The Latent Factor Structure of Young's Early Maladaptive Schemas: Are Schemas Organized Into...

The Latent Factor Structure of Young’s Early Maladaptive Schemas:Are Schemas Organized Into Domains?

Levente Kriston,1∗ Janina Schafer,2 Alessa von Wolff,1 Martin Harter,1and Lars P. Holzel2

1University Medical Center Hamburg-Eppendorf, Germany,2University Medical Center Freiburg, Germany,

Objective: In previous research it has not been clarified whether the first-order schema factors ofthe Young Schema Questionnaire (YSQ) can be structured into higher order domains. We examinedwhether investigators’ subjective choices between complex models of the YSQ or chance as opposedto clinical diversity of the samples may be responsible for the heterogeneity of results reported in theliterature. Method: We used confirmatory factor analysis to compare several a priori defined do-main models in a sample of 542 undergraduate students (82.8% female; mean age 24.1 years) and 590nonstudent adults (73.9% female; mean age 34.5 years) form an Internet survey. An additional MonteCarlo simulation study was performed to gain further insights on model selection. Results: Theanalyses did not provide unequivocal support for the presence of a second-order domain structure.However, study findings suggested that the structure of the YSQ could be represented by a bifactormodel including a first-order generic factor on which all items load and correlated first-order specificschema factors on which only the items load that were meant to measure the respective schemas.Conclusion: In the YSQ either several second-order domain structures are present that cannot beranked by statistical measures alone or a first-order generic factor is present making second-orderdomains dispensable. Future research should include theoretical arguments and incorporate the clinicalexperience of practitioners. C© 2012 Wiley Periodicals, Inc. J. Clin. Psychol. 00:1–15, 2012.

Keywords: early maladaptive schema; Young Schema Questionnaire; factor structure; confirmatoryfactor analysis; reliability; validity

Beck introduced cognitive functions into the theory of mental disorders (1967; Beck, Rush,Shaw, & Emery, 1979) by proposing a hierarchical model of cognition. He focused his researchmainly on the surface and middle level of conditional cognitive contents (e.g., core beliefs).At a deeper level of cognitive representations, rather unconditional and unconscious schemasorganize perception and behavior. Young (1990) proposed a classification system for schemasand identified a set of early maladaptive schemas. Early maladaptive schemas can be describedas stable and enduring themes regarding oneself, one’s relationship with others, and the worldthat developed during childhood and became elaborated throughout the course of a lifetime. Itwas assumed that although these schemas were present in asymptomatic populations, they wereparticularly distorted, rigid, and dysfunctional in symptomatic individuals. Currently, Young’stheory includes 18 early maladaptive schemas organized into five domains. The complete list,classification scheme, and definitions are available at http://schematherapy.com/id73.htm.

Young (1990) developed the Young Schema Questionnaire (YSQ) to measure early mal-adaptive schemas. This self-report instrument was revised several times; shorter versions of theoriginal 205-item scale have been developed, and the current version, the YSQ-Short Form3 (YSQ-SF3), includes 90 items. As Oei and Baranoff (2007) have reviewed the psychomet-ric research on this instrument, we summarize the central findings of previous research briefly.

∗We thank two anonymous reviewers for their helpful comments on the manuscript and all those whoparticipated in the online survey.

Please address correspondence to: Dr. phil. Levente Kriston, Department of Medical Psychology,University Medical Center Hamburg-Eppendorf, Martinistr. 52, D-20246 Hamburg, Germany. E-mail:[email protected]

JOURNAL OF CLINICAL PSYCHOLOGY, Vol. 00(00), 1–15 (2012) C© 2012 Wiley Periodicals, Inc.Published online in Wiley Online Library (wileyonlinelibrary.com/journal/jclp). DOI: 10.1002/jclp.21846

2 JOURNAL OF CLINICAL PSYCHOLOGY, Vol. xx(x), XXX-XXX (2012)

Research findings have been consistent despite considerable variation in the versions (long, short,versions 1 to 3), languages (e.g., English, French, Turkish), settings (e.g., students, nonclinicaland clinical samples), and the statistical techniques used (exploratory factor analysis [EFA] andconfirmatory factor analysis [CFA]). Measures of internal consistency have found most schemasubscales to be reliable. Although some items and subscales exhibited unsatisfactory charac-teristics, most studies confirmed the first-order factor structure at the schema level. However,the consistent finding of high intercorrelations among the first-order schema factors suggestedthat these strong associations might reflect a hierarchical organization of schemas into domains(Lockwood, 2011). Table 1 presents a brief summary of research investigating the second-orderfactor structure of the YSQ and reveals that findings of a number of studies are conflicting.

The goal of the present study was to investigate the second-order factor structure of the YSQto determine the extent to which the first-order schema factors could be structured into higherorder domains.

First, we compared several second-order models using data from an online sample of con-venience. The tested models included Young’s model with five domains, a global factor modelwith a single domain, and models with two to five second-order domains identified in previousresearch. A bifactor model was also investigated as a possible explanation for the strong schemafactor associations (Holzinger & Swineford, 1937; Reise, Morizot, & Hays, 2007). In a bifac-tor model, all items load on a first-order generic factor, and the items associated with specificschemas load on the associated first-order schema factors. This type of factor structure does notappear to have been considered to describe the latent structure of the YSQ in previous research.

Second, because different structural equation models might fit a particular dataset equallywell (Lee & Hershberger, 1990; MacCallum, Wegener, Uchino, & Fabrigar, 1993), we performeda small-scale Monte Carlo simulation study to examine the extent to which other than clin-ical factors (e.g., chance or investigators’ subjective choices between complex models of theYSQ) might be responsible for the heterogeneity of the results reported in the literature. Weassumed that because of the complexity of the models of the latent structure of schemas, whichincluded a large number of observed and latent variables with multiple unidirectional and/orbidirectional associations, varying findings might have reflected sampling fluctuation or the sub-jective decisions of the investigator rather than “true” clinical variation in the factorial structure(MacCallum, Roznowski, & Necowitz, 1992).

Methods

Study Design and Sample Characteristics

Data were collected in a cross-sectional Internet survey of one month’s duration in June and July2009. The survey link was distributed via e-mail to psychology faculties at German universities,several Internet forums, and other contacts of the authors. The invitation to participate includeda brief description of the study, a guarantee of anonymity, the time needed to complete the survey,a request for further dissemination of the invitation to participate, and contact information forquestions and comments. No reimbursement or other incentives were provided and no personaldata (e.g., IP addresses) were recorded. The local institutional review board approved the studyprotocol.

The online questionnaire was accessed 2608 times and 1150 individuals completed the survey.Participants younger than 18 years were excluded and data from the remaining 1132 participantswere included in the analysis. Table 2 presents the demographic and clinical characteristics ofthe sample and reveals that most participants tended to be either students (47.9%) or employed(39.8%). Most were female (78.2%), young (mean age 29.5 years), single (78.6%), and highlyeducated (79.0%). Approximately one of five participants (21.1%) reported a lifetime diagnosisof a mental disorder. Affective disorders (n = 134), anxiety disorders (n = 43), eating disorders(n = 32), and borderline personality disorders (n = 25) occurred the most frequently. A substan-tial proportion of the sample reported treatment in an outpatient (20.0%) or inpatient (7.1%)setting.

The Latent Structure of Schemas 3

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Table 2Sample Characteristics

Student sample Nonstudent sample Total samplen = 542 n = 590 N = 1132

Sex; n (%)Female 449 (82.8) 436 (73.9) 885 (78.2)Male 93 (17.2) 154 (26.1) 247 (21.8)Age; yrs.Mean (standard

deviation)24.1 (4.5) 34.5 (11.1) 29.5 (10.0)

Marital status; n (%)Single 506 (96.4) 372 (63.1) 878 (78.6)Married 29 (5.4) 159 (26.9) 188 (16.6)Separated 4 (0.7) 11 (1.9) 15 (1.3)Divorced 3 (0.6) 45 (7.6) 48 (4.2)Widowed 0 (0) 3 (0.5) 3 (0.3)Education; n (%)Low 0 (0) 19 (3.3) 19 (1.7)Middle 52 (9.6) 167 (28.3) 219 (19.3)High 490 (90.4) 404 (68.5) 894 (79.0)Occupational status; n (%)Employed 0 (0) 450 (76.3) 450 (39.8)Homemaker 0 (0) 23 (3.9) 23 (2.0)Retired 0 (0) 23 (3.9) 23 (2.0)Student 542 (100) 0 (0) 542 (47.9)Unemployed 0 (0) 34 (5.8) 34 (3.0)Other 0 (0) 60 (10.2) 60 (5.3)Lifetime diagnosis of a mentalhealth disorder; n (%)Yes 99 (18.3) 140 (23.7) 239 (21.1)No 443 (81.7) 450 (76.3) 893 (78.9)Health care utilization due tomental health disorder; n (%)a

None 374 (69.0) 372 (63.1) 746 (65.9)Counseling 99 (18.3) 114 (19.3) 213 (18.8)Outpatient treatment 90 (16.6) 136 (23.1) 226 (20.0)Inpatient treatment 27 (5.0) 53 (9.0) 80 (7.1)

aMultiple selections possible.

Instruments

The 90-item YSQ-SF3 is a self-report instrument that assesses 18 early maladaptive schemaswith five items in each schema subscale. Individuals rated each descriptive statement on a 6-pointLikert-type scale ranging from one (completely untrue of me) to six (describes me perfectly). Forall scale items, higher ratings corresponded to stronger schemas. The YSQ-SF3 was administeredin German language.

In addition to the administration of the YSQ-SF3, demographic and health utilization datawere collected. Participants reported their sex, age, marital status, education, occupationalstatus, lifetime diagnosis of a mental disorder, and utilization of any health service (counseling,outpatient, or inpatient) because of a mental disorder.

Confirmatory Factor Analysis of the Online Survey Data

The data from undergraduate students and nonstudent adults were analyzed separately tocontrol for possible differences between the two groups. Confirmatory factor analyses (CFA)


assessed the fit of several a priori defined models, which were chosen to be clinically andmethodologically heterogeneous with different factor structures. Models obtained using theYSQ-SF3 were preferred. Table 1 presents the information used to define the models derivedfrom previous studies.

The baseline model, which was based on Young’s revised theory (Young, Klosko, & Weishaar,2003), assumed 18 correlated first-order schema factors. A theory-driven model with five second-order factors that was consistent with Young’s revised theory (Young et al., 2003) was also fitted,as well as a model empirically derived from a student sample with five second-order domainfactors (Soygut, Karaosmanoglu, & Cakir, 2009). The influential four-domain model originallydeveloped by Lee, Taylor, and Dunn (1999) and confirmed by Hoffart et al. (2005) in patientswith mental disorders (referred to as the “Lee-Hoffart model” in the present study) was the onlytested model that had been independently confirmed. Because this model defined domains foronly 15 schemas, the remaining three first-order schema factors were allowed to freely correlatewith each other and the second-order domains. A two-domain and a three-domain model basedon samples of adults with and without chronic pain were also fitted (Saariaho, Saariaho, Karila,& Joukamaa, 2010), as well as a model with a single global domain. Finally, bifactor modelswith and without correlations between the first-order schema factors were examined.

In the CFA, we applied the maximum likelihood estimation method to analyze the covariancematrix of the items. A robust estimator was used to account for possible non-normality ofobservations yielding adjusted chi-squared statistics and robust standard errors. Latent variableswere scaled by fixing the loading of one indicator for each latent variable.

To evaluate and compare models, both local and global goodness-of-fit measures were cal-culated. Because previous research had found that the first-order local fit characteristics (e.g.,the intercorrelations of items within schemas) of the YSQ were satisfactory, we focused on theglobal fit indexes, although we also report some rarely investigated local fit indexes. The discrep-ancy chi-squared statistic, the normed chi-squared statistic, the comparative fit index (CFI), theTucker-Lewis index (TLI), the root mean squared error of approximation (RMSEA), the stan-dardized root mean squared residual (SRMR), and the Bayes information criterion (BIC) werechosen as fit indexes for the structural equation models (Hair, Black, Babin, & Anderson, 2009;Hu & Bentler, 1999; Schermelleh-Engel, Moosbrugger, & Muller, 2003). However, it should benoted that the CFI and TLI were unlikely to reach acceptable levels when modeling at item leveldue to the large number of items (Hair et al., 2009).

Because the online survey required that all questions be answered, the data included in theanalyses were complete. All analyses were performed using Mplus 5.1 (Muthen & Muthen,1998).

The Monte Carlo Simulation Study

We performed a small-scale Monte Carlo simulation study with 2000 generated samples of 500subjects each, which approximated the median sample size in the studies displayed in Table 1.The data-generating model included 15 normally distributed first-order (schema) factors andfour normally distributed second-order (domain) factors, following the standardized estimatesreported in Hoffart et al. (2005). We chose the Lee-Hoffart model as the data-generating modelbecause of its popularity and because the reported model characteristics provided sufficientdetail to replicate the correlation matrix of the 15 first-order schema factors. We investigated theglobal fit of all of the models presented in Table 1 to the 2000 simulated data sets, with the meanfit indexes reported for evaluation.

In addition to the models described above, the set of models tested included the following: thefive-domain model based on Young’s original theory (Young, 1990), a three-domain model froma student sample (Schmidt, Joiner, Young, & Telch, 1995), a two-domain and a three-domainmodel from a sample of psychiatric outpatients and healthy controls (Mihaescu et al., 1997),a four-domain model from a student sample (Cecero, Nelson, & Gillie, 2004), a three-domainmodel from a student sample (Calvete, Estevez, Arroyabe, & Ruiz, 2005), and a four-domainmodel from a sample of inpatients with depression (Csukly et al., 2011). If the tested model hadoriginally investigated the factor structure of more or less than 15 schemas, the nonoverlapping


schemas of the data-generating and the tested models were omitted from the analyses. Becausein the Monte Carlo study the first-order factor (schema-level) data were simulated, the resultingmeasures of fit were not directly comparable to the measures of fit obtained using the onlinesurvey data (item-level).

Results

CFA of the Online Survey Data

First, univariate and bivariate item descriptive statistics of the total sample were examined todetermine whether the assumptions of CFA were met. On a scale from one to six, the means ofthe 90 items ranged from 1.4 to 4.5 with a median of 2.6. Skewness and kurtosis values rangedfrom -1.0 to 2.6 and -1.1 to 6.4 with medians of 0.6 and -0.5, respectively. Most of the Pearsonproduct-moment correlations of items (99.6%) were positive, with a small proportion (0.5%)exceeding 0.70. The median correlation between items was 0.30.

In general, analyses of the student and nonstudent samples provided comparable estimates,with the normed chi-squared and RMSEA exhibiting a modestly superior model fit for thestudent sample and the CFI, TLI, and SRMR exhibiting a marginally better fit for the nonstudentsample. The relative ranking of the models based on their fit to the data, however, remainedconstant across the samples. Table 3 presents the global goodness-of-fit indexes of the testedmodels.

Young’s revised theoretical model (S1 and N1 in Table 3) with 18 correlated first-order schemafactors without second-order domains exhibited an acceptable fit in both samples. Althoughneither the CFI nor the TLI reached the threshold for an acceptable model (≥0.90), the normedchi-squared statistic (<3), the RMSEA(<.05), and the SRMR (<.08) indicated that the model fitthe data. The first-order schema factors exhibited internal consistency. For the student sample,the factor reliability coefficients ranged from 0.65 for “Entitlement/Grandiosity” to 0.91 for“Defectiveness/Shame” with a median of 0.80, while the coefficients for the nonstudent sampleranged from 0.66 for “Punitiveness” to 0.92 for “Defectiveness/Shame” with a median of 0.83.The median explained variance of items within factors was 0.45 for students and 0.50 fornonstudent adults. Thus, on average, the model explained approximately half of the varianceof the item responses. Of the 153 inter-factor correlations, 24 in the student sample and 44 inthe nonstudent sample were 0.70 or higher. The median inter-schema correlation was 0.52 forstudents and 0.63 for nonstudent adults.

In both samples, all models with second-order domains (S2 to S7, and N2 to N7 in Table 3)were inferior to the models without second-order schema domains, even though their absolutefit was satisfactory with regard to all the indexes but the CFI and TLI. Among the second-orderdomain models, the Lee-Hoffart model (S4 and N4) exhibited marginally better fit comparedwith the other models.

The bifactor model with uncorrelated specific factors (S8 and N8 in Table 3) exhibited agood fit that was comparable to the model with correlated first-order schema factors withoutsecond-order domains (S1 and N1). This indicates that modeling the intercorrelations of the first-level schema factors could be “replaced” by the inclusion of a generic factor without seriouslyendangering the model fit. However, due to the parsimonious nature of the bifactor model,complexity-adjusted measures such as the BIC (where lower values indicate better fit) clearlysupported it over the baseline model with correlated first-order factors.

An even better fit was achieved with the bifactor structure that included the correlationsbetween the specific factors (S9 and N9), which was the model with the best fit in both groups. Theinclusion of a generic factor, on which all items loaded considerably reduced the intercorrelationsof the first-order schema factors; the median of the intercorrelations was zero in the studentsample and 0.10 in the nonstudent adult sample. In the student sample, the loadings on thegeneric factor ranged from 0.03 to 0.77 with a median of 0.51, while in the nonstudent sample thecorresponding values were 0.08, 0.77, and 0.54, respectively. The amount of variance explainedby the generic factor was 0.22 in the student sample and 0.23 in the nonstudent sample, while theaverage variance explained by the specific factors was 0.27 for students and 0.30 for nonstudents.


Tab

le3

Glo

balG

oodn

ess-

of-F

itIn

dexe

sof

the

Test

edM

odel

s

Chi

-sq

(df)

Chi

-sq/

dfC

FI

TL

IR

MSE

ASR

MR

BIC

Thr

esho

ldfo

rgo

odm

odel

sna

≤2≥.

95≥.

95≤.

05≤.

05na

Thr

esho

ldfo

rac

cept

able

mod

els

na≤3

≥.90

≥.90

≤.08

≤.08

na

SSt

uden

tsa

mpl

eS1

18co

rrel

ated

first

-ord

erfa

ctor

s(Y

oung

’ssc

hem

as)

7703

(376

2)2.

047

.830

.819

.044

.066

1471

74.0

40S2

5co

rrel

ated

seco

nd-o

rder

fact

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Figure 1. Average explained variance by the generic and specific factors of the bifactor model in thenonstudent adult sample.Note. ED = Emotional Deprivation; AB = Abandonment/Instability; MA = Mistrust/Abuse; SI = So-cial Isolation/Alienation; DS = Defectiveness/Shame; FA = Failure; DI = Dependence/Incompetence;VH = Vulnerability to Harm or Illness; EM = Enmeshment/Undeveloped Self; SB = Subjugation; SS =Self-Sacrifice; EI = Emotional Inhibition; US = Unrelenting Standards/Hypercriticalness; ET = Entitle-ment/Grandiosity; IS = Insufficient Self-Control/Self-Discipline; AS = Approval-Seeking/Recognition-Seeking; NP = Negativity/Pessimism; PU = Punitiveness.

For example, Figure 1 displays the average variance explained by the generic and specific factorsin the nonstudent group. The generic factor explained a fifth to a quarter of the total variance; thespecific factors explained another quarter to third of the variance, although there was substantialvariation across the schemas.

The reliability of the specific schema factors was considerably diminished by the inclusion ofthe generic factor. The reliability coefficients ranged from 0.40 to 0.78 with a median of 0.67in the student sample and from 0.27 to 0.82 with a median of 0.70 in the nonstudent adultsample. Thus, several schema factors did not reach the threshold of 0.70 typically required forinternal consistency. The lowest reliability estimates were observed for the schemas of “Depen-dence/Incompetence” (0.40 for students and 0.27 for nonstudents) and “Subjugation” (0.41 forstudents and 0.49 for nonstudents).

The Monte Carlo Simulation Study

The Lee-Hoffart model (Hoffart et al., 2005) with 15 first-order schema factors and foursecond-order domains was used to generate each of the 2000 data sets with 500 cases inthe Monte Carlo simulation study. Table 4 displays the study results. On average, the data-generating model (M0) exhibited a very good fit to the data, confirming that the data gen-eration was successful. For the mean normed chi-squared index, four models (M2, M4, M5,and M6) exhibited an acceptable fit and two models (M3 and M12) exhibited a good fit. Ac-cording to the mean CFI and the mean TLI, all models provided at least an acceptable fit(≥.90) and several (M2, M3, M4, M5, M6, M8, and M12) provided a good fit (≥.95). Forthe mean RMSEA, all but two of the models (M1 and M11) exhibited an acceptable (≤.08)fit. For the mean SRMR, all but one model (M9) provided a good fit (≤.05). For the meanBIC (where lower values indicate better fit), some of the fitted models exhibited a complexity-adjusted fit that was superior to the data-generating model (particularly M3, M4, M5, M7, M9,and M11).


Tab

le4

Mea

nF

itIn

dexe

sfo

rM

odel

sF

itte

dto

Sim

ulat

edL

ee-H

offa

rtD

ata

ina

Mon

teC

arlo

Sim

ulat

ion

Stu

dy

Num

ber

ofN

umbe

rof

Mea

nM

ean

Mea

nM

ean

Mea

nM

ean

Mea

ndo

mai

nses

tim

ated

para

met

ers

Chi

-sq

(df)

Chi

-sq/

dfC

FI

TL

IR

MSE

ASR

MR

BIC

Thr

esho

ldfo

rgo

odm

odel

sna

≤2≥.

95≥.

95≤.

05≤.

05na

Thr

esho

ldfo

rac

cept

able

mod

els

na≤3

≥.90

≥.90

≤.08

≤.08

na

M0

Lee

-Hof

fart

mod

el(1

999/

2006

)4

5185

.972

(84)

1.02

3.9

991.

000

.008

.016

1651

8.17

3M

1G

loba

ldom

ain

mod

el1

4542

9.19

6(9

0)5.

667

.932

.921

.087

.040

1682

2.25

3M

2Y

oung

’sor

igin

alth

eory

(199

0)5

5522

8.10

6(8

0)2.

851

.970

.961

.061

.031

1668

2.54

1M

3Sc

hmid

tet

al.(

1995

)3

4173

.716

(49)

1.50

4.9

93.9

90.0

30.0

2013

842.

797

M4

Mih

aesc

uet

al.I

(199

7)2

3713

5.39

7(5

3)2.

555

.978

.972

.055

.025

1358

7.76

6M

5M

ihae

scu

etal

.II

(199

7)3

3913

1.83

0(5

1)2.

585

.978

.972

.056

.025

1359

6.63

4M

6Y

oung

’sre

vise

dth

eory

(200

3)5

5521

3.20

4(8

0)2.

665

.973

.965

.057

.030

1666

9.08

7M

7C

ecer

oet

al.(

2004

)4

4625

9.31

8(7

3)3.

552

.958

.947

.071

.037

1586

2.98

2M

8C

alve

teet

al.(

2005

)3

4827

5.26

6(8

7)3.

164

.962

.955

.066

.038

1668

7.19

6M

9So

ygut

etal

.(20

09)

547

289.

226

(72)

4.01

7.9

51.9

38.0

78.1

0315

898.

193

M10

Saar

iaho

etal

.I(2

010)

246

378.

606

(89)

4.25

4.9

42.9

32.0

80.0

4116

777.

260

M11

Saar

iaho

etal

.II

(201

0)3

4532

2.38

3(7

4)4.

357

.947

.935

.082

.041

1563

6.49

7M

12C

sukl

yet

al.(

2011

)4

5714

9.66

4(7

8)1.

919

.986

.981

.041

.024

1661

7.48

5

Not

e.C

hi-s

q=

disc

repa

ncy

chi-

squa

red

stat

isti

c;df

=de

gree

sof

free

dom

;Chi

-sq/

df=

norm

edch

i-sq

uare

dst

atis

tic;

CF

I=

com

para

tive

fitin

dex;

TL

I=

Tuc

ker-

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isin

dex;

RM

SEA

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red

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appr

oxim

atio

n;SR

MR

=st

anda

rdiz

edro

otm

ean

squa

red

resi

dual

;BIC

=B

ayes

info

rmat

ion

crit

erio

n;na

=no

tap

plic

able

(no

abso

lute

thre

shol

dde

finab

le).


Discussion

We performed CFA of data from an online survey and a Monte Carlo simulation study toinvestigate the dimensionality of the YSQ, with a particular focus on its second-order factorstructure.

The present study appears to have been one of the few investigations testing the YSQ-SF3,as well as the first study to examine the psychometric properties of the German version of thisinstrument. An advantage of the study was its design, which compared the results provided bydifferent a priori defined models. In addition, the study was preceded by a review of previousempirical findings on the second-order factor structure of the instrument, and the model-fittingapproach involved only confirmatory analyses of theoretically or empirically based modelswithout model modifications.

A major limitation of the confirmatory analyses of the data obtained from the online surveyconcerns the generalizability of the findings. Although the survey provided complete data from alarge sample, the online format allowed no control over the selection of the participants. However,despite the largely nonclinical sample, findings of the present study may contribute to clinicalpractice for several reasons. First, Young’s schema theory assumes a continuum from “normal”functioning to the clinically relevant manifestation of schemas. Second, a nonnegligible propor-tion of the sample had received treatment for a diagnosed mental disorder. Finally, responses inthe sample reflected the frequent occurrence of schemas with a presence ranging from minimalto pronounced, which provided a solid basis for the analysis of variance-covariance structures.Nevertheless, the heterogeneous nature of the sample and the broad range of demographic andclinical characteristics of respondents did not provide a distinct population to which the studyfindings could be generalized.

The data did meet the assumptions for CFA. The results of the analyses were consistent withprevious research and provided evidence supporting the first-order factor structure of Young’sscale with 18 correlated schema factors that exhibited acceptable internal consistency.

Our central finding was that the analysis of the online survey data provided no support for aparticular second-order factor structure of the measure. The results of the CFA suggested thata model with correlated first-order schema factors provided a better fit than the models thatdefined second-order domains both for the student and nonstudent adult groups. Calvete et al.(2005), who also performed a CFA with a student sample, reported similar findings. However,the findings of the present study conflicted with those of Hoffart et al. (2005), who reported thata higher order four-domain model provided a somewhat better fit than the correlated first-ordermodel without domains for a clinical sample. In the present study, most higher order domainmodels provided an acceptable fit to the empirical data. A detailed examination of the Hoffartet al. (2005) findings reveals that their study also identified several higher order models with anacceptable fit with only minor differences among the models. Unfortunately, this issue cannotbe resolved completely at present because these appear to be the only studies applying CFA tothe investigation of the hierarchical factor structure of maladaptive schemas.

We performed a Monte Carlo simulation study to determine whether previous findings re-garding the domain structure of the YSQ should be attributed to chance, sample fluctuation,or inconsistent methodology rather than clinical sample characteristics. Because the Hoffartet al. (2005) study was the only independently confirmed higher order domain structure modeland because the reported findings were sufficiently detailed, this model was chosen to generatethe Monte Carlo data. Surprisingly, almost all tested models provided an acceptable fit to thisdata, regardless of features such as the language or version of the instrument, the number ofhigher order domains, the factor rotation method, the nature of the sample, or the presence ofcross-loadings. One may interpret these findings as clear evidence that the Lee-Hoffart model isthe best approximation of the “true” population model. However, one may also assume that thechoice of the model is arbitrary and similar findings would have been obtained if another modelfor data generation had been chosen. Unfortunately, because of low reporting quality, we couldnot test if another data generation model had behaved the same way.

Thus, our conclusion is somewhere between these two extremes stating that whatever thepopulation model (the “true” domain structure) is, several models can be identified that fit to


it. Thus, one cannot exclude that the seemingly different findings on the domain structure ofschemas may be attributed to other than clinical factors. For example, most studies used EFAtechniques, which may result in a kind of “overfitting” of the resulting models and overempha-sizing their generalizability. Research on personality traits (Eysenck, 1991) and on the SymptomChecklist-90 (Cyr, McKenna-Foley, & Peacock, 1985; Derogatis, 1983; Vassend & Skrondal,1999) has demonstrated that in the search for dimensions underlying psychosocial constructs,the choice of the number of factors to extract is not always straightforward.

In the present study, a bifactor model in which all items loaded onto a single generic factor aswell as on Young’s first-order schema factors fit the data better than any other tested model. Thisfactor structure for the YSQ does not appear to have been considered in previous research. Inaddition to its beneficial statistical properties (e.g., partitioning the explained variance betweenthe generic and specific factors), this model also has implications for practical measurementissues and hypotheses regarding the organization of schemas. The fit of this model to the dataprovided clear empirical support for the existence of a first-order generic factor that accountedfor approximately half of the explained variance in the test items.

In the case of the SCL-90-R, a similarly emerging generic factor has been primarily viewed asgeneral distress (Cyr et al., 1985; Vassend & Skrondal, 1999). However, it would not be straight-forward to interpret the generic factor of the YSQ as general distress or global psychopathology,because these terms refer to alterable states rather than prevailing traits and are incompatiblewith the conception of the schema as a stable pattern. In agreement with empirical findings sup-porting a weak to moderate association of schema scores and global psychopathology (Soygutet al., 2009; Stopa, Thorne, Waters, & Preston, 2001; Welburn, Coristine, Dagg, Pontefract, &Jordan, 2002), we rather suppose that the generic schema factor can be considered a relativelystable trait that influences the development of psychopathological characteristics. FollowingVassernd and Skrondal (1999), we presume that it is likely that dispositional characteristicsunderlie the generic schema factor, such as neuroticism (Eysenck, 1947) or negative affectivity(Watson & Clark, 1984). Other investigations that report stability of schemas over time as well asa substantial overlap between schemas and neuroticism (Muris, 2006; Riso et al., 2006; Thimm,2010a; Wang, Halvorsen, Eisemann, & Waterloo, 2010) support this view.

It is also possible that the generic factor is artifactual and reflects participant differencesin test-taking habits, such as a “response level shift” due to interpersonal differences in theperceived difficulty of scale items or different connotations of the response category labelsfor respondents. This explanation cannot be ruled out without further investigation becausethe imprecise wording of responses (e.g., “moderately true of me”) allows for differences ininterpretation.

Based on our findings, we recommend clinicians who use the YSQ not to focus solely on theabsolute scores (or other quantitative summaries) reported on each schema scale, but also toconsider the global response pattern as an indicator of the generic factor. By analyzing graphicalprofiles it would mean not only to scan for high values exceeding a certain threshold, but alsoto look for peaks in the profile, i.e., values above the average score of the individual.

We encourage researchers to test the bifactor model either prospectively or even retrospectivelyon existing data collected with the YSQ. As most researchers probably would not decide to extractonly one factor in EFA when the amount of explained variance is below 30% or even 50%, it isnot surprising that this structure has not been considered before. However, our findings suggestthat the presence of a bifactor structure is possible in every explorative factor analytical study onthe item level, where the first factor in the unrotated solution accounts for around 20% or morevariance. If the findings of the present study were replicated, then they would raise concernsabout the reliability of the different schema subscales once the generic factor was partitionedout. However, in contrast to the SCL-90 research, the presence of a generic factor does notpreclude the need to identify first-order schema factors. Although the reliability of the schemasubscales was reduced after the generic factor was included, it was still substantial in severalcases, and further research would clarify the extent to which the reliability of these factors wasmaintained.

On the other hand, the fact that the influence of the generic factor on items varied betweenschemas should not be ignored. This complex “blending” may be responsible for the frequently


reported inconsistence, or even inconsequence, in studies that seek to identify unique schemaprofiles for certain disorders or disorder subtypes (Oei & Baranoff, 2007; Specht, Chapman, &Cellucci, 2009; Thimm, 2010b; Zeigler-Hill, Green, Arnau, Sisemore, & Myers, 2011). In general,the findings of the present study strongly suggest that future analyses should employ confirma-tory factor techniques, model first-order and second-order factor structures simultaneously, andcompare several a priori defined models instead of assessing the fit of a single model.

Conclusions

In the present study, we were unable to definitively determine whether higher order schemadomains exist. However, extending the model to include a generic first-order factor in addition tofirst-order schema factors proved to be informative. If the bifactor model proves to be superior toother models also in future studies, then this would provide strong evidence against the existenceof higher order domains. At the moment, it is only possible to conclude either that several higherorder domain structures are present but cannot be ranked by statistical measures alone or that afirst-order generic factor is present and higher order domains are dispensable. Although we didnot directly address this issue, our findings also suggest questioning the hypothesis that clinicaland nonclinical populations differ with regard to their schema structure. In clinical populations,schemas are likely to have a more pronounced presence, but the associational structure ofthe schemas can still be similar to that in nonclinical populations. Although further empiricalresearch seems necessary, we go along with the conclusions of Vassend and Skrondal (1999, p.699): “As should be clear by now, statistical arguments alone are in principle incapable of leadingto final decisions about choice of model.” Accordingly, future empirical research should includeconceptual and theoretical arguments that incorporate the clinical experience of practitioners.

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The Latent Factor Structure of Young's Early Maladaptive Schemas: Are Schemas Organized Into...

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