Original Research Article

A Psychometric Evaluation of the
Expanded Version of the Inventory ofDepression and Anxiety Symptoms(IDAS-II) in Children and Adolescents

Assessment
2024, Vol. 31(3) 588–601
Ó The Author(s) 2023
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DOI: 10.1177/10731911231170841
journals.sagepub.com/home/asm

Matti Cervin1 , Carla Martı́ Valls1 , Stefan Möller1, Andreas Frick2,
Johannes Björkstrand1, and David Watson3

Abstract
The expanded version of the Inventory of Depression and Anxiety Symptoms (IDAS-II) is a self-report measure of 18 empirically derived internalizing symptom dimensions. The measure has shown good psychometric properties in adults but has
never been evaluated in children and adolescents. A Swedish version of the IDAS-II was administered to 633 children andadolescents (Mage =16.6 [SD = 2.0]) and 203 adults (Mage = 35.4 [SD = 12.1]). The model/data fit of the 18-factor structure
was excellent in both samples and measurement invariance across age groups was supported. All scales showed good to
excellent internal consistency and psychometric properties replicated in the younger youth sample (\ 16 years). Among
youth, good convergent validity was established for all scales and divergent validity for most scales. The IDAS-II was betterat identifying youth with current mental health problems than an internationally recommended scale of internalizing symptoms. In conclusion, the IDAS-II shows promise as a measure of internalizing symptoms in youth.
Keywords
IDAS-II, psychometrics, HiTOP, children, adolescents, internalizing, validation

Mental health difficulties and mental disorders aremajor contributors to everyday impairment in children
and adolescents (Gore et al., 2011). The most common
and most impairing difficulties are internalizing symptoms, including fear, anxiety, and depression (Caspiet al., 2020; Polanczyk et al., 2015). A vast majority of
all individuals who develop mental disorders develop
their first disorder before adulthood (Caspi et al., 2020)
and the peak age of onset is 14 years (Solmi et al., 2022).
The early development of mental health problems makes
research with children and adolescents important, andadequate measurement of symptoms is key to improving
our understanding of the development and impact ofmental disorders among youth.
Mental disorders in youth are commonly classified
according to the Diagnostic and Statistical Manual of
Mental Disorders (DSM; American Psychiatric
Association, 2022), where subsets of symptoms are usedas criteria for different disorders (e.g., major depression,
post-traumatic stress disorder [PTSD], social anxiety
disorder). Based on symptom presentation, distress, andfunctional impairment, an individual is considered toeither meet or not meet criteria for a specific disorder.

Albeit a useful framework within health services, where
treatment decisions are often categorical and where
diagnoses are used as treatment indicators and prognostic markers, there are downsides of the current diagnostic classification system. First, discrete disorder
categories do not align with strong evidence indicatingthat mental health difficulties are best viewed as dimensional continua without clear thresholds between normal and abnormal (Kotov et al., 2017). Second,
psychiatric comorbidity is ubiquitous in the mental
health field, that is, a majority of all individuals whomeet criteria for one psychiatric disorder also meet criteria for another disorder, challenging the notion of discrete disorders (Caspi et al., 2014). Third, there is
1

Lund University, Sweden
Uppsala University, Sweden
3
University of Notre Dame, IN, USA
2

Corresponding Author:
Matti Cervin, Department of Clinical Sciences Lund, Child and Adolescent
Psychiatry, Faculty of Medicine, Lund University, Sofiavägen 2D, Lund SE22241, Sweden.
Email: matti.cervin@med.lu.se

Cervin et al.
substantial heterogeneity within disorders and overlapping criteria between disorders (Newson et al., 2021).
These challenges, along with evidence of questionable
reliability, discriminant validity, and content validity for
psychiatric diagnoses have led to the development ofalternative models aimed to explain the structure ofmental health symptoms (Insel et al., 2010; Watson
et al., 2022).
One alternative (or complementary) model is the
Hierarchical Taxonomy of Psychopathology (HiTOP)(Kotov et al., 2017), which attempts to overcome theabove challenges by addressing mental health difficulties
through a quantitative and hierarchical approach. The
HiTOP model is hierarchically based because althoughvirtually all forms of psychopathology are positively
correlated with one another (as demonstrated by theexistence of an overarching p factor), some forms are
more strongly related than others. For instance, social
anxiety is more strongly related to depressed mood than
it is to antisocial behavior. In a hierarchical model, morestrongly correlated variables are placed closer together(e.g., social anxiety and depressed mood both are indicators of the internalizing dimension, whereas antisocial
behavior is placed within the externalizing dimension).
In HiTOP, psychopathological phenomena are classified into different levels of specificity that move from
specific signs, symptoms, and behaviors (e.g., insomnia
or irritability), to tightknit symptom components (e.g.,agoraphobia, social anxiety), dimensional syndromes(e.g., distress, fear), broad spectra (e.g., internalizing,externalizing, thought disorder), and finally a general
psychopathological factor, often referred to as the p factor, which indicates a broad propensity toward manydifferent types of mental health difficulties (Caspi et al.,2014; Watson et al., 2022). In youth with anxiety disorders, the p factor has been shown to be a stronger predictor of long-term outcomes than DSM diagnoses andother established risk factors, such as poor family functioning (Cervin, Norris, et al., 2020), lending support forthe possible benefits of addressing youth mental healthsymptoms using an hierarchical dimensional framework
as suggested by HiTOP.
In the HiTOP system, symptoms of depression andanxiety are included under the broad internalizing spectrum, which also includes obsessive–compulsive, trauma
and stressor-related, personality disorder, and eating
disorder symptoms (Watson et al., 2022). To assess
symptoms within this spectrum, Watson et al. (2007)
developed the Inventory of Depression and Anxiety
Symptoms (IDAS), a 64-item self-report measure assessing various symptoms related to the dimensional syndromes of fear and distress. The original IDAS had 11
non-overlapping scales: dysphoria, insomnia, lassitude(or fatigue), suicidality, appetite loss, appetite gain, ill589
temper (i.e., anger or hostility), well-being, panic, social
anxiety, and traumatic intrusions (Watson et al., 2007).
A goal of the measure was to provide a broad screening
tool for internalizing disorders. Broad screening isimportant because of the vast comorbidity between
internalizing symptoms and disorders. In addition, a single measure would eliminate the methodological problems stemming from assessing different symptoms using
different scales (Irak & Albayrak, 2020).
The IDAS-II (Watson et al., 2012) expanded upon
the original IDAS with the goal of including additional
symptoms of anxiety (claustrophobia), PTSD (traumatic
avoidance), obsessive–compulsive disorder (OCD)(cleaning, ordering, and checking), and bipolar disorder(BD) (euphoria and mania). The IDAS-II encompasses
99 items that yield 18 non-overlapping scales.1
Interestingly, many of the symptom scales of the IDASII (e.g., mania, checking, ordering, cleaning) may relate
to both the internalizing and thought disorder spectra of
HiTOP. According to Watson et al. (2012), the resulting
overarching factor structure of IDAS-II includes threecomponents: (a) Distress, including symptoms of major
depressive disorder (MDD), generalized anxiety disorder (GAD), and PTSD; (b) Obsessions/Fear, grouping
symptoms of claustrophobia, social anxiety, and OCD;
and (c) Positive Mood, defined by well-being andeuphoria.
Several translations of the IDAS-II exist and the measure is extensively used in research, including in researchwith children and adolescents (Bartlett et al., 2019; B. D.
Nelson et al., 2015), but no psychometric evaluation of
the scale has been conducted with youth. Adult evaluations support that the proposed 18-dimension model ofthe measure fits self-reported data well, that is, selfreported symptoms within the proposed dimensions cooccur in a way that is predicted by the model (De la
Rosa-Cáceres et al., 2020; Irak & Albayrak, 2020;
Weitzner et al., 2020; Wester et al., 2022). The IDAS-II
scales have shown good internal consistency (most estimates above a Cronbach’s alpha of .80) (De la RosaCáceres et al., 2020; Weitzner et al., 2020) and temporalstability (with most test–retest correlations being above.70) (De la Rosa-Cáceres et al., 2020; Irak & Albayrak,
2020; Watson et al., 2012; Weitzner et al., 2020).
Evaluations have also supported that IDAS-II showsadequate convergent and discriminant validity whencompared with other self-report and interview-based
measures of internalizing symptoms (De la RosaCáceres et al., 2020; Stasik-O’Brien et al., 2019; Watson
et al., 2012; Weitzner et al., 2020). In terms of criterion
validity, the IDAS-II subscales present generally highcorrelations (rs ;.50) with interview-based measuresdisorders included in the Diagnostic and Statistical
Manual of Mental Disorders (4th ed.; DSM-IV;

590
American Psychiatric Association, 1994) (Watson et al.,2012). Prior studies have also reported adequate discrimination between clinical and non-clinical samples,indicating medium to large effect sizes for most scales(Cohen’s ds . .70), but smaller effects for the OCD
scales, which has been interpreted as reflecting common
subclinical manifestations of OCD-related symptoms
and behaviors in the general population (Irak &Albayrak, 2020; Watson et al., 2012). Furthermore,
scores on the euphoria scale have been found to behigher in non-clinical than in clinical samples, raisingsome concerns about the complex relationship between
euphoria and BD (Watson et al., 2012). Finally, an overarching three-factor structure (distress, obsessions/fear,and positive mood) has largely been supported (De la
Rosa-Cáceres et al., 2020; Irak & Albayrak, 2020;
Weitzner et al., 2020; Wester et al., 2022).
In sum, previous research indicates that IDAS-II is a
useful assessment tool in both research and clinical settings, and it has unique characteristics given that it
assesses a broad range of empirically derived symptom
dimensions using a single measure. The scale is based on
the HiTOP framework and can support research using
this taxonomy. Nevertheless, its psychometric properties
in children and adolescents are unknown. Prior research
into the hierarchical dimensional structure of psychopathology has largely supported similar dimensions andhierarchical structures across age groups (Hankin et al.,2017; Martel et al., 2017; Patalay et al., 2015), which
would suggest that IDAS-II may be valid also for children and adolescents, but empirical evaluations areneeded. Because mental health problems typically begin
during childhood or adolescence, theoretically informed
measures that can be used in this age group are important for both clinical and research purposes. The aim of
this study is to conduct a comprehensive psychometricevaluation of a Swedish version of the IDAS-II in children and adolescents. In line with adult findings, we
expect the scale to show adequate psychometricproperties.

MethodParticipants and ProcedureChild and adolescent participants were recruited
through social media and school platforms and completed an anonymous online survey that included the
IDAS-II and other measures of mental health.
Participants provided informed assent before completing the online survey. The survey was hosted by Sunet
Survey, which is an online questionnaire research tool
available through Lund University. Only basic sociodemographic information was collected to secure

Assessment 31(3)anonymity. The only inclusion criterion was being
between 10 and 19 years of age. No exclusion criteria
were applied. The youth sample was collected in two
waves. The first data collection (Survey 1) was conducted between September and December 2018. The second data collection (Survey 2) was conducted between
January and May 2022. The youth study was approvedby the regional ethics committee as part of an amendment to a larger clinical research project (Dnr: 2018/668). Based on the strong psychometric properties of the
IDAS-II in adult evaluations and since we expected thatthe measure would work also with youth, we set out torecruit five youth participants per item, yielding a target
sample size of 495 participants. Two surveys wereneeded to reach this number.
The adult sample was recruited using social media
advertisements and was part of a study examining emotional processing and psychopathology. Participants
were required to be aged 18 years or older and have selfreported normal or corrected-to-normal vision andhearing. However, to produce samples that did not overlap in age, we eliminated participants from this sample
who were 18 or 19 years old (n = 9). Participants provided informed consent prior to participation and were
reimbursed with a gift card worth 200 SEK (approximately 20 USD). Consent and IDAS-II data were collected online using REDCap (Research Electronic Data
Capture), a secure, web-based software platform
designed to support data capture for research studies(Harris et al., 2019). The study was approved by the
Swedish Ethical Review Authority (2020-05777). The
adult sample was a convenience sample. Table 1 presentssociodemographic characteristics of the youth and adultsamples.

Measures
Expanded Version of the Inventory of Depression and Anxiety
Symptoms. The IDAS-II includes 99 items and each item
is scored on a 5-point Likert-type scale (1 = Not at all
to 5 = Extremely) (Watson et al., 2012). The measureyields 18 subscales (dysphoria, lassitude, insomnia, suicidality, appetite loss, appetite gain, well-being, ill temper, mania, euphoria, panic, social anxiety,claustrophobia, traumatic intrusions, traumatic avoidance, checking, ordering, and cleaning). As described in
the introduction, the measure has shown adequate psychometric properties in adults (G. H. Nelson et al.,2018; Watson et al., 2012) but has never been evaluatedusing youth samples. The IDAS-II was translated to
Swedish by a team of mental health experts, including
the first author (MC) of this study. Translated items
were presented to children and adolescents who provided feedback on clarity and understandability. This

Cervin et al.

591

Table 1. Sample Characteristics.
Youth samplenAdult sample
633

Age
M (SD)
Min.–max.
10 to \12 years, n (%)
12 to \14 years, n (%)
14 to \16 years, n (%)
16 to \18 years, n (%)
18 to \19 years, n (%)

16.56 (1.98)
10–19
29 (4.6%)
22 (3.5%)
68 (10.7%)282 (44.5%)232 (36.7%)

GenderGirl, n (%)
433 (68.4%)
Boy, n (%)
182 (28.8%)
Other, n (%)
6 (0.9%)Unsure, n (%)
10 (1.6%)
Do not want to report, n (%)
2 (0.3%)Current mental health problems
Yes, n (%)
321 (50.7%)
No, n (%)
312 (49.3%)
Current treatment for mental health problems
Yes, n (%)
119 (18.8%)
No, n (%)
514 (81.2%)
Previous mental health problems
Yes, n (%)
400 (63.2%)
No, n (%)
233 (36.8%)
Previous treatment for mental health problems
Yes, n (%)
217 (34.3%)
No, n (%)
416 (65.7%)

led to clarifications of certain words and phrases (e.g.,
self-conscious, accomplished, lose temper), which wereadded within parentheses. No other age adjustments/
adaptations were made. The Swedish translation wasthen back translated into English by a bilingual clinicalpsychologist not involved in the first translation. The
back translated version was reviewed by the original
scale developer (Prof. David Watson) and some items
were discussed and corrected before the final version of
the scale was approved. The youth and adult samples
completed the same Swedish version of the IDAS-II.
Revised Child Anxiety and Depression Scale. The RevisedChild Anxiety and Depression Scale (RCADS) includes
47 items (Chorpita et al., 2000). Each item is scoredusing a 0 to 3 scale with higher scores indicating more
frequent symptoms. The RCADS yields six subscales:
depression, social anxiety, panic, generalized anxiety,separation anxiety, and OCD. The measure has shownadequate psychometric properties (e.g., internal consistency in the form of Cronbach’s a . .70 and adequate
model/data for the proposed six-factor model) in several
studies (Piqueras et al., 2017), including in Swedish
youth (Cervin, Veas, et al., 2022), and is annAge
M (SD)
Min.–max.
20 to \30 years, n (%)
30 to \40 years, n (%)
40 to \50 years, n (%)
50 + years, n (%)
GenderFemale, n (%)
Male, n (%)
Highest education
University, n (%)
Short post high school, n (%)
Theoretical high school, n (%)
Practical high school, n (%)
No high school, n (%)

203
35.39 (12.09)
20–71
83 (39.2%)
53 (25.0%)
37 (17.5%)30 (14.2%)
170 (87.2%)
25 (12.8%)
95 (54.9%)
11 (6.4%)
27 (15.6%)
33 (19.1%)
7 (4.0%)internationally recommended measure for the assessment of internalizing symptoms in children and adolescents (Krause et al., 2021). In the present study, theinternal consistency (Cronbach’s alpha, a) of each
RCADS scale was adequate: depression (a = .92), social
anxiety (a = .92), panic (a = .94), generalized anxiety (a= .87), separation anxiety (a = .87), and OCD (a =.88). We used the RCADS to examine convergent anddivergent validity of the IDAS-II scales. The RCADS
was only administered in the Youth Survey 1.
Child Sheehan Disability Scale. Functional impairment
stemming from mental health difficulties was assessedusing the Child Sheehan Disability Scale (CSDS), which
includes impairment in three areas: school functioning,family functioning, and social functioning (Whiteside,2009). Each area is rated using a 0 to 10 scale with higherscores indicating greater impairment. Prior research has
shown that CSDS has adequate psychometric properties(e.g., internal consistency . .70), strong convergent anddiscriminant validity (i.e., moderate to strong correlations with related constructs), and known groups validity (i.e., ability to discriminate youth with and without
mental disorders) (Whiteside, 2009), including in

592
Swedish settings (Soler et al., 2021). The three areas areusually pooled into an overall impairment score andwere so also in the present study; the Cronbach’s alphawas adequate (a = .89). The CSDS was administered in
Youth Surveys 1 and 2 but not in the adult sample.

Statistical Analysis
All statistical analyses were conducted in R Studio (version 2021.09.0) (R Core Team, 2020). First, we usedconfirmatory factor analysis (CFA) to test the proposed
factor structure of the IDAS-II that includes 18 firstorder factors that are allowed to correlate freely. The
CFAs were conducted using the R library lavaan(Rosseel, 2012). Because the IDAS-II items are ordinal,
we used the diagonally weighted least squares estimatorthat can model non-normal responses as indicators of anormally distributed latent factor. Model/data fit wasevaluated by inspecting four fit indices: comparative fit
index (CFI), Tucker–Lewis index (TLI), root meansquare error of approximation (RMSEA), and standardized mean square residual (SRMR). An RMSEA
below 0.06, an SRMR below 0.08, and CFI and TLI
estimates greater than 0.90 are indicative of acceptablemodel–data fit; CFI and TLI estimates above 0.95 areindicative of good model–data fit (Hu & Bentler, 1999).
We estimated scaled fit indices because of the ordinal
nature of the data. The proportion of missing data waslow (Youth Survey 1, 2.9%; Youth Survey 2, 2.6%;
adults, 0.0%—the digital system used with the adultsample did not allow for skipping items) and missing
data were handled using pairwise deletion. Finally,
because the youth sample was predominantly in theolder adolescent age range, we split the youth sample
into those under 16 years of age (n = 119) and those 16–
19 years of age (n = 514), fitted the model separately ineach sample, and examined model/data fit.
To examine whether the proposed 18-factor IDAS-II
structure showed measurement invariance across youth
and adults, we conducted multi-group confirmatory factor analysis (MG-CFA) using lavaan. MG-CFA canempirically test whether a measure psychometricallyassesses the same constructs across different groups(e.g., children/adults, different countries), which is a prerequisite to compare factor scores across these groups.
In a first step, we used MG-CFA to test for configuralinvariance. Configural invariance indicates that thesame items load onto the same factors across groups.
We then tested for scalar invariance, which indicates thesame thresholds and factor loadings across groups. The
common step of metric invariance was omitted because
when items are ordinal, loadings and intercepts jointly
define item function and thus should be freed/restrainedin tandem (Sass et al., 2014). Finally, we tested for strict

Assessment 31(3)invariance, which indicates the same factor loadings,
thresholds, and residuals across groups. MG-CFA is
conducted by restraining model parameters in a stepwise
fashion according to the three steps described above andinvariance is evaluated by inspecting change in fit
indices. We interpreted a drop in CFI (DCFI) \.01 as
an indicator of invariance (Kim et al., 2017). In all MGCFA analyses, two items were omitted because they had
zero observations in some categories in the adult sample(Item 22, ‘‘I cut or burned myself on purpose’’ and Item90, ‘‘I was afraid of tunnels’’).
Internal consistency (i.e., a quantification of theamount of random measurement error) for the items ofeach scale was examined using Cronbach’s alpha andMcDonald’s omega. Internal consistency coefficientsabove 0.70 are often considered adequate but coefficients above 0.80 are preferable. McDonald’s omega
was calculated as it is less restrictive in its assumptions
compared with the more widely used Cronbach’s alpha(Dunn et al., 2014). Coefficients above .90 were considered excellent, coefficients between .80 and .90 were considered good, coefficients between .70 and .80 adequate,
and coefficients below .70 problematic.
CFA does not include systematic examination of
overarching factor structures, that is, how the first-orderfactors of the IDAS-II relate to each other. Instead, toexplore overarching factor structures, we conducted
exploratory factor analysis (EFA) using the 18 IDAS-II
scales as input. The Kaiser–Meyer–Olkin (KMO) test
values were used to examine whether the full set of scales
as well as each individual scale was suitable for EFA.
KMO values indicate the proportion of variance in variables that might be explained by latent factors and values above .80 are considered to indicate that EFA is well
suited. We used parallel analysis implemented in the R
library psych to determine the number of factors toretain during EFA. Factors were extracted using principal axis factoring and promax rotation. Principal axis
factoring was used because it does not include a multivariate normal assumption and promax rotation wasused because the IDAS factors are known to be significantly correlated with one another.
To examine convergent and divergent validity in the
youth sample, we estimated associations (correlations)
between the IDAS-II and RCADS scales. The IDAS-IIand RCADS both include scales pertaining to depressive
symptoms (IDAS-II: dysphoria; RCADS: depression),social anxiety, panic, and OCD (IDAS-II: checking,ordering, cleaning; RCADS: OCD). To test whether thecorrelation between scales measuring similar constructs(e.g., IDAS-II dysphoria and RCADS depression) wasstronger than between scales of dissimilar constructs(e.g., IDAS-II dysphoria and RCADS OCD), we used
the method presented in the work of Hittner et al.

Cervin et al.
(2003), which is implemented in the R library cocor. We
compared correlations using the following convergent
associations: IDAS-II dysphoria with RCADS depression; IDAS-II social anxiety with RCADS social anxiety; IDAS-II panic with RCADS panic; and IDAS-II
checking, ordering, and cleaning with RCADS OCD.
Correlations above .50 were considered to indicate adequate convergent validity.
Associations between the IDAS-II scales and functional impairment in the youth sample were examinedby correlating each of the IDAS-II scales with the
CSDS. Correlations of ;.10 were considered small,;.30 medium, and ..50 large. The ability of the IDASII scales to discriminate between those who self-identified
as suffering from current mental health problems fromthose who did not experience such difficulties was examined by conducting independent samples t-tests andquantifying differences using Cohen’s d, where ds
between .50 and .80 were considered to indicate moderate differences and ds . .80 to indicate large differences.
Finally, we again used the self-report item in whichyouth participants reported on whether they experienced
current mental health problems and examined thedegree to which the IDAS-II and RCADS could be used
to correctly classify participants who affirmed this item.
This was considered a test of the screening properties ofeach measure. Survey 1 was used in these analyses since
the RCADS was not included in Survey 2. We randomly
split the sample 70/30% and estimated regression coefficients for all symptom scales using a logistic regression
model based on the subsample that included 70% of theparticipants. The item indicating current mental healthsymptoms was used as the dependent variable. We thenclassified participants (current mental health problems
or not) in the remaining 30% of the sample using thesecoefficients and examined sensitivity (proportion of participants reporting mental health symptoms who wereclassified as having mental health symptoms), specificity(proportion of participants reporting no mental healthsymptoms who were classified as having no mental
health symptoms), the positive predictive value (PPV;
proportion of participants classified as having mental
health symptoms who reported mental health symptoms), and the negative predictive value (NPV; proportion of participants classified as not having mental
health symptoms who reported not having mental healthsymptoms) of the IDAS-II and RCADS. No formal statistical comparison of classification performance wasconducted because of low power; rather, we compared
the point estimates.
Statistical code and the youth data used in the present
study are freely available on the Open Science
Framework repository (https://osf.io/n5um8/). The
adult data are not freely shared because of regulations at

593
one of the participating universities but it is available
upon reasonable request. The study was not preregistered, but all factor analyses were based on the model
proposed in the original validation of the IDAS-II(Watson et al., 2012). Questions about analyses and/or
data will be answered by the corresponding author.

Results
Data Quality
All data were screened for straightlining responses (i.e.,
when a respondent gives identical or nearly identicalresponses to all items) by estimating the individual variation around the individual means for scales as well as
the full measure, which was then manually screened. No
cases of straightlining were identified.

Model/Data Fit and Internal Consistency
Model/data fit in the youth sample (both in the full sample, and in the younger and older age groups) and theadult sample as well as for Surveys 1 and 2 for the youth
sample are presented in Table 2. Model/data fit wasgood according to all fit indices in all samples andsurveys (CFI/TLI = .94–.96, RMSEA = .03–.04,
SRMR = .06–.09). Strict invariance between youth andadults was established (i.e., the drop in CFI did exceed.01 with increasing constraints; see bottom of Table 2).
Standardized factor loadings in the youth and adultsamples are presented in the Supplemental Table S1.
The internal consistency of the items of each scale ineach sample is presented in Supplemental Table S2 (fullyouth and adult samples plus Surveys 1 and 2 for the
youth sample) and Supplemental Table S3 (younger andolder youth samples). Internal consistency was good to
excellent (alpha/omega . .80 and . .90, respectively)
for all scales in all samples except for appetite gain,
euphoria, and checking which were adequate (alpha/omega . .70 to \.80) in some samples and good in others (alpha/omega ..80 to \.90). Very similar internalconsistencies were found across youth and adults as well
as separately for Surveys 1 and 2 and for the younger
and older age groups within the youth sample.
Overarching Factor Structure. We estimated the correlation
matrix for the 18 IDAS-II scales in youths and adults,
respectively. The overall KMO test value for youths was
0.92 with the lowest dimension-level value being 0.55
(euphoria) and the second lowest being 0.86 (well-being
and ordering). For adults, the overall KMO test value
was 0.88, with the lowest value being 0.56 (euphoria) andthe second lowest being 0.71 (appetite gain). Thus, overall, the IDAS-II scales were well suited for EFA.

594

Assessment 31(3)Table 2. Model/Data Fit for the Original IDAS-II Factor Structure Using the Different Samples and Measurement Invariance Tests for
Youth Versus Adults.
Sample

x2

df

p

CFI

TLI

RMSEA

SRMR

Full youth sample (n = 633)
Youth sample, Survey 1 (n = 408)
Youth sample, Survey 2 (n = 225)
Adult sample (n = 202)
Younger youth sample (n = 119)
Older youth sample (n = 514)
Invariance tests (youth vs. adults)
Configural
ScalarStrict

8,354
7,042
5,478
5,431
5,091
7,657

4,599
4,599
4,599
4,599
4,599
4,599

\.001
\.001
\.001
\.001
\.001
\.001

0.94
0.95
0.95
0.95
0.96
0.94

0.94
0.94
0.95
0.95
0.96
0.94

0.04
0.04
0.03
0.03
0.03
0.04

0.06
0.07
0.08
0.08
0.09
0.07

12,581
12,51012,626

8,812
9,085
9,182

\.001
\.001
\.001

0.95
0.95
0.96

0.95
0.95
0.95

0.03
0.03
0.03

0.07
0.07
0.07

Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized mean square
residual.

We examined the number of factors to extract. For
both youth and adult data, parallel analysis suggested
five factors. We compared three-, four-, five-, six-, andseven-factor solutions in both age groups. To find themost suitable solution, we examined three different
properties: (a) cross loadings, (b) correlations among
dimensions included in each factor, and (c) total variance explained. A four-factor solution received most
support in youth and a three-factor solution in adults.
The four-factor solution in youth explained 54.0% of
the covariance among the 18 dimensions. In adults, thethree-factor solution explained 53.2% of the covariance.
The respective factor solutions are presented in
Figure 1 together with factor loadings and covariancesamong the factors. The overarching factor structures
were identical with two exceptions. In adults, the two
PTSD dimensions (intrusions and avoidance) loaded
onto the distress factor while in youth they formed theirown factor. Furthermore, appetite gain did not have afactor loading . .30 on any factor in adults, whereas it
loaded onto the distress factor in youth. In youth, only
well-being had a cross loading . .30 (a negative loadingon the distress factor). In adults, well-being (negative
loading on the distress factor), mania (positive loadingon the positive mood factor), and social anxiety (positive
loading on the OCD factor) had cross loadings . .30.2

Convergent and Divergent Validity. Convergent and divergent validity of the IDAS-II scales in the youth sample,
assessed using correlations between the IDAS-II scale and
RCADS scales, are presented in Table 3. With regard toconvergent validity, each of the IDAS-II scales correlated
moderately to strongly with the corresponding RCADS
scale. Further, for convergent validity, the IDAS-II dysphoria scale showed a strong correlation with the
RCADS depression scale, and this correlation was

statistically significantly stronger than the correlation
between the IDAS-II dysphoria scale and each of theother RCADS scales. Similar results were found for the
IDAS-II checking and ordering scales, which showed significantly stronger correlations with the RCADS OCD
scale than with all other RCADS scales. The IDAS-II
cleaning scale also showed a significantly stronger correlation with the RCADS OCD scale than with all other
RCADS scales except social anxiety. The IDAS-II panic
scale was significantly more strongly correlated with the
RCADS panic scale than with all other RCADS scales
except for the RCADS depression scale. The IDAS-IIsocial anxiety scale was significantly more strongly correlated with the RCADS social anxiety scale than with the
RCADS OCD and separation anxiety scales, but no significant differences were found compared with the correlations with the other RCADS scales. Finally, we estimatedcorrelations between the IDAS-II well-being scale and the
RCADS scales. The well-being scale correlated negatively
with all RCADS scales with the strongest negative correlation emerging in relation to RCADS depression.
We also examined the convergent/divergent validityof the four broader IDAS-II dimensions in youth (distress, OCD, PTSD, and positive mood) by correlating
predicted factor scores with the RCADS scales. Results
are at the bottom of Table 3. The broad distress dimension was moderately to strongly associated with all
RCADS scales and the broad PTSD dimension wasmoderately associated with all RCADS scales. For thebroad OCD dimension, we expected that it would be
most strongly associated with the broad OCD RCADS
scale, which was confirmed compared with RCADS
panic, RCADS social anxiety, and RCADS separation
anxiety. The broad positive mood dimension had small
and negligible correlations with all RCADS scales.
See Table 4 for correlations between the IDAS-II
scales and self-reported functional impairment in theCervin et al.

595

Figure 1. Factor Structures for Youth and Adults With Factor Loadings and Covariance Among Overarching Dimensions.
Note. OCD = obsessive–compulsive disorder; PTSD = post-traumatic stress disorder.

youth sample. All scales correlated statistically significantly with functional impairment except euphoria andthe significant correlations were in the moderate tostrong range. Similarly, all scales discriminated, in a statistically significant way, those who self-identified ashaving current mental health problems from those without such problems, with large between-group differences
for all scales except euphoria (where those without mental health symptoms had higher scores), claustrophobia,
and the three OCD scales, where differences were small
to moderate in magnitude.

Classification Performance
The 18 IDAS-II scales correctly classified 81.5% of all
youth who self-identified as having ongoing mental healthsymptoms (i.e., a sensitivity of 81.5%). Furthermore,among those who were classified as having ongoing mental health symptoms, 81.5% were correctly classified (aPPV of 81.5%). The specificity was 78.2% and the NPV
was also 78.2%. In contrast, the six RCADS scales had asensitivity of 78.8%, a PPV of 74.3%, a specificity of
67.9%, and an NPV of 73.1%. In Supplemental Table S4,
we present the 2 by 2 tables that were used to estimate theabove reported classification performance.

Discussion
The aim of this study was to examine the psychometricproperties of the IDAS-II in children and adolescents.
Based on evidence suggesting that the structure of psychopathological symptoms is similar in youths andadults, we expected that the measure would show good
psychometric properties in youths. The findings of thepresent study confirmed our expectations.
First, the model/data fit of the proposed factor structure with 18 first-order dimensions was good to excellent

Note. RCADS = Revised Child Anxiety and Depression Scale; OCD = obsessive–compulsive disorder; GAD = generalized anxiety disorder; IDAS-II = expanded version of the Inventory of Depression
and Anxiety Symptoms; PTSD = post-traumatic stress disorder.
a
The correlation is statistically significantly smaller in the expected direction. b A statistically significantly stronger correlation in the unexpected direction. c No statistically significant differences for thecorrelation coefficients..

0.40
0.31a
0.31
0.03
0.58
0.46 c
0.48
0.00
0.50
0.50
0.46
0.07
0.56
0.44a
0.47
0.01
0.71
0.48c
0.55
20.04

0.57
0.43a
0.49
0.03

0.58
0.46c
0.47a
0.36a
0.29a
0.29a
20.38
0.48
0.36a
0.43a
0.42
0.35
0.37
20.30
0.52
0.42c
0.54
0.32a
0.25a
0.28a
20.34

0.57
0.48
0.46a
0.33a
0.26a
0.32c
20.39
0.69
0.51c
0.58b
0.35a
0.28a
0.31a
20.54

IDAS-II dysphoria
IDAS-II social anxiety
IDAS-II panic
IDAS-II checking
IDAS-II ordering
IDAS-II cleaning
IDAS-II well-being
Broader dimensions
IDAS-II distress
IDAS-II OCD
IDAS-II PTSD
IDAS-II positive mood

RCADS GADaa

RCADS OCDRCADS panicaa

RCADS social anxiety
RCADS depression
Scale

Table 3. Convergent and Divergent Correlations Between IDAS-II and RCADS. The Corresponding Correlations are Highlighted in Bold.

0.41a
0.30a
0.34a
0.24a
0.19a
0.22a
20.26

Assessment 31(3)RCADS separation anxiety

596

according to all fit indices. The fit indices were also very
similar in the youth and adult samples, and in theyounger and older youth samples. These findings indicate that the items of the IDAS-II, which were developed for adults, group together in a very similar way inchildren and adolescents. Second, when comparing thefactor loadings across the youth and adult samples,
remarkable similarities were found and strict invariance
was established, establishing that the IDAS-II behavesvery similarly in youth as compared with adult samples.
Third, the internal consistency of the items of each scale
was adequate to excellent and again very similar results
emerged for youths and adults.
Although adequate psychometric properties of the
IDAS-II in youths were expected, the almost completely
preserved psychometric properties, compared with estimates from adult samples, and excellent properties also
in the youngest age group, were somewhat surprising.
Several items of the IDAS reflect complex internal
experiences and no items were changed in the youth version, although clarifications of some terms were added.
Children and adolescents are expected to have more difficulties than adults in operationalizing and reportinginternal experiences, which potentially could affect thepsychometric properties of the measure. We found noevidence of such effects in the present study and it may
be that the careful and step-wise development of IDASII (Watson et al., 2012) has resulted in final pools of
items that are easy to understand and clearly load onto
their expected factors. With regard to variability in internal consistency, our results were similar to those fromprevious evaluations, showing the lowest internal consistency for appetite gain (Irak & Albayrak, 2020) andeuphoria (Watson et al., 2012; Wester et al., 2022).
However, the scale with the lowest internal consistency
in the youth sample in this study (appetite gain) still had
adequate internal consistency and all other scales
showed good to excellent internal consistency.
We also explored the overarching factor structure
defined by the 18 symptom dimensions of the IDAS-IIand similar results again emerged for youths and adults,
which were also largely in line with previous adult evaluations (De la Rosa-Cáceres et al., 2020; Irak &Albayrak, 2020; Watson et al., 2012; Weitzner et al.,2020; Wester et al., 2022). Three highly similar factors
emerged in both youth and adults (distress, positive
mood, and OCD), and a fourth for youth (PTSD). Few
cross loadings were present and all but two scales in the
youth sample and all scales in the adult sample loaded .
.50 onto its proposed factor. Taken together, several
studies have now suggested that the IDAS-II dimensions
can be sorted according to overarching symptom dimensions and this study suggests that these overarching
dimensions are broadly similar in youths and adults.

Cervin et al.

597

Table 4. Association Between Each IDAS-II Scale and Functional Impairment and the Difference Between Those Self-Identifying as
Having Current Mental Health Problems Versus Those Without Such Problems Expressed as Cohen’s ds.
Scale
Dysphoria
Panic
Suicidality
Lassitude
Ill temper
Social anxiety
Traumatic avoidanceAppetite loss
Traumatic intrusions
Insomnia
Mania
Claustrophobia
Checking
Appetite gain
Ordering
Cleaning
Euphoria
Well-being

Correlation withfunctional impairment

Difference for current mental healthproblems vs. no current problems Cohen’s d

.75**
.59**
.57**
.56**
.52**
.52**
.52**
.51**
.51**
.50**
.46**
.38**
.34**
.27**
.26**
.26**
2.07
2.55**

1.70**
1.18**
1.06**
0.96**
1.03**
1.00**
0.91**
0.94**
1.01**
0.97**
0.84**
0.66**
0.59**
1.07**
0.37**
0.41**
20.23*br25-1073191
21.19**

Note. Scales are Ordered According to the Strength of Their Association With Functional Impairment. IDAS-II = expanded version of the Inventory ofDepression and Anxiety Symptoms.
**p \ .001. *p \ .01.

However, future research is needed to test whether the
proposed overarching structure shows adequate model/
data fit when tested in a confirmatory fashion.
After establishing sound psychometric properties of
the IDAS-II in youths and examining its overarching
structure, we examined convergent and divergent validity by correlating both the first-order scales and theoverarching dimensions with the subscales from the
RCADS. The RCADS is an internationally recommended measure of internalizing symptom dimensions
in youth (Krause et al., 2021) and we evaluated whether
scales assessing similar symptoms (i.e., corresponding
scales) were at least moderately correlated and morestrongly correlated than scales assessing nonoverlapping symptoms (i.e., non-corresponding scales).
Corresponding scales were used to examine convergent
validity. These included the dysphoria scale of IDASII—a scale assessing core emotional and cognitive symptoms of depression and anxiety (Watson et al., 2012)—
and the depression scale of RCADS; the social anxiety
scales of both measures; the panic scales of both measures; and the OCD scales of both measures. Findings
indicated strong correlations for all corresponding scales
except between the OCD scales of the two measures,
which showed moderate correlations. Lower correlations between the OCD scales of IDAS-II and the OCD
scale of RCADS are expected since OCD symptoms areheterogeneous in youth, both topographically andfunctionally (Cervin, Miguel, et al., 2022; Cervin, Perrin,
Olsson, Aspvall, et al., 2020; Cervin, Perrin, Olsson,
Claesdotter-Knutsson, & Lindvall, 2020). This heterogeneity is appreciated by the IDAS-II, which assesses
checking, ordering, and cleaning, dimensions whichlargely corresponds to the three major symptom dimensions of OCD in youth, namely, disturbing thoughts andchecking, symmetry and ordering, and contamination
and washing (Cervin, Miguel, et al., 2022). The RCADS,
on the other hand, only includes a single OCD scale thatpools together OCD symptoms of different types.
Of the 30 indicators, 25 supported divergent validity
for the IDAS-II scales, that is, that the corresponding
scales of the IDAS-II and RCADS correlated significantly more strongly than non-corresponding scales.
The scale with the weakest evidence for divergent validity was social anxiety. The reasons for this are unclear,and it is important to point out that it may reflect problems in this IDAS-II scale and/or one or more RCADS
scales. Future research should revisit this issue, which
will be best examined by including diagnostic interviews
and more in-depth information about the nature of psychopathological problems in participants. Finally, thewell-being scale of IDAS-II showed moderate to strong
negative correlations with all RCADS scales, indicatingthat this scale is associated with the absence or reduced
frequency of a broad range of internalizing mental
health symptoms in youth.

598
We also examined correlations between the overarching IDAS-II dimensions derived from exploratory factor
analysis and the RCADS scales. The overarching distress, OCD, and PTSD dimensions all correlated moderately to strongly with all RCADS scales, with the
distress dimension showing the strongest correlations.
As expected, the correlation between the overarching
OCD dimension and the RCADS OCD scale was stronger than the correlations between the three separate
IDAS-II OCD scales and the RCADS OCD scale. The
overarching positive mood dimension was very weakly
correlated with the RCADS scales, making it uncertain
what this dimension reflects. The IDAS-II well-being
scale showed clear negative correlations with all
RCADS scales and, although well-being and euphoriagrouped together into an overarching factor using
exploratory factor analysis, this combination yielded
unclear relations to convergent validators. Of note, during EFA, the euphoria scale stood out as the scale showing least amount of shared variance with other scales.
Further support for construct validity was foundwhen we correlated the IDAS-II scales with selfreported mental health-related functional impairment.
Findings showed that all scales except euphoria correlated moderately to strongly with functional impairment. All scales also discriminated those who selfidentified as having current mental health difficulties
from those who self-identified as not having such difficulties. These results indicate that although the IDAS-II
includes a long list of symptom dimensions, virtually allthese dimensions are related to functional impairment inyouth, which is important both for the construct validityof the measure and for its clinical usefulness.
Having said that, the euphoria scale was not significantly correlated with impairment and only yielded asmall between-group difference with higher scoresamong those without current mental health problems.
Although the euphoria scale was developed to assess a
symptom dimension of BD, it has previously showed
weak convergent validity (De la Rosa-Cáceres et al.,2020; Weitzner et al., 2020) and in the present study it
discriminated significantly between those with and without mental health difficulties but in the opposite direction of what was expected (i.e., higher scores among
those without mental health difficulties). Thus, although
the items of the euphoria scale load onto a single factor
and have adequate internal consistency, it is unclear
what construct these items measure. If anything, they
seem to mostly capture positive aspects of mental healthin youth, which contrasts findings with adults where thisscale has been shown to be positively associated withinterview-based indicators of mania and BD (Watson &O’Hara, 2017; Watson et al., 2012). The OCD scales also
showed weaker correlations with functional impairment

Assessment 31(3)than the other IDAS-II scales, as well as the weakest
ability to discriminate those with current mental healthdifficulties. This is in line with previous evaluations of
the IDAS-II in adults and has been suggested to reflectsubclinical manifestations of OCD-related behaviors
and traits in the general population (Watson et al.,2012).
We examined the degree to which the IDAS-II could
identify those who rated themselves as having ongoingmental health problems using a stringent two-step
approach, with classification performance being basedon data not used for coefficient estimation. To put
results in context, we compared the classification performance of the IDAS-II with that of the RCADS. Overall,the IDAS-II showed excellent classification performancewith a sensitivity of 82% and a PPV of 82%. Specificity
and NPV were 78% for both indicators. The IDAS-II
had better values than RCADS for all four indicators ofclassification performance, which is somewhat surprisingsince the different indicators often compete (i.e., higher
sensitivity lower PPV). These results indicate that the
IDAS-II may be a useful screening measure in clinicalpractice, but it should be noted that no formal statisticalcomparisons of classification performance were conducted because of low statistical power. Furthermore,
the measure of current mental health difficulties waspurely self-reported.
Several limitations of the current study deserve mentioning. First, as mentioned above, no diagnostic interviews were conducted and information about current
mental health was purely self-reported. Future studies
should include diagnostic interviews and otherinterview-based information on current mental health;information from caregivers would be valuable since
youths and caregivers provide unique information in
relation to mental health difficulties in children and adolescents (Aebi et al., 2017; De Los Reyes et al., 2015).
Second, no repeated measurement was conducted,
which precluded evaluation of temporal stability andsensitivity of the IDAS-II to change. More knowledge
about the measure’s sensitivity to changes in mental
health and everyday functioning following treatment isimportant for the measure to be used clinically. Third,
the youth participants were anonymous and only limited
personal information was collected. It is therefore hardto infer to what degree the current results can be generalized. For example, half of the youth sample reported
that they experienced current mental health difficulties,
suggesting that the survey disproportionately drew theattention of youth with such difficulties. Future researchshould be conducted with representative samples of children and adolescents, which would also allow for the
calculation of norms. Finally, we had few participants inthe lower age range. Nevertheless, we conductedCervin et al.
separate analyses in those under 16 years of age and theresults (i.e., model/data fit and internal consistency ofeach scale) were very similar when compared with older
youths and the adult sample. Although this is promising,larger samples of younger youths are needed to provide
more certainty about the suitability of IDAS-II in thisage range.
In sum, the present study supports the use of the
IDAS-II as a measure of internalizing symptom dimensions in children and adolescents. The psychometricproperties of its 18 scales are consistent with the adultversion; moreover, the scales correlate with mental
health-related impairment in youth and can discriminate
youths with current mental health difficulties from those
without such difficulties. The measure shows promisingclinical applicability, with excellent classification performance, and may be better at identifying youths withmental health problems than the currently recommended measure of youth internalizing symptoms.
Future research should include more comprehensive
clinical information, administer the IDAS-II repeatedly
to examine its temporal stability and sensitivity tochange in youths, and evaluate psychometric properties
in younger youth samples.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest withrespect to the research, authorship, and/or publication of thisarticle.

Funding
The author(s) received no financial support for the research,authorship, and/or publication of this article.

ORCID iDs
Matti Cervinhttps://orcid.org/0000-0003-1188-8706
Carla Martı́ Valls
https://orcid.org/0000-0002-4566-1529
https://orcid.org/0000-0002-9605-0576
David Watson

Supplemental Material
Supplemental material for this article is available online.

Notes
1. Both versions of the IDAS also include a 20-item General
Depression scale that includes items from several scales. It
will not be examined in our study.
2. Since most previous studies have reported an overarching
three-factor structure of the IDAS-II, we report the threefactor solution for youth: Factor 1: dysphoria, lassitude,
insomnia, suicidality, appetite loss, appetite gain, ill temper, mania, panic, social anxiety, traumatic intrusions,599
traumatic avoidance; Factor 2: claustrophobia, checking,ordering, cleaning; Factor 3: well-being, euphoria.

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