Testing the 8-Syndrome Structure of the Child Behavior Checklist in 30 Societies Masha Y. Ivanova, Thomas M. Achenbach, and Levent Dumenci Department of Psychiatry, University of Vermont Leslie A. Rescorla Department of Psychology, Bryn Mawr College Fredrik Almqvist and Sheila Weintraub Department of Child Psychiatry, University of Helsinki Niels Bilenberg Department of Child and Adolescent Psychiatry, University of Southern Denmark Hector Bird Department of Psychiatry, Columbia University Wei J. Chen Institute of Epidemiology, National Taiwan University Anca Dobrean Department of Psychology, Babes-Bolyai University Manfred Do ¨pfner Department of Child and Adolescent Psychiatry and Psychotherapy, University of Cologne Nese Erol Department of Child and Adolescent Psychiatry, Ankara University Eric Fombonne Department of Psychiatry, McGill University Antonio Castro Fonseca Faculty of Psychology and Educational Sciences, University of Coimbra Alessandra Frigerio Child and Adolescent Psychiatry Unit, Eugenio Medea Scientific Institute Hans Grietens Faculty of Psychology and Educational Sciences, Katholieke University of Leuven Helga Hannesdottir Division of Psychiatry, Landspı ´talinn University Hospital Journal of Clinical Child and Adolescent Psychology 2007, Vol. 36, No. 3, 405–417 Copyright # 2007 by Lawrence Erlbaum Associates, Inc. 405
Transcript
Testing the 8-Syndrome Structure of the Child Behavior Checklistin 30 Societies
Masha Y. Ivanova, Thomas M. Achenbach, and Levent DumenciDepartment of Psychiatry, University of Vermont
Leslie A. RescorlaDepartment of Psychology, Bryn Mawr College
Fredrik Almqvist and Sheila WeintraubDepartment of Child Psychiatry, University of Helsinki
Niels BilenbergDepartment of Child and Adolescent Psychiatry, University of Southern Denmark
Hector BirdDepartment of Psychiatry, Columbia University
Wei J. ChenInstitute of Epidemiology, National Taiwan University
Anca DobreanDepartment of Psychology, Babes-Bolyai University
Manfred DopfnerDepartment of Child and Adolescent Psychiatry and Psychotherapy,
University of Cologne
Nese ErolDepartment of Child and Adolescent Psychiatry, Ankara University
Eric FombonneDepartment of Psychiatry, McGill University
Ant�oonio Castro FonsecaFaculty of Psychology and Educational Sciences, University of Coimbra
Alessandra FrigerioChild and Adolescent Psychiatry Unit, Eugenio Medea Scientific Institute
Hans GrietensFaculty of Psychology and Educational Sciences, Katholieke University of Leuven
Helga Hannesd�oottirDivision of Psychiatry, Landspıtalinn University Hospital
Journal of Clinical Child and Adolescent Psychology2007, Vol. 36, No. 3, 405–417
Copyright # 2007 byLawrence Erlbaum Associates, Inc.
405
Yasuko KanbayashiDepartment of Letters, Chuo University
Michael LambertDepartment of Human Development and Family Studies,
University of Missouri–Columbia
Bo LarssonThe Norwegian University of Science and Technology, Trondheim
Patrick LeungDepartment of Psychology, Chinese University of Hong Kong
Xianchen LiuDepartment of Psychiatry, University of Pittsburgh
Asghar MinaeiSensory Disabilities Department, Research Institute of Exceptional Children
Mesfin S. MulatuThe MayaTech Corporation
Torunn S. NovikDepartment of Child and Adolescent Psychiatry, Buskerud Hospital
Kyung Ja OhDepartment of Psychology, Yonsei University
Alexandra RoussosAttiki Child Psychiatric Hospital
Michael SawyerDepartment of Paediatrics, University of Adelaide and Research and Evaluation Unit,
Women’s and Children’s Hospital
Zeynep SimsekHarran University
Hans-Christoph Steinhausen and Christa Winkler MetzkeDepartment of Child and Adolescent Psychiatry, University of Zurich
Tomasz WolanczykDepartment of Child Psychiatry, Medical University of Warsaw
Hao-Jan YangChung Shan Medical University
IVANOVA ET AL.
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Nelly ZilberFalk Institute for Mental Health Studies, Jerusalem and CRFJ (French Research
Center in Jerusalem)
Rita ZukauskieneDepartment of Psychology, Mykolas Romeris University
Frank C. VerhulstDepartment of Child and Adolescent Psychiatry, Erasmus University Medical
Center-Sophia’s Children’s Hospital
There is a growing need for multicultural collaboration in child mental healthservices, training, and research. To facilitate such collaboration, this studytested the 8-syndrome structure of the Child Behavior Checklist (CBCL) in30 societies. Parents’ CBCL ratings of 58,051 6- to 18-year-olds were subjectedto confirmatory factor analyses, which were conducted separately for eachsociety. Societies represented Asia; Africa; Australia; the Caribbean; Eastern,Western, Southern, and Northern Europe; the Middle East; and North America.Fit indices strongly supported the correlated 8-syndrome structure in each of30 societies. The results support use of the syndromes in diverse societies.
Mental health workers and educators increasinglydeal with cultural and ethnic variations amongthe children they serve. Assessment instrumentsmust therefore be appropriate for children ofdiverse backgrounds. Assessment instrumentsdeveloped for U.S. children are also widely usedin other societies by indigenous mental healthworkers. However, before assessment instrumentsdeveloped in one society can be applied inanother society, it is important to determineempirically whether they function similarly in thetwo societies.
Translations of standardized assessmentinstruments offer opportunities for testing theapplicability of these instruments across societies.Patterns of co-occurring problems can beidentified by performing multivariate statisticalanalyses on problem items reported for large sam-ples of children. These statistically derived patternscan be viewed as ‘‘syndromes’’ of problems thattend to co-occur. Syndromes are often derived stat-istically via factor analytic methods. Exploratoryfactor analysis (EFA) is applied to correlationsamong ratings of problem items to find patternsof co-occurring problems. After EFA has identifiedpatterns of co-occurring items, they are usuallytested via confirmatory factor analysis (CFA) in adifferent sample. CFA tests how well a specifiedmodel of item groupings fits a particular dataset.
A key empirical question is whether syndromesderived from particular problem items in onesociety would also be found for the same problemitems in other societies. If an instrument’s syn-drome structure is replicated across societies, thenservices, training, and research can focus on thesame syndromes in these societies. Of course,additional syndromes might also be found byusing different items, informants, and assessmentmethods.
The similarity of an instrument’s syndromestructure across groups is termed configural invar-iance (Vandenberg & Lance, 2000). Configuralinvariance is the most basic component ofmeasurement invariance. Measurement invariancerefers to the notion that an assessment instrumentmeasures the same construct across popula-tions. In addition to configural invariance, othercomponents of measurement invariance includemetric invariance (similarity of factor loadings),scalar invariance (similarity of item inter-cepts), residual invariance (similarity of item resi-duals), factor variance invariance (similarity offactor variances), factor covariance invariance(similarity of factor covariances), and latent meaninvariance (similarity of latent means) (Vandenberg& Lance, 2000). Components of measurementinvariance can be conceptualized as a pyramid, withconfigural invariance as the base on which the othercomponents rest.
The present study was designed to provide a testof the configural invariance of the Child BehaviorChecklist (CBCL; Achenbach & Rescorla, 2001) in
Correspondence should be sent to Masha Y. Ivanova, Psy-
chiatry Department, University of Vermont, 1 S. Prospect St.,
each of 30 societies. The CBCL assesses 120emotional, behavioral, and social problemsreported by parents of children ages 6 to 18. Par-ents rate each item as 0-not true as far as you know,1-somewhat or sometimes true, and 2-very trueor often true, based on the preceding 6 months.The CBCL is part of a multi-informant familyof empirically based assessment instrumentsdeveloped by Achenbach and colleagues (Achen-bach, 1991; Achenbach & Rescorla, 2001).
The CBCL syndrome model tested in our studywas found to be the best-fitting model for dataobtained from parents’ ratings of combined U.S.general population and clinical samples of 6- to18-year-olds (Achenbach & Rescorla, 2001). The8-syndrome model was derived and tested viaEFA and CFA. The EFA consisted of exploratoryUnweighted Least Squares (ULS) analyses ofpolychoric correlations and principal componentsanalyses (PCA) of Pearson correlations. TheCFA employed techniques that were robust to vio-lations of multivariate normality: The correlated8-syndrome model derived from EFA was fittedon tetrachoric correlations using the weightedleast squares with standard errors and mean- andvariance-adjusted chi-square estimator (WLSMV)via Mplus (Muthen & Muthen, 2001, 2004). Thefollowing eight syndromes were obtained:Anxious=Depressed, Withdrawn=Depressed, SomaticComplaints, Social Problems, Thought Problems,Attention Problems, Rule-Breaking Behavior,and Aggressive Behavior. These 2001 syndromeswere highly correlated with earlier versions of thesyndromes published in 1991 (Achenbach, 1991).
The WLSMV (Muthen & Muthen, 2004) esti-mator represents a significant advance in CFA ofdata such as ratings on the CBCL. Because it isan asymptotically distribution-free (ADF) esti-mator, the WLSMV can be used with ordinal datawithout assuming multivariate normality. This isextremely important in CFA of problem ratingsbecause problem item distributions are usuallynon-normal, which limits the utility of estimationprocedures that assume multivariate normality. Inaddition to being robust to violations of multivari-ate normality, the WLSMV estimator is extremelyefficient in comparison to other ADF estimators,such as the ULS.
Prior to our study, CFA was used to testthe CBCL factor structure in samples fromFrance, the Netherlands, Turkey, and Korea(e.g., Albrecht, Veerman, Damen, & Kroes, 2001;Berg, Fombonne, McGuire, & Verhulst, 1997;De Groot, Koot, & Verhulst, 1994; Dumenci,Erol, Achenbach, & Simsek, 2004; Dumenci, Oh,& Achenbach, 2003; Van den Oord, 1993). Mostof these studies applied the ULS estimation to
polychoric correlations to avoid assuming multi-variate normality and supported the 1991 CBCLfactor model. In one of the few studies that exam-ined the configural invariance of the CBCL in sev-eral societies, Hartman et al. (1999) tested the 1991syndromes in general population samples fromGreece, the Netherlands, Israel, Norway, Portugal,and Turkey. With maximum likelihood esti-mation, all models converged, and the Root MeanSquare Error of Approximation (RMSEA; Browne& Cudeck, 1993) met the authors’ criterion forgood fit in all six societies (RMSEA < .07). WithULS estimation, the model failed to converge forone sample and the RMSEA was outside theacceptable range for the remaining samples.
The present study was designed to test the con-figural invariance of the correlated 8-syndromestructure of the CBCL in 30 societies. The studydiffered from previous tests of configural invar-iance of the CBCL syndrome structure by test-ing the 2001 CBCL syndromes in each of 30diverse societies and capitalizing on advancesin CFA methodology by using the WLSMVestimator.
Method
Samples
We analyzed data for 58,051 6- to 18-year-oldsfrom the 30 general population samples describedin Table 1. The 1991 version of the CBCL wasused to rate children in 27 societies, whereas the2001 CBCL was used to rate children in Iran,Lithuania, and Romania. Conventions requiredby each investigator’s institution for obtainingthe parents’ informed consent were followed.Consistent with standard procedures (Achenbach& Rescorla, 2001), CBCLs with > 8 missing itemratings were excluded (6% of the CBCLs). Ns ran-ged from 628 for Denmark to 4,858 for China. Weincluded data used by Rescorla et al. (in press) intheir comparisons of distributions of CBCL scalescores across societies, plus data for 4,331 childrenwho were older than those used by Rescorla et al.Three CBCLs from Portugal and 18 from Taiwanhad missing data for age. We included theseCBCLs in our analyses after we determined thatthey were for children whose ages were between6 and 18.
Tested Model
Figure 1 illustrates the 2001 correlated 8-syndromemodel (Achenbach & Rescorla, 2001) that wastested in our study. Each item was assigned toonly one factor. Of the 103 items that loaded
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significantly on the eight factors derived from the2001 CBCL, we analyzed the 96 items that werethe same on the 1991 and 2001 CBCL. Weexcluded six items that were new on the 2001CBCL, plus Item 105, which was changed from‘‘Uses alcohol or drugs for nonmedical purposes(describe)’’ on the 1991 CBCL to ‘‘Uses drugsfor nonmedical purposes (don’t include alcoholor tobacco)’’ on the 2001 CBCL. The 2001 versionof Item 105 excluded alcohol and tobacco becausenew items assessed alcohol and tobacco usage.
Data Analyses
Tetrachoric correlations between items scored 0versus 1 or 2 were used. To take account of thebinary item scores, we used WLSMV implementedvia Mplus 3.0 (Muthen & Muthen, 2004). The pri-mary index of model fit was the RMSEA, which isconsidered the best fit index for the WLSMV, withvalues 6.06 indicating good fit (Yu & Muthen,2002). Although other fit indicas do not perform wellfor binary variables, we also computed the Com-parative Fit Index (CFI; Bentler, 1990) and theTucker-Lewis index (TLI; Tucker & Lewis, 1973).We considered their results to be secondary to theresults of the RMSEA. Hu and Bentler (1999) pro-posed that CFI and TLI values > .95 be requiredfor good model fit. However, this criterion has beencriticized for incorrectly rejecting correctly specifiedcomplex models (Marsh, Hau, & Wen, 2004). Asour model was complex, we used Browne andCudeck’s (1993) criterion of > .90 for good fit and.80 to .90 for acceptable fit.
Results
The model converged in all 30 samples. AsTable 2 shows, the RMSEA ranged from .026
(China) to .055 (Ethiopia), indicating good fit forall 30 societies (25th percentile ¼ .037, 50th ¼ .039,and 75th ¼ .041). The CFI ranged from .730(Lithuania) to .947 (Puerto Rico), indicating accept-able to good fit for all societies, except Ethiopia,Germany, Hong Kong, and Lithuania (25thpercentile ¼ .840, 50th ¼ .870, and 75th ¼ .897).The TLI ranged from .790 (Ethiopia) to .964(Australia), indicating acceptable to good fit for allsocieties, except Ethiopia (25th percentile ¼ .900,50th ¼ .918, and 75th ¼ .944).
For 25 societies, the correlated 8-factor modelconverged smoothly. Table 2 shows that five socie-ties (Belgium, Jamaica, Norway, Sweden, andThailand) had one negative residual item varianceeach. Thus, 5 (.0008) of the 6,600 estimated para-meters were outside of the admissible parameterspace. We used Van Driel’s (1978) procedure fortesting out-of-range parameter estimates, whichhas been recommended by Chen, Bollen, Paxton,Curran, and Kirby (2001) and McDonald (2004).Van Driel’s procedure determines whether theinadmissible parameter estimate (e.g., negativeresidual item variance) is due to sampling fluctua-tions or a model specification error. It involvesforming confidence intervals around the inadmis-sible parameters using their asymptotic standarderrors. If the confidence interval and the admiss-ible parameter space overlap, then the inadmissiblepoint estimate is concluded to be due to samplingfluctuations. For all five negative residual itemvariances in our study, the 95% confidence inter-vals included admissible values.
For the 24 societies for which the modelconverged smoothly, all 96 items loaded signifi-cantly on their predicted factors (factor loadingz > 1.96). For the remaining six societies, all itemshad significant loadings on their predicted factors,except Item 59 for Taiwan; Item 32 for Jamaica;
Figure 1. The model that was tested in the study. A/D = Anxious/Depressed, W/D = Withdrawn/Depressed,
SC = Somatic Complaints, SP = Social Problems, TP = Thought Problems, AP = Attention Problems, RBB = Rule-
Breaking Behavior, AB = Aggressive Behavior.
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Items 59 and 60 for Puerto Rico; and Items 30,56d, 59, 60, 72, 79, and 83 for Ethiopia.
Table 3 presents descriptive statistics for itemfactor loadings for each society, including means,medians, standard deviations, and ranges. Meanfactor loadings for each society ranged from .48(Ethiopia) to .70 (Australia). The mean of meanfactor loadings for each society was .62 (25thpercentile ¼ .59, 50th ¼ .62, and 75th ¼ .64,respectively). Table 4 presents descriptive statisticsfor factor loadings separately for each item acrossthe 30 societies, including means, medians, stan-dard deviations, and ranges. Mean factor loadingsacross society ranged from .37 (Item 32. Feelshe=she has to be perfect) to .87 (Item 103. Sad).The mean of mean factor loadings across societieswas also .62 (25th percentile ¼ .55, 50th ¼ .64, and75th ¼ .69). When considered by syndrome, the
mean factor loadings ranged from .55 for ThoughtProblems to .68 for Aggressive Behavior.
As presented in Table 3, we also computeddescriptive statistics for factor covariances foreach society, including means, medians, standarddeviations, and ranges. Mean factor covariancesranged from .60 (France) to .78 (Iran), with anoverall mean of .70.
Discussion
Our results indicated that the correlated8-syndrome structure fit well when tested separatelyin 30 societies. The societies were quite diverse,representing world regions differing greatly inpolitical, educational, and mental health systems,as well as childrearing practices. RMSEAs were
Table 2. Results of Confirmatory Factor Analyses for 30 Societies
Note: RMSEA ¼ Root Mean Square Error of Approximation; CFI ¼ Comparative Fit Index; TLI ¼ Tucker-Lewis index.aItems with negative residual variances.bThe number is the item’s number on the Child Behavior Checklist.
CFA IN 30 SOCIETIES
411
< .06 for all samples, indicating good model fit.The model fit the data with minimal problemsfor all societies except Ethiopia, as Table 2 shows.Even for Ethiopia, the RMSEA was .055 and themean loading for items on their predicted factorswas a substantial .48. Small model underidentifica-tion problems were found for five societies. Theyconstituted less than .0008 of all estimated para-meters. Across all societies, the mean loading ofitems on their predicted factors was a substantial.62. The factors were correlated, with the meanof factor covariances across societies being .70.
As another test of the cross-cultural validityof the CBCL, Rescorla et al. (in press) performedcomparisons of CBCL syndrome scores for the6- to 16-year-old children in the 30 data setsdescribed here, plus a U.S. general populationsample. For the eight syndromes, effect sizes formean score differences among societies rangedfrom 4% (Rule-Breaking Behavior) to 9% (Anxious=Depressed), with most clustering in the 5 to 6%
range. These values were in the small to mediumeffect size range, using Cohen’s (1988) criteria.Societies differed more on scores for internali-zing kinds of problems (i.e., Anxious=Depressed,Withdrawn=Depressed, and Somatic Complaintssyndromes) than on scores for externalizing kinds ofproblems (i.e., Rule-Breaking Behavior and Aggress-ive Behavior syndromes). Thus, although the resultsof our study indicated that the correlated 8-syndromestructure fit the data well when tested separately in 30societies, the results of the Rescorla et al. study indi-cated some variations in the mean level of syndromescores across these societies.
Assessment of children’s problems requires datanot only from parents but from teachers and thechildren themselves. The Teacher’s Report Form(TRF) and the Youth Self-Report (YSR) are tea-cher- and self-report counterparts to the CBCLthat have similar syndrome structures. For theTRF, Ivanova et al. (in press) tested a correlated7-syndrome structure comprising the Anxious=
Table 3. Descriptive Statistics for Factor Loadings and Factor Covariances by Society
Note: Values in italics are descriptive statistics for syndromes.a Loadings >1 are for the 5 out of 6,600 estimated parameters that were outside of that admissable
parameter space.
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Depressed, Withdrawn=Depressed, Somatic Com-plaints, Social Problems, Thought Problems,Rule-Breaking Behavior, and Aggressive Behaviorsyndromes, plus a hierarchical 3-syndrome struc-ture for attention problems in 20 societies. The20 societies included Lebanon and all societiestested in this study except Belgium, Ethiopia,Germany, Iceland, Israel, Korea, Norway, Russia,Sweden, Switzerland, and Taiwan. The modelconverged for all 20 samples, and the RMSEAindicated acceptable to good model fit in eachsociety. For the YSR, Ivanova et al. (in press b)tested the fit of the correlated 8-syndrome struc-ture comprising the eight syndromes tested in thepresent study in 23 societies, which included Spainand all societies tested in the study, exceptBelgium, China, France, Italy, Portugal, Russia,Taiwan, and Thailand. The model converged inall 23 samples, and RMSEA values indicated goodmodel fit in each society. The results the presentstudy and the Ivanova et al. (in press a, in press b)studies provide preliminary evidence for the simi-larity of syndromes measured by the CBCL, TRFand YSR in very different societies.
Limitations and Implications
Our findings do not necessarily imply a univer-sal or exhaustive syndrome structure for child psy-chopathology. Because it was developed in the(U.S.), the CBCL may assess children’s problemsthat are particularly relevant for U.S. childrenbut may fail to assess additional problems thatcould be relevant for children elsewhere. Althoughthey tap diverse problems, the items could be sup-plemented by additional items that might yield dif-ferent syndromes in particular societies or even inall societies. Other assessment methods, such asclinical interviews, are needed for comprehensivemental health evaluations of children and adoles-cents. Furthermore, other syndrome structurescould have fit the data as well.
To account for the nonnormal distribution ofour data, we used the WLSMV estimator. TheWLSMV is a recently developed advanced esti-mator that is robust to violations of multivariatenormality (Muthen & Muthen, 2004). Becausethe WLSMV is so computationally intensive, it isnot now feasible to use the WLSMV to test com-ponents of measurement invariance other thanconfigural invariance. To conclude that an assess-ment instrument measures the same constructacross different societies, it is necessary to formallytest all components of measurement invariance. Itis important to recognize that the results of thepresent study and the Ivanova et al. (in press a,in press b) studies do not necessarily imply that
the CBCL, TRF, and YSR measure the samepsychological constructs across the 30 societies.Rather, the finding of configural invariancefor the correlated 8-syndrome structure acrosssocieties should be interpreted as preliminary evi-dence that the patterning of problem itemsassessed by these instruments is similar in thetested societies.
The findings of our study have important prac-tical implications for mental health professionals.Efficient assessments of child psychopathologythat are calibrated across societies are badlyneeded, especially where mental health resourcesare scarce. The syndromes tested in this studycan be readily assessed by parent-, teacher-, andself-ratings. Translations of the CBCL, TRF, andYSR are available in over 79 languages. Theresults also have implications for our evolvingunderstanding of child psychopathology. The find-ings of our study and the Ivanova et al. (in press a,in press b) studies support the tested syndromes astemplates for conceptualizing children’s emotionaland behavioral difficulties in many societies. Thesetemplates can provide mental health professionalsaround the world with a common language forcommunicating about child psychopathology. Sucha language can facilitate international collaborationin clinical care and research, which can promotecross-fertilization of research programs and sharingof resources. Because they have empirical supportin many societies, the syndromes can also contrib-ute to a shared framework for training mentalhealth professionals in these societies.
References
Achenbach, T. M. (1991). Manual for the Child Behavior
Checklist=4–18 and 1991 Profile. Burlington: University
of Vermont, Department of Psychiatry.
Achenbach, T. M., Bird, H. R., Canino, G., Phares, V., Gould,
M. S., & Rubio-Stipec, M. (1990). Epidemiological
comparisons of Puerto Rican and U.S. mainland children:
Parent, teacher, and self-reports. Journal of the American
Academy of Child and Adolescent Psychiatry, 29, 84–93.
Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the
ASEBA School-Age Forms & Profiles. Burlington: Univer-
sity of Vermont, Research Center for Children, Youth, and
Families.
Albrecht, G., Veerman, J. W., Damen, H., & Kroes, G. (2001).
The Child Behavior Checklist for group care workers: A
study regarding the factor structure. Journal of Abnormal
Child Psychology, 29, 83–89.
Bentler, P. M. (1990). Comparative fit indices in structural
models. Psychological Bulletin, 107, 238–246.
Berg, I., Fombonne, E., McGuire, R., & Verhulst, F. (1997). A
cross-cultural comparison of French and Dutch disturbed
children using the Child Behavior Checklist (CBCL).
European Child and Adolescent Psychiatry, 6, 7–11.
Bilenberg, N. (1999). The Child Behavior Checklist (CBCL)
and related material: Standardization and validation in
CFA IN 30 SOCIETIES
415
Danish population based and clinically based samples.
Acta Psychiatrica Scandinavica, 100, 2–52.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of
assessing model fit. In K. A. Bollen & J. S. Long (Eds.),
