Stress Responsivity and the Structure of Common Mental Disorders: Transdiagnostic Internalizing and Externalizing Dimensions Are Associated With Contrasting Stress Appraisal Biases

Abstract

Biased stress appraisals critically relate to the origins and temporal course of many—perhaps most—forms of psychopathology. We hypothesized that aberrant stress appraisals are linked directly to latent internalizing and externalizing traits that, in turn, predispose to multiple disorders. A high-risk community sample of 815 adolescents underwent semistructured interviews to assess clinical disorders and naturalistic stressors at ages 15 and 20. Participants and blind rating teams separately evaluated the threat associated with acute stressors occurring in the past year, and an appraisal bias index (i.e., discrepancy between subjective and team-rated threat) was generated. A 2-factor (Internalizing and Externalizing) latent variable model provided an excellent fit to the diagnostic correlations. After adjusting for the covariation between the factors, adolescents’ threat overestimation prospectively predicted higher standing on Internalizing, whereas threat underestimation prospectively predicted elevations on Externalizing. Cross-sectional analyses replicated this pattern in early adulthood. Appraisals were not related to the residual portions of any diagnosis in the latent variable model, suggesting that the transdiagnostic dimensions mediated the connections between stress appraisal bias and disorder entities. We discuss implications for enhancing the efficiency of emerging research on the stress response and speculate how these findings, if replicated, might guide refinements to psychological treatments for stress-linked disorders.

Full text

Stress Responsivity and the Structure of Common Mental Disorders:
Transdiagnostic Internalizing and Externalizing Dimensions Are Associated
With Contrasting Stress Appraisal Biases
Christopher C. Conway
College of William and Mary
Lisa R. Starr
University of Rochester
Emmanuel P. Espejo
Veterans Affairs San Diego Healthcare System, San Diego,
California, and University of California, San Diego
Patricia A. Brennan
Emory University
Constance Hammen
University of California, Los Angeles
Biased stress appraisals critically relate to the origins and temporal course of many—perhaps most—
forms of psychopathology. We hypothesized that aberrant stress appraisals are linked directly to latent
internalizing and externalizing traits that, in turn, predispose to multiple disorders. A high-risk commu-
nity sample of 815 adolescents underwent semistructured interviews to assess clinical disorders and
naturalistic stressors at ages 15 and 20. Participants and blind rating teams separately evaluated the threat
associated with acute stressors occurring in the past year, and an appraisal bias index (i.e., discrepancy
between subjective and team-rated threat) was generated. A 2-factor (Internalizing and Externalizing)
latent variable model provided an excellent fit to the diagnostic correlations. After adjusting for the
covariation between the factors, adolescents’ threat overestimation prospectively predicted higher stand-
ing on Internalizing, whereas threat underestimation prospectively predicted elevations on Externalizing.
Cross-sectional analyses replicated this pattern in early adulthood. Appraisals were not related to the residual
portions of any diagnosis in the latent variable model, suggesting that the transdiagnostic dimensions mediated
the connections between stress appraisal bias and disorder entities. We discuss implications for enhancing the
efficiency of emerging research on the stress response and speculate how these findings, if replicated, might
guide refinements to psychological treatments for stress-linked disorders.
General Scientific Summary
Biased perceptions of stressful or emotional events confer vulnerability to a wide array of psycho-
logical disorders. We found here that aberrant appraisals of real-world stressors directly predict
liability to Internalizing and Externalizing traits, which serve as substrates for diverse mental
disorders, but in markedly different ways. Exaggerated perceptions of stressor severity predicted
higher standing on the Internalizing trait—predisposing to anxiety and depression—whereas a
tendency to downplay stressor impact predicted elevations on externalizing—predisposing to anti-
social behavior and substance misuse.
Keywords: appraisal, externalizing, internalizing, stress reactivity, transdiagnostic
Editor’s Note. Jennifer L. Tackett served as the Guest Editor for this
article.—SHG
Christopher C. Conway, Department of Psychology, College of William and
Mary; Lisa R. Starr, Department of Clinical and Social Sciences in Psychology,
University of Rochester; Emmanuel P. Espejo, Psychology Service, Veterans
Affairs San Diego Healthcare System, San Diego, California, and Department of
Psychiatry, University of California, San Diego; Patricia A. Brennan, Department
of Psychology, Emory University; Constance Hammen, Department of Psychol-
ogy, University of California, Los Angeles.
This research was supported by the National Health and Medical
Research Council, the Mater Misericordiae Mother’s Hospital in
Queensland, Australia, and the National Institute of Mental Health
Grant R01MH52239. We thank Jake Najman of the University of
Queensland and Mater Misericordiae Mothers’ Hospital-University of
Queensland Study of Pregnancy colleagues William Bor and Gail
Williams.
Correspondence concerning this article should be addressed to Christo-
pher C. Conway, Department of Psychology, College of William and
Mary, Box 8795, Williamsburg, VA 23187. E-mail: conway@wm.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Abnormal Psychology © 2016 American Psychological Association
2016, Vol. 125, No. 8, 1079–1089 0021-843X/16/$12.00 http://dx.doi.org/10.1037/abn0000163
1079

Psychopathology tends to be closely linked to environmental
stressors. Both acute stressful life events and chronically adverse
conditions can shape the onset, temporal course, and consequences
of depressive, bipolar, anxiety, substance use, antisocial, and psy-
chotic disorders (e.g., Conway, Rutter, & Brown, 2016;Hammen,
2005;Hlastala et al., 2000;Kim, Conger, Elder, & Lorenz, 2003;
Monroe, Slavich, & Georgiades, 2014;Myin-Germeys & van Os,
2007;Sinha, 2001). Thus, comprehensive models of the origins
and treatment of psychopathology require consideration of an
individual’s stressful environments and the mechanisms that link
such experiences to symptoms and disorders.
Clearly, however, serious stressors do not invariably trigger
psychopathology. Life stress research has revealed diverse trajec-
tories of vulnerability and resilience in response to adversity.
Adaptive stress responses, mediated by homeostatic biological and
psychological systems, are common. For instance, although a
significant stressor precedes most depressive episodes, only one in
five of those exposed to acute stress becomes depressed (Brown &
Harris, 1978). Pathological stress responses, in contrast, often
include hyperbolic cognitive-emotional reactions instigated by
misrepresentations of stressor impact and meaning. That is, ac-
cording to a large body of evidence on emotion regulation pro-
cesses (e.g., Gross & Jazaieri, 2014;Joormann & Vanderlind,
2014), faulty appraisals of stressor impact are largely responsible
for initiating maladaptive responses.
The most extensively studied instances of stress appraisal bias
involve depressed and anxious populations, consistent with Beck’s
(1976) classic cognitive theory, which posits that a tendency to
distort the significance of stressful situations— exaggerating
threat, depletion, and loss—sets the stage for, and later sustains,
internalizing symptoms. Over recent years, a vast amount of re-
search has found evidence for a variety of attentional, interpretive,
and memory biases associated with depression and anxiety that are
relevant to stress appraisal. Biases include preferential attention to
negative information, negative inferential style, overgeneral auto-
biographical memory, and difficulties disengaging from negative
information (e.g., reviews by Gotlib & Joormann, 2010;Joormann
& Vanderlind, 2014;Mathews & MacLeod, 2005;Yoon & Zin-
barg, 2008). These biases can accentuate the negative aspects of
life circumstances, creating and prolonging distressing emotions.
Less research has explored biased appraisals of personal stres-
sors as they relate to externalizing disorders. In contrast to ampli-
fied stress responses observed in internalizing populations, antiso-
cial behavior and substance use disorders generally feature
hyposensitivity to threatening, emotional, or stressful circum-
stances (e.g., Birbaumer et al., 2005;Dawes et al., 1999;Raine,
Venables, & Williams, 1990;Syngelaki, Fairchild, Moore, Savage,
& van Goozen, 2013). Some evidence suggests that malfunction-
ing inhibitory mechanisms account for this blunted reactivity (e.g.,
Krueger & South, 2009). For instance, inhibitory control deficits in
the face of probable punishment are commonly detected among
people at risk for externalizing problems (reviewed in Byrd, Loe-
ber, & Pardini, 2014;Frick & Dickens, 2006). Similarly, a variety
of studies document that impulsive and aggressive individuals
experience less negative emotional arousal when encountering
potential threat because they assess dangerous or stressful situa-
tions as nonthreatening (De Vries-Bouw et al., 2011). Thus, risky
and antisocial behaviors are theorized to be more common in these
populations because they are not deterred by fear of punishment or
negative outcomes (Raine et al., 1990).
Together, these lines of research signal discriminant validity
between, but not within, internalizing and externalizing disorder
domains. That is, stress overreactions pervade the anxiety and
depressive disorders, whereas insensitivity to stress characterizes
diverse antisocial behavior and substance use conditions. This lack
of discriminant validity among commonly co-occurring syndromes
is the norm in many research areas—not just research on the stress
response—and it has propelled the shift toward the Research
Domain Criteria (RDoC) initiative and other transdiagnostic mod-
els of psychopathology. One possible explanation for this pattern
of observations is that stress reactivity processes, such as stress
appraisal, are directly related to a small set of core psychopatho-
logical traits that underpin multiple disorders (e.g., Nolen-
Hoeksema & Watkins, 2011). From this perspective, diagnostic
entities (e.g., panic disorder, persistent depressive disorder) are
manifestations of underlying transdiagnostic traits, and they are
not expected to have strong associations with clinical outcomes
independent of those basic transdiagnostic dimensions (Krueger &
Markon, 2006).
There are two goals of the present article. First, we present a
method of measuring characteristic ways of appraising stressful
life events that is based on individuals’ own recent stressors.
Second, we test the hypothesis that transdiagnostic dimensions
underlying internalizing disorders and externalizing disorders are
each associated with aberrant stress appraisals, but in markedly
different ways.
Why do we propose a measure based on naturally occurring
stressors? We argue that much of the functional impairment ac-
companying psychological disorders results from maladaptive
emotions and behaviors that stem from misappraisals of life cir-
cumstances. In the real world, the ability to appropriately cope
with challenging events and situations requires accurate interpre-
tation of the likelihood, meaning, and consequences of actual
circumstances; these may be difficult to validly reproduce in the
laboratory setting. We operationalize appraisal bias as the discrep-
ancy between objective, contextually informed ratings of the se-
verity of participants’ recent stressful life events as rated by blind,
independent raters on the one hand, and participants’ subjective
judgments of stressor severity on the other. In an earlier study,
Espejo, Hammen, and Brennan (2012) reported that youth whose
stress appraisals were more negative than those of the objective
rating team were at heightened risk for new onsets of depressive
and anxiety disorders over late adolescence. In the current study,
we extend this method to create a bipolar appraisal bias construct,
in the sense that misperceptions can reflect either over- or under-
estimation of stressor impact relative to objective standards. The
stress assessment is based on a gold standard interview method of
evaluating occurrence and impact of stress based on the context in
which it occurs (Hammen, 2016; see also Monroe, 2008).
Construct Validity of a Quantitative Transdiagnostic
Model of Mental Disorders
We rely on recent developments in the quantitative modeling of
the latent structure of psychopathology to relate appraisal bias to
transdiagnostic traits. Structural research on the internalizing and
externalizing domains can be traced back to Achenbach’s (e.g.,
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1080 CONWAY, STARR, ESPEJO, BRENNAN, AND HAMMEN

Achenbach, 1966) latent variable modeling of child behavior prob-
lems. In a pair of highly influential studies, Krueger and his
collaborators (Krueger, 1999;Krueger, Caspi, Moffitt, & Silva,
1998) extended this modeling framework by analyzing diagnostic
correlations from large community samples of adults. Paralleling
Achenbach’s early findings, Krueger et al. (1998) uncovered an
Internalizing factor underlying the anxiety and depressive disor-
ders and an Externalizing factor underlying the antisocial behavior
and substance use disorders. This two-factor configuration has
demonstrated impressive consistency across several large-scale
epidemiological studies (reviewed in Eaton, Rodriguez-Seijas,
Carragher, & Krueger, 2015). Additionally, research has shown
the Internalizing and Externalizing factors to be stable over time
and present across a variety of cultures and age ranges (Krueger et
al., 1998;Krueger, Chentsova-Dutton, Markon, Goldberg, &
Ormel, 2003).
The available research on the latent structure of disorders sum-
marized above has been largely descriptive. Expressed in latent
variable modeling terminology, the focus has been on the mea-
surement model, as compared with structural relations of the
transdiagnostic dimensions with external constructs. The next
phase, which is just beginning, will evaluate the predictive, con-
vergent, and discriminant validity of the Internalizing and Exter-
nalizing dimensions. The results of these investigations will deter-
mine the value of the proposed transdiagnostic model as a research
and diagnostic tool. Specifically, strong correlations between the
transdiagnostic dimensions and biobehavioral outcomes would
validate the Internalizing and Externalizing dimensions as useful
levels of analysis (Krueger & Markon, 2006). On the other hand,
weak correlations— either in absolute terms or relative to effects of
observed diagnostic categories— of the transdiagnostic dimensions
with external constructs would suggest limited utility.
A small group of existing studies has investigated the construct
validity of the Internalizing and Externalizing dimensions. This
research evaluates the extent to which the transdiagnostic dimen-
sions explain previously observed associations between psychos-
ocial factors and individual diagnostic categories. For instance,
South, Krueger, and Iacono (2011) found that the association
between marital problems and individual disorders (e.g., major
depression) was mediated by the overarching Internalizing and
Externalizing traits. That is, after adjusting for the transdiagnostic
dimensions, marital discord was unrelated to specific mental dis-
orders (cf. Krueger & Piasecki, 2002). Analogous patterns of
results have been reported in other substantive areas, such as
childhood maltreatment, intergenerational transmission of depres-
sion, and gender differences in psychopathology (Eaton et al.,
2012;Keyes et al., 2012;Starr, Conway, Hammen, & Brennan,
2014). Findings from this line of research carry potentially signif-
icant implications for research design strategies. For instance, in
the current research context, if aberrations in stress responding are
indeed traced to cross-cutting psychopathological traits, as op-
posed to specific diagnoses, then studies designed to compare a
single disorder to a healthy control group might be considered
inefficient. Instead, the maximally informative investigation would
examine correlations between stress responses and relevant trans-
diagnostic dimensions, which presumably represent the driving
forces behind prior evidence of association between categorical
disorders and the stress response.
Present Study
We applied the quantitative model of comorbidity to understand
the relationship between dysfunctional stress appraisals and com-
mon mental disorders. We focused on biased appraisals of natu-
ralistic stressors, which, as mentioned above, are conceptually
related to several cognitive processes previously investigated in
the context of internalizing disorders. Exploiting the hierarchical
organization of the quantitative model (see Krueger & Piasecki,
2002), we examined stress appraisal bias in relation to transdiag-
nostic and diagnosis-specific components of a latent variable
model of mental disorders.
The appraisal bias index was computed at two time periods in a
longitudinal study: mid-adolescence (age 15) and young adulthood
(age 20). We first examined the age 15 bias as a predictor of new
onsets of disorder occurring between ages 15 and 20 based on the
hypothesis that cognitive bias may serve as a developmental an-
tecedent to psychopathology. We then examined the age 20 bias as
a correlate of lifetime diagnoses up to age 20. Evidence from both
preclinical and human research suggests that stress reactivity peaks
in adolescence, and that stress-related disorders in adolescence and
early adulthood shape the trajectory of psychopathology and psy-
chosocial functioning long into adulthood (e.g., Dahl & Gunnar,
2009;Ge, Conger, & Elder, 2001;Rohde, Lewinsohn, Klein,
Seeley, & Gau, 2013). Thus, cognitive processes implicated in the
stress response during this time period are theorized to have
enduring, clinically significant consequences.
We hypothesized that overestimations of event impact would
predict higher standing on the Internalizing factor given prior
findings of hyperactive biological and cognitive-emotional stress
reactivity mechanisms in anxiety and depressive disorders (e.g.,
Craske et al., 2009;Mathews & MacLeod, 2005). In contrast, we
expected that Externalizing would be associated with underesti-
mation of event impact. As briefly reviewed above, externalizing
disorders are related to hypoactivation of several stress response
systems, which might contribute to a discounting of the possible
negative consequences of stressful situations (e.g., van Leeuwen,
Rodgers, Gibbs, & Chabrol, 2014). Finally, we hypothesized that
any associations between individual diagnoses and appraisal bias
would be comparatively small after statistically adjusting for the
effects of transdiagnostic dimensions.
Method
Participants
Eight hundred fifteen mothers and their 15-year-old children
(412 males) were recruited from an ongoing birth cohort study of
health and social outcomes, originally consisting of 7,223 pregnant
women who gave birth between 1981 and 1984 in Brisbane,
Australia (Keeping et al., 1989). Women were recruited to repre-
sent a wide range of depressive experiences based on scores on a
questionnaire measure of depressive symptoms administered four
times between pregnancy and child age five (Delusions-Symp-
toms-States-Inventory; Bedford & Foulds, 1978). Depression di-
agnoses were later confirmed by diagnostic interview based on the
Structured Clinical Interview for DSM–IV (SCID; First, Spitzer,
Gibbon, & Williams, 1995) at youth age 15; 358 mothers had a
lifetime diagnosis of major depressive episode and/or dysthymic
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1081
STRESS APPRAISAL BIAS AND PSYCHOPATHOLOGY

disorder, and 457 were never depressed. The families were pre-
dominantly lower middle/working class and 91.4% were Cauca-
sian (3.6% Asian, 5% other). Sixty-seven percent of the mothers
were currently married to the father of the child, 12% were
currently not married, and 21% were married to someone other
than the biological father. Further details of sample selection are
reported by Hammen and Brennan (2001).
At age 20, families were recontacted for follow-up and, among
those located, 705 youth agreed to participate (86.5%; 342 males).
There were no statistically significant differences in the rates of
participation at age 20 compared with age 15 by maternal depres-
sion status by youth age 15, ␹
2
(1) ⫽3.60, p⫽.06, or youth
depressive diagnoses by 15, ␹
2
(1) ⫽0.25, p⫽.25. However,
males were less likely retained at age 20, ␹
2
(1) ⫽8.71, p⬍.01.
Procedures
At ages 15 and 20, the youth and their mothers separately
completed interviews and questionnaires in their home. All par-
ticipants completed informed consent (assent) procedures at both
time points, and procedures were approved by institutional review
boards of the University of Queensland, Emory University, and the
University of California, Los Angeles.
Measures
Youth diagnoses. At age 15, youth current and lifetime men-
tal disorders were ascertained using the Schedule for Affective
Disorders and Schizophrenia for School-Age Children—Revised
for DSM–IV (K-SADS-E; Orvaschel, 1995), administered sepa-
rately to the parent and the child by interviewers blind to mothers’
depression status. Diagnostic decisions were reviewed by the clin-
ical rating team with best-estimate judgments based on all avail-
able information. In the current sample, weighted kappas for youth
diagnoses were .82 for current depressive diagnoses (major de-
pressive disorder or dysthymia) or subclinical depression, and .73
for past depressive diagnoses or subclinical depression. Reliabili-
ties for current anxiety, substance use, disruptive behavior (con-
duct disorder or oppositional defiant disorder), and “other” (pri-
marily eating) disorders ranged from .67 to 1.0, with a mean of .82;
reliabilities for past diagnoses ranged between .72 and 1.0, with a
mean of .81.
At the age 20 follow-up, youth were administered the SCID,
which covered the 5-year interval since the age 15 assessment.
Interrater reliabilities (i.e., kappa coefficients) for current diagno-
ses were .83 for depressive disorders, .94 for anxiety disorders, and
.79 for substance abuse disorders. The corresponding kappa values
for past disorders were .89, .89, and .96, respectively.
Stressful life events. At age 15 and age 20, the UCLA Life
Stress Interview (LSI; Hammen & Brennan, 2001;Hammen et al.,
1987) was administered to assess occurrence and severity of stress-
ful life events in the past 12 months. Similar to the Life Events and
Difficulties Schedules (Brown & Harris, 1978), the LSI is a
semistructured interview with two defining characteristics. First,
following identification and careful dating of each negative life
event, the event is probed to determine the factual elements of the
context in which it occurred in order to fully define the impact and
meaning of the event for that individual. Second, event impact/
severity is evaluated by an independent rating team following
presentation of a written narrative of each event and its context.
The ratings of severity on a 5-point scale (from no negative impact
to extremely negative impact) are assigned by consensus discus-
sion. The interviewer elicits the individual’s subjective appraisal of
each event on a 5-point scale (from no negative impact to ex-
tremely negative impact), but this information is not available to
the rating team. Various studies have reported on the reliability and
validity of the LSI for acute life events (Hammen & Brennan,
2001;Hammen, Brennan, Keenan-Miller, & Herr, 2008;Hammen,
Kim, Eberhart, & Brennan, 2009). In the current study, interrater
reliability estimates between independent rating teams for stressor
severity were .92 at .95 at age 15 and age 20, respectively.
Additionally, previous research has documented good test–retest
reliability of subjective ratings of stressor impact (Espejo et al.,
2011).
The appraisal bias score for each individual was based on
established methods for comparing subjective and objective rat-
ings (Cole, Martin, Peeke, Seroczynski, & Hoffman, 1998;De Los
Reyes & Prinstein, 2004;Krackow & Rudolph, 2008). Mean
subjective severity rating scores were regressed on mean objective
severity rating scores in the full sample, yielding an index of
subjective perceptions of stressfulness that adjusts for objective
levels of threat. The standardized residuals constitute the outcome
variables in the present analyses, with higher scores representing
perceptions of more negative impact (Conway et al., 2012).
1
Data Analytic Plan
Age 15 appraisal bias. We first examined the prospective
association between appraisal bias at age 15 and subsequent new
onsets of psychopathology (hereafter called the prospective
model). That is, the transdiagnostic outcomes in this analysis were
defined by diagnoses occurring for the first time between the age
15 and age 20 (inclusive) assessments. Indicators of the Internal-
izing factor were major depressive disorder (MDD), dysthymia
(DYS), generalized anxiety disorder (GAD), panic disorder
(PAN), social anxiety disorder (SOC), specific phobia (SPEC),
obsessive– compulsive disorder (OCD), and posttraumatic stress
disorder (PTSD) diagnoses, whereas Externalizing indicators were
alcohol abuse or dependence (ALC), and drug abuse or depen-
dence (DRUG) diagnoses. Conduct disorder (COND) and opposi-
tional defiant disorder (ODD) were assessed during this period, but
excluded as factor indicators due to low incidence during this
period (1 new onset of COND and 0 of ODD). (The prevalence of
all disorders prior to age 15 was too low to permit similar latent
variable analysis of disorders that onset before the age 15 assess-
ment.)
The prospective model analyses proceeded in three parts:
1. We compared the correlated two-factor model with two
other configurations—labeled the three-factor model and
the bifactor model— of common mental disorders sup-
ported in prior structural research. In the three-factor
1
In supplementary analyses, we examined whether the association be-
tween appraisal bias and transdiagnostic factors changed if the number of
reported acute life events was included as a covariate. The pattern and
statistical significance of results were unchanged when we adjusted for the
count of acute stressors. Full results are available from Christopher C.
Conway upon request.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1082 CONWAY, STARR, ESPEJO, BRENNAN, AND HAMMEN

model, the Internalizing factor bifurcated into Distress
and Fear subfactors, an arrangement that has been found
to fit in some samples but not others (Kessler et al., 2011;
Krueger & Markon, 2006). The Distress subfactor was
defined by MDD, DYS, GAD, and PTSD, whereas the
Fear subfactor was defined by PAN, SOC, OCD, and
SPEC (see Watson, 2005). In the bifactor model, each
diagnosis loaded onto a superordinate General Psycho-
pathology factor (termed the p-factor by Caspi et al.,
2014) and onto one specific factor representing Internal-
izing or Externalizing. This model has been found to fit
diagnostic- and symptom-based data well in several prior
studies (e.g., Caspi et al., 2014;Laceulle, Vollebergh, &
Ormel, 2015;Lahey et al., 2012;Tackett et al., 2013).
2. We regressed the transdiagnostic factors from the best
fitting model onto the age 15 appraisal bias index. Ad-
ditionally, given the moderate correlation between Inter-
nalizing and Externalizing observed here (see Results)
and in prior research, we carried out supplementary re-
gressions to determine the association between bias and
Internalizing while controlling for variation on External-
izing (and vice versa). This step was based on previous
findings that indicated the Internalizing and Externaliz-
ing factors exhibited different correlations with external
constructs once their overlap was partialed out (e.g.,
Caspi et al., 2014).
3. Modification indices from the regressions in the point
above (No. 2) were then inspected to determine whether
estimating any paths from age 15 appraisal bias to diag-
nostic residual terms would improve model fit. Following
prior conventions (Keyes et al., 2012), we set a modifi-
cation index threshold of 3.84 (i.e., critical chi-square
value for expected model improvement corresponding to
a .05 alpha level) to evaluate for the presence of statis-
tically significant diagnosis-specific associations. Any
statistically significant residual associations would indi-
cate that appraisal bias predicted unique portions of man-
ifest disorders while holding constant levels of the trans-
diagnostic factor of which the diagnosis is an indicator.
Age 20 appraisal bias. We next examined the correlations
between age 20 appraisal bias and lifetime psychopathology up to
age 20 (concurrent model). Transdiagnostic outcomes in this anal-
ysis were defined by current or past disorders documented at the
age 20 assessment point (i.e., combining information from the age
15 K-SADS-E evaluation and the age 20 SCID evaluation). The
factor indicators were the same as above, except that COND and
ODD were included as indicators of the Externalizing factor. We
followed the same sequence of analyses as the prospective model:
4. We compared the two-factor, three-factor, and bifactor
models for lifetime disorders.
5. We regressed the transdiagnostic factors from the best
fitting model onto the age 20 appraisal bias index. To
statistically control for the overlap between transdiagnos-
tic dimensions, we then covaried Externalizing in the
regression of Internalizing on appraisal bias and, like-
wise, covaried Internalizing when regressing Externaliz-
ing on appraisal bias.
6. We inspected modification indices from the regressions
in the point above (No. 5) to determine whether there
were any statistically significant disorder-specific asso-
ciations with age 20 appraisal bias.
To summarize, this latter set of analyses examined the correla-
tions between appraisal bias at age 20 and lifetime (i.e., ages 0 –20)
history of mental disorders. These analyses were considered a test
of concurrent validity, whereas the prospective model was consid-
ered a test of predictive validity. Results from these two models
allowed us to determine whether appraisal bias at age 20 showed
the same pattern of associations with psychopathology as bias at
age 15 despite variation across the different time periods in the
rates of disorder and latent variable model indicators.
Data were analyzed in Mplus (Version 7.11; Muthén & Muthén,
1998 –2014) using the weighted least squares means and variance
adjusted (WLSMV) estimator. We evaluated model goodness of fit
with the comparative fit index (CFI), the Tucker–Lewis index
(TLI), the root-mean-square error of approximation (RMSEA),
and the weighted root-mean-square residual (WRMR). Acceptable
fit was defined according to guidelines offered by Hu and Bentler
(1999): RMSEA values close to 0.06 or below, CFI and TLI values
close to .95 or above, and WRMR values close to 1.00 or below.
We also judged model fit on the basis of interpretability of param-
eter estimates and localized areas of strain (i.e., modification
indices).
Results
Descriptive Statistics
Table 1 displays lifetime (to age 20) diagnostic frequencies and
correlations. As expected, internalizing disorders were more often
comorbid with each other than with externalizing disorders (and
vice versa). Rates of new disorder onsets after age 15 were as
follows: 148 MDD, 21 DYS, 43 GAD, 22 PAN, 114 SOC, 77
SPEC, 36 PTSD, 17 OCD, 190 ALC, and 164 DRUG.
Table 1 also shows that a history of internalizing disorder—with
the exception of specific phobia— generally was associated with
appraisal bias (i.e., overestimation of impact) at both assessment
waves. In contrast, externalizing diagnoses were largely unrelated
to appraisal bias (mean r⫽.03).
Latent Structure of Mental Disorders
Prospective model. The initial correlated two-factor model
did not offer acceptable fit to the data, ␹
2
(34) ⫽57.85, p⫽.01;
CFI ⫽.90; TLI ⫽0.86; RMSEA ⫽0.03; WRMR ⫽0.91. The
factor loadings of all indicators (range: .40 –.77) were statistically
significant at the .001 alpha level, except for specific phobia (␭⫽
.21, p⬍.05) and OCD (␭⫽.34, p⫽.08). We removed these two
diagnoses from subsequent iterations of the prospective model.
The revised two-factor model (i.e., without SPEC and OCD)
yielded a much better fit to the data, ␹
2
(19) ⫽27.96, p⫽.08;
CFI ⫽.97; TLI ⫽0.95; RMSEA ⫽0.03; WRMR ⫽0.77. The
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1083
STRESS APPRAISAL BIAS AND PSYCHOPATHOLOGY

correlation between the Internalizing and Externalizing factors in
this revised model was .43 (p⬍.001).
Our attempt to fit a hierarchical three-factor model, in which a
second-order Internalizing factor bifurcated into Distress and Fear
subfactors, produced an improper solution. Specifically, the stan-
dardized factor loading of Fear on Internalizing was greater than
unity. Ad hoc inspection of an oblique three-factor model, in
which correlations between first-order Fear, Distress, and Exter-
nalizing factors were freely estimated, revealed a correlation
greater than one between Fear and Distress. We concluded that
Fear and Distress dimensions could not be meaningfully distin-
guished in these data. Similarly, attempts to fit the bifactor model
did not produce an interpretable solution (i.e., negative variance
estimates for the specific factors). Thus, the two-factor measure-
ment model was applied in subsequent structural analyses.
Concurrent model. The two-factor model fit the data well,
␹
2
(53) ⫽75.56, p⫽.02; CFI ⫽.96; TLI ⫽0.95; RMSEA ⫽0.02;
WRMR ⫽0.92. The factor loadings of all indicators (range:
.42–.82) were statistically significant at the .001 alpha level, ex-
cept for OCD (␭⫽.25, p⫽.09). We therefore omitted OCD from
subsequent models. A revised two-factor model (i.e., excluding
OCD) produced a nearly identical pattern of factor loadings and
equivalent fit to the data, ␹
2
(43) ⫽64.43, p⫽.02; CFI ⫽.96;
TLI ⫽0.95; RMSEA ⫽0.02; WRMR ⫽0.93. The correlation
between the Internalizing and Externalizing factors in this revised
model was .39 (p⬍.001). Attempts to fit more complicated
structural models were unsuccessful for the same reasons reported
for the prospective model above. Thus, the two-factor configura-
tion was applied in structural analyses involving the concurrent
model as well.
Transdiagnostic and Diagnosis-Specific Associations
With Stress Appraisal Bias
Prospective model. Internalizing and Externalizing factors
were regressed simultaneously on age 15 appraisal bias. As shown
in Figure 1, bias was positively related to Internalizing (b⫽0.17,
SE ⫽0.04, p⬍.001, ␤⫽.27, f
2
⫽.07) and negatively, though
nonsignificantly, related to Externalizing (b⫽⫺0.07, SE ⫽0.05,
p⫽.15, ␤⫽⫺.08, f
2
⫽.01). Next, Internalizing was regressed on
age 15 appraisal bias while statistically controlling for variation in
Externalizing (and vice versa). As mentioned above, this step was
intended to partial out overlap among the transdiagnostic factors,
essentially computing a partial correlation between appraisal bias
and each factor. Holding Externalizing constant, the positive effect
of bias on Internalizing was even stronger (b⫽0.19, SE ⫽0.04,
p⬍.001, ␤⫽0.30). In contrast, after adjusting for Internalizing,
the appraisal bias was significantly associated with Externalizing
but in the opposite direction (b⫽⫺0.17, SE ⫽0.05, p⬍.01,
␤⫽⫺.21). The direction of this effect indicated that, after
accounting for the overlap between Internalizing and Externaliz-
ing, more benign perceptions of stressor impact, relative to the
objective rating team, predicted higher Externalizing levels.
We followed up with a planned test of the equality of the
magnitude of structural associations of the Internalizing versus
Externalizing factors with appraisal bias. That is, we evaluated the
extent to which appraisals had divergent associations with Inter-
nalizing versus Externalizing. Chi-square difference tests (per-
formed with the DIFFTEST procedure in Mplus) indicated that
model fit deteriorated significantly when the effects of Internaliz-
ing and Externalizing were constrained to equality, ␹diff
2(1) ⫽
17.21, p⬍.001, suggesting that the size—and, indeed, the direc-
tion— of these effects was significantly different. Comparison of
other model fit indices in the restricted (CFI
r
⫽.96; TLI
r
⫽0.95;
RMSEA
r
⫽0.02) versus unrestricted (CFI
u
⫽.99; TLI
u
⫽0.98;
RMSEA
u
⫽0.01) models corroborated this result.
We also used difference tests to examine gender as a moderator
of transdiagnostic effects on stress appraisal. Nonsignificant Wald
tests demonstrated that the associations of appraisal bias with
Internalizing and Externalizing did not differ by gender, ␹diffs
2(1) ⬍
2, ps⬎.05. Likewise, Wald tests indicated that neither maternal
Table 1
Correlations Among Lifetime Mental Disorders and Appraisal Bias Indices
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. MDD —
2. DYS .47
ⴱⴱⴱ
—
3. GAD .56
ⴱⴱⴱ
.38
ⴱⴱⴱ
—
4. PAN .60
ⴱⴱⴱ
.51
ⴱⴱⴱ
.52
ⴱⴱⴱ
—
5. SOC .29
ⴱⴱⴱ
.25
ⴱⴱ
.46
ⴱⴱⴱ
.25
ⴱ
—
6. SPEC .13 .16 .32
ⴱⴱⴱ
.22 .25
ⴱⴱⴱ
—
7. PTSD .58
ⴱⴱⴱ
.37
ⴱⴱⴱ
.36
ⴱⴱⴱ
.41
ⴱⴱⴱ
.09 .25
ⴱⴱ
—
8. OCD .04 .01 .25 .24 ⫺.01 ⫺.02 .30
ⴱ
—
9. CON .22 .31
ⴱ
.08 .28 .18 .17 ⫺.04 ⫺.01 —
10. ODD .30
ⴱⴱ
.03 .29
ⴱ
.10 .42
ⴱⴱⴱ
⫺.09 .31
ⴱ
.31 ⫺.02
ⴱ
—
11. ALC .18
ⴱⴱ
.01 .23
ⴱⴱ
.12 .22
ⴱⴱ
⫺.04 .19
ⴱ
.18 .42
ⴱⴱⴱ
.42
ⴱⴱⴱ
—
12. DRUG .20
ⴱⴱ
.23
ⴱⴱ
.21
ⴱ
.31
ⴱⴱ
.18
ⴱ
⫺.03 .30
ⴱⴱⴱ
.22 .63
ⴱⴱⴱ
.35
ⴱⴱ
.62
ⴱⴱⴱ
—
13. BIAS15 .24
ⴱⴱⴱ
.12
ⴱ
.22
ⴱⴱⴱ
.15 .13
ⴱ
.08 .23
ⴱⴱ
.00 .15 .01 ⫺.03 ⫺.09 —
14. BIAS20 .28
ⴱⴱⴱ
.16
ⴱ
.36
ⴱⴱⴱ
.22
ⴱ
⫺.02 .08 .26
ⴱⴱⴱ
.01 ⫺.04 .16 ⫺.01 ⫺.06 .16
ⴱⴱⴱ
—
N(%) 220 (27) 71 (9) 56 (7) 24 (3) 144 (18) 110 (13) 48 (6) 21 (3) 21 (3) 22 (3) 198 (24) 177 (22)
Note. Correlations among diagnoses are tetrachoric correlations. N⫽number of participants qualifying for a diagnosis; MDD ⫽major depressive
disorder; DYS ⫽dysthymia; GAD ⫽generalized anxiety disorder; PAN ⫽panic disorder; SOC ⫽social phobia; SPEC ⫽specific phobia; PTSD ⫽
posttraumatic stress disorder; OCD ⫽obsessive-compulsive disorder; CON ⫽conduct disorder; ODD ⫽oppositional defiant disorder; ALC ⫽alcohol
abuse/dependence; DRUG ⫽drug abuse/dependence; BIAS15 ⫽age 15 stress appraisal bias; BIAS20 ⫽age 20 stress appraisal bias.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1084 CONWAY, STARR, ESPEJO, BRENNAN, AND HAMMEN

depression history (44% of sample) or psychiatric medication use
(3.4%) moderated the effects of age 15 appraisal bias on transdi-
agnostic dimensions, ␹diffs
2(1) ⬍3, ps⬎.05. Moreover, the pattern
and statistical significance of results were unaltered if any or all of
these variables (i.e., gender, maternal depression, medication use)
were included as a covariate (full results available from Christo-
pher C. Conway upon request).
In a final step, we examined the diagnosis-specific pathways
between psychopathology and appraisal bias. We inspected the
modification indices to determine whether age 15 appraisal bias
had a statistically significant association with any residual com-
ponents of individual disorders. No modification index exceeded
our predetermined minimum value of 3.84. Thus, it appeared that
associations between diagnostic categories and appraisal bias were
accounted for by the transdiagnostic dimensions.
Concurrent model. As above, we began by regressing the
two factors simultaneously onto age 20 appraisal bias. Young adult
appraisal bias was positively associated with Internalizing (b⫽
0.25, SE ⫽0.04, p⬍.001, ␤⫽.31, f
2
⫽.10), but it was virtually
uncorrelated with Externalizing (b⫽⫺0.02, SE ⫽0.04, p⫽.46,
␤⫽⫺.04, f
2
⫽.001). To evaluate the unique relationship of
appraisal bias with Internalizing, we added Externalizing as a
covariate. Holding Externalizing constant, appraisal bias had a
moderate, positive association with Internalizing levels (b⫽0.45,
SE ⫽0.08, p⬍.001, ␤⫽0.34). Analogous to our prospective
model result, when adjusting for the overlap with Internalizing, a
statistically significant inverse association emerged between age
20 appraisal bias and Externalizing (b⫽⫺0.13, SE ⫽0.04, p⬍
.01, ␤⫽⫺.18).
As before, a chi-square difference test confirmed that the mag-
nitude of the association between appraisal bias and Internalizing
significantly differed from (i.e., was greater than) the association
between appraisal bias and Externalizing, ␹diff
2(1) ⫽29.26, p⬍
.001 (restricted model: CFI
r
⫽.89; TLI
r
⫽0.87; RMSEA
r
⫽0.04;
unrestricted model: CFI
u
⫽.96; TLI
u
⫽0.94; RMSEA
u
⫽0.03).
Mirroring results from the prospective model, the connections
between young adult appraisal bias and the transdiagnostic dimen-
sions were equally strong across gender, maternal depression his-
tory, and medication status, ␹diffs
2(1) ⬍2, ps⬎.05. Finally, there
was no evidence of statistically significant associations between
age 20 appraisal bias and any diagnostic residual (i.e., no modifi-
cation index value of 3.84 or greater).
Discussion
Quantitative modeling of the latent structure of mental disorders
has provided compelling evidence for a transdiagnostic model
anchored by Internalizing and Externalizing dimensions that un-
derpin major forms of psychopathology. A new phase of investi-
gation now aims to evaluate the predictive validity of the transdi-
agnostic dimensions to determine their applied value. That is, it
remains to be seen whether transdiagnostic factors emerging from
quantitative structural research actually stand to improve mental
health research and treatment practices. In the present study, we
examined associations of the Internalizing and Externalizing di-
mensions with stress appraisal bias, a cognitive risk marker for a
wide array of disorders. Our data supported the discriminant va-
lidity of the transdiagnostic constructs. After adjusting for the
covariation between dimensions, adolescent stress hypersensitivity
was prospectively linked to higher Internalizing levels, whereas
stress hyposensitivity was prospectively related to higher Exter-
nalizing levels. Further, once standing on the Internalizing and
Externalizing factors was held constant, none of the constituent
diagnoses were linked to appraisal bias. Cross-sectional analyses
Figure 1. Path diagram (with standardized path coefficients) illustrating the contrasting predictive effects of
age 15 appraisal bias on Internalizing and Externalizing factors. These effects diverge further if the overlap
between factors is partialed out (see main text). MDD ⫽major depressive disorder; DYS ⫽dysthymia; GAD ⫽
generalized anxiety disorder; PAN ⫽panic disorder; SOC ⫽social phobia; PTSD ⫽posttraumatic stress
disorder; ALC ⫽alcohol abuse/dependence; DRUG ⫽drug abuse/dependence. All factor loadings are statis-
tically significant at the .001 alpha level.
ⴱⴱⴱ
p⬍.001.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1085
STRESS APPRAISAL BIAS AND PSYCHOPATHOLOGY

of appraisal bias and psychopathology in young adulthood re-
vealed an identical pattern of results.
Elevation on the Internalizing dimension was associated with
exaggerated cognitive responses to naturally occurring stressors.
This result aligns with data from an extensive body of observa-
tional and experimental research examining the stress response in
anxious and depressed populations. That is, investigators have
consistently documented abnormal cognitive styles—in both sub-
jective (e.g., attitudinal) and implicit (e.g., speeded information-
processing) domains (see Gotlib & Joormann, 2010)—in anxiety
and depressive disorders. Moreover, our findings concur with
laboratory studies that report disruptions in online biological pa-
rameters of the stress response (e.g., limbic metabolism, cortisol
secretion) in emotional disorders (e.g., Craske et al., 2009;Hariri,
Drabant, & Weinberger, 2006). We extend these lines of research
by finding cognitive distortions in response to naturally occurring,
as opposed to laboratory-based, stressors among those at risk for
anxiety and depression. More importantly, our results suggest that
the commonalities among the various internalizing disorders drive
the associations between these conditions and stress-related phe-
notypes.
Our analysis of the Externalizing spectrum offers a new per-
spective on prior studies of antisocial behavior and substance use
disorders. These syndromes generally have been associated with
blunted biological and psychological responses to stressful and
emotional cues (e.g., Gao, Tuvblad, Schell, Baker, & Raine, 2015;
Syngelaki et al., 2013). In the present study, bivariate associations
between the Externalizing spectrum and stress appraisal bias re-
vealed little evidence of hyposensitivity. However, a very different
pattern of findings—wherein Externalizing was associated with
appraisals that discounted the potential negative impact accompa-
nying stressful events— emerged after adjusting for the overlap
between the transdiagnostic dimensions. It is a testament to the
scientific utility of the structural model that this association be-
tween the Externalizing spectrum and appraisal bias would have
been overlooked if either (a) individual antisocial behavior and
substance use diagnoses had been analyzed (i.e., the Externalizing
factor was greater than the sum of its parts), or (b) comorbidity
with Internalizing psychopathology had been neglected. The di-
vergence in biases characterizing Internalizing and Externalizing is
consistent with prior work by Caspi et al. (2014), who uncovered
different psychosocial correlates of Internalizing and Externalizing
traits once the overlap between these two dimensions was partialed
out.
Our findings advance the growing research literature on the
concurrent and predictive validity of the Internalizing and Exter-
nalizing dimensions. Prior investigations have found that variation
on transdiagnostic dimensions parsimoniously accounts for asso-
ciations between categorical disorders and suicide, marital distress,
intergenerational transmission of psychopathology, and childhood
maltreatment (e.g., Keyes et al., 2012;South et al., 2011;Starr et
al., 2014). In combination with these previous studies, our findings
reinforce the notion that disorders historically examined indepen-
dently are, in many research contexts, better understood in terms of
their common elements (Krueger & Markon, 2006). Future inquiry
into the correlates (i.e., nomological network) of the transdiagnos-
tic traits is needed to evaluate that hypothesis across various
outcomes (e.g., biological, cognitive, interpersonal) and research
settings. Convincing empirical evidence of the superior construct
validity of transdiagnostic dimensions, relative to categorical dis-
orders, is needed for the transdiagnostic model to gain momentum
as a research and clinical tool.
If supported, the quantitative model has the potential to unify
research practices and treatment protocols that traditionally have
fractured along diagnostic lines. For instance, the present results
suggest that future studies on stress reactivity phenotypes may
benefit from focusing on transdiagnostic effects rather than com-
paring stress responses in one disorder category versus a healthy
control group. Thus, paralleling RDoC-informed recruitment strat-
egies (e.g., Cuthbert & Kozak, 2013), patients with any anxiety or
depressive disorder might be recruited for an investigation of the
Internalizing spectrum and stress responses. Aside from enhancing
the efficiency of investigations, transdiagnostic models may align
more closely with basic genetic, biological, and cognitive param-
eters that notoriously show a pattern of multifinality with respect
to mental disorder categories (Ofrat & Krueger, 2012). Thus,
transdiagnostic dimensions may serve as useful intermediate phe-
notypes in the causal chain from distal biological and cognitive
processes to complex clinical syndromes.
Analogous implications follow for the treatment and prevention
of psychological disorders. If closely related disorders have a
common substrate, perhaps that shared pathology could be an
effective target for psychological or psychotropic treatment. Ame-
liorating core psychopathological processes, including stress ap-
praisal biases, theoretically could address related conditions simul-
taneously, enhancing the efficiency of treatment for comorbid
conditions. Barlow and colleagues are leading the way in the
development of a transdiagnostic program for the treatment of
internalizing disorders based on quantitative models of anxiety and
depressive disorder comorbidity (Barlow, Sauer-Zavala, Carl, Bul-
lis, & Ellard, 2014; see also Reinholt & Krogh, 2014). Similarly,
if certain risk factors are shown to predispose to an array of related
disorders (e.g., Nolen-Hoeksema & Watkins, 2011), prevention
efforts might be streamlined by targeting these transdiagnostic
vulnerabilities. For instance, the present results suggest indirectly
that addressing the extremes of cognitive appraisals of stressful
circumstances might mitigate risk for internalizing and external-
izing conditions.
Our study’s contributions should be considered in the context of
several limitations. First, the temporal sequencing of bias and
disorder onset could be resolved only for the age 15 appraisal bias
index. The conceptual status of stress appraisal bias in young
adulthood as an antecedent versus correlate of psychopathology
could not be determined here. Second, coverage of clinical disor-
ders was limited. It is possible that the inclusion of additional, less
prevalent (e.g., bipolar, psychotic, eating) disorders could alter the
number and nature of transdiagnostic dimensions (e.g., Forbush &
Watson, 2013) or their pattern of association with stress appraisal
bias. Along these same lines, the incidence of new antisocial
behavior disorders in late adolescence was too low for them to be
included in prospective analyses, consistent with epidemiological
data showing that these disorders typically onset prior to age 15
(Kessler et al., 2005). As a consequence, the Externalizing factor
in these analyses was limited to substance use disorders. Although
it is encouraging that the prospective results were consistent with
those from the cross-sectional model, which included a wider
range of disorders, further work assessing a broader range of
psychopathology is needed. In fact, the rates of antisocial behavior
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1086 CONWAY, STARR, ESPEJO, BRENNAN, AND HAMMEN

disorders were low across the study timeframe, perhaps due to
recruitment procedures that overselected for maternal depression.
This imbalance in the rates of Internalizing and certain External-
izing disorders may limit, to some extent, generalizability of the
results.
Third, recent quantitative research has pointed out that including
symptom dimensions, as opposed to diagnostic categories, as
manifest indicators of latent psychopathology constructs can re-
veal a more nuanced transdiagnostic architecture to psychopathol-
ogy (e.g., Markon, 2010;Prenoveau et al., 2010;Wright et al.,
2013). Symptom-level data were not available in the present study.
Thus, continuous factor indicators (e.g., a symptom-based ap-
proach) may have improved our chances of fitting more compli-
cated factor models (e.g., three-factor model including the subdi-
vision of Internalizing into Fear and Distress factors). Fourth, the
stability of the appraisal bias index across assessment waves was
lower than anticipated (r⫽.16). It is possible that the changing
nature of stressors experienced from adolescence to adulthood, or
simply normative developmental changes in stress response pro-
cesses over the five years between assessment waves, contributed
to the relatively low autocorrelation. We hypothesize that stress
appraisal bias is a traitlike process, perhaps one that becomes more
stable with age. Finally, the present study concentrated on the
transition from adolescence to adulthood. It is conceivable that the
latent architecture of mental disorders or associations between
disorders and stress reactivity might differ across developmental
epochs (e.g., Wittchen et al., 2009). Similarly, the relatively high
prevalence of maternal depression may have altered the meaning
or configuration of transdiagnostic factors that emerged in our
analyses, relative to what might be observed in unselected sam-
ples.
In sum, we found that transdiagnostic Internalizing and Exter-
nalizing dimensions explained associations between common
mental disorders and stress appraisal biases. Additionally, we
provided novel evidence of discriminant validity for the transdi-
agnostic constructs, in that Internalizing and Externalizing were
associated with distinct appraisal bias profiles. Continued empir-
ical research into the transdiagnostic dimensions is needed to
determine whether these constructs help our theories and treat-
ments work better. Indeed, the transdiagnostic model has the
potential to compete with RDoC to serve as the organizing frame-
work for nosology, etiological research, and treatment going for-
ward. However, persuasive evidence for its concurrent and predic-
tive validity is needed before it can be accepted as a viable
research or diagnostic tool. The present findings provide promising
evidence that it can enhance the efficiency and validity of life
stress research.
References
Achenbach, T. M. (1966). The classification of children’s psychiatric
symptoms: A factor-analytic study. Psychological Monographs, 80,
1–37. http://dx.doi.org/10.1037/h0093906
Barlow, D. H., Sauer-Zavala, S., Carl, J. R., Bullis, J. R., & Ellard, K. K.
(2014). The nature, diagnosis, and treatment of neuroticism: Back to the
future. Clinical Psychological Science, 2, 344 –365. http://dx.doi.org/10
.1177/2167702613505532
Beck, A. T. (1976). Cognitive therapy and the emotional disorders. New
York, NY: International Universities Press.
Bedford, A., & Foulds, G. (1978). Delusions-Symptoms-States Inventory of
Anxiety and Depression. Windsor, England: National Foundation for
Educational Research.
Birbaumer, N., Veit, R., Lotze, M., Erb, M., Hermann, C., Grodd, W., &
Flor, H. (2005). Deficient fear conditioning in psychopathy: A functional
magnetic resonance imaging study. Archives of General Psychiatry, 62,
799 – 805. http://dx.doi.org/10.1001/archpsyc.62.7.799
Brown, G. W., & Harris, T. (1978). Social origins of depression: A study
of psychiatric disorder in women. London, England: Tavistock.
Byrd, A. L., Loeber, R., & Pardini, D. A. (2014). Antisocial behavior,
psychopathic features and abnormalities in reward and punishment pro-
cessing in youth. Clinical Child and Family Psychology Review, 17,
125–156. http://dx.doi.org/10.1007/s10567-013-0159-6
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington,
H., Israel, S.,...Moffitt, T. E. (2014). The pfactor: One general
psychopathology factor in the structure of psychiatric disorders? Clinical
Psychological Science, 2, 119 –137. http://dx.doi.org/10.1177/
2167702613497473
Cole, D. A., Martin, J. M., Peeke, L. G., Seroczynski, A. D., & Hoffman,
K. (1998). Are cognitive errors of underestimation predictive or reflec-
tive of depressive symptoms in children: A longitudinal study. Journal
of Abnormal Psychology, 107, 481– 496. http://dx.doi.org/10.1037/0021-
843X.107.3.481
Conway, C. C., Hammen, C., Espejo, E. P., Wray, N. R., Najman, J. M.,
& Brennan, P. A. (2012). Appraisals of stressful life events as a
genetically-linked mechanism in the stress– depression relationship.
Cognitive Therapy and Research, 36, 338 –347. http://dx.doi.org/10
.1007/s10608-011-9368-9
Conway, C. C., Rutter, L. A., & Brown, T. A. (2016). Chronic environ-
mental stress and the temporal course of depression and panic disorder:
A trait-state-occasion modeling approach. Journal of Abnormal Psychol-
ogy, 125, 53– 63. http://dx.doi.org/10.1037/abn0000122
Craske, M. G., Rauch, S. L., Ursano, R., Prenoveau, J., Pine, D. S., &
Zinbarg, R. E. (2009). What is an anxiety disorder? Depression and
Anxiety, 26, 1066 –1085. http://dx.doi.org/10.1002/da.20633
Cuthbert, B. N., & Kozak, M. J. (2013). Constructing constructs for
psychopathology: The NIMH research domain criteria. Journal of Ab-
normal Psychology, 122, 928 –937. http://dx.doi.org/10.1037/a0034028
Dahl, R. E., & Gunnar, M. R. (2009). Heightened stress responsiveness and
emotional reactivity during pubertal maturation: Implications for psy-
chopathology. Development and Psychopathology, 21, 1– 6. http://dx.doi
.org/10.1017/S0954579409000017
Dawes, M. A., Dorn, L. D., Moss, H. B., Yao, J. K., Kirisci, L., Ammer-
man, R. T., & Tarter, R. E. (1999). Hormonal and behavioral homeo-
stasis in boys at risk for substance abuse. Drug and Alcohol Dependence,
55, 165–176. http://dx.doi.org/10.1016/S0376-8716(99)00003-4
De Los Reyes, A., & Prinstein, M. J. (2004). Applying depression-
distortion hypotheses to the assessment of peer victimization in adoles-
cents. Journal of Clinical Child and Adolescent Psychology, 33, 325–
335. http://dx.doi.org/10.1207/s15374424jccp3302_14
De Vries-Bouw, M., Popma, A., Vermeiren, R., Doreleijers, T. A., Van De
Ven, P. M., & Jansen, L. M. (2011). The predictive value of low heart
rate and heart rate variability during stress for reoffending in delinquent
male adolescents. Psychophysiology, 48, 1597–1604. http://dx.doi.org/
10.1111/j.1469-8986.2011.01233.x
Eaton, N. R., Keyes, K. M., Krueger, R. F., Balsis, S., Skodol, A. E.,
Markon, K. E.,...Hasin, D. S. (2012). An invariant dimensional
liability model of gender differences in mental disorder prevalence:
Evidence from a national sample. Journal of Abnormal Psychology, 121,
282–288. http://dx.doi.org/10.1037/a0024780
Eaton, N. R., Rodriguez-Seijas, C., Carragher, N., & Krueger, R. F. (2015).
Transdiagnostic factors of psychopathology and substance use disorders:
A review. Social Psychiatry and Psychiatric Epidemiology, 50, 171–
182. http://dx.doi.org/10.1007/s00127-014-1001-2
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1087
STRESS APPRAISAL BIAS AND PSYCHOPATHOLOGY

Espejo, E. P., Ferriter, C. T., Hazel, N. C., Keenan-Miller, D., Hoffman,
L. R., & Hammen, C. (2011). Predictors of subjective ratings of stressor
severity: The effects of current mood and neuroticism. Stress and
Health, 27, 23–33. http://dx.doi.org/10.1002/smi.1315
Espejo, E. P., Hammen, C., & Brennan, P. A. (2012). Elevated appraisals
of the negative impact of naturally occurring life events: A risk factor for
depressive and anxiety disorders. Journal of Abnormal Child Psychol-
ogy, 40, 303–315. http://dx.doi.org/10.1007/s10802-011-9552-0
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1995).
Structured clinical Interview for DSM–IV Axis I disorders. Washington,
DC: American Psychiatric Press.
Forbush, K. T., & Watson, D. (2013). The structure of common and
uncommon mental disorders. Psychological Medicine, 43, 97–108.
http://dx.doi.org/10.1017/S0033291712001092
Frick, P. J., & Dickens, C. (2006). Current perspectives on conduct
disorder. Current Psychiatry Reports, 8, 59 –72. http://dx.doi.org/10
.1007/s11920-006-0082-3
Gao, Y., Tuvblad, C., Schell, A., Baker, L., & Raine, A. (2015). Skin
conductance fear conditioning impairments and aggression: A longitu-
dinal study. Psychophysiology, 52, 288 –295. http://dx.doi.org/10.1111/
psyp.12322
Ge, X., Conger, R. D., & Elder, G. H., Jr. (2001). Pubertal transition,
stressful life events, and the emergence of gender differences in adoles-
cent depressive symptoms. Developmental Psychology, 37, 404 – 417.
http://dx.doi.org/10.1037/0012-1649.37.3.404
Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current
status and future directions. Annual Review of Clinical Psychology, 6,
285–312. http://dx.doi.org/10.1146/annurev.clinpsy.121208.131305
Gross, J. J., & Jazaieri, H. (2014). Emotion, emotion regulation, and
psychopathology: An affective science perspective. Clinical Psycholog-
ical Science, 2, 387– 401. http://dx.doi.org/10.1177/2167702614536164
Hammen, C. (2005). Stress and depression. Annual Review of Clinical
Psychology, 1, 293–319. http://dx.doi.org/10.1146/annurev.clinpsy.1
.102803.143938
Hammen, C. (2016). Depression and stressful environments: Identifying
gaps in conceptualization and measurement. Anxiety, Stress, and Cop-
ing. Advance online publication. http://dx.doi.org/10.1080/10615806
.2015.1134788
Hammen, C., & Brennan, P. A. (2001). Depressed adolescents of depressed
and nondepressed mothers: Tests of an interpersonal impairment hy-
pothesis. Journal of Consulting and Clinical Psychology, 69, 284 –294.
Hammen, C., Brennan, P. A., Keenan-Miller, D., & Herr, N. R. (2008).
Early onset recurrent subtype of adolescent depression: Clinical and
psychosocial correlates. Journal of Child Psychology and Psychiatry,
49, 433– 440. http://dx.doi.org/10.1111/j.1469-7610.2007.01850.x
Hammen, C., Gordon, D., Burge, D., Adrian, C., Jaenicke, C., & Hiroto, D.
(1987). Maternal affective disorders, illness, and stress: Risk for chil-
dren’s psychopathology. The American Journal of Psychiatry, 144,
736 –741. http://dx.doi.org/10.1176/ajp.144.6.736
Hammen, C., Kim, E. Y., Eberhart, N. K., & Brennan, P. A. (2009).
Chronic and acute stress and the prediction of major depression in
women. Depression and Anxiety, 26, 718 –723. http://dx.doi.org/10
.1002/da.20571
Hariri, A. R., Drabant, E. M., & Weinberger, D. R. (2006). Imaging
genetics: Perspectives from studies of genetically driven variation in
serotonin function and corticolimbic affective processing. Biological
Psychiatry, 59, 888 – 897. http://dx.doi.org/10.1016/j.biopsych.2005.11
.005
Hlastala, S. A., Frank, E., Kowalski, J., Sherrill, J. T., Tu, X. M., Anderson,
B., & Kupfer, D. J. (2000). Stressful life events, bipolar disorder, and the
“kindling model”. Journal of Abnormal Psychology, 109, 777–786.
http://dx.doi.org/10.1037/0021-843X.109.4.777
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance
structure analysis: Conventional criteria versus new alternatives. Struc-
tural Equation Modeling, 6, 1–55. http://dx.doi.org/10.1080/
10705519909540118
Joormann, J., & Vanderlind, W. M. (2014). Emotion regulation in depres-
sion: The role of biased cognition and reduced cognitive control. Clin-
ical Psychological Science, 2, 402– 421. http://dx.doi.org/10.1177/
2167702614536163
Keeping, J. D., Najman, J. M., Morrison, J., Western, J. S., Andersen,
M. J., & Williams, G. M. (1989). A prospective longitudinal study of
social, psychological and obstetric factors in pregnancy: Response rates
and demographic characteristics of the 8,556 respondents. BJOG: An
International Journal of Obstetrics & Gynaecology, 96, 289 –297. http://
dx.doi.org/10.1111/j.1471-0528.1989.tb02388.x
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., &
Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions
of DSM–IV disorders in the National Comorbidity Survey Replication.
Archives of General Psychiatry, 62, 593– 602. http://dx.doi.org/10.1001/
archpsyc.62.6.593
Kessler, R. C., Cox, B. J., Green, J. G., Ormel, J., McLaughlin, K. A.,
Merikangas, K. R.,...Zaslavsky, A. M. (2011). The effects of latent
variables in the development of comorbidity among common mental
disorders. Depression and Anxiety, 28, 29 –39. http://dx.doi.org/10.1002/
da.20760
Keyes, K. M., Eaton, N. R., Krueger, R. F., McLaughlin, K. A., Wall,
M. M., Grant, B. F., & Hasin, D. S. (2012). Childhood maltreatment and
the structure of common psychiatric disorders. The British Journal of
Psychiatry, 200, 107–115. http://dx.doi.org/10.1192/bjp.bp.111.093062
Kim, K. J., Conger, R. D., Elder, G. H., Jr., & Lorenz, F. O. (2003).
Reciprocal influences between stressful life events and adolescent inter-
nalizing and externalizing problems. Child Development, 74, 127–143.
http://dx.doi.org/10.1111/1467-8624.00525
Krackow, E., & Rudolph, K. D. (2008). Life stress and the accuracy of
cognitive appraisals in depressed youth. Journal of Clinical Child and
Adolescent Psychology, 37, 376 –385. http://dx.doi.org/10.1080/
15374410801955797
Krueger, R. F. (1999). The structure of common mental disorders. Archives
of General Psychiatry, 56, 921–926. http://dx.doi.org/10.1001/archpsyc
.56.10.921
Krueger, R. F., Caspi, A., Moffitt, T. E., & Silva, P. A. (1998). The
structure and stability of common mental disorders (DSM–III–R): A
longitudinal-epidemiological study. Journal of Abnormal Psychology,
107, 216 –227. http://dx.doi.org/10.1037/0021-843X.107.2.216
Krueger, R. F., Chentsova-Dutton, Y. E., Markon, K. E., Goldberg, D., &
Ormel, J. (2003). A cross-cultural study of the structure of comorbidity
among common psychopathological syndromes in the general health
care setting. Journal of Abnormal Psychology, 112, 437– 447. http://dx
.doi.org/10.1037/0021-843X.112.3.437
Krueger, R. F., & Markon, K. E. (2006). Reinterpreting comorbidity: A
model-based approach to understanding and classifying psychopathol-
ogy. Annual Review of Clinical Psychology, 2, 111–133. http://dx.doi
.org/10.1146/annurev.clinpsy.2.022305.095213
Krueger, R. F., & Piasecki, T. M. (2002). Toward a dimensional and
psychometrically-informed approach to conceptualizing psychopathol-
ogy. Behaviour Research and Therapy, 40, 485– 499. http://dx.doi.org/
10.1016/S0005-7967(02)00016-5
Krueger, R. F., & South, S. C. (2009). Externalizing disorders: Cluster
5 of the proposed meta-structure for DSM–V and ICD-11.Psycho-
logical Medicine, 39, 2061–2070. http://dx.doi.org/10.1017/
S0033291709990328
Laceulle, O. M., Vollebergh, W. A. M., & Ormel, J. (2015). The structure
of psychopathology in adolescence: Replication of a general psychopa-
thology factor in the TRAILS study. Clinical Psychological Science, 3,
850 – 860. http://dx.doi.org/10.1177/2167702614560750
Lahey, B. B., Applegate, B., Hakes, J. K., Zald, D. H., Hariri, A. R., &
Rathouz, P. J. (2012). Is there a general factor of prevalent psychopa-
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1088 CONWAY, STARR, ESPEJO, BRENNAN, AND HAMMEN

thology during adulthood? Journal of Abnormal Psychology, 121, 971–
977. http://dx.doi.org/10.1037/a0028355
Markon, K. E. (2010). How things fall apart: Understanding the nature of
internalizing through its relationship with impairment. Journal of Ab-
normal Psychology, 119, 447– 458. http://dx.doi.org/10.1037/a0019707
Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional
disorders. Annual Review of Clinical Psychology, 1, 167–195. http://dx
.doi.org/10.1146/annurev.clinpsy.1.102803.143916
Monroe, S. M. (2008). Modern approaches to conceptualizing and mea-
suring human life stress. Annual Review of Clinical Psychology, 4,
33–52. http://dx.doi.org/10.1146/annurev.clinpsy.4.022007.141207
Monroe, S. M., Slavich, G. M., & Georgiades, K. (2014). The social
environment and depression: The role of life stress. In I. H. Gotlib &
C. L. Hammen (Eds.), Handbook of depression (3rd ed., pp. 296 –314).
New York, NY: Guilford Press.
Muthén, L. K., & Muthén, B. O. (1998 –2014). Mplus (Version 7) [Com-
puter software]. Los Angeles, CA: Author.
Myin-Germeys, I., & van Os, J. (2007). Stress-reactivity in psychosis:
Evidence for an affective pathway to psychosis. Clinical Psychology
Review, 27, 409 – 424. http://dx.doi.org/10.1016/j.cpr.2006.09.005
Nolen-Hoeksema, S., & Watkins, E. R. (2011). A heuristic for developing
transdiagnostic models of psychopathology: Explaining multifinality
and divergent trajectories. Perspectives on Psychological Science, 6,
589 – 609. http://dx.doi.org/10.1177/1745691611419672
Ofrat, S., & Krueger, R. F. (2012). How research on the meta-structure of
psychopathology aids in understanding biological correlates of mood
and anxiety disorders. Biology of Mood and Anxiety Disorders, 2, 13–17.
http://dx.doi.org/10.1186/2045-5380-2-13
Orvaschel, H. (1995). Schedule for Affective Disorder and Schizophrenia
for School-Age Children Epidemiologic (Version 5). Ft. Lauderdale, FL:
Nova Southeastern University, Center for Psychological Studies.
Prenoveau, J. M., Zinbarg, R. E., Craske, M. G., Mineka, S., Griffith, J. W.,
& Epstein, A. M. (2010). Testing a hierarchical model of anxiety and
depression in adolescents: A tri-level model. Journal of Anxiety Disor-
ders, 24, 334 –344. http://dx.doi.org/10.1016/j.janxdis.2010.01.006
Raine, A., Venables, P. H., & Williams, M. (1990). Relationships between
central and autonomic measures of arousal at age 15 years and crimi-
nality at age 24 years. Archives of General Psychiatry, 47, 1003–1007.
http://dx.doi.org/10.1001/archpsyc.1990.01810230019003
Reinholt, N., & Krogh, J. (2014). Efficacy of transdiagnostic cognitive
behaviour therapy for anxiety disorders: A systematic review and meta-
analysis of published outcome studies. Cognitive Behaviour Therapy,
43, 171–184. http://dx.doi.org/10.1080/16506073.2014.897367
Rohde, P., Lewinsohn, P. M., Klein, D. N., Seeley, J. R., & Gau, J. M.
(2013). Key characteristics of major depressive disorder occurring in
childhood, adolescence, emerging adulthood, and adulthood. Clinical
Psychological Science, 1, 41–53. http://dx.doi.org/10.1177/2167702
612457599
Sinha, R. (2001). How does stress increase risk of drug abuse and relapse?
Psychopharmacology, 158, 343–359. http://dx.doi.org/10.1007/
s002130100917
South, S. C., Krueger, R. F., & Iacono, W. G. (2011). Understanding
general and specific connections between psychopathology and marital
distress: A model based approach. Journal of Abnormal Psychology,
120, 935–947. http://dx.doi.org/10.1037/a0025417
Starr, L. R., Conway, C. C., Hammen, C. L., & Brennan, P. A. (2014).
Transdiagnostic and disorder-specific models of intergenerational trans-
mission of internalizing pathology. Psychological Medicine, 44, 161–
172. http://dx.doi.org/10.1017/S003329171300055X
Syngelaki, E. M., Fairchild, G., Moore, S. C., Savage, J. C., & van Goozen,
S. H. (2013). Affective startle potentiation in juvenile offenders: The
role of conduct problems and psychopathic traits. Social Neuroscience,
8, 112–121. http://dx.doi.org/10.1080/17470919.2012.712549
Tackett, J. L., Lahey, B. B., van Hulle, C., Waldman, I., Krueger, R. F., &
Rathouz, P. J. (2013). Common genetic influences on negative emotion-
ality and a general psychopathology factor in childhood and adoles-
cence. Journal of Abnormal Psychology, 122, 1142–1153. http://dx.doi
.org/10.1037/a0034151
van Leeuwen, N., Rodgers, R. F., Gibbs, J. C., & Chabrol, H. (2014).
Callous-unemotional traits and antisocial behavior among adolescents:
The role of self-serving cognitions. Journal of Abnormal Child Psychol-
ogy, 42, 229 –237. http://dx.doi.org/10.1007/s10802-013-9779-z
Watson, D. (2005). Rethinking the mood and anxiety disorders: A quan-
titative hierarchical model for DSM–V.Journal of Abnormal Psychol-
ogy, 114, 522–536. http://dx.doi.org/10.1037/0021-843X.114.4.522
Wittchen, H. U., Beesdo-Baum, K., Gloster, A. T., Höfler, M., Klotsche, J.,
Lieb, R.,...Kessler, R. C. (2009). The structure of mental disorders
re-examined: Is it developmentally stable and robust against additions?
International Journal of Methods in Psychiatric Research, 18, 189 –203.
http://dx.doi.org/10.1002/mpr.298
Wright, A. G., Krueger, R. F., Hobbs, M. J., Markon, K. E., Eaton, N. R.,
& Slade, T. (2013). The structure of psychopathology: Toward an
expanded quantitative empirical model. Journal of Abnormal Psychol-
ogy, 122, 281–294. http://dx.doi.org/10.1037/a0030133
Yoon, K. L., & Zinbarg, R. E. (2008). Interpreting neutral faces as
threatening is a default mode for socially anxious individuals. Journal of
Abnormal Psychology, 117, 680 – 685. http://dx.doi.org/10.1037/0021-
843X.117.3.680
Received October 22, 2015
Revision received March 23, 2016
Accepted March 23, 2016 䡲
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1089
STRESS APPRAISAL BIAS AND PSYCHOPATHOLOGY