conceptualizations - the reective model - the attribute is
seen as the common cause of observed scores: neuroticism
causes worrying about things going wrong. In the second
conceptualization - the formative model - observed scores
dene or determine the attribute. The classic example of
such a model involves socio-economic status (SES), which
is viewed as the joint effect of variables like education, job,
salary and neighborhood.
In the present paper, we argue that the dichotomy of
reective/formative models does not exhaust the possibil-
ities that can be used to connect psychological attributes
and observable variables. We advocate an alternative
conceptualization, in which psychological attributes are
conceptualized as networks of directly related observables.
We discuss the possibilities that this addition to the
psychometric arsenal offers, the inferential techniques that
it allows for, and the consequences it has for the ontology of
psychopathological constructs and the epistemic status of
validation strategies.
The structure of this paper is as follows. First, we discuss
the ideas that underlie reective and formative models.
Second, we highlight important problems that the models
face. Third, we discuss the network approach. Fourth, we
touch on the ramications that this approach has in the
context of validity theory.
1. Reective and formative models
1.1. Re ective models
In reective models, observed indicators (e.g., item or
subtest scores) are modeled as a function of a common
latent variable (i.e., unobserved) and item-specic error
*Corresponding author.
E-mail address: (D. Borsboom).
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New Ideas in Psychology xxx (2011) 111
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logical phenomena, New Ideas in Psychology (2011), doi:10.1016/j.newideapsych.2011.02.007
variance. Reective models are commonly presented as
measurement modelsin modern test theory (Mellenbergh,
1994). Examples are the IRT models of Rasch (1960),
Birnbaum (1968) and Samejima (1969), common factor
models (Jöreskog, 1971; Lawley & Maxwell, 1963), latent
class models (Lazarsfeld, 1959), and latent prole models
(Bartholomew, 1987; McLachlan & Peel, 2000). In these
models, a latent variable is introduced to account for the
covariance between indicators. In a nontrivial sense, it
explains this covariance; in most models it is assumed that
conditioning on the latent variable makes the covariance
vanish (this is an implication of local independence). The
latent variable then functions analogously to an unobserved
common cause (Pearl, 2000).
This model matches the way in which many theorists in
psychology think about the relation between psychological
attributes and observations. For instance, in clinical
psychology, the conceptual idea of reective models is
often used as a blueprint for a realistic picture of a mental
disorder and its symptoms; that is, a mental disorder is
thought to be a reective construct that causes its observ-
able symptoms (e.g., depression causes fatigue and
thoughts of suicide). Likewise, personality variables like
neuroticism may be considered as the common cause of
observable neurotic behaviors, such as feeling jittery and
worrying about things going wrong. A set of indicators that
measure the observable consequences of such attributes
can then be used to make inferences about individual
differences in the underlying attributes: Alice has a higher
total score on a neuroticism questionnaire than John
because Alice is more neurotic than John. Fig. 1 presents
areective model for the items of the Big Five Neuroticism
scale of the Dutch NEO-PI-R (Hoekstra, Ormel, & De Fruyt,
2003), obtained from 500 rst-year Psychology students
(Dolan, Oort, Stoel, & Wicherts, 2009).
In reective models, indicators are regarded as
exchangeable save for measurement parameters like reli-
ability (Bollen, 1989). That is, although the indicators in
Fig. 1 may differ in their factor loadings (as indicated by the
thickness of arrows between N and items) and residual
variances, the relation they bear to neuroticism is qualita-
tively the same. Therefore, it does not make a qualitative
difference which neuroticism items one uses.
Second, the observed correlations between the indica-
tors are spurious in the reective model (as indicated by the
absence of edges between individual items). That is,
observed indicators should correlate; but they only do so
because they share a cause, namely neuroticism. Such
thinking makes perfect sense in the case of the reective
construct temperaturethat is measured with three
different thermometers: any correlation between the
thermometers is caused by the fact that they measure the
same thing, namely temperature. There is no direct causal
relation between the thermometers, in that the functioning
of thermometer A does not directly cause the temperature
reading on thermometer B. Thus, it is feasible to regard the
correlations between the thermometers as essentially
spurious, and this is indeed a sensible assumption of
models that aspire to capture the idea that the relation
between indicators and a particular construct is one of
1.2. Formative models
In formative models, possibly latent composite variables
are modeled as a function of indicators. Without residual
variance on the composite, models like principal compo-
nents analysis and clustering techniques serve to construct
an optimal composite out of observed indicators. However,
one can turn the composite into a latent composite if one
introduces residual variance on it. This happens, for
instance, if model parameters are chosen in a way that
optimizes a criterion variable. Conditional independence of
observed indicators given the composite variable is not
assumed. Rather, the independence relation is reversed: in
formative models, conditioning on the composite variable
induces covariance among the observables even if they
were unconditionally independent; hence the composite
variable functions analogously to a common effect (Pearl,
2000). Fig. 2 presents a formative model for the same
dataset as above.
Formative models differ from reective models in many
aspects. Indicators are not exchangeable because indicators
are hypothesized to capture different aspects of the
construct. In the neuroticism example, this implies that
feeling jitteryand worrying about things going wrong
represent different aspects of the construct neuroticism.
As such, removing an indicator potentially alters the
formative construct (Bollen & Lennox, 1991; Edwards &
Bagozzi, 2000). Also, contrary to reective models, there
is no a priori assumption about whether indicators of
a formative construct should correlate positively, nega-
tively or not at all.
2. Problems with the reective and formative
The status and nature of reective and formative
measurement models have been the source of various
discussions (Bagozzi, 2007; Bollen, 2007; Howell, Breivik, &
Wilcox, 2007a; Howell, Breivik, & Wilcox, 2007b; see also
a special issue of the Journal of Business Research, vol. 16,
issue 12, 2008). These have centered on desirable proper-
ties of indicators in formative and reective models (Bollen,
1984; Jarvis, Mackenzie, & Podsakoff, 2003; Wilcox, Howell,
& Breivik, 2008), the status of the error term in formative
models (Coltman, Devinney, Midgley, & Venaik, 2008;
Diamantopoulos, 2006; Edwards, 2011), model selection
(e.g. Baxter, 2009; Diamantopoulos & Siguaw, 2006; Jarvis
et al., 2003), referential (in)stability (e.g. Burt, 1976;
Franke, Preacher, & Rigdon, 2008), and (causal) interpre-
tations of the relation between indicators and latent vari-
ables (e.g. Blalock, 1964; Bollen & Lennox, 1991; Borsboom,
Mellenbergh, & Van Heerden, 2003; Diamantopoulos,
Rieer, & Roth, 2008; Edwards, 2011; Edwards & Bagozzi,
Such debates have often focused on the question
whether there are general reasons to favor one or the other
model. Both ends of the spectrum have been defended in
this respect, from questioning whether formative models
are ever appropriate on the one hand (e.g. Edwards, 2011;
Wilcox et al., 2008) to arguing that reective models are
adopted too readily, and that formative models may usually
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be more appropriate (e.g. Coltman et al., 2008;
Diamantopoulos et al., 2008; Jarvis et al., 2003). It seems
clear that these debates are far from settled. However, they
largely rage within the connes of a dichotomous choice
between formative and reective models. An intriguing
possibility is that more fundamental issues plague both
models and that these issues may be the source of at least
some of the debates discussed above.
For instance, although the resemblance of reective and
formative models to common cause and effect models is
indeed striking, in many instances of psychological testing
the causal relations suggested by these conceptualizations
are extremely problematic. Three particular problems
concern the role of time, the inability to articulate causal
relations between construct and observables in terms of
processes, and the subordinate treatment of relations
between observables.
2.1. The role of time
In most conceptions of causality, causes are required to
precede their effects in time. However, in psychometric
models like the reective and formative models, time is
generally not explicitly represented. That is, the dynamics of
the system are not explicated. It is therefore unclear whether
the latent variables relate to the observables in whatever
dynamical process generated the observations; in fact, it is
unclear whether the latent variables in question would
gure in a dynamic account at all (Borsboom et al., 2003;
Molenaar, 2004). This puts the causal interpretation of
latent variable models, as for instance ttedto data gathered
at a single time point, in a difcult position. For instance, in
areective model, are we supposed to consider the latent
differences to exist beforethe observed differences? Or, in
a formative model with a latent variable, is the latent vari-
able to be considered a consequenceof the observables?
Edwards and Bagozzi (2000) suggested that the causal
order between latent and observed variables is to be xed
through a thought experiment. In such a thought experi-
ment, the researcher considers whether it is more plausible
that, say, SES causes a raise in salary, or that a raise in salary
causes a higher SES. In this example, intuition tends to the
latter possibility; therefore SES should be modeled as
a formative construct. Thought experiments and intuition,
however, are dubious guides in determining causality; and
even in the simple SES case it could be questioned whether
47 48
Fig. 1. Areective model of Neuroticism items of the NEO-PI-R questionnaire. One underlying Neuroticism factor, depicted as a circle, determines the variation in
the items, depicted as rectangles. The thicker and darker green an arrow from the factor to an item, the higher the factor loading. Residual variances are not
represented. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
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they are adequate. From a temporal perspective, it is easy to
imagine that when John gets a raise that increases his
salary, he is able to buy a house in a better neighborhood;
this may increase his social status, as a result of which he
gets invited to fancy parties, where he becomes friendly
with the CEO of a big rm, who offers him a better job; the
outcome of which is that he gets a better salary, etc (i.e.,
a cycle). Given the plausibility of such cyclic developmental
trajectories it appears naive to consider the relation
between indicators like salary and theoretical variables like
SES to be one way trafc.
2.2. Inability to articulate processes
The identication of causal relations is arguably an
essential ingredient of the scientic enterprise. Typically,
after a causal relation is discovered, it is broken down into
constituent processes to illustrate the precise mechanism(s)
that realize(s) that relation. For instance, after the general
causal relation between smoking and lung cancer was
discovered through the standard routes of scientic
research researchers endeavored to nd out what processes
made the causal relation work. That is, they studied
processes that lead elements of the causal factor (constitu-
ents of tobacco smoke) to trigger mediational processes (tar
build up in lung cells) that result in the effect (lung cancer).
Such progress, or even the ambition to realize it, is mostly
lacking in psychological measurement. There is rarely
a progressive research program that identies how,say,
neuroticism causes someone to worry about things going
wrong or that identies the mechanisms that embody the
effect of general intelligence on IQ-scores. In fact, it is in
many cases quite hard to imagine how such effects could be
realized at all. A plausible cause of this problem is that most
constructs in psychology are not empirically identiable
apart from the measurement system under validation; no
one has been able to identify general intelligencein the
brain, for example. This hampers causal research; one may
imagine how hard it would be to investigate the effect of
smoking on lung cancer if the only measure of smoking were
the observation of lung cancer itself (i.e., when smoking
would be structurally latent).
47 48
Fig. 2. A formative model of Neuroticism items of the NEO-PI-R questionnaire. Arrows point from the items (rectangles) to the composite variable Neuroticism
(N). The thicker and darker green an arrow, the higher the contribution of the item to the composite score. Correlations between items are not represented. (For
interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
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2.3. Relations between observables
A third important issue in both reective and formative
models is the neglect or subordinate treatment of causal
relations between the observed indicators themselves. The
reective model relies on the assumption that no direct
causal relations exist between observables, and in the
formative model relations between observables that are
not accounted for by the latent variables are typically
treated as a nuisance. However, causal relations between
observables are likely to exist in many psychological
constructs. Moreover, such causal relations between
observables may be the reason why a phenomenon is
perceived or interpreted as an entity. Consider again SES.
This concept is commonly operationalized as summary
statistic over a group of variables that clearly do not
measure the same attribute. However, the system is so
coherent that researchers discuss topics such as "the rela-
tion between SES and intelligence" as if SES in fact did
denote a single measured attribute. Why is this? In our
view, the perception of SES as a single theoretical entity
may arise precisely because its constituents are causally
interrelated (education may inuence job choice, which
may constrain income, which in turn constrain the neigh-
borhood one chooses to live in).
Possibly, these three problems (among others) are at
least partly to blame for the intensity and breadth of the
debates centering on the use and interpretation of forma-
tive versus reective models. For this reason, a different
conceptualization of the relationship between indicators
may be appropriate.
3. The network perspective: constructs as dynamical
We propose that the variables that are typically taken to
be indicators of latent variables should be taken to be
autonomous causal entities in a network of dynamical
systems. Instead of positing a latent variable, one assumes
a network of directly related causal entities as a result of
which one avoids the three problems discussed above.
First, consider criteria for a major depressive episode
(MDE; American Psychiatric Association, 1994). These
criteria involve symptoms like lack of sleep,”“fatigue,and
concentration problems.In empirical research, scores on
these criteria are usually added to form a total score which
then functions as a measure of depression. This practice
ignores the likely presence of direct relations between
symptoms (e.g., lack of sleep /fatigue /concentration
problems). Similarly, in personality psychology, one nds
items that relate to the ability to get organized, to the
tendency of nishing once initiated projects, and to the
tendency to adopt a clear set of goals, which are taken to
reect the conscientiousnessfactor of the Big Five model
(Hoekstra, Ormel, & De Fruyt, 2003); in a dynamic scheme,
however, it appears to be reasonable that, say, having
a clear set of goals is an important determinant of getting
organized, and that getting organized facilitates nishing
projects. In all these cases, the indicatorsfunction auton-
omously in the system, rather than being passive indicators
of a common construct. These elements are connected
causally. We argue that such causal relations can be artic-
ulated in terms of processes. For some relations, these
processes are already known, for example homeostatic
processes that are involved in mediating the relation
between lack of sleepand fatigue(both symptoms of
depression: Achermann, 2004; Finelli, Baumann, Borbély, &
Achermann, 2000).
Fig. 3 gives a avor of what such a network of autono-
mous causal entities may look like. It represents Neuroti-
cism items as nodes, and the empirical correlations
between them as edges. After constructing such a network,
one can use techniques from network analysis to visualize
the system (Boccaletti, Latora, Moreno, Chavez & Hwang,
2006). For Fig. 3, we used an algorithm for the placement
of the nodes, which causes strongly correlated sets of items
to cluster together (Fruchterman & Reingold, 1991). For
example, item 6 (content relates to feelings of helplessness
and the wish for help), is located in the center of the gure,
because that item is strongly correlated with other
Neuroticism items.
From a network perspective, a construct is seen as
a network of variables. These variables are coupled in the
sense that they have dependent developmental pathways,
because a change in one variable causes a change in
another. Studying the construct means studying the
network; and such investigation would naturally focus on
a) network structure and b) network dynamics. The rela-
tion between observables and the construct should not be
interpreted as one of measurement, but as one of mereol-
ogy: the observables do not measure the construct, but are
part of it. Therefore, studying the relation between
observables and the construct means studying the function
of the observables in the network (e.g., which observables
are dominant in a network in terms of the strength of
relations with other observables?). In the following
sections, we will outline a few key concepts of a general
framework to investigate such networks of variables, and
discuss related methodological procedures.
3.1. Dynamical systems
A general framework to formalize and study the behavior
of a network of interconnected variables over time is
dynamical systems theory. In psychology, it has for instance
been applied to cognitive processes (Van Gelder, 1998), to
the construct of intelligence (Van der Maas et al., 2006), and
in the area of developmental psychology (Van der Maas &
Molenaar, 1992). Put briey, a dynamical system changes
its state (which is represented by a set of interrelated vari-
ables) according to equations that describe how the
previous state determines the present state, i.e., how
the variables inuence each other. Given an initial state, the
system will move through a trajectory of states over time.
Particularly relevant are attractor states of the system. If
the system is close to an attractor state, it will converge to
it, and remain in there in equilibrium. For example,
a depression network may have two attractor states:
a disordered, depressed state and a healthy state. A suf-
ciently large perturbation to the system, such as stressful
life events, may propel the person from the healthy state
towards the depressed state. In dynamical systems,
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parameters in the state-transition function determine the
number and the type of the equilibrium points. Therefore, if
we allow these parameters to change, the system may
show qualitative changes in its structure (e.g., a new
attractor emerges). Thus the line between depression and
health may be continuous for some people, while it is
categorical for others.
It would be extremely helpful if we possessed a theory
on state-transition-functions that govern psychological
systems like those of depression, but these are generally
unknown. Thus, we currently lack rules that determine
system behavior: who will get depressed and who will not,
and in what circumstances? Therefore, in order to study
networks of variables, simplifying assumptions will often
be made, and simulation methods can be helpful in inves-
tigating the plausibility of network properties. For instance,
Van der Maas et al. (2006) showed that a mutualistic model
of intelligence could produce the positive manifold of
intelligence tests; and Borsboom, Cramer, Schmittmann,
Epskamp & Waldorp (submitted for publication) were
able to show that a network of depression and anxiety
symptoms could plausibly reproduce comorbidity statistics
regarding these problems.
3.2. Causal inference
A problem with formal theories of dynamical systems is
that almost all of the known mathematical results concern
deterministic systems. In psychology, we typically deal with
probabilistic systems and data characterized by high levels
of noise. The difculty is then to derive, from a statistical
pattern, that changes in A are structurally related to changes
in B. One way to arrive at a viable method for inferring such
relationships between variables is to adopt the assumption
of linearity and normality. This assumption gives one access
to well-developed causal inference methods (Pearl, 2000;
Spirtes, Glymour, & Scheines, 2000). The construction of
causal systems through such inference methods is a statis-
tical route that may be used to get a grip on the architecture
of networks.
These methods typically work through the detection of
conditional independence relations. For instance, consider
the graph in Fig. 4(a), which is held to be a representation of
the causal relation in the population. Because in the pop-
ulation there is a connection from A to B through C, it is
likely that A and B are correlated in the sample. However, A
and B are not directly connected, and the explanation for
the observed correlation is that C is in between A and B, or
put differently, that C separates A and B. So, an easy test to
see whether A and B are directly causally connected is to
test for a correlation between A and B with C taken out, that
is, a partial correlation between A and B. If there is no partial
correlation between A and B, then this is taken as evidence
that there is no direct causal connection between A and B.
Under the assumptions of normality and acyclicity, this
result implies that A and B are independent conditional on C.
Knowing this, however, we still do not know the causal
relations between A and C and between C and B. In this
simple case with three variables, one can distinguish only
the graphs of Fig. 4(a)(c) versus Fig. 4(d). Conditioning on
C renders A and B independent in Fig. 4(a)(c), but yields
dependence between A and B in Fig. 4(d). In the latter case,
Fig. 3. A network of Neuroticism items of the NEO-PI-R questionnaire. Nodes represent items; edges the empirical correlation between items. Numbers in nodes
refer to the order of appearance in the questionnaire. A stronger correlation (positive green; negative red) results in a thicker and darker edge. (For interpretation
of the references to colour in this gure legend, the reader is referred to the web version of this article.) Graph generated with R-package qgraph, Available from
Graph generated with R-package qgraph (Epskamp, Cramer, Waldorp, Schmittmann, Borsboom, 2011)
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C is called a collider. A and B become correlated when C is
conditioned on, because knowing that, for instance, A is not
the cause of C increases the likelihood of B being the cause
of C. For instance, suppose that possible causes of a burglar
alarm (C) are a dog (A) or a burglar (B); then, given the
alarm, learning the presence of a dog decreases the likeli-
hood of a burglar being present (Neapolitan, 2004). To
distinguish between cases 4(a)-4(c), one needs additional
variables that either cause A or C (creating a collider at A or
C) to infer the causal direction between A and C. Generally,
variables must be included that could create colliders on
the set of variables of interest.
As an illustrative example of inferring causal relations as
described above, four participants reported ve constitu-
ents of depression (tiredness, concentration difculties,
self-content, sad mood, and pleasure in the current
activity) on a continuous scale hourly in the daytime on ve
consecutive days.
Fig. 5 shows the development of those constituents in
time for one participant. To these data, we tted seven
conrmatory models, in which we formalized different
assumptions about the causal relations between the vari-
ables. The best tting model (as judged by the AIC) was the
one shown in Fig. 6.
In general, the relations appear to
conform to common sense (e.g. if you are tired, you will
experience concentration difculties). The results are
illustrative only but can serve to demonstrate how to
investigate developmental trajectories of psychological
3.3. Network analysis
Once the network structure has been inferred in one of
the aforementioned ways, the network may be subject to
further analysis. Many network structure analysis methods
are implemented in free software such as the R-package
iGraph (Csárdi & Nepusz, 2006). Such methods allow one
for instance to examine whether a network has small-
world properties (e.g., high clustering of items within one
Big Five factor combined with relatively few separating
nodes between different Big Five factors; Watts & Strogatz,
1998). The resilience of networks to the removal of their
nodes could be of particular importance in psychopa-
thology, where the "removal" of a node might correspond
to the administration of medication that directly remedies
a symptom. In addition, one can analyze properties of
individual nodes, such as their centrality, that is, how
strongly a particular node is connected to all the other
nodes in a network. Studying network and node properties
may help to nd meaningful individual differences with
respect to the construct.
4. Constructs and their interrelations
The ontological status of psychological constructs as
well as the epistemic question of how to measure them has
been the topic of considerable controversy in psychology.
Borsboom, Mellenbergh, and Van Heerden (2004) have
argued that, in order to be plausible candidates for
measurement, constructs should in fact refer to structures
in reality; structures that play a causal role in determining
individual differences in test scores. Maraun and Peters
(2005) have suggested that the entire idea of constructs
being unobservable constituentsof natural realityis
intrinsically misguided. Cronbach and Meehl (1955)
famously espoused an agnostic position with respect to
this question, arguing that in some cases constructs would
refer to causally active latent variables, and in some cases
would merely be inductive summaries, much like the
composites typically formed in formative modeling.
In the network view, a construct label (e.g., the word
depression) does not refer to a latent variable or inductive
summary, but to a system. Since there is no latent variable
that requires causal relevance, no difcult questions con-
cerning its reality arise. Naturally, the components of the
system have to be capable of causal action, but this is typi-
cally not much of problem (e.g., returning to the depression
example, the reality and causal relevance of sleep loss can
hardly stand in doubt). Although psychological constructs
are the source of considerable conceptual headaches, indi-
vidual indicator variables (items, symptoms, response
times, etc.) often are tractable and associated with precisely
the kinds of progressive scientic research that applied
psychometrics typically lacks.
In our view, the referential connection between construct
labels and systems may therefore be a comparatively simple
affair. A short outline is as follows. Scientists use terms like
Fig. 4. Four possible relations between three ctitious random variables: A,
B, and C. (a): A causes C and C causes B. C is the middle node in a chain and
therefore, A and B are independent given C. (b): B causes C and C causes A. C
is the middle node in a chain and therefore, A and B are independent given
C. (c): C causes both A and B. C functions as the common cause of A and B
and therefore, A and B are independent given C. (d): A and B both cause C.
C is the middle node of a collider and therefore, A and B are dependent
given C.
Besides the best tting model, the other models, in ascending AIC
value, were: 1) tired /concentration AND self-content /sad mood /
pleasure (AIC: -1004885); 2) tired /concentration /self-content /
sad mood AND pleasure /self-content (AIC: -1004813); 3) tired /
concentration /self-content /sad mood AND self-content /pleasure
(AIC: -1004813); 4) tired /concentration /self-content /pleasure /
self-content (AIC: -1003106); tired /concentration /self-content )
sad mood AND self-content )pleasure (AIC: -997422).
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depressionto indicate a system that can be identied
through its constituents (e.g., symptoms) and systematic
connections between them: an architecture.Theterm
depressed, in contrast, indicates a certain state of the person
that is analyzable in terms of the networks dynamics, char-
acterized for instance by a attractor state in which a signicant
number of symptoms is present. This state may either be
a gradual property of the system on which we can place
a measure, or a discretely identiable state that we can name.
Which of these situations obtains depends on the network
architecture and the resulting dynamic properties, with the
interesting corollary that, say, levels of depression may be
measurable continua in some people but discrete states in
others. Networks are likely to differ over people (traits)and
over time (states) and a considerable psychometric adven-
ture may be entered by guring out exactly how to determine
these from observed data. Conceptually, however, little more is
needed to furnish the connection of a construct label to
Importantly, even though borders between networks
are likely to be fuzzy, this does not make the systems
themselves arbitrary, in the sense that collections of
formative measures are arbitrary; systems to which
constructs like depression refer can have denite charac-
terizations and are eligible for scientic inquiry. However
such characterizations are inherently complex, and scien-
tic theorizing ideally respects that complexity. In this
sense, our position is closely related to that of McGrath
(2005) who suggests that complexity is an intrinsic prop-
erty of many psychological constructs, and to that of
Kendler, Zachar, and Craver (2010) who argue the very
similar position that psychopathological constructs are best
construed as mechanistic property clusters.
4.1. Validity
If the question of validity is constructed as whether a set
of items really measuresa given attribute, the answer to
that question requiresan account of item response processes
in which that attribute plays a causal role (Borsboom,
Cramer, Kievit, Zand-Scholten, & Franic, 2009; Borsboom
et al., 2004). Such an account has not been forthcoming in
most areas where the test validity is at issue. This is unsur-
prising from a network perspective. The essence of
a network construct is not a common cause; rather, it resides
in the relations between its constituents. These relations
lead to a clustering of symptoms picked up both by formal
methods to detect clustering (e.g. factor analysis) and by
people (e.g. psychiatrists constructing the DSM). However, if
a construct like depression is a network, searching for the
common cause of its symptoms is like searching for actors
inside ones television set. For this reason, the question
whether symptoms really measuredepression, under-
stood causally, is probably moot, and causal processes that
0 1020304050
Fig. 5. The developmental trajectory of ve constituents of depression in one participant. The x-axis represents discrete time points while the y-axis is divided in
ve parts, from top to bottom: 1) pleasure in current activity, 2) sad mood, 3) self-content, 4) concentration difculties and 5) tiredness. For each part, the y-axis
represents the continuous scale on which the participants rated the constituents.
Fig. 6. The best tting conrmative time series model of the following ve constituents of depression: tiredness; concentration difculties (concentration); self-
content; sad mood; pleasure in current activity (activity).
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connect depression to item responses on a questionnaire
will not be found because they do not exist.
4.2. Relations with other constructs
Importantly, it should be noted that unless a network is
completely isolated an unlikely situation in psychology
construct labels like depressiondenote in an inherently
fuzzy sense. In particular, the distinction between different
traits or disorders or abilities is itself a matter of degree,
depending on the extent to which the networks are sepa-
rated. Networks that are not well separated are likely to
show entangled behavior that may often cause researchers
to wonder whether they are dealing with one or two
A highly interesting example in clinical psychology
where such a situation may arise is comorbidity, i.e., the
simultaneous satisfaction of symptoms that belong to
multiple disorders. In earlier work we have shown that
a network perspective on comorbidity is feasible (Cramer,
Waldorp, Van der Maas, & Borsboom, 2010) using the
example of the extremely high comorbidity between
depression and generalized anxiety. Depression and
anxiety each have unique symptoms, but also share
symptoms (e.g., fatigue and loss of concentration). Such
symptoms may function as bridge symptoms that transfer
symptom activation from one network to the other, like
a virus may spread from one community to another via
people who are in contact with both.
Where the dividing line between intertwined networks
lies is a question that has no sharp factual answer, even
though the optimal allocation of symptoms to disorders
may of considerable practical signicance. In accordance,
any theory on such a system will have to admit a multi-
plicity of exceptions arising from the fact that science
requires the isolated study of a system, and thus neglects
the fact that it is ordinarily situated in a larger network of
4.3. Causes and effects in a network structure
In a network perspective, causes do not work on a latent
variable, and effects do not spring from it. Since the indi-
vidual observables are viewed as causally autonomous,
they are responsible for incoming and outgoing causal
action. This motivates the study of such observables
themselves as gateways of causal action, a perspective that
has rarely been taken in psychometric thinking.
Depression again illustrates these issues nicely. Even
though its symptoms appear to behave as a unidimensional
scale in psychometric research, causal antecedents of
depression seem to impact (clusters of) symptoms differ-
entially. For example, adverse life events seem to have
stronger ties to psychological symptoms (e.g., depressed
mood) than to vegetative symptoms of depression (e.g.,
concentration problems; see Lux & Kendler, 2010; Tennant,
2001). This suggests that etiological pathways into
depression may themselves depend on external events. In
addition, it is likely that there are many such pathways,
since individual symptoms of depression (e.g., concentra-
tion problems or being unable to sleep) can be activated by
anything from lower back pain to babies. Any combination
from these factors, like babies that cause lower back pain,
may be involved as well. Moreover these causes may
themselves form new, ever more complicated networks,
such as when the sleep loss caused by the lower back pains
leads to new babies. As result, even for network constructs
whose dynamics are understood there is little hope that
science in time will come up with a manageable laundry list
of their causes.
Similar concerns involve the study of outgoing effects
(e.g., consequences like losing ones job in the context of
depression). In some cases, these consequences may be
seen as a result of the overall state of the network; in other
cases it is more plausible that only a few of the symptoms
are responsible. The network perspective offers a natural
way to accommodate this, as in dynamical systems even
simple interactions between variables may cause emergent
phenomena to arise as a result of nonlinear interactions
between components of the system (most psychological
systems must feature nonlinear relations because at least
some of their variables are naturally bounded; e.g., one
cannot sleep less than 0 h a night).
Take for instance a suicide attempt by someone who is
depressed. Such an act may not be a result of the latent
variable, depression, but rather the result of interactions
between symptoms like depressed mood, self-reproach,
and suicidal ideation, i.e., three symptoms of depression.
Such interactions may lead to a downward spiral from
which a person cannot escape, and that spiral could be
viewed as an emergent phenomenon with novel causal
powers that none of its generating elements possessed. The
network approach accommodates these issues naturally,
and in a way that no reective or formative model can do,
because it allows us to reason about dynamics within the
psychometric context of the indicator variables themselves.
5. Conclusion
In this paper, we have presented a network approach in
which the constituents of psychological constructs are
directly related in a nontrivial and non-spurious manner.
The network approach is intuitively attractive and naturally
accommodates what we know about the elusive nature of
psychological constructs.It also offers an explanation of why
our traditional psychometric approaches have met with so
little success, that is, of why after all these years we still do
not know whether typical psychometric instruments really
measure something and, if so, what that something could be.
While the network approach is not necessarily adequate
for all psychological constructs, it may turn out to be so for
more constructs than intuition suggests. For instance, one
may think that while depression is a nice test case, network
models are unlikely to be useful in other domains, like
intelligence testing. However, Van der Maas et al. (2006)
present a convincing case for a network model of intelli-
gence, the explanatory resources of which rival those of any
other contemporary theory. Similarly, personality research
seems a feasible area for network applications, because
personality items typically list items that are plausible
causes and effects (consider I plan ahead when doing
a joband I always nish jobs on timeas indicators of
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logical phenomena, New Ideas in Psychology (2011), doi:10.1016/j.newideapsych.2011.02.007
conscienciousness). Longitudinal studies and novel meth-
odologies are needed to investigate the dynamics of
psychological constructs. An important question is whether
network structure and dynamics differ between persons. In
addition, studies investigating the relation between
network properties (e.g., the distribution of connection
weights, or the number and type of equilibrium points) on
one hand, and the possible range of congurations of cross-
sectional data obtained at a single time point (e.g., data
conforming to a one-factor model or a ve factor model
with correlated factors) on the other may provide useful
starting points for dynamic accounts of psychological
constructs. For instance, if a cross-sectional data set
conforms to a ve factor model, what are the necessary and
possible properties of a network that generated these data,
and vice versa?
Past decades have resulted in a signicant set of tools
that can be used to study and evaluate the structure and
dynamics of networks. Such approaches have gone largely
unnoticed in psychometrics, validity theory, and psycho-
logical testing; however, they may offer signicant poten-
tial for advancing our understanding of psychological
This work was supported by NWO innovational research
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... Traditionally, research about the epidemiology and etiology of major depression follows the latent factor approach (Everett, 2013). This approach postulates that depression is the unobserved entity that is manifested by observable symptoms called indicators (Schmittmann et al., 2013). In this view, depression is the common cause of the observed symptoms, and without it, the symptoms would have zero correlations with each other (Brown, 2006;Everett, 2013;Schmittmann et al., 2013). ...
... This approach postulates that depression is the unobserved entity that is manifested by observable symptoms called indicators (Schmittmann et al., 2013). In this view, depression is the common cause of the observed symptoms, and without it, the symptoms would have zero correlations with each other (Brown, 2006;Everett, 2013;Schmittmann et al., 2013). This is contradicted by the finding that anhedonia, hopelessness or tiredness usually correlated with each other even when the diagnostic criteria for MDD were not fulfilled (Borsboom, 2008;Santos et al., 2017). ...
... An alternative approach, called network analysis, focuses instead on symptoms and their relations, without assuming that a latent factor causes the symptoms (Borsboom & Cramer, 2013). Unlike the latent variable approach, the symptoms are considered "active ingredients of mental disorders" instead of mere by-products (Borsboom & Cramer, 2013). ...
Full-text available
Background The coronavirus disease 2019 (COVID-19) pandemic disrupted the working lives of Macau residents, possibly leading to mental health issues such as depression. The pandemic served as the context for this investigation of the network structure of depressive symptoms in a community sample. This study aimed to identify the backbone symptoms of depression and to propose an intervention target. Methods This study recruited a convenience sample of 975 Macao residents between 20th August and 9th November 2020. In an electronic survey, depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9). Symptom relationships and centrality indices were identified using directed and undirected network estimation methods. The undirected network was constructed using the extended Bayesian information criterion (EBIC) model, and the directed network was constructed using the Triangulated Maximally Filtered Graph (TMFG) method. The stability of the centrality indices was evaluated by a case-dropping bootstrap procedure. Wilcoxon signed rank tests of the centrality indices were used to assess whether the network structure was invariant between age and gender groups. Results Loss of energy, psychomotor problems, and guilt feelings were the symptoms with the highest centrality indices, indicating that these three symptoms were backbone symptoms of depression. The directed graph showed that loss of energy had the highest number of outward projections to other symptoms. The network structure remained stable after randomly dropping 50% of the study sample, and the network structure was invariant by age and gender groups. Conclusion Loss of energy, psychomotor problems and guilt feelings constituted the three backbone symptoms during the pandemic. Based on centrality and relative influence, loss of energy could be targeted by increasing opportunities for physical activity.
... However, alternatively to the factor approach, the network approach has recently emerged [32,33] and has been applied in several psychological research areas, such as personality [34,35], emotions [36], empathy [37], perfectionism [38], the dark triad [39], mental disorders [40], well-being [41], meaning at work, and decent work [42,43]. According to this approach, psychological self-perception of the SDGs does not arise from latent factors but derives from the reciprocal interaction between its observable indicators (scale items) [44]. Thus, following the SDGPI measurement model, the network realm can provide a novel perspective to analyze the link between interest, motivation, and self-efficacy. ...
... We performed a network analysis to expand our knowledge on the psychological self-perception of SDGs in our participants. Findings from network analysis expand those yielded via the factorial approach [18] by providing the SDGPI as a network composed of its constituent elements (items) [44]. In this framework, the first network analysis was run to inspect the different paths that connect interest, motivation, and self-efficacy for each SDG. ...
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The Sustainable Development Goals Psychological Inventory (SDGPI) is a recently developed self-report questionnaire that assesses interest, motivation, and self-efficacy associated with each of the 17 Sustainable Development Goals (SDGs) advanced by the United Nations. This study aims to investigate, via network analysis, (a) the relationships between interest, motivation, and self-efficacy for each SDG and (b) the most central SDGs. To this end, 417 Italian university students (73.9% females and 26.1% males; mean age: 22.20; DS = 3.02) were assessed through the SDGPI, and two network structures were estimated. The first network structure investigates links (edges) between interest, motivation, and self-efficacy in relation to each specific SDG. The second network structure investigates most central SDGs as the sum of interest, motivation, and self-efficacy for each specific SDG. Regarding results, the first network structure showed that five SDGs had strong and statistically significant edges between interest, motivation, and self-efficacy; seven SDGs had strong and statistically significant edges between interest and motivation but not self-efficacy; five SDGs had no statistically significant edges linking the other dimensions. The second network structure revealed that SDG 2 (Zero Hunger) and SDG 7 (Affordable and clean energy) were the most central nodes. Implications for research, tailor-made interventions, and prevention were discussed.
... However, it should be noted that these analyses ignore the potential associations to arise from their interaction with another variable, as it is the case of Pearson Product-Moment Correlations, or ignore the possibility of bidirectional relationships between the variables, as it is the case of linear regression [9]. These potential limitations can be addressed using network analysis [10]. ...
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Evidence supports that migraine is a complex pain condition with different underlying mechanisms. We aimed to quantify potential associations between demographic, migraine-related, and psychophysical and psychophysical variables in women with migraine. Demographic (age, height, and weight), migraine-related (intensity, frequency, and duration), related-disability (Migraine Disability Assessment Scale, Headache Disability Inventory), psychological (Hospital Anxiety and Depression Scale), and psycho–physical (pressure pain thresholds -PPTs-) variables were collected from a sample of 74 women suffering from migraine. We calculated adjusted correlations between the variables by using a network analysis. Additionally, we also calculated centrality indices to identify the connectivity among the variables within the network and the relevance of each variable in the network. Multiple positive correlations (ρ) between PPTs were observed ranging from 0.1654 (C5-C6 and tibialis anterior) to 0.40 (hand and temporalis muscle). The strongest associations within the network were those between migraine attack frequency and diagnosis of chronic migraine (ρ = 0.634) and between the HDI-E and HDI-P (ρ = 0.545). The node with the highest strength and betweenness centrality was PPT at the second metacarpal, whereas the node with the highest harmonic centrality was PPT at the tibialis anterior muscle. This is the first study applying a network analysis to understand the underlying mechanisms in migraine. The identified network revealed that a model where each subgroup of migraine-related, psychological, and psycho–physical variables showed no interaction between each variable. Current findings could have clinical implications for developing multimodal treatments targeting the identified mechanisms.
... To date, traditional models of psychopathology have conceptualized depression and other psychiatric syndromes on the basis of latent models wherein individual clinical symptoms (i.e., observable indicators) are equally-weighted manifestations of latent variables (i.e., unobservable factors) Schmittmann et al., 2013). Relatedly, a key premise of traditional approaches is that the severity of syndromes such as depression is best measured as the sum of individual symptoms endorsed on interview or questionnaire measures (Eaton, 2015;. ...
Background Various populations have experienced significant increases in depression and decreased quality of life (QOL) during the coronavirus disease 2019 (COVID-19) pandemic. This network analysis study was designed to elucidate interconnections between particular depressive symptoms and different aspects of QOL and identify the most clinically important symptoms in this network among adults in Wuhan China, the initial epicenter of the COVID-19 pandemic. Methods This cross-sectional, convenience-sampling study (N = 2459) was conducted between May 25 to June 18, 2020, after the lockdown policy had been lifted in Wuhan. Depressive symptoms and QOL were measured with the Patient Health Questionnaire-9 (PHQ-9) and first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. A network structure was constructed from the extended Bayesian Information Criterion (EBIC) model. Network centrality strength and bridge strength were evaluated along with the stability of the derived network model. Results Loss of energy (DEP-4) and Guilt feelings (DEP-6) were the two central symptoms with the highest strength as well as the two most prominent bridge symptoms connecting the clusters of depression and quality of life (QOL) in tandem with the two nodes from the QOL cluster. Network structure and bridge strengths remained stable after randomly dropping 75 % of the sample. Conclusion Interventions targeting “Loss of energy” and “Guilt feelings” should be evaluated as strategies for reducing depressive symptoms and promoting improved QOL in COVID-19-affected populations.
... Network analysis allows for quantification and depiction of the strength by which symptoms react with and influence each other, as well as those symptoms that are most central to a disorder 19,32 . In a psychopathological network model, symptoms are depicted as individual nodes connected by edges that reflect the strength and direction of the relationship between pairs of symptoms. ...
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Attenuated positive symptoms (APS), transient psychotic-like symptoms (brief, limited intermittent psychotic symptoms, BLIPS), and predictive cognitive-perceptive basic-symptoms (BS) criteria can help identify a help-seeking population of young people at clinical high-risk of a first episode psychosis (CHRp). Phenomenological, there are substantial differences between BS and APS or BLIPS. BS do not feature psychotic content as delusion or hallucinations, and reality testing is preserved. One fundamental problem in the psychopathology of CHRp is to understand how the non-psychotic BS are related to APS. To explore the interrelationship of APS and predictive BS, we fitted a network analysis to a dataset of 231 patients at CHRp, aged 24.4 years (SD = 5.3) with 65% male. Particular emphasis was placed on points of interaction (bridge symptoms) between the two criteria sets. The BS ‘unstable ideas of reference’ and “inability to discriminate between imagination and reality” interacted with attenuated delusional ideation. Perceptual BS were linked to perceptual APS. Albeit central for the network, predictive cognitive basic BS were relatively isolated from APS. Our analysis provides empirical support for existing theoretical accounts that interaction between the distinct phenomenological domains of BS and APS is characterized by impairments in source monitoring and perspective-taking. Identifying bridge symptoms between the symptom domains holds the potential to empirically advance the etiological understanding of psychosis and pave the way for tailored clinical interventions.
Understanding how student teacher professional agency (STPA) develops during teacher education is crucial for educators and curriculum developers interested in strengthening it. To explore this process comprehensively, environmental factors (i.e., curriculum coherence between theory and practice and the learning environment) and individual factors (i.e., professional identity) were considered. This study utilized quantitative cross-sectional data of 362 students who were in different stages of their teacher education in a Finnish university. Structural equation modeling (SEM) and network analysis were applied over the data to better understand the interactive processes of the factors being investigated. The results indicated that different aspects of curriculum coherence, learning environment and professional identity dynamically influence STPA during teacher education by changing their relationship with it over time.
Zahlreiche Klassifikationsmöglichkeiten gesundheitsfördernder Maßnahmen im Arbeitskontext wie z. B. die Unterscheidung personenbezogener und bedingungsbezogener Maßnahmen oder die Einordnung als primäre, sekundäre oder tertiäre Prävention beziehen sich auf die inhaltliche Ausrichtung der Verfahren. Darüber hinaus lassen sich gesundheitsfördernde Interventionen hinsichtlich ihres klassifizieren. Das bei der Planung einer Maßnahme festgelegte Design determiniert in der Regel deren Evaluation (z. B. hinsichtlich der Akzeptanz) als auch die Überprüfung der Wirksamkeit. Während bei face-to-face Ansätzen Prä-Post-(Follow-up)-Designs am häufigsten umgesetzt werden, erleichtern web/app-basierte und blended learning Ansätze eine engmaschigere Erfassung und damit Evaluation der intendierten Veränderungsprozesse. Obwohl sowohl in der Praxis als auch im Forschungsalltag die Planung von gesundheitsfördernden Maßnahmen pragmatische oder Kosten-Nutzen-Aspekte einbeziehen muss, möchten wir im vorliegenden Kapitel einige ausgewählte Themen diskutieren, deren Berücksichtigung bei der Implementierung von Interventionsdesigns von Relevanz für eine adäquate Evaluation des beabsichtigten Ziels der Maßnahme sein können. Wir erörtern versuchsplanerische Voraussetzungen, aber auch konzeptuelle Aspekte (z. B. Fragen der Operationalisierung relevanter Konstrukte, deren Stabilität versus Fluktuation) und schließlich methodische Aspekte diverser Designs und Auswertungsmöglichkeiten.
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Background Traumatic events related to war and displacement may lead to development of posttraumatic stress symptoms (PTSS), but many war trauma survivors also report experiencing posttraumatic growth (PTG). However, the phenomenon of PTG remains poorly understood among refugees. Previous findings are also contradictory on whether more PTSS associate with PTG and what specific symptoms or aspects of growth may account for any possible link. Objective and Method Here, we aimed to better understand posttraumatic growth among refugees, especially its structure and most important constituent elements, as well as how it associates with PTSS. We employed regression and network analysis methods with a large sample (N = 3,159) of Syrian and Iraqi refugees living in Turkey self-reporting on PTG and PTSS. Results We found PTG and PTSS to be clearly distinct phenomena. Still, they often co-occurred, with a positive, slightly U-shaped relationship found between levels of PTSS and PTG. The main bridge between the constructs was identified from intrusive symptoms to having new priorities in life, although new priorities were more peripheral to the overall network structure of PTG. Meanwhile, discovering new psychological strengths and abilities and a new path in life emerged as elements most central to PTG itself. Conclusions Many refugees report elements of PTG, even as they suffer from significant PTSS. The two phenomena appear distinct but positively associated, supporting the idea that intense cognitive processing involving distress may be necessary for growth after trauma. Our findings may inform efforts to support refugee trauma survivors in finding meaning and perhaps even growth after highly challenging experiences.
Introduction: Suicide is a substantial public health burden, particularly among veterans. Risk factors have been delineated for suicide; however, the dynamic interrelations between risk factors have not been fully examined. Such research has the potential to elucidate processes that contribute to suicide risk between individuals with a past suicide attempt (attempters) and those without a past suicide attempt (nonattempters). Methods: In the current study, network analysis was used to compare networks between attempters and nonattempters in a high-risk veteran sample (N = 770; Mage = 32.3 years, SD = 6.8; 326 with a past suicide attempt) who were followed over 1 year. Networks were estimated to examine (1) concurrent relations of suicide risk factors at baseline and (2) predictability of prospective suicidal behavior (SB). Results: There were no differences in the overall connectivity of attempter and nonattempter networks. Perceived burdensomeness and posttraumatic stress disorder (PTSD) symptoms were most central in the attempters' network, whereas PTSD symptoms and insomnia were most central in the nonattempters' network. The risk factors prospective SB in either network. However, attempters were more likely to engage in SB over the course of the study. Conclusion: These findings highlight the difficulty in predicting who will attempt suicide.
Background Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches. Methods Data are from ‘the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)’. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms. Results Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks. Conclusions We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.
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Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.
This chapter addresses the problem of learning the parameters from data. It also discusses score-based structure learning and constraint-based structure learning. The method for learning all parameters in a Bayesian network follows readily from the method for learning a single parameter. The chapter presents a method for learning the probability of a binomial variable and extends this method to multinomial variables. It also provides guidelines for articulating the prior beliefs concerning probabilities. The chapter illustrates the constraint-based approach by showing how to learn a directed acyclic graph (DAG) faithful to a probability distribution. Structure learning consists of learning the DAG in a Bayesian network from data. It is necessary to know which DAG satisfies the Markov condition with the probability distribution P that is generating the data. The process of learning such a DAG is called “model selection.” A DAG includes a probability distribution P if the DAG does not entail any conditional independencies that are not in P. In score-based structure learning, a score is assigned to each DAG based on the data such that in the limit. After scoring the DAGs, the score are used, possibly along with prior probabilities, to learn a DAG. The most straightforward score, the Bayesian score, is the probability of the data D given the DAG. Once a DAG is learnt from data, the parameters can be known. The result will be a Bayesian network that can be used to do inference. In the constraint-based approach, a DAG is found for which the Markov condition entails all and only those conditional independencies that are in the probability distribution P of the variables of interest. The chapter applies structure learning to inferring causal influences from data and presents learning packages. It presents examples of learning Bayesian networks and of causal learning.
The problem of assigning empirical meaning to unobserved variables in structural equation models is discussed. Interpretational confounding is discussed as the assignment of the other than a priori assigned empirical meaning of an unobserved variable. Hypotheses conceming the possibility of interpretational confounding as a concomitant of a lack of point variability in unobserved variables are specified, and corresponding chi-square statistics are given. Numerical illustration is provided
"Construct validation was introduced in order to specify types of research required in developing tests for which the conventional views on validation are inappropriate. Personality tests, and some tests of ability, are interpreted in terms of attributes for which there is no adequate criterion. This paper indicates what sorts of evidence can substantiate such an interpretation, and how such evidence is to be interpreted." 60 references. (PsycINFO Database Record (c) 2006 APA, all rights reserved).