Categories versus dimensions in the classification and ...

Annual Research Review: Categories versusdimensions in the classification and

conceptualisation of child and adolescentmental disorders: implications of recent

empirical study

David Coghill1 and Edmund J.S. Sonuga-Barke2,31Division of Medical Sciences, Centre for Neuroscience, University of Dundee, Dundee; 2Institute for Disorder

of Impulse and Attention, University of Southampton, Southampton, UK; 3Department of ExperimentalClinical and Health Psychology, Ghent University, Ghent, Belgium

The question of whether child and adolescent mental disorders are best classified using dimensional orcategorical approaches is a contentious one that has equally profound implications for clinical practiceand scientific enquiry. Here, we explore this issue in the context of the forth coming publication of theDSM-5 and ICD-11 approaches to classification and diagnosis and in the light of recent empiricalstudies. First, we provide an overview of current category-based systems and dimensional alternatives.Second, we distinguish the various strands of meaning and levels of analysis implied when we talkabout categories and dimensions of mental disorder – distinguishing practical clinical necessity, formaldiagnostic systems, meta-theoretical beliefs and empirical reality. Third, we introduce the differentstatistical techniques developed to identify disorder dimensions and categories in childhood popula-tions and to test between categorical and dimensional models. Fourth, we summarise the empiricalevidence from recent taxometric studies in favour of the ‘taxonomic hypothesis’ that mental disordercategories reflect discrete entities with putative specific causes. Finally, we explore the implications ofthese findings for clinical practice and science. Keywords: Assessment, classification, diagnosis, DSM,factor analysis, ICD, taxometrics.

IntroductionFew matters can polarise groups of clinicians andresearchers in our field more than the question ofwhether mental disorder should be classified andconceptualised in categorical or dimensional terms.This debate is common to both those working withchildren and adolescents and our counterpartsworking with adults (Lawrie, Hall, Mcintosh, Owens,& Johnstone, 2010). On the one hand, those whopropose a categorical approach regard mental dis-orders as qualitatively different from variation acrossthe normal range of expression in the population,and as having their own pattern of rather distinctcauses – disorder differs from normality in bothdegree and kind. On the other hand, those who re-gard disorder as an extreme expression of normal

variation in the population emphasise continuity inunderlying causes – disorder and normality differonly in degree but not kind. This tension betweencategorical and dimensional conceptualisations isreflected in the debate, energised recently by therevision of DSM-5 and the consequent renewedinterest in nosology and diagnostics, over whethercurrent category-based systems for the classificationof child and adolescent mental disorders are fit forpurpose or whether they should be superseded bydimensional alternatives: a debate which clearly hasfar reaching implications for both clinical practiceand scientific enquiry.

Although at first sight this debate seems relativelysimple to resolve by addressing some rather straightforward and apparently tractable questions – it is, infact rather complex as it raises issues about therelationship between clinical and scientific reality,the role of values in science and clinical practice andthe practical politics of mental disorder classifica-tion. Understanding these different levels of analysisdepends on rather subtle differences in the use of theterms category and dimension in different contextsand for different purposes. This review sets out to (a)describe the nature of the category/dimensiondebate and its influence on current models of clas-sification (b) understand the practical, political andphilosophical origins of the current category-based

Conflict of interest statement: DC has received research sup-

port from Lilly, Janssen Cilag and Shire; served in an advisory

or consultancy role for Lilly, Janssen Cilag, Pfizer, Shire,

Flynn, Shering-Plough and UCB and has received conference

attendance support or was paid for public speaking by Lilly,

Janssen Cilag, Shire, Flynn, Novartis and Medice.

EJSS-B has received research support from Janssen Cilag,

Shire, Qbtech, Flynn Pharma; served in an advisory or con-

sultancy role for Shire, Flynn Pharma, UCB, Astra Zeneca; and

has received conference attendance support or was paid for

public speaking by Shire, UCB.

Journal of Child Psychology and Psychiatry *:* (2012), pp **–** doi:10.1111/j.1469-7610.2011.02511.x

� 2012 The Authors. Journal of Child Psychology and Psychiatry � 2012 Association for Child and Adolescent Mental Health.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA

system and its implications for clinical practice andscientific enquiry (c) distinguish these philosophicaland practical issues from the empirical questionabout the existence of categories of disorder as dis-crete causal entities (d) review the empirical datadirectly addressing these empirical questions and (e)explore the implications of these data for approachesto classification and diagnosis in the light of theupcoming DSM-5 and ICD-11 systems. Our analysisis founded on the notion that seemingly contradic-tory views are, in fact, often resolvable when differentlevels of analysis of the strengths and weaknessesand uses of the concepts of categories and dimen-sions are distinguished. Our approach is also basedon the idea that systems based on these conceptscan coexist with each other, serving a different butequally useful purpose, even within the context of asingle disorder.

Section 1: Current category-based approachesand dimensional alternatives to classificationof child and adolescent mental disordersHistorically, psychiatry emerged as a discipline whenshared systems of diagnosis, based on informal rulesof categorisation emerging out of everyday experi-ence were developed not only to aid the drawing ofappropriate distinctions between ‘health’ and ‘ill-ness’ but also to better characterise different disor-ders in psychiatric settings (Mack, Forman, Breown,& Frances, 1994). Initial attempts at classificationwere, however, rather idiosyncratic and their usewas inconsistent. This led to the first attempts tointroduce more formal and universal diagnostic cri-teria, guided by attempts to promote clear commu-nication, reliability (Spitzer & Fleiss, 1974), validity(Cloninger, 1989), and consistency of applicationacross clinicians who often held very different viewsof the causes of disorder. Although version 6 of theWHO International Classification of Diseases (ICD-6)included a chapter on mental health, it was thepublication in 1977 of ICD-9 (WHO, 1977), and ofDSM-III in 1980 (APA, 1980) that really representedthe start of the modern era of classification. Thissignalled an active attempt to shift away from apredominantly psychodynamic approach to mentalhealth and illness, where boundaries between healthand disorder were vague and poorly specified, to-wards a more scientific one, which shared a commonframe of reference with mainstream medicine – apronounced bio-medical focus (Spitzer & Endicott,1978). The introduction of this category-based ap-proach with its clear and explicit criteria for makinga diagnosis meant that identifying the presence of adisorder changed, at least in theory, from somethingof an art-form linked to the nuanced interpretationsof symptom meaning, to a more technical taskinvolving the application of algorithms based on‘data’ from systematic observation. Whilst here wefocus on issues relating to childhood disorders it is

important to recognise that many of these changeswere initiated from within the adult mental healtharena. In that context, the categorical view wasproposed partly to oppose the psychoanalyticalviews, as mentioned above but also to bring psychi-atry back closer to other medical specialties, asgovernmental and private insurance had cut reim-bursements to psychiatry due to the confusion andlack of reliability of mental disorder diagnoses(Wilson, 1993). Whilst its clinical utility (ease of use,ability to improve communication and inform treat-ment planning; Mullins-Sweatt & Widiger, 2009) andscientific approach (Kendler, 1990) are reasons whycategorical approaches have been widely adopted,there are also political, economic, sociological andpsychological reasons why these systems have cometo hold such a strong position in the field.

1. Practical clinical reality: It is a clinician’s job tomake difficult practical decisions about whetheran individual should or should not receive spe-cialist health interventions and which interven-tions they should receive. It is, therefore, just asimportant to ensure that individuals for whom anintervention is unlikely to be beneficial are notexposed to unnecessary risk, as it is to ensurethat those who may benefit from an interventionare accurately identified. By definition these arecategorical decisions (see also below).

2. Politics and economics: Childhood disordersarouse strong, but very different, public andpolitical reactions from different groups and indi-viduals with different agendas. Despite these dif-ferences, it is important for both the advocates ofthe diagnosis and treatment of child and adoles-cent mental health problems and those whocampaign against their existence to be able topoint to the label and diagnostic criteria – evenwhere the ultimate aim is to dismantle these samecriteria. At the same time, it is universally the casethat service provision for children and youngpeople with mental health problems lags waybehind those for children with physical illness oradults with mental health problems. As a conse-quence, it has become important for those lobby-ing for better services to be able to point to the‘validity’ of disorders and their impact on quality oflife. To do this, they will often rely on recogniseddiagnostic categories to make the argument formore financial resources. Similarly, it is the casethat specialist healthcare services are almostalways rationed, whether at the level of the state orby private insurance companies. It seems easier tojustify the need for clinical care if one can asso-ciate this need with a discrete diagnostic entity.

Following this economic imperative can result inclinicalmisunderstanding. This was highlightedrecently by the case of severe mood dysregula-tion (SMD). In many healthcare systems, thelack of a formal diagnostic label for those with

2 David Coghill and Edmund J. S. Sonuga-Barke J Child Psychol Psychiatry 2012; *(*): **–**

� 2012 The Authors. Journal of Child Psychology and Psychiatry � 2012 Association for Child and Adolescent Mental Health.

SMD is a barrier to funding for treatment. Therecognition that certain symptoms of SMD par-alleled those of bipolar disorder (e.g. rapid fluc-tuating mood) led to the widening of the conceptof paediatric bipolar disorder (PBD) as a clinicalentity. This in turn eased problems with reim-bursement. However, althoughwell intentioned,the concept of PBD resulted in much confusionand led to claims and counterclaims about thevalidity of this new variant of an establisheddisorder (See The DSM-5 Childhood and Ado-lescent Disorders and Mood Disorders WorkGroups, 2010, for discussion of the currentstanding of the debate).From a different perspective, the application ofcategory-based diagnostics can be used to sup-port the rationing of treatment thatmayhave thepotential for much broader beneficial applica-tion. An extreme, but interesting, example is theuse of stimulant medication, currently re-stricted to those with ADHD, but which can be(and indeed in practice are; Mccabe, Teter, &Boyd, 2006; Mccabe, Brower, West, Nelson, &Wechsler, 2007) used as cognitive enhancers toboost performance in the general population(Sahakian & Morein-Zamir, 2007). If we were toeschew the use of categorical diagnoses but stillwish to ration healthcare, where would one drawthe line about who should and who should notreceive reimbursement for such ‘treatment?’

3. Psychology: Studies in experimental psychologyshow that human beings are natural categorisers.Children as young as few months can learn todistinguish between stimuli along categoricallines (Blewitt, 1994; Eimas & Costello, 1994;Younger, 1993), and people, when presented witha stimulus array with little or no formal structure,will group its component parts into categories(but see Dagostino & Beegle, 1996). This mani-fests as categorical perception when relating tophysical stimuli and group polarisation whenrelating to social stimuli. It is, therefore, thecase that the clinical tendency towards catego-rising ‘cuts with the grain of human nature’(Schoeeman, Segerstrom, Griffin, & Greshham,1993). It is also the case that the act of categori-sation itself serves to reorganise the categorisers’perception of the category members (Harnad,1987). In particular, there is an increase in theperceived separation between people in differentgroups and an increase in the perceived of simi-larity within groups (Mcgarty & Turner, 1992).This observation seems to hold just as well in theclinical situation as it does in the many groupsocial psychology experiments in which it wasfirst described (Sonuga-Barke, 1998).

Leaving these more general issues about the ori-gins of the category-based systems to one side, itremains to be seen whether these systems have

improved clinical practice. Certainly clinicians, whohave received special training, achieve good levels ofinterrater and test–rest reliability (see Blashfield &Livesley, 1991). Evidence is lacking, however, forimprovements in the day-to-day performance ofuntrained clinicians (Kirk & Kutchins, 1994). Thismay be because clinicians simply do not apply theclassification rules properly or consistently. This inturn may be because of a mismatch between thedecision rules of the system and the clinician’s nat-ural approach to categorising [i.e. clinicians appearto categorise patients by using typical exemplar orprototype based decision-making models (Blashfield,Sprock, Pinkston, & Hodgin, 1985)]. There are alsoissues associated with heterogeneity and comorbid-ity that may be especially undermining of the cate-gorical approach to classification (Sonuga-Barke,1998). Comorbidity is common in childhood mentaldisorder and the relationships between disorders iscomplex (Ford, Goodman, & Meltzer, 2003; Nottel-mann & Jensen, 1995) and appears to work againstthe concept of disorders as discrete entities withclear boundaries (Clark, Watson, & Reynolds, 1995).Whilst heterogeneity has not been as well studied ascomorbidity, the dual problems of within categoryheterogeneity (not everybody with a disorder has thesame pattern of symptoms or even defining features)and mixed symptom patterns that fall between cat-egories (or just below diagnostic thresholds) are alsolikely to impact heavily on the clinical utility of cat-egory-based diagnostic systems. The problem here isthat if one seeks to reduce comorbidity by reducingcategories, one is faced with increasing levels ofheterogeneity and vice versa (Morgan, Hynd, Riccio, &Hall, 1996; Sonuga-Barke, 1998). The strict applica-tion of categorical diagnostic rules can also result inindividuals with significant symptoms and impair-ments, but who fall just short of the diagnostic crite-ria, being denied support and treatment.Whilstmanyof the current criteria include definitions for sub-types, the true meaning of these groupings is oftenunclear and within a disorder the evidence for sta-bility within these subgroups is poor, with individualsoften moving between different subgroups over time(Lahey, Pelham, Loney, Lee, & Willcutt, 2005).

These criticisms have been accompanied by callsto abandon current category-based approaches.First, some call for the scrapping of the concept ofmental disorder altogether, echoing the antipsychi-atry movement of the 1960s following such figures asFoucault, Laing, Szasz and Basaglia (Foucault,Khalfa, & Murphy, 2006). The central thesis of thisloosely constituted movement was that diagnosticcriteria are too vague and arbitrary to meet scientificstandards and that psychiatric treatments causemore harm than good to patients. They also focusedon an inappropriate use of medical concepts andmiscategorisation of normal reactions to extremecircumstances as psychiatric disorders, the stigma-tising nature of psychiatric labelling and misuse of

doi:10.1111/j.1469-7610.2011.02511.x Categories and dimensions 3


power by doctors, and the state, against patients(Cooper, 1967; Whitaker, 2004). Although less pre-valent now, there are still those within the child andadolescent mental health professions who activelyquestion the existence of mental disorder and theuse of labelling – a view perhaps most prominentlyexpressed by the huge variation in the acceptanceand treatment of ADHD even within relatively welldefined regions (e.g. NHS Quality ImprovementScotland, 2008).

Second, there are those who work from a theoret-ically driven position and advocate the developmentof aetiology-based categories as is seen across much,but not all, of physical medicine (Andreasen & Car-penter, 1993). From this perspective, symptoms thatshare the same cause should be seen as indicatingthe same disorder. There are two contrasting, butrelated, problems with this approach. On the onehand, it has been noted, at several levels of analysisthat causal factors do not map on a one-to-one basiswith other indicators of disorder-integrity such astreatment response or prognosis (Coghill, Rhodes, &Matthews, 2007). One cause or set of causes canlead to different and apparently unrelated clinicalmanifestations, while the same manifestation can belinked to many different causes (Coghill, Nigg,Rothenberger, Sonuga-Barke, & Tannock, 2005).For example, deficits in working memory have beenidentified in conditions as diverse as schizophrenia(Park & Holzman, 1992), autism (Steele, Minshew,Luna, & Sweeney, 2007), ADHD (Rhodes, Coghill, &Matthews, 2005) and oppositional defiant disorder(Rhodes, Park, Seth, & Coghill, 2011a). At the sametime a single disorder, ADHD is a good example, canbe underpinned by different neuropsychologicaldeficits in different individuals with the same clinicalexpression. Rhodes et al. (2005) described separablepatterns of executive and nonexecutive functioningand Sonuga-Barke, Bitsakou, and Thompson (2010)reported discrete and dissociable patterns of defi-cient executive function, delay-related respondingand temporal processing. Given this heterogeneitywithin clinical manifestations and causal overlapbetween aspects of functioning, any such aetiology-based system of classification is likely to beunworkable, given the many thousands of categoriesthat would likely be needed to map identified causesonto clinical manifestation in a specific and sensitiveway. Dimensional approaches that characterisedisorders ‘on a linear continuum of graded severity’offer a third alternative (Clark et al., 1995, p. 145).Advocates of dimensional approaches point out thatthey avoid the waste of potentially important infor-mation associated with categorical approaches.From a research perspective, dimensions have beenfound to some times have greater predictive validitythan do their diagnostic counterparts (Fergusson &Horwood, 1995). Others have simply argued thatdimensional approaches are preferable as they pro-vide a better fit with the data than the categorical

ones. For example, Gjone, Stevenson, and Sundet(1996), investigated the heritability of attentionproblems in a general population sample of twins.They found evidence of substantial heritability andimportantly that the degree of heritability was simi-lar for those with low levels of attention problems asit was for those with moderate or even high levels ofproblems. This failure to find different estimates ofheritability at either extreme of the continuumclearly supports a dimensional rather than a cate-gorical model of ADHD.

We can distinguish between several differentdimensional approaches. These propose either (a)the replacement of specific categorical disorders withequivalent dimensional concepts, (b) the wholesalereplacement of the current categorical structure withan common set of empirically derived dimensionsrepresenting the major aspects of behaviour andcognition that can lead to impairment and distress or(c) a mixed approach whereby both dimensions andcategories are used alongside each other. The firstapproach has been proposed for ADHD. Confirma-tory factor analytical studies have suggested that anonhierarchical bi-factor model for ADHD, with ageneral factor and specific factors of inattention andhyperactivity-impulsivity, fits the available databetter than either simple one-, two-, and three-factormodels, or a second-order factor model (See below;Martel, Von Eye, & Nigg, 2010). It has therefore beenargued that the ADHD category could be replaced bythese three dimensions and individuals described bytheir ratings on each, an overall rating for the ADHDg factor and separate ratings for inattention andhyperactivity/impulsivity. Typically such ratingswould be made using symptom based measuressuch as the SNAP (Bussing et al., 2008) or theADHD-IV rating scale (Du Paul, Power, Anastopou-los, & Reid, 1998), although truly dimensionalmeasures that measures the continuum from goodthrough average to poor attention have also beenadvocated (Swanson, Wigal, Lakes, & Volkow, 2011).

The second approach, the wholesale replacementof the current categorical structure with empiricallyderived dimensions, has been advocated by devel-opmental psychopathologists such as Achenbach,who developed the Child Behaviour Checklist (CBCL;Achenbach, 1991). This instrument was designed tocapture information across the broad range ofbehavioural and emotional difficulties in childhood.Indeed, the driving force that led Achenbach todevelop the CBCL in the 1960s was the desire for amore differentiated picture of child and adolescentpsychopathology than was provided by the prevailingversion of DSM. Starting with a long list of descrip-tors of problematic childhood behaviours that werenot necessarily the same as the symptoms of recog-nised disorders, Achenbach used multivariate sta-tistical analyses of correlations and factor analytictechniques to derive subscales that measuredbehaviour across multiple continuous dimensions.



These data are used to (a) develop reports of skillsand involvement in activities, social relations,school, and work (b) assess competencies andadaptive functioning and (c) construct profiles ofscales and subscales on which to display individuals’scores in relation to norms for their age and gender.

Factor analytic analyses of CBCL data have gen-erally supported the existence of two broad-baseddimensions of disorder, that is, externalising andinternalising (Achenbach, Conners, Quay, Verhulst,& Howell, 1989) and several other more specificdimensions represented by different subscales.Whilst several of these original empirically derivedsubscales bore similarities to recognised diagnosticcategories (e.g. anxious/depressed, withdrawn/de-pressed and attention problems, rule-breakingbehaviour, aggressive behaviours and somatic com-plaints), there were clear differences in both contentand orientation and also other categories (e.g.thought problems and social problems) that do notreadily map on to particular diagnostic categories.One distinct benefit of such scales is that symptomoverlap among subscales is considered as clinicallyrelevant information rather than nuisance and con-tamination across diagnostic categories (Cummings,Davies, & Campbell, 2000). Although not unique tothese types of scales, such empirically derived datacan be used much more flexible for examiningdiverse developmental trajectories compared to cat-egorical systems. This is particularly relevant todevelopmental psychopathology where equifinality(multiple pathways ending with the same outcome)and multifinality (children with similar risks endingwith different outcomes) are particularly common(Cicchetti & Rogosch, 1996). The CBCL has changedover time and in its current form also includes sixDSM orientated scales. These represent a clear nodto the dominance of the categorical approach as theywere not empirically derived but instead compriseitems identified by experts as ‘very consistent withDSM-IV categories’ (affective problems, ADHD prob-lems, anxiety problems, oppositional defiant prob-lems, somatic problems, conduct problems).Perhaps a more interesting development has beenthe construction of subscales that reflect potentiallyimportant clinical groupings such as those with‘Sluggish Cognitive Tempo’.

The CBCL, like most other measures of psycho-pathology, truncates the range of aptitudes that canbe rated by collapsing ratings of normal and super-normal characteristics under one rating of ‘noproblem’ and distinguishing these from differentlevels of subnormal aptitudes (Swanson et al.,2001). When applied to a normal population thisresults in an extremely skewed data, with a J shapedor exponential distribution of scores in the popula-tion. Whilst such a scale can still be clinically usefulin situations where the behaviours upper (superior)levels of function have no meaning or importance forclinical decision making, this psychometric flaw

makes it difficult, if not impossible, to use such ascale to classify individuals in situations wherebehaviour across the full range of aptitudes is rele-vant to the decision-making process.

As a response to this problem, Swanson developedthe Strengths and Weaknesses of ADHD-symptomsand Normal-behaviour (SWAN) rating scale. Thisre-conceptualised the DSM symptom domains ofADHD as a continuum that covers the full range ofrelevant aptitudes (from super- to subnormal),allowing the ADHD construct to have a trulydimensional characterisation. Several studies haveprovided empirical support for the advantageousdistributional characteristics of the SWAN (e.g. Cor-nish et al., 2005; Hay, Bennett, Levy, Sergeant, &Swanson, 2007; Polderman et al., 2007), althoughSwanson et al., 2011 report that, whilst the SWANproduced normally distributed scores in a schoolwide sample, it did not do so in a clinical sample.This suggests that the value of this approach may beless apparent in clinical studies than it is in epi-demiologic studies where the full range of behaviouris the primary interest.

The third approach, using a combination of cate-gorical and dimensional systems, can be viewed fromtwo different perspectives. In the first, both ap-proaches are used together within the same class ofbehaviour. For example, the diagnostic category ofADHD as defined by one of the recognised diagnosticsystems would be retained, but with individuals alsodescribed dimensionally with respect to their posi-tion on the ADHD g factor and the inattentivenessand hyperactivity/impulsivity dimensions. Thisseems attractive at first glance as it allows identifi-cation of those who are subthreshold for diagnosisand gives a clear picture of an individual’s standingon each dimension. On closer inspection, however,one could ask what relevance the diagnostic categoryhas if it can be trumped by the dimensional data. It isalso the case that if such an approach was adoptedinto clinical practice, there would be many difficultchoices to make for those cases that were at theborders either dimensionally and/or categorically.Whilst clinicians may see this as a problem as itexacerbates ethical dilemmas around treatmentdecisions, others may regard it a strength as ithighlights both the uncertainty and the fact that thecurrent solutions are rather arbitrary and provide aspurious sense of certainty. A second way of inte-grating categories and dimensions is to allow differ-ent classes of problem to be described differently.For example, for those disorders such as autismspectrum or endogenous depression, risk factorssuch as schizotypy and anxiety sensitivity andbehavioural patterns such as antisocial behaviourwhere categorical entities have been demonstrated(see Section 4) a categorical approach could beretained. Whereas for those such as ADHD, PTSD,nonendogenous depression, psychopathy andattachment status, where a dimensional approach



seems more appropriate, the category would bedismissed and the condition described only indimensional terms.

Whilst the dimensional approach to classificationcertainly seems to be ‘correct’ in certain situationsand can avoid the waste of information that isinherent to the categorical approach, many cliniciansexpress concern as to how they should best apply adimensional approach within a clinical situationand, more specifically, when and how to use thedimensional data to enhance clinical decision mak-ing. It is, however, important to recognise thatdichotomous decision making is not an inevitableconsequence of adopting a categorical approach.For example, evidence-based medicine (EBM)approaches to assessment and diagnosis emphasisethe use of clinical data to estimate the probability ofthe presence of a categorical diagnosis (Mash &Hunsley, 2005). Using this approach, where the ba-sic clinical data suggest that the probability of aparticular categorical disorder is low, the clinicianwill often decide that no further assessment is re-quired and discharge the patient. If the probability issomewhat higher more detailed and expensiveassessments may be required. Where it is still higherbut still not in the range of strong probability, theclinician may give a provisional diagnosis andrecommend less risky or expensive treatmentapproaches with minimal potential for adverse ef-fects (e.g. parent training, CBT and/or educationalaccommodations in ADHD). If we are, at least incertain circumstances, to move towards a moredimensional approach within clinical practice itwould seem that the initial use of a combined cate-gorical and dimensional approach would allow for asmoother transition to amore dimensional approach,allowing clinicians to be familiarised to dimensionalapproaches while not deleting a categorical approachwith which they are much more used to.

Section 2: The category/dimension debate:important distinctions between practicalnecessity, meta-theoretical convictions andempirical realityAs described above, both the categorical anddimensional approaches have strengths and weak-nesses as well as advocates and detractors, witheach group making apparently reasonable claims.The category/dimension debate can be confused andconfusing. This is because it operates at a number ofdifferent levels of analysis, with the notions of cate-gory and dimension being employed in subtly dif-ferent ways at different levels, with differentimplications for clinicians and scientists. Here, wedraw a number of important distinctions betweendifferent levels of analysis and different usages.

First, as we have noted above, there is the practicallevel – it is self-evident that there are good practicalreasons why clinicians must, to some degree, be

categorisers. Effective management is premised onthe ability to distinguish those that are in need oftreatment from those that are not, and then to decidewhich treatment is most appropriate. In this sense,there is a boundary (irrespective of how arbitrary itis) between those who do and don’t require particulartreatments. ‘I distinguish X from Y because X needstreatment A and Y doesn’t’. Such decisions requireclinicians and commissioners of health services to becategorisers in this practical sense, irrespective oftheir political, philosophical or theoretical bent andirrespective of the underlying structure of the disor-der in question.

Second, this practical need to categorise must bedistinguished from a second level whereby the dis-tinctions drawn between those in need of treatmentare considered to reflect (or not) a discrete causalentity. Whilst one group of clinicians may believethat ‘my distinction between X and Y on practicalgrounds also reflects a more deep seated belief thatX’s disorder places him in a group of the populationthat is qualitatively distinct from the norm’, othersmay make the same decisions without holding sucha view. Where such a conviction is present it oftenreflects a more deep seated distinction betweendifferent clinical/philosophical ‘world views’ –grounded in what has come to be regarded as thebio-medical model of mental disorder (as opposed tothe psychosocial model). Both Sonuga-Barke (1998)and Beauchaine (2003) highlighted the overlap be-tween the biomedical and the psychosocial ‘worldviews’ and the opposing stances of ‘essentialist’ and‘nominalist’ views of human behaviour (Flanagan &Blashfield, 2002). Essentialists argue that mentaldisorders reflect objective underlying causal realitiesthat are independent of human values, whilst nom-inalists argue that psychiatric disorders reflectdeviations from socially constructed prescriptionsfor behaviour, and that there are no objective meansof demarcating normality from abnormality. Thenominalists are, therefore, likely to interpret allbehaviours, including those characterised by othersas symptoms of a mental health problem, as fallingalong a continuum of social acceptability (they arelikely to be dimensionisers), consider diagnostic cut-offs as arbitrary and are extremely wary of diagnosisaltogether. However, it is important to recognise thatthe practical necessity of categorising and a philo-sophical conviction about what that practical cate-gory reflects, in terms of underlying reality, areindependent and essentially orthogonal. This meansthat there is no inherent contradiction betweenholding a dimensional view of disorder and in mak-ing clinical decisions about clinical need. ‘I am happyto make a practical distinction between X and Y but Ido not believe that X is part of a discrete and quali-tatively distinct group’.

Third, the relationship between the use ofcategorical approaches to diagnosis and classifica-tion such as those used in DSM and ICD and cate-



gorisation, both as a practical necessity and aphilosophical conviction, need to be understood andclarified. As we have stated above, in one senseclassification systems build on the practical clinicalneed to categorise. They were introduced as anattempt to provide clear and consistent way ofmaking those clinical distinctions and so to increasethe validity, reliability and consistency of diagnosisin everyday clinical practice, by providing clinicianswith simple, explicit symptom and impairment-based rules, independently of the particular theorypreferences of clinicians – they are practical aides.However, alongside their support of the practicalvalue of diagnostic categories, there was also, atleast in the beginning, a strong implicit adherence tothe assumption that mental disorders are concep-tually equivalent to medical diseases – a convictionheld by Spitzer, who was the driving force behind thedevelopment of modern diagnostic approaches. Inthis sense, the DSM and ICD systems have promotedthe view that the diagnostic categories are manifes-tations of underling discrete causal entities.

Fourth, and crucially for the purposes of the currentreview, we need to distinguish these practical (I needto distinguish X from Y so X get the right treatment)and meta-theoretical levels (the conviction that Xdiffers in both degree and kind from Y) from theempirical question – ‘Is X a member of a discrete cat-egory that is not only quantitatively but also qualita-tively different from the category which Y inhabits?’When seen from the empirical point of view, thisstatement, which may represent a philosophicalconviction on the part of the clinician or researcher, isturned in to ahypothesis that is scientifically testable.In recognition of the important work of Paul Meehl inthis field this may be referred to as the ‘taxonomichypothesis’ (Meehl, 1995). That the category to whichX belongs is not an arbitrary division or merely apractical grouping, but actually describes a discretecausal entity that is qualitatively different from thenormal range – a taxon. Sonuga-Barke (1998) arguedthat there was little available evidence at that time totest the ‘taxonomic hypothesis’ for childhood mentalhealth problems in general and with respect to ADHDin particular. Additional evidence and alternativemethodologies have become available since that time.These will be reviewed below.

Fifth, the category/dimensions debate is of as fun-damental importance for clinical scientists too as it isfor clinicians. Beauchaine (2003) clearly enunciatedthe various fundamental but elusive questions facingthose who try to investigate the latent structure ofmental health problems; do mental disorders reflectfailures of biological systems to perform naturallyselected functions (e.g. Wakenfield, 1992, 1993,1997, 1999), or are they defined by somewhat arbi-trary distinctions derived from social values (e.g. Li-lienfeld & Marino, 1995, 1999)? Should a set ofsymptoms be considered a disorder when induced byahigh risk environment, or is evidence of independent

internal mechanisms necessary (Wakefield, Pottick,& Kirk, 2002)? Can environmental risk and internalmechanisms even be considered as separate causalagents (e.g. Bremner&Vermetten, 2001)? Could all ofthe 365 categories inDSM-IV possibly be distinct (e.g.Houts, 2002; Kendell, 1989)? Does the DSM-IVframework pathologise normal behaviour (e.g. Rich-ters & Cicchetti, 1993)? Again, the issues relating tocategory/dimension operate here at a number of lev-els to influence and constrain the scientific study ofpsychopathology. First, as we have seen with clini-cians, it can be argued that there may be some prac-tical benefits fromcategorisingparticipants into thosewith a disorder and those without it. Assuming thepresence of clear and clean boundaries between cat-egories, may reduce the recruitment burden forstudies. However, several researchers have shownthat in some cases when categorical latent structureis identified, dimensional measurement approachescan still have superior psychometric properties andpredictive validity (Peralta & Cuesta, 2003). Adimensional view would of course require samplingacross a broader range of symptom expression. Therewill be a balance between the increased power asso-ciated with a wider range of scores and the require-ment to recruit an adequate sample size to ensuresufficient power to adequately test hypotheses. Fordisorders where taxometric analyses have failed toidentify taxa andadimensional structure is assumed,adopting a categorical approach will seriouslyundermine the statistical power of a study. Second,science, like clinical practice, is performed by humanbeings and human beings have convictions and val-ues about what they are studying which the philoso-phers of science suggest influence the science carriedout (see Sonuga-Barke, 1998 for a discussion). Thereare epistemic values (i.e. values about what is a goodexplanation in science) and nonepistemic values inscience (more general values about the phenomenonto be studied which are often derived from a broaderworld view). These second set of values can be seen inthe sorts of assumptions that are made about thematter under investigation. The philosophers of sci-ence also tell us that such assumptions are inevitable(even in the ‘hard sciences’). They are often seen as‘natural’ and so often go unnoticed remaining im-plicit. In as much as they are inevitable, they arenecessary for the practice and progress of science.This is because they provide a set of sharedmeaningsand a common set of references that allow commu-nication between scientists. Finally, in as much asthey are necessary they are also constraining – theydetermine the questions that can be asked, the waythey are addressed and therefore the sorts of answersthat are found. In the field of mental disorders, if oneassumes that mental disorders are discrete entitieswhich have a specific set of causes (the assumptionbehind much categorical thinking), this promotes aparticular model of cause that focuses on discretecore dysfunctions rather than continuous risk. The



assumptions inherent in categorical models ofclassification can have very real implications for sci-entific study (Sonuga-Barke, 2011).

Whilst some developmental psychopathologistshave entered these philosophical debates about thevalue of categorisation and diagnosis, most havefocused more on the methodological issues andpractical constraints imposed by categorical sys-tems. Mirowsky & Ross (1989) propose that the cat-egorical approach (a)confounds interpretation withmeasurement by presenting attributes as entities (b)wastes information about meaningful differencesboth within classes and between classes by groupingtogether people who differ on potentially significantcharacteristics (c) and collapses causal structuresand so wastes valuable information about the aetio-logical origins of disorder. Poland, Von Eckardt, &Spaulding (1994) suggest that the use of categoricalsystems promote neither acceptable nor productiveresearch. They argue that by conducting researchthat is founded on ‘protoscientifically conceived,polythetic and massively heterogeneous groups’such as those derived from the DSM manuals andthen using these groups as independent variableswithin experimental research, we will confound theeffects of symptoms and limit the likelihood that ef-fects will be properly explored. However, as Sonuga-Barke (1998) points out, these claims are probablymotivated as much by a rejection of the whole polit-ical and ideological basis of categorisation andlabelling as they are by pure scientific reasoning.

Sixth, we need to understand how categoriesoperate at the intercept between the clinic and thelaboratory. Clearly, clinical science needs to begrounded in clinical concepts if it is to address rele-vant themes and issues and to be able to commu-nicate its findings in a meaningful way to clinicians,and facilitate translation from the laboratory into theclinic (Poland et al., 1994). In this sense, there is aconnection between the practical necessity of cate-gorisation in clinical practice, the codification andreification of those categories in diagnostic manualsand the categorical assumption that underpinsmuch of scientific investigation in the field. So longas categorical models persist in clinical practice,scientists have to (at least in part) base their work onclinical categories. For scientists to unilaterallyreject categorical models and replace them withdimensional ones (even of those proved to be in factmore accurate), would break a crucial bridge ofcommunication between the clinic and the lab thatdiagnostic categories provide and render the mean-ing of findings from science hard to implement inclinical practice. On the other hand, it is not yet clearwhether a failure to find taxa for some or all mentaldisorders and an acceptance that some or all mentaldisorders are dimensions and not categories shouldresult in a change in the way that these disorders arediagnosed in everyday practice, and/or the way thatthey are represented in the diagnostic manuals. On

the face of it, the answer seems obviously yes.However, the huge practical implications that suchchanges would have on clinical practice make thismuch less clear. We discuss this question further inSection 4.

Section 3: Statistical approaches to testingbetween categorical and dimensional modelsof disorder in childhood populationsSince the publication of DSM-IV and ICD-10 therehave been several important developments in dataanalysis that have started to impact on our ability todescribe more accurately the underlying structure ofmental health problems. Some of these start with theassumption that disorder is dimensional and/orcategorical, and are designed to identify the numberand form of independent and dissociable dimensionsor classes of mental disorders as they are expressedin a population of individuals (factor analysis, latentclass analysis, etc.). Other techniques, more usefulto our current purposes, make no prior assumptionsand are specifically designed to identify patterns ofdiscontinuity in underlying structure of observeddata, and so can provide a test between categoricaland dimensional models of data.

Starting with the assumption that a disorder iseither dimensional or categorical and usingcategorical techniques to testing how manydimensions/classes there are

Factor analysis comprises a series of approaches thatstart by assuming that there is an underlyingdimensional structure and aim to identify the latent(underlying) factor structure of an entity. When usedin an exploratory way they are more suited to gener-ating hypotheses, while confirmatory factor analysis(CFA) is designed to test a predefined model ofunderlying latent structure against data. CFA cantherefore be used to: (a) establish the validity of asingle model, (b) compare the ability of two differentmodels to account for the same set of data, (c) test thesignificance of a specific factor loading or the rela-tionship between two or more factor loadings, (d) testwhether a set of factors are correlated or uncorrelatedor (e) assess the convergent and discriminant validityof a set of measures. CFA has been used to addressspecific questions about the distinctions that can bedrawn between subdimensions within broader cate-gories. Studies have sought to investigate the rela-tionship between anxiety and depression in children(e.g. Cole, Peeke, Martin, Truglio, & Seroczynski,1998; Cole, Truglio, & Peeke, 1997; Murphy, Mare-lich, & Hoffman, 2000). In the autism field, Frazier,Youngstrom, Kubu, Sinclair, and Rezai (2008)investigated the factor structure of the Autism Diag-nostic Interview-Revised (ADI-R) using EFA and CFAmethods. Whilst the EFA indicated strong support fortwo-factor structure, with social communication and



stereotyped behaviour factors, the CFAs gave roughlyequal support for this two-factor model and a three-factor model separating peer relationships and playfrom other social and communicative behaviours.Both the two and three-factor models showedgood stability across age with only slight changes infactor relationships. In the ADHD field, CFA hassupported bi-factor models (with dissociable factorsfor inattention and impulsiveness/hyperactivity;Amador-Campos, Forns-Santacana, Martorell-Balanzo, Guardia-Olmos, & Pero-Cebollero, 2005;Burns, Boe, Walsh, Sommers-Flanagan, & Teegar-den, 2001; Wolraich et al., 2003). However, morerecent work by Martel et al. (2010) and others(Dumenci, Erol, Achenbach, & Simsek, 2004; Toplaket al., 2009) used the CFA to support a nonhierar-chical model that includes a ‘g’ factor as well as twospecific factors of inattention and hyperactivity–impulsivity against other simple one, two or three-factor models or hierarchical bi-factor models (Martelet al., 2010). Whilst this finding could be seen aspartially supporting the current DSM-IV model, it isimportant to remember that these statistical tech-niques cannot distinguish whether the different fac-tors represent dimensions or categories.

In contrast to CFA, latent class analyses (LCA)assumes the presence of discrete classes or group-ings rather than dimensions. LCA is a form of cate-gorical data analysis which hypothesises that it ispossible to account for the observed symptom pro-files in terms of a small number of mutually exclu-sive respondent classes (M), each class with its ownset of symptom probabilities. One benefit of thisapproach over say cluster analysis, is that it simul-taneously defines the structure and estimates theprobabilities of membership. The parameter esti-mates obtained from an LCA are (a) class member-ship probabilities and (b) symptom endorsementprobabilities for each class. Using ADHD as anexample one might anticipate, based on its DSMconceptualisation, that within a population-basedsample at least four latent classes would be identi-fied for this disorder, (a) an unaffected class with lowprobability of endorsement of all symptoms (b) apredominantly inattentive class with high probabil-ity of endorsement of attention problem items andlow probability of hyperactive/impulsive items (c) apredominantly hyperactive/impulsive class with theopposite profile and (d) a combined class with a highprobability of endorsement of all items. In one of thefirst studies to use LCA to investigate the structure ofchildhood mental disorders, Szatmari, Volkmar, andWalter (1995) compared the sensitivity and speci-ficity of three competing diagnostic systems for aut-ism: DSMIII, DSM-III-R, and ICD-10. They reportedthat, when the results of the LCA were used as areferent, the ICD-10 criteria for autism had bettersensitivity and specificity than either of the DSMcriteria. Wadsworth, Hudziak, Heath, and Achen-bach (2001) conducted an LCA of the CBCL anxiety

and depression items. These analyses revealed threelevels of problem presentation across two indepen-dent samples. Children in a nonreferred sample wereclassified as having no problems, mild problems, ormoderate anxiety/depression problems, whilst chil-dren and adolescents in a referred sample wereclassified as having mild, moderate, or severe levelsof problems. No pure anxiety or depression classeswere found – only classes containing a mixture ofboth anxiety and depressive problems. These resultssuggest that the comorbid conditions of anxiety anddepression may be best thought of as part of thesame continuum of problems. Todd and colleaguesconducted a series of LCA analyses on several ADHDsamples (Hudziak et al., 1998; Neuman et al., 1999).They consistently identified three ADHD subtypes,primarily inattentive and hyperactive/impulsivetypes and a combined inattentive and hyperactive/impulsive type, even though the samples differedsignificantly from one another with respect to diag-nostic criteria, sex, age, and method of ascertain-ment and data collection. Whilst these observedlatent classes were somewhat similar to the threesubtypes of ADHD defined in DSM-IV, they also in-cluded individuals who did not meet DSM-IV ADHDcriteria. Conversely, the ADHD latent classes did notcontain all of the cases that met DSM-IV criteria. In alater study, designed to take comorbidities into ac-count, the same group used LCA to subdivide apopulation sample of adolescent female twins intomutually exclusive classes based upon their patternof responses to a series of questions relating tosymptoms of ADHD, ODD, separation anxiety, anddepression (Neuman et al., 2001). Whilst the LCAagain revealed three ADHD categories of clinicalinterest, these were different from the previousanalyses: an inattentive subtype without comorbid-ity, a second inattentive subtype with increasednumber of ODD symptoms, and a combined inat-tentive/hyperactive-impulsive type with elevatedlevels of ODD, separation anxiety, and depressivesymptoms. The LCA also distinguished an ODDclass and a separation anxiety class, each withoutincreased levels of other comorbid symptoms, asecond ODD class co-occurring with increased sep-aration anxiety and depression symptoms, and apure depression class. This pattern of latent classes,along with associated genetic data, suggested that inthe general female adolescent population there arethree highly heritable ADHD subtypes, two of whichare comorbid with other disorders. These classeswere consistent with a genetic hypothesis for ADHD,with each class potentially reflecting a uniquegenetic subtype.

Testing between dimensional and categoricalmodels

Whilst they begin to illuminate the latent structure ofpsychopathology neither CFA nor LCA can answer



the question; is the underlying structure of a disorder

dimensional or categorical? Developing empiricalapproaches to addressing this question has been achallenge. This is partly because, even where dis-crete groups exist within a population, they are likelyto overlap at the symptom level with each otherbecause of measurement error and within groupvariability. As a consequence two groups with dis-crete and quite distinct underlying distributions canoften appear to be unimodal at the level of symptomexpression. The converse can also be seen where, asa consequence of unusual measurement propertiesassociated with particular instruments, a bimodalscore distribution is observed when the latentstructure is dimensional (Waller & Meehl, 1998; andsee Beauchaine, 2003 for a comprehensive discus-sion of these issues). It was in an attempt to addressthese problems that Paul Meehl developed the taxo-metric approach over 30 years ago (summarised inMeehl, 1995). Meehl recognised that whatever gen-eral concerns existed concerning the use of a cate-gorical approach to classifying and conceptualisingmental disorders, the answer to the question aboutwhether a particular disorder represents a discreetcausal entity (a real category) or simply one end of acontinuum (a part of a dimension) is an empiricalone that can be addressed mathematically.

At the core of Meehl’s approach to this problemwas the profound insight that one could distinguishbetween categories and dimensions by examiningdiscontinuities in the underlying latent structure ofmultiple correlated manifest disorder-related vari-ables across continua of symptom severity (Meehl,1995). To take a general case, if one measuredsymptoms of a disorder, genetic risk (in some way)and levels of disorder-related cognitive impairmentin a full population and plotted the pattern of asso-ciations between these three manifest variables as afunction of the severity of the disorder symptoms,(perhaps by calculating correlations for separatemultiple windows across the symptom severity dis-tribution), then it would follow from Meehl’s logicthat where a taxon is present, there should be anabrupt change in the strength of these associationsas one moves to towards the extreme. Such a patternof results would be consistent with the notion of acategorical model of this hypothetical disorder.Where no such abrupt change occurs a dimensionalmodel would be favoured.

Meehl was particularly interested in applying thislogic to identifying the underlying genetic diathesisfor schizophrenia, which he labelled schizotaxia, andthe core set of observable phenotypic markers forthis diathesis that he labelled schizotypy. In hisquest to achieve this, he developed a series ofmathematical algorithms that are able to distinguishbetween discrete types and continua. Together hereferred to these techniques as coherent cut kinetics(CCKs). Commonly used CCK algorithms include theMAXSLOPE, MAXCOV, MAMBAC MAXEIG and

L-Mode procedures. Given space limitation it is notpossible to describe these techniques in any detailhere but detailed descriptions are available else-where (Beauchaine, 2003; Meehl, 1995). Meehl wasable to identify four putative markers for theschizotaxic genotype, each representing a discreteentity with the broader population: anhedonia,interpersonal aversiveness, ambivalence and cogni-

tive slippage to which smooth pursuit and saccadic

eye tracking abnormalities have been subsequentlyadded. Although a single gene locus for schizophre-nia has never been identified, repeated taxometricstudies have shown that these schizotypic traits doindeed mark a manifestation of a discrete taxon orlatent category. In the context of the current dis-cussion, there are obvious benefits of being able toidentify whether a disorder represents a true taxonor not. Beauchaine (2003) described these potentialbenefits, which, with some additions, are summar-ised below.

1. Supporting/refuting the validity of current cate-

gory-based models: Finding evidence of taxonicitywould strongly support the validity of the currentcategorical approaches to classifying and diag-nosing disorders. Failure to do so would castdoubt on the extent to which such categoricalmodels accurately capture the underlying realityof the disorder and suggest that the use of thesecategories may distort both clinical practice andscientific enquiry.

2. Refining diagnostic thresholds: Where taxonicboundaries are identified it may help to specifymore precisely the diagnostic thresholds of adisorder.

3. Identifying disorder subtypes: Taxometric analy-sis can be used to identify discrete subgroups ofindividuals within disorders. A wide range ofbiomarkers and clinical factors, such as symptompatterns, longitudinal course or treatmentresponse, could potentially be used as indicatorsto identify such subgroups. Such analyses mayhelp answer questions such as; does Asperger’sdisorder reflect a discrete behavioural syndromeas is assumed by DSM-IV or does it simplyrepresent a particular point on a continuousautism spectrum as suggested in the draft DSM-5proposals?

4. Confirming the biological/environmental basis of a

condition: Where evidence to support the taxoncomes from multiple levels of analysis (i.e.genetic, environmental anatomical, physiological,neuropsychological, observational), this may helpto pinpoint the underlying causal basis for adisorder.

5. Tailoring therapeutic approaches: Therapeuticapproaches may differ based on whether a persondoes or does not belong to a certain taxon group.

6. Identifying moderators of treatment outcome:

Understanding which factors are associated with



taxon membership may help identify potentialresponders or nonresponders. For instance,around 30% of those with ADHD do not respondto methylphenidate and similar proportions ofthose with anxiety disorders or conduct problemsfail to response to cognitive behavioural therapyor parent training, respectively. It has, however,proved extremely difficult to identify clinicallyrelevant moderators of treatment outcome inchild and adolescent mental health.

7. Identifying at risk groups for early intervention:

For example, around 5% of children can beidentified as having constellation of observabletraits and symptoms that place them in theschizotypy taxon, which itself is associated withan increased genetic liability for schizophreniaspectrum disorders (Blanchard, Gangestad,Brown, & Horan, 2000; Golden & Meehl, 1979;Korfine & Lenzenweger, 1995; Lenzenweger,1999; Lenzenweger & Korfine, 1992; Tyrka et al.,1995). This base rate is considerably higher thanthat for the prevalence of schizophrenia in thegeneral population which is estimated at 1.1%(Regier et al., 1993), implying that the trait is notfully penetrant. High levels of expressed emotionhave been consistently linked with the course andprognosis of schizophrenia (Falloon et al., 1985).It is, therefore, possible that measures to reduceexpressed emotion, if targeted towards those withthe high risk taxon, could reduce or postpone therisk of these individuals developing schizophre-nia. It is possible that similar taxons can beidentified for other disorders. This may allow theopportunity for primary prevention by targetingthese individuals with early interventions.

8. Locating bifurcation points in the development of

discrete traits: Where taxometric traits have beenidentified in adults, developmental taxometricstudies could help answer questions about atwhat point during development do these discretegroups arise. For example, Harris, Rice, & Quin-sey (1994) identified a taxon for psychopathy.Does this discrete group first appear duringadulthood or adolescence, or is it present in theyounger child, or even from birth? Identificationof such developmental bifurcation points may goon to help to identify the optimal timing of variousinterventions.

9. Elucidating mechanisms of equifinality and mul-

tifinality: Similar to the identification of treat-ment moderators, taxometric studies have thepotential to identify those factors that result insome individuals with apparently similar start-ing points following very different paths andreaching very different endpoints from eachother (multifinality). Alternatively, they couldalso identify the common thread that results inindividuals who start from seemingly differentstarting points but converge on a single outcome(equifinality).

Whilst the discussion here will concentrate on thetaxometric approaches described above there areother, recently developed, latent variable modellingmethods, such as factor mixture or latent class factoranalysis models that are able to address the samequestions. The empirically recommended approachis now to examine both factor and latent class modelsand then compare the fit of these models to plausiblefactor mixture models to determine whether data isbest represented by a combination of categories anddimensions or whether only one or the other is nee-ded (Muthen, Asparouhov, & Rebollo, 2006).

Section 4: What is the evidence for taxa in thechild and adolescent disorders?Coherent cut kinetic methods have only recentlybeen applied to child and adolescent disorders andwhile the field remains in its infancy, with many ofthe above options remaining unstudied, there issufficient evidence to start to draw some provisionalconclusions about the taxonic status of differentdiagnoses. Due to the very technical nature of thiswork we have resisted the temptation to describe thedetail of the analyses used in each study. Interestedreaders should consult the original papers whichdescribe the precise methods in detail.

Schizophrenia

In the first application of taxometrics to the field ofdevelopmental psychopathology, Erlenmeyer-Kim-ling, Golden, and Cornblatt (1989) used variousmeasures of cognitive and neuromotor performancein a sample comprised of children of schizophrenic,depressed or healthy parents. They were able toidentify a taxon that chiefly comprised children ofschizophrenic patients rather than those from theother two groups. The authors note that whilst thetaxon rate for those with healthy parents (4%) wassimilar to the rate of schizotypy in the normal pop-ulation, the proportion of subjects with schizo-phrenic parents (the high risk group) was higherthan expected at 47%. Whilst this may indicate thepresence of a dominant, single major locus geneticmodel for the taxon, the authors are rightly cautiousabout drawing any strong conclusions on causalityon the basis of a single study. Interestingly, thetaxon group also identified those individuals in thehigh risk group with the worst psychiatric outcomesby early adulthood, as measured by inpatientadmission rates, although this association did notextend to history of outpatient treatment. Unfortu-nately, this work has not been followed up with fur-ther studies.

Insecure attachment classification

In another early study, Fraley and Spieker (2003)investigated whether individual differences in



attachment status, as measured by the strange sit-uation test, are more consistent with a continuous ora categorical model. Using data on 1,139 fifteenmonth old children from the NICHD Study of EarlyChild Care, they were unable to identify the presenceof any taxa and therefore concluded that variation islargely continuous rather than categorical. This is, ofcourse, interesting as the longstanding and widelyheld view within the field, and based on traditionalstatistical methods of identifying groups, was thatchildren’s attachments could be organised intosecure, avoidant and resistant groupings. On thebasis of their CCK analyses, the authors proposed atwo dimension model of attachment based on theobservations of the Strange Situation; a Proximity-

Seeking Versus Avoidant Strategies characterised byvariability in the degree by which children’s attach-ment systems are organised by the goal of proximitymaintenance, and an Angry and Resistant Strategies

dimension that refers to the variability in the amountof overt conflict and anger towards the caregiver whenthe attachment status is stressed during the strangesituation. They hypothesised that variation in thisfactor may reflect a history of inconsistent caregivingand the insecurity that arises from such experiences.

Anxiety sensitivity

Bernstein, Zvolensky, Weems, Stickle, and Leen-Feldner (2005) and Bernstein, Zvolensky, Stewart,and Comeau (2007) conducted two taxometric stud-ies of anxiety sensitivity. Anxiety sensitivity reflectsrelatively stable individual differences in the fear ofanxiety and anxiety-related sensations (Mcnally,2002). It is proposed that when individuals with highlevels of anxiety sensitivity become anxious, they willhave additional worries that an event will have neg-ative consequences which will further increase theanxiety, and that may be a key factor in the devel-opment of panic attacks. Whilst some previousstudies have suggested that the basic structure ofanxiety sensitivity may be hierarchical with a higherorder factor and a number of lower order factors(Zinbarg, Mohlman, & Hong, 1999), others havesuggested that it is a dimensional construct thatvaries along a latent dimension. Whilst the firsttaxometric study of anxiety sensitivity in adults failedto find a taxonic structure (Taylor, Rabian, & Fedor-off, 1999), this study has been criticised as havingseveral significant limitations. Two further studies inadults (Bernstein et al., 2006; Schmidt, Kotov, Ler-ew, Joiner, & Ialongo, 2005) suggested a taxonicstructure. In the first of their investigations into thestructure of anxiety sensitivity in youth, Bernsteinet al. (2005) used two sets of indicators from theChildhood Anxiety Sensitivity Questionnaire (CASI);three composite indicators indexing disease con-cerns, unsteady concerns and mental health con-cerns and nine single item indicators representingeach of these three facets of anxiety sensitivity. The

taxometric analyses indicated that the latent struc-ture of anxiety sensitivity in this group of youngpeople (n = 371) was taxonic, with a base rate ofbetween 13.6% and 16.5% for the anxiety sensitivitytaxon. These rates are similar to those found in adultpopulations (Bernstein et al., 2006; Schmidt et al.,2005). The second study, Bernstein et al. (2007)conducted using similar data from the CASI and alarger sample (n = 4,462) of North American youth,also supported a taxonic latent class structure,although with a slightly lower base rate of 9%. Sub-sequent confirmatory factor analysis supported acontinuous multidimensional four-factor model ofanxiety sensitivity among the nontaxonic ‘normativeform’ of anxiety sensitivity, but not the taxonic ‘highrisk’ form. Interestingly, and in contradiction toprevious studies, the social concerns indicator didnot contribute to the distinction between the taxoniclatent classes, suggesting that these social concernsare similarly distributed between youth in both thenormative and high risk groups. The authors spec-ulate that this may indicate that the anxiety sensi-tivity taxon may confer vulnerability for somedisorders such as panic and PTSD but not for socialanxiety. On the other hand, the mental concernsfactor was highly discriminating, leading the authorsto suggest that future studies could focus on thepotentially important role of fears of cognitive dys-control in the anxiety sensitivity taxon.

Depression

Studies in adults with depression have suggestedthat depression per se is best conceptualised as adimensional, not categorical, construct (Prisciandaro& Roberts, 2005; Ruscio & Ruscio, 2000, 2002).Early taxometric studies of so called melancholia, orendogenous depression (depression with prominentphysical or biological symptoms) have, however,supported melancholia being a discrete subtype witha taxonic structure (Grove et al., 1987; Haslam &Beck, 1994), although these studies have been criti-cised on methodological grounds (Ruscio & Ruscio,2000). Several studies have investigated adolescentdepression from a taxometric perspective. Hankin,Fraley, Lahey, and Waldman (2005) collected datausing structured diagnostic interviews from a popu-lation-based sample of 845 children and young peo-ple aged 9–17 years. Using taxometric proceduresand analyses that explicitly took into account theskewness of depressive symptoms, they found con-sistent evidence that adolescent depression is adimensional not categorical construct. This dimen-sional structure held for all of the DSM-IV majordepressive symptoms as well as for different domainsof depression (emotional distress symptoms andvegetative, involuntary defeat symptoms) and also forboth parent and youth reports, as well as for severalsubsamples (boys vs. girls, younger vs. older). Theysuggest that future depression research should be



based around dimensional rather categorical modelsof depression, and that a failure to do so will result inthe loss of important information. Richey et al. (2009)utilised taxometric procedures to analyse self-reportdata derived from the Child Depression Inventory(CDI) from three independent samples using multiplenonredundant taxometric methods, and found con-sistent evidence to support taxonicity in childhooddepression. They argued that the differences betweentheir findings and those of Hankin et al. (2005) maybe accounted for by methodological weakness withinthe Hankin study. From their results Richey et al.(2009) constructed a 9 item CDI taxon scale thatdespite containing fewer items than the originalscale, had as good validity and predictive utility asthe full scale and accurately identified taxon mem-bers. This represents a significant development as itwill allow future researchers to taxon memberswithout the logistic and computational burden ofconducting taxometrics. Ambrosini, Bennett,Cleland, and Haslam (2002) utilised a taxometricapproach to investigate adolescent melancholiausing data from the KIDDIE-SADS interview and theBeck Depression Inventory in a sample of 378 re-ferred adolescents. As with the adult samples theyfound consistent support for a taxon. The suggestionhere is that these data imply that ‘melancholic/endogenous’ depression may be a discrete categorywithin those occupying the extreme end of thedepression dimension. Schmidt et al. (2007) investi-gated whether mixed anxiety depression (MAD),which is a provisional diagnosis in DSM-IV and isproposed as a free-standing diagnosis in DSM-5, is adiscrete category. To qualify for a diagnosis of MAD,the patient must have three or four of the symptomsof major depression (which must include depressedmood and/or anhedonia), accompanied by anxiousdistress (two or more of the following symptoms:irrational worry, preoccupation with unpleasantworries, having trouble relaxing, motor tension, fearthat something awful may happen). They used taxo-metric procedures and mixture modelling to discernwhether indicators derived from the Child BehaviourChecklist and the KIDDIE-SADS constitute a discretetaxon in a school-based population of 706 adoles-cents. Both the taxometric andmodelling approachesidentified a taxon with a prevalence of between 11%and 15%. Taxon membership was predictive of thedevelopment of mood and anxiety disorders over a14 month prospective follow-up.

Posttraumatic stress disorder (PTSD)

Taxometric studies in predominantly adult sampleshave supported a dimensional model of PTSD(Broman-Fulks et al., 2006; Ruscio, Ruscio, & Ke-ane, 2002). The only published study in adolescentsinvestigated a national epidemiologic sample of2,885 adolescents (Broman-Fulks et al., 2009) andthe results were consistent with these adult studies

in supporting a dimensional model of posttraumaticstress reactions.

Aggression and psychopathy

Adult studies have suggested that the constructs ofpsychopathy (Edens,Marcus, Lilienfeld, & Poythress,2006; Guay, Ruscio, Knight, & Hare, 2007; Marcus,John, & Edens, 2004; Walters, Duncan, & Mitchell-Perez, 2007b; Walters et al., 2007c) and antisocialpersonality disorder (Marcus, Lilienfeld, Edens, &Poythress, 2006; Walters, Diamond, Magaletta,Geyer, & Duncan, 2007a) are dimensional in nature.Several groups have investigated the taxometricstructure of various aspects of aggression, antisocialbehaviour and psychopathy in adolescence. Skilling,Quinsey, and Craig (2001) and Skilling, Harris, Rice,and Quinsey (2002) found consistent evidence for ataxon indexing antisocial behaviour across severalsamples using various different measures. Vasey,Kotov, Frick, and Loney (2005) specifically investi-gated the taxometric structure of psychopathy inyouth, using data from the antisocial processscreening device and a sample of referred, nonre-ferred adolescents enriched with a sample of adoles-cents from a juvenile justice diversion programme.Similar to the findings of Skilling et al., they identifiedan antisocial behaviour taxon but also found evi-dence for an overlapping, less common but moresevere psychopathy taxon. Murrie et al. (2007) wereunable to replicate these findings using similar pro-cedures, but a broader set of indicators of psychop-athy, among delinquent boys. Their investigationsfailed to identify a discrete taxon and supported adimensional structure for psychopathy. A strength oftheMurrie study was their use of a fairly homogenoussample of youth which reduced the risk of identifyingpseudo-taxons. Walters, Ronen, and Rosenbaum(2010) investigated the broader concept of general, asopposed to pathological, aggression in a large sampleof Israeli school children. Taxometric analysis of selfand teacher-reported data gave consistent supportfor a dimensional latent structure with no evidence ofa discrete taxon. These are consistent with findings inadults (e.g. Edens et al., 2006; Guay et al., 2007).

Attention deficit/hyperactivity disorder

There have been three taxometric investigations intothe latent structure of ADHD. The first and largest ofthese analysed data on almost 3,000 children andadolescents drawn from an Australian epidemiolog-ical study (Haslam et al., 2006) and found no evi-dence for a discrete taxon. Marcus and Barry (2011)conducted similar analyses on data from anotherlarge community sample. Their data support adimensional rather than categorical latent structurefor ADHD as well as for the separate inattention andhyperactivity/impulsivity domains. They went on toinvestigate the strength of association between both



dimensional and dichotomous models of ADHD andvarious associated features (e.g. academic perfor-mance, externalising and internalising symptomsand social problems), and found that the dimen-sional model demonstrated stronger validity coeffi-cients than the dichotomous models. Frazier,Youngstrom, and Naugle (2007) again found supportfor a dimensional model of ADHD after conducting ataxometric analysis of subjective report and neuro-psychological data from a referred population. Unlikeother types of research where the use of a clinicalsample is associated with the possibility of Berkson’sBias, the inclusion of a sample with a relatively highbase rate can be helpful in taxometric studies. Takentogether, these analyses suggested that ADHD is bestmodelled as a continuum in both children andadolescents, thus supporting the genetic and otherevidence that suggests a dimensional structure andmultifactorial aetiology (Coghill et al., 2005).

Autism spectrum disorder

Several questions about autism and the proposedautism spectrum would appear to be obvious candi-dates for taxometric analysis. Frazier et al. (2010)investigated the taxonic structure of autismspectrumdisorders (ASD) using data from an autism registrythat recruits families with at least one child with anASD. They included 6,621 families in total and datafrom 6,901 affected and 4,606 unaffected childrenusing the Social Responsiveness Scale and the SocialCommunication Questionnaire. Taxometric and la-tent variable analyses strongly supported the pres-enceofa taxon that is very closelyalignedwithexistingDSM diagnostic criteria when all of the spectrumdisorders (DSM-IV-TR autistic disorder, PDD NOS,and Asperger’s disorder) are combined. This taxon issimilar to the criteria for ASD proposed for DSM 5.Studies aimed at identifying the package of nonre-dundant measures that are associated with thehighest pretest probabilities of ASD diagnosis areplanned. Ingram, Takahashi, and Miles (2008) foundevidence for a categorical structure for a social inter-action/communication taxon that appeared to berelated to anASD/no-ASDdistinction, a second taxonrelated to intelligence and a third essential/complexphenotype that identified individuals with geneticsyndromes many of which have large head circum-ference and/or dysmorphology. No taxa were identi-fied for adaptive functioning, insistence on sameness,repetitive sensory motor actions or language acquisi-tion.

Section 5: Implications of taxometric evidencefor clinical practice and scienceWhile far from giving a complete picture of thetaxometrics of child and adolescent mental disorder,these studies described above provide initial evi-dence about the underlying structure across a

number of conditions. More specifically, they help usto start to answer the question of whether the diag-nostic categories in current approaches reflect dis-crete classes within the wider population marked bynonarbitrary boundaries or whether they representan extreme expression of normal variation withessentially arbitrary pragmatically defined thresh-olds. In this final section, we reflect on the implica-tions of the results so far from taxometric analysesfor current category-based models of classificationand their relation to clinical practice and scientificenquiry. There are of course many caveats. Manydisorders remain unstudied, many studies havebeen conducted on relatively small samples, almostall have only used symptom data and have not madeuse of data from other levels of analysis (e.g. physi-ological, neuroimaging, neuropsychological). It isalso the case that the latent structure may differ byrater (parent-report vs. clinician report) and that thisdifference may arise as a consequence of rater biasesrather than actual differences in the structure of thedisorder. Studies have also not yet used measures,like the SWAN described above, that are able tocapture the full range of behaviour or experience.

The most striking and perhaps important findingof the review of taxometric studies is that differentdisorders appear to ‘behave’ very differently when itcomes to their categorical or dimensional underpin-nings. Maybe the most important general contribu-tion of these taxometric analyses is to emphasise thepoint that there is no single or simple resolution tothe category versus dimension debate in relation tomental disorder per se. Taken together, they dem-onstrate that within the field of developmental psy-chopathology, there are likely to be situations wherea categorical solution is appropriate and otherswhere a dimensional approach is both more correctand more useful. Interestingly, for several of thoseconditions where discrete taxa have been identified,these taxa seem likely to represent high risk groupsrather than the disorder per se, and in the case ofdepression the taxon is for a subgroup, melancholic/endogenous depression, rather than the broadercategory of major depressive disorder. In other cases,such as ADHD, the evidence seems to point stronglyto a dimensional rather than categorical structurewhereby those currently identified as having ADHDrepresent the extreme end of a continuum. Here, wedistinguish the implications for those disorderswhere there is evidence for taxonicity and thosewhere a dimensional model seems more appropriate.

Implications where evidence to date supportscategories rather than dimensions

According to our survey of the extant literature, thereare a number of disorder-domains where, despite themethodological limitations listed above, evidence fortaxa are emerging. These include schizotypy, anxietysensitivity, melancholic/endogenous depression,



mixed anxiety depression disorder, antisocialbehaviour and ASD. For those disorders that maponto the current classification systems there is adegree of consistency between the taxon and theDSM structure of the disorder. This is most clear formelancholic/endogenous depression and mixedanxiety depression disorder. It is interesting to notethat for autism the presence of a taxon representingthe ASDs suggests that the ‘spectrum’ exists withinthe disorder and not merely as an extreme repre-sentation of normality. In other cases, the taxaappears to describe a broader group of individualsthan are currently captured by the diagnostic man-uals (e.g. schizotypy), or a grouping that does notreally match up to any of the traditional diagnosticgroups (e.g. anxiety sensitivity and antisocialbehaviour). Here, the taxa may be describing a groupat risk of mental health problems rather than thosesuffering from a disorder. The identification of taxausing empirical means should only be regarded as astarting point. As noted above, the identification oftaxa can result in a broad range of benefits to bothscientists and clinicians. From the science perspec-tive, identification of such discrete taxa may be ofbenefit to those searching to understand the causesof a disorder. The identification of a taxon, whether itis for a high risk group or the disorder itself, suggestsa unit of analysis that would be more likely to rep-resent the outcome of a common causal pathway andcan be used to refine study populations into morehomogeneous groupings. Taxa can also be used toinvestigate the mechanisms of equifinality and mul-tifinality within and across different groups. From adevelopmental perspective, it will be important toidentify the point in development at which discretetaxa first appear. This could have significant clinicalimplications, for example, where it is possible toidentify a discrete bifurcation point during develop-ment it may be possible to time interventions in sucha way as to prevent individuals at risk from devel-oping a full blown disorder. It should also be notedthat just because a particular latent structure (eithercategorical or dimensional) has been demonstratedat the symptom level it does not necessarily followthat the same structure holds at other levels ofanalysis (e.g. genetic, brain structure and function,neuropsychological). Thus, the presence of a clearlyidentified categorical latent structure for a particulardisorder does not imply a homogeneous causality.For example, a significant minority of those withautism have thought to be due to large, rare, clini-cally meaningful copy number variants (CNV;Glessner et al., 2009). However, very few of thesecases share the same CNVs and many arise as denovo mutations (Sebat et al., 2007). The implicatedCNVs do, however, tend to cluster in particularregions of the genome that contain genes known tobe important for brain development and on-goingneuroplasticity (Glessner et al., 2009). As a conse-quence, whilst knowing the latent structure at the

symptom level may give hints as to underlyingmechanism these relationships should not be over-stated or over-interpreted.

From the clinical perspective the identification of atrue taxon opens the door to the search for nonar-bitrary diagnostic cut-offs and potentially aidsidentification of stable subtypes within a disorder.Whilst there is currently not enough data to describereliable cut-offs for any of the child and adolescentdisorders, this may become possible in the not toodistant future. Where a taxon identifies a risk grouprather than a disorder, this information can be usedto identify the unique characteristics of those indi-viduals that distinguish them from low risk individ-uals, distinctions that may help develop/facilitateearly intervention strategies and treatments. Treat-ment outcomes will need to be compared for taxonicand nontaxonic groups and this data used to tailortherapeutic interventions and identify importantmoderators of treatment outcome.

Implications where the balance of evidence to datesupports dimensions rather than categories

Where the evidence supports a dimensional ratherthan a categorical structure (e.g. ADHD, psychopa-thy, PTSD or nonmelancholic depression, attach-ment status and general aggression), the approachtaken in response to these findings is likely to berather different. We will explore this in the case ofADHD as an example where all three taxometricstudies fail to confirm the existence of an ADHDtaxon. Confidence in the initial findings is supportedby their consistency with behavioural genetic studiesthat demonstrate similar patterns of heritabilityacross the full range of symptom expression (Gjoneet al., 1996). There are, however, a number of cave-ats to these findings. First, ADHD is a pathophysio-logically heterogeneous condition with differentpatients being affected to different degrees by dif-ferent types of underlying deficits (Rhodes, Riby,Matthews, & Coghill, 2011b; Rhodes et al., 2005;Sonuga-Barke, 2002, 2005; Sonuga-Barke &Halperin, 2010). It, therefore, remains possible thatthere may be subgroups, marked by specific causalfactors that show a taxonic structure. Second, eventhough the studies already conducted include manythousands of individuals they probably still do nothave sufficient power to identify taxa at the extremeright of the distribution. This is important as there isdata to suggest that those with the most seven ADHDwho meet criteria for ICD Hyperkinetic Disorderrespond differently to ADHD treatments whencompared to those with DSM defined combined typeADHD but not ICD hyperkinetic disorder (Santoshet al., 2005). It is still therefore possible that adiscrete more restricted taxa for ADHD exists andfurther studies are required.

Leaving these caveats aside, we can ask what arethe implications for the failure to find a general



ADHD taxon? At first sight this seems obvious – thecurrent category-based diagnostic system should bereplaced by a dimensional approach, perhaps of thetypes we described in Section 1. Such a systemwould have the obvious advantage of actuallyreflecting ‘reality’. But would it have value as asystem for classification and diagnosis of ADHD?This must be the overriding priority. In Section 2 ofthis review, we highlighted distinctions betweenclinical pragmatics, philosophical conviction andempirical reality in relation to the category/dimen-sion debate. There, we made the case that clinicianshave to make categorical decisions for clinicalpragmatic reasons and that the tendency to createcategories out of continua goes with the grain ofhuman cognition. In this sense, it could be arguedthat dimensional systems, which potentially betterreflect reality, may be difficult to implement and touse in everyday clinical practice. Our guidingquestion must therefore be – would dimensionalsystems help (or improve) clinical decision making?The case for this has yet to be made convincingly(see Section 1). Until such a case can be made and

until a dimensional approach that both adds clini-cal value and is acceptable and workable for clini-cians is developed, this mismatch between thedimensional structure of the disorder and thepragmatic benefits of diagnostic categories willcontinue to create a tension. Whilst clinicians willcontinue to treat ADHD as if it is a category they, atthe same time, ought to acknowledge that this is infact not so and that ADHD represents the extremeexpression of normal variation with cut-offs anddiagnostic thresholds that are to some degree arbi-trary. The evidence may appear to cast doubt overthe clinical validity of ADHD and other apparentlydimensional disorders – If there is no ADHD taxon

does it really exist as mental disorder? In responseto this question, it is important to point out that, atleast from a psychometric and pragmatic point ofview, the evidence continues to support the clinicalvalidity and utility of the ADHD disorder dimensionwith clustering of symptoms of inattention overac-tivity and impulsivity (e.g. Neuman et al., 1999). Itis also clear that ADHD can be reliably distin-guished from other disorder dimensions and cate-gories (Neuman et al., 2001). High levels of thissymptom cluster are associated with suffering andimpairment and there are treatments that canalleviate these problems (Taylor et al., 2004). Thepossibility of dimensional mental disorders hascertain precedents in physical medicine. For exam-ple, hypertension and obesity are rarely questionedas genuine health problems but neither are taxonicin nature and for both, the cut-offs between healthand disease are somewhat arbitrary. Indeed, in bothcases, the cut-offs have changed over time asunderstanding of the risk associated with each hasbeen refined. In both cases it is their associationwith future risk that identifies them as important

health indicators. A similar logic can be applied todisorders such as nonmelancholic depression andADHD, both of which are associated with increasedrisk of negative health and social outcomes. Thedecision to persist with category-based classifica-tion systems for putative dimensional disorders(such as ADHD) does, however, raise an interestingdilemma for scientists. On the one hand, main-taining a categorical system which does not reflectthe underlying structure of the disorder and forcesthe dichotomising of continuous measures is aclearly suboptimal approach to design and mea-surement (see Section 2 for a discussion of theseissues). In this case, correlational designs usingtruly dimensional measures (Swanson et al., 2001)with appropriate sampling techniques would be thescientifically preferred and statistically more pow-erful option. However, a unilateral break from theuse of category-based systems would breach a cru-cial bridge of communication between the science ofpsychopathology and clinical practice which pro-vides its ultimate purpose and end. Such a breachwould potentially reduce the ability of scientists toask clinically meaningful questions and provideanswers to those questions that can translate basicscience findings into therapeutic innovations. Thus,the finding that ADHD appears to be dimensionalrather than categorical creates a dilemma for theclinical scientist about which way it should becharacterised. One possible way forward is forstudies to routinely include both categorical anddimensional conceptualisations and measurementsof disorder. They should also explore the distinctivefeatures and overlap between these different modelsso that over time, the meaning of dimensional con-cepts for category-based classifications can be builtup within the scientific community. This wouldallow researchers and clinicians to eventuallytranslate between one approach and the other. Suchan approach may throw up some interesting andunexpected findings. For instance, studies ofmolecular genetics of ADHD have tended to showsomewhat different patterns of association whenADHD is characterised categorically than when it isanalysed as a continuous quantitative trait.

ConclusionsIn this review, we have explored contemporaryaspects to the category/dimension debate within thefields of child and adolescent mental health anddevelopmental psychology. The application of mod-ern analytic techniques, especially taxometricapproaches, has made it clear that there is no simpleor overall resolution to this debate from an empiricalpoint of view. Both categorical and dimensionalsolutions appear to have a value and this variesfrom disorder to disorder. Whilst some disorders,such as melancholic/endogenous depression andmixed anxiety and depression disorder, appear to be



associated with discrete taxa others, such as ADHD,PTSD, nonmelancholic depression, and psychopa-thy, do not. In other cases such as schizotypy, thetaxa appear to identify a high risk group rather thanthe disorder per se. These data, whilst interesting,only represent the start of the journey and there isstill investigating need to investigate these taxa anddimensions in greater detail. There now needs to befurther focus on the scientific underpinnings ofidentified taxa, and their clinical implications withrespect to course outcome and treatment effects.Where a dimensional model appears more appro-priate, scientists and clinicians need to worktogether, rather than against each other as has oftenbeen the case in the past, to address and betterunderstand the tensions and relationships betweenthese dimensional disorders and the traditionalcategorical models.

Correspondence toDavid Coghill, Centre for Neuroscience, Division ofMedical Sciences, University of Dundee, Dundee, UK,DD1 9SY; Tel: +441382204004; Email: [email protected]

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Accepted for publication: 8 November 2011



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