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Typology of emotional and behavioral adjustment for low-income children:A child-centered approach

Rebecca J. Bulotsky-Shearer a,⁎, John W. Fantuzzo b, Paul A. McDermott b

a University of Miami, Department of Psychology, Child Division, P.O. Box 248185, Coral Gables, FL 33124-0751, USAb University of Pennsylvania, Graduate School of Education, 3700 Walnut Street Philadelphia, PA 19104-6216, USA

a b s t r a c ta r t i c l e i n f o

Article history:Received 5 March 2009Received in revised form 26 November 2009Accepted 12 December 2009Available online 11 February 2010

Keywords:Preschool emotional and behavioraladjustmentContextual and developmental assessmentSchool readinessTypologyHead Start

An empirical typology of classroom emotional and behavioral adjustment was developed for preschoolchildren living in urban poverty. Multistage hierarchical cluster analyses were applied to identify six distinctand reliable subtypes of classroom adjustment, differentiated by high and low levels of behavioral(aggressive, inattentive, oppositional, withdrawn/low energy, socially reticent) and situational adjustment(structured learning, peer interactions, and teacher interactions). Differences among profile types werefound across child age, special needs status, and peer social and classroom learning outcomes. Patterns ofoveractive behavior with problems in socially-mediated learning situations predicted peer disruption.Patterns of withdrawn behavior, and problems in socially-mediated and teacher-directed learning situationspredicted peer disconnection and poor learning outcomes. More resilient patterns were associated witholder age, lower percentage of special needs, and higher readiness outcomes. Implications of the findings toextend prior research and to inform strategic early identification and mental health intervention arediscussed.

© 2009 Elsevier Inc. All rights reserved.

There is increased national concern for the social and emotionaldevelopment of young children. Epidemiological studies suggest that8–22% of preschool children exhibit moderate to clinically significantemotional and behavioral problems (Campbell, 1995; Lavigne et al.,1996; Mash, 2003) and prevalence rates for children living in povertyare cited as high as 38% (Barbarin, 2007; Feil et al., 2005; Qi & Kaiser,2003). There is also consistent evidence that early behavior problemsinterferewith the ability to engage in classroom learning activities andto form important relationships with peers and teachers, placingchildren at risk for future social and academic difficulties (Denham,2006; Huffman, Mehlinger, & Kerivan, 2000; Raver, 2002). Nationalstudies of early childhood educators echo these concerns—teachersreport that a growing number of children enter kindergarten “notready to learn” because they lack basic social and emotional skills as afoundation for their learning (Rimm-Kaufman, Pianta, & Cox, 2000).These concerns are heightened in school districts serving childrenliving in urban areas with disproportionately high concentrations ofpoverty (Cooper, 2008).

Unfortunately, there are major gaps in our understanding ofappropriate and effectivemethods to address the social and emotionalneeds of vulnerable children and to identify which children are at

greatest risk for poor school adjustment (Cooper et al., 2008;Fantuzzo, McWayne, & Childs, 2006; Fisher et al., 2002; U.S.Department of Health and Human Services (USDHHS), 2001). Earlychildhood educational programs such as Head Start are in a strategicposition to respond to the mental health needs of low-incomechildren (Fantuzzo, McWayne, & Bulotsky, 2003; Lopez, Tarullo,Forness, & Boyce, 2000; Yoshikawa & Zigler, 2000). Based on adevelopmentally appropriate comprehensive approach to interven-tion, Head Start attends to the needs of the nation's most vulnerablechildren and fosters development across multiple readiness domains(National Education Goals Panel, 1999) within important contextssuch as the family and school (Zigler & Bishop-Josef, 2006).

Logically programmatic interventions are contingent upon theavailability of reliable and valid assessment tools to identify children'semotional and behavioral needs and that provide practical informationteachers can use within the classroom context. In large municipalprograms teacher rating scales are themost efficient way to identify thegreatest number of children in need of intervention (McDermott, 1993).These are helpful to screen large numbers of children; however, ingeneral, available teacher rating scales have been found to havequestionable reliability and validity for low-income minority popula-tions (Lopez et al., 2000; USDHHS, 2001). In addition, commonly usedteacher or parent rating scales identify problem behavior via checklistsof static symptoms ofmental disorders, rather than identify problems asthey arise within proximal contexts (such as within classroom learningor social situations) limiting their contextual and practical relevance.Such tools have been criticized as inappropriate for preschool children

Journal of Applied Developmental Psychology 31 (2010) 180–191

⁎ Corresponding author. University of Miami, Department of Psychology, ChildDivision, 5665 Ponce de Leon, Coral Gables, FL 33146, USA. Tel.: +1 305 284 8439;fax: +1 305 284 3402.

E-mail addresses: rshearer@miami.edu (R.J. Bulotsky-Shearer),johnf@gse.upenn.edu (J.W. Fantuzzo), drpaul4@verizon.net (P.A. McDermott).

0193-3973/$ – see front matter © 2009 Elsevier Inc. All rights reserved.doi:10.1016/j.appdev.2009.12.002

Contents lists available at ScienceDirect

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due to their psychiatric orientation, subjectivity required to inferchildren's internal emotional states, and lack of contextual sensitivity(Fantuzzo&Mohr, 1999; Sherrod, 1999). Empirical studies indicate thatwhen asked to use these tools, teachers seriously underreport children'sbehavioral needs to avoid stigmatizing children with labels that are notassociated with classroom-based services (Mallory & Kearns, 1988;Piotrowski, Collins, Knitzer, & Robinson, 1994). Further, such tools focuson the “type”of problem(e.g., internalizingor externalizing) rather than“situations where” problems occur.

Amore comprehensive, contextualized understanding of children'sbehavior is necessary to inform developmentally and ecologicallyappropriate classroom-based intervention for low-income children. Inlarge municipal programs serving children living in urban poverty,resources to address children's behavioral needs are scarce (Cooperet al., 2008). Access to timely and appropriate psychological services(evaluation and intervention) are limited (Fantuzzo et al., 1999). Toolsare needed that accurately identify mutable patterns of behavioraladjustment and that provide practical, useful information for teachersand staff to address children's needs directly within the classroom(Bulotsky-Shearer, Fantuzzo, & McDermott, 2008; Fantuzzo & Mohr,1999).

Fortunately, a contextually relevant assessment has been devel-oped to assess low-income children in early childhood programs. TheAdjustment Scales for Preschool Intervention (ASPI; Lutz, Fantuzzo, &McDermott, 2002) was developed in partnershipwith early childhoodeducators, parents, and professional staff to ensure developmentallyappropriate item content, and directly observable adaptive andmaladaptive behavior within the context of 22 routine classroomsituations (Lutz et al., 2002). (See Appendix A for an example of theitem format.) Behaviors are described in the language of earlychildhood educators and do not require inferences regardingchildren's internal thoughts or feelings (e.g., “seems sad”). Problembehavior is defined by its multi-situational occurrence rather thanupon the frequency or severity of more global behaviors (McDermott,1993). The ASPI is unique from other preschool behavioral assess-ments such as the BASC or CBCL because behavior problems areidentified within the context of expectations of functioning withinclassroom situations. In accord with a developmental-ecologicalmodel, problem behaviors are seen as manifestations or indicatorsof the child's difficulty navigating or negotiating the developmentaldemands of these situations (Sameroff, 1975; Sameroff & Fiese, 2000;Sroufe, 1997). A hierarchy of social and emotional skills are requiredto navigate each classroom situation; for example, learning activitiessuch as circle time require children to self-regulate, inhibit verbal andmotor activity, listen carefully, and pay attention, while in free play,another repertoire of skills are required. When the cognitive or socialdemands of classroom situations do not match the child's develop-mental capacities, problem behavior may result.

Research in Head Start has established two sets of reliable andvalid dimensions of emotional and behavioral adjustment: (a) fivebehavioral dimensions, assessing types of behavior across classroomdemands (aggressive, inattentive/hyperactive, oppositional, with-drawn/low energy, and socially reticent behavior) (Lutz et al.,2002), and (b) three situational dimensions, assessing situationswhere problem behavior occurs (including both teacher- and peer-mediated structured learning activities, peer interactions, and teacherinteractions) (Bulotsky-Shearer et al., 2008). Problems in structuredlearning describe behavior problems that occur within 7 organizedlearning situations (e.g., paying attention in class, sitting duringteacher-directed activities, working with hands (art), free play/individual choice time, involvement in class activities). Problems inpeer interaction reflect problems occurring within 6 social situationswith peers (e.g., getting along with agemates, behaving in classroom,standing in line). Problems in teacher interactions consist of problemswithin 6 situations (e.g., talking to teacher, answering teacherquestions, greeting teacher, helping teacher with jobs).

To date, research in Head Start has employed a variable-centeredapproach to understand the influence of both what type of behaviorand situations where problematic behavior occurs on children'sschool readiness trajectories. A variable-centered approach allowsfor the study of behavior grouped on the basis of constructs(dimensions) and the linear relations of these dimensions within apopulation (Achenbach & Edelbrock, 1981; Beg, Casey, & Saunders,2007). This research provided evidence for the unique and combinedcontribution of both sets of dimensions to social and academicoutcomes. Findings indicated that overactive classroom behaviorpredicted socially disruptive play outcomes, as well as poorapproaches to learning with respect to socially-mediated classroomlearning experiences (Fantuzzo, Bulotsky, McDermott, Mosca, & Lutz,2003; Fantuzzo, Bulotsky-Shearer, Fusco, & McWayne, 2005). Under-active classroom behavior was found to predict lower emergentliteracy and mathematics skills, lower adaptive emotional regulationand affective engagement, and greater disconnected peer play(Fantuzzo, Bulotsky, et al., 2003; Fantuzzo, McWayne, et al., 2003;Fantuzzo et al., 2005).

When examined simultaneously, Bulotsky-Shearer et al. (2008)found that ASPI classroom behavioral and situational problemscontributed greater variance in social and academic outcomes thaneither set alone; aswell, the negative influence of behavioral problemson readiness outcomes was moderated by situational problems. Forexample, inattentive problems in combination with problems withpeer interactions predicted significantly lower socially-mediatedclassroom learning outcomes (e.g., social interaction, initiation andsocial problem solving skills). Other research in fact suggests thatbehavior problems often co-occur across multiple contexts forchildren living in poverty (Brooks-Gunn, Duncan, & Aber, 1997;Garbarino, 1995); and the cumulative effect ofmultiple behavioral andsituational problems predicts greater risk for poor outcomes than anyone problem alone (Campbell, 2002; Rutter, 2009).

To inform early identification efforts, further research using theASPI behavioral and situational dimensions is needed to differentiatespecific subtypes of children demonstrating early patterns ofemotional and behavioral needs and strengths within the classroomcontext. Developmental psychologists are increasingly employinga person-centered (or child-centered) approach. This approach allowsthe researcher to move beyond examining the linear co-variancerelations among variables, to examine unique profiles of homoge-neous groups of children (Hirsh-Pasek, Kochanoff, Newcombe, &deVilliers, 2005; McWayne, Fantuzzo, & McDermott, 2004). Inother words, rather than examining the linear relations betweenspecific variables across an entire population (e.g., how doesaggressive behavior relate to cognitive skills?), this child-centeredapproach allows for examination of variability across behavioraldimensions within children (e.g., what combinations of high or lowlevels of aggressive, oppositional or withdrawn problem behavior dochildren exhibit within the population?). This holistic approach canobtain a rich understanding of within-child variability (Bergman &Magnusson, 1997; Cicchetti & Toth, 1997). Common patterns ofbehavioral (what) and situational (where) problems can be iden-tified and multiple co-occurring behavioral and situational risks canbe examined simultaneously.

Multivariate hierarchical cluster analysis is an empirical approachthat permits examination of multiple domains of functioningsimultaneously (e.g., classroom behavioral and situational adjust-ment) (Youngstrom, 2008) and enhances early identification andaccuracy of risk prediction by avoiding making multiple univariatedecisions about an individual when examining correlated measures(McDermott & Weiss, 1995). Statistically, homogenous profiles ofindividual children are defined by level (position toward the upper,middle or lower end of a continuum), shape (the pattern of peaks andvalleys across multiple scores), and dispersion (variance around eachbehavioral or situational score) (Konold, Glutting, McDermott, Kush,

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& Watkins, 1999). Because cluster analyses can produce clusters evenin random data (Huberty, DiStefano, & Kamphaus, 1997) employingan empirically-rigorous, hierarchical analytic approach is necessary toestablish distinct, reliable and replicable profile types. Results canguide targeted interventions and differentiated instruction forsubgroups of children manifesting similar patterns of behavioraladjustment (Roeser, Eccles, & Sameroff, 1998; Youngstrom, 2008)—for example, children in greatest need of classroom-based interven-tion or those demonstrating resilience (Flanagan, Bierman, & Kam,2003).

For elementary school children, a number of studies have em-ployed rigorous hierarchical cluster analyses to identify empirically-based behavioral typologies. With large or nationally representativeelementary school samples, researchers have analyzed parent- andteacher-report measures, including: The Behavioral AssessmentSystem for Children–Teacher Rating Scales–Child (BASC-TRS-C;DiStefano, Kamphaus, Horne, & Winsor, 2003; Huberty et al., 1997;Kamphaus, Huberty, DiStefano, & Petoskey, 1997); the MissouriChildren's Behavior Checklist (MCBC; Curry & Thompson, 1985;Thompson, Kronenberger, & Curry, 1989); the Child BehaviorChecklist—Parent Report (CBCL; McConaughy, Achenbach, & Gent,1988); and theAdjustment Scales for Children andAdolescents (ASCA;McDermott, 1993). While these typologies provide useful informationto guide intervention for elementary school children, their generaliz-ability to low-income, preschool populations is limited.

Empirically-based typologies of preschool behavior problems arescant in the literature and none currently exist that examineclassroom adjustment using a reliable and ecologically valid assess-ment for low-income, diverse children. In their recent review of theliterature, Beg, Casey, and Saunders (2007) identified four empiricalstudies that applied cluster analyses to preschool behavioral problemsusing behavioral rating scales and/or psychiatric diagnostic inter-views (e.g., McGuire & Richman, 1986; Richman, Stevenson, &Graham, 1982; Sonuga-Barke, Thompson, Stevenson, & Viney, 1997;Wolkind & Everitt, 1974). Findings revealed several subtypes,differentiated by normative adjustment, or patterns of high overactiveor high underactive behavioral adjustment problems. However, thestudies had several limitations: None employed a rigorous hierarchi-cal cluster analytic approach; samples were small or limited to clinic-referred children, with the exception of Sonuga-Barke et al. (1997)who employed a large, representative sample of 3-year-old children;only one study included both 3- and 4-year-old children (McGuire &Richman, 1986); and all of the studies except Wolkind and Everitt(1974) were conducted in Britain using the same behavior rating scaleassessing static symptoms or problematic behaviors (BSQ; Richmanet al., 1982) or a variant of the BSQ (PBCL, McGuire & Richman, 1986;BCL; Richman, 1977).

In their recent study, Beg, Casey, and Saunders (2007) used amulti-stage hierarchical clustering method with the eight clinicalscales of the parent rating scale of the BASC (PRS-P; Reynolds &Kamphaus, 2002). Five subtypes were identified: No behaviorproblems, attention problems, externalizing and attention problems,disruptive behavior problems and atypical behavior, and a mixedsevere externalizing/internalizing type. While Beg, Casey, andSaunders (2007) employed a rigorous hierarchical clustering methodfor 3-, 4- and 5-year-old children in the United States, their samplewas relatively small (N=268) and comprised predominantly of maleCaucasian, clinic-referred children. Also, the BASC scales used werelimited to parent report of clinical problems only and subtypes werenot examined with respect to social or learning outcomes.

No studies currently exist that apply an empirically rigorousperson-centered approach to identify patterns of preschool emotionaland behavioral adjustment for children living in urban poverty. Toinform strategic early intervention efforts for children at greatestrisk of poor school adjustment, the purpose of the present study was:(a) to employ a rigorous multistage hierarchical cluster analytic

strategy to identify psychometrically sound profile types of classroombehavioral adjustment for a large, representative sample of urbanHead Start children; and (b) to identify associations betweenresultant profile types and child demographic characteristics, specialneeds status, and a comprehensive set of readiness competencies atthe end of the Head Start year. Based on prior research, it washypothesized that multiple reliable and distinct profile types wouldbe identified, including a well adapted profile type comprising thelargest percentage of children. Additional profile types would includechildren with patterns of overactive or underactive behavioralproblems, and co-occurring situational problems. Finally, it washypothesized that lower problem behavior types would be associatedwith older children, fewer special needs children, and higher socialand cognitive school readiness outcomes.

Method

Participants

Participants included 829 children enrolled in a large urban schooldistrict Head Start program in the northeast who were representativeof the entire program. The school district Head Start program servesapproximately 5000 children (in over 200 classrooms) in the mostimpoverished areas of the city. Children in the study ranged in agefrom 36 to 73 months (M = 52.8, SD = 7months) and sex was splitevenlywith 48%male. Childrenwere predominantly African American(73%) with 14% Caucasian, 9% Latino, 5% Asian or other. Annual familyincome of 94% of families was below $12,000.

Children were enrolled in 46 Head Start classrooms. All teacherswere credentialed in early childhood education with at least abachelor's degree. Thirty-five percent had experience teaching inHead Start less than 10 years, 27% between 10 and 20 years, and 38%over 20 years experience. Teachers were predominantly Caucasian(66%), with 31% African-American.

Measures

Preschool emotional and behavioral adjustmentThe Adjustment Scales for Preschool Intervention (ASPI; Bulotsky-

Shearer et al., 2008; Lutz et al., 2002) was the primary measure usedin this study to assess emotional and behavioral problems acrossroutine preschool classroom situations. The scale's 144 behavioralitems (122 items reflect problem behaviors, 22 reflect positivebehaviors) are framed by 22 classroom situations and 2 categoriesof non-situationally specific behavior problems. Teachers completethe scale by endorsing as many behaviors as apply to a child in each ofthe classroom situations. The ASPI was standardized on a sample ofurban Head Start children and validated for use with this population.

Construct validity studies with urban, low-income preschoolpopulations have revealed two distinct and reliable sets of dimen-sions: (a) five behavioral dimensions (Lutz et al., 2002) and (b) threesituational dimensions that assess where behavioral problems occur(Bulotsky-Shearer et al., 2008). Each of the behavioral dimensionsdemonstrated adequate internal consistency, with Cronbach's alphacoefficients of .92, .78, .79, .85 and .79 for aggressive, oppositional,inattentive/hyperactive, withdrawn/low energy and socially reticentadjustment, respectively (Lutz et al., 2002). Cronbach's alphacoefficients for ASPI situational dimensions were also high (.84, .81,and .75, for problems in structured learning, peer interactions, andteacher interactions, respectively). Both sets of dimensions werefound to be replicable and generalizable to subgroups of thestandardization sample (i.e., younger and older children, boys andgirls). Convergent and divergent validity of the five behavioraldimensions was established with constructs of interactive peer play,behavior problems, temperament, emotion regulation, classroomlearning competencies, receptive language skills, learning behaviors,

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and observations of classroom behavior problems (Bulotsky-Shearer& Fantuzzo, 2004; Fantuzzo, Bulotsky, et al., 2003; Fantuzzo, Bulotsky-Shearer, Frye, McDermott, McWayne & Perlman, 2007; Fantuzzo,McWayne, et al., 2003; Fantuzzo et al., 2005). Criterion-relatedvalidity of situational dimensions was established with peer socialand classroom learning competencies in Head Start (Bulotsky-Sheareret al., 2008).

Outcome measures

Peer social competenceThe teacher version of the Penn Interactive Peer Play Scale (PIPPS-T;

Fantuzzo, Coolahan, Mendez, McDermott, & Sutton-Smith, 1998) wasused to assess children's interactive peer play competencies withinthe classroom context at the end of the Head Start year. The PIPPS-T isa 32-item rating scale used to measure common play behaviors thatfacilitate or interferewith prosocial peer interactions in the classroom.The PIPPS-T was developed in collaboration with Head Start parentsand teachers specifically for use with low income, urban Head Startchildren. Construct validity studies of the PIPPS-T have revealed threedimensions: Play interaction, play disruption and play disconnection,each demonstrating high internal consistency (Cronbach's alpha =.92, .91, and .89, respectively). Validity has been established usingdirect observations of play, peer sociometrics, and measures oflearning behaviors, temperament, emotion regulation, psychologicaladjustment, and social skills (Coolahan, Fantuzzo, Mendez, &McDermott, 2000; Fantuzzo et al., 1998; Mendez, McDermott, &Fantuzzo, 2002).

Classroom learning competenceThe Child Observation Record (COR; High Scope Educational

Research Association, 1992) is a 30-item observationally-based tooldesigned for use with children ages 2 1/2 to 6 years in early childhoodsettings. It measures emergent literacy, numeracy, social and motorcompetencies (Schweinhart, McNair, Barnes, & Larner, 1993). Explor-atory factor analysis with urban, low-income preschool childrenyielded three factors: Cognitive skills, social engagement, andcoordinated movement (Fantuzzo, Hightower, Grim, & Montes,2002) demonstrating high internal consistency (Cronbach's alpha =.95, .93, and .86, respectively). Validity has been established withassessments of peer play, receptive vocabulary, psychological adjust-ment, early mathematics, reading, and learning behaviors (Fantuzzoet al., 2002; Sekino & Fantuzzo, 2005).

Peabody Picture Vocabulary Test—Third versionThe Peabody Picture Vocabulary Test—Third edition (PPVT-III;

Dunn & Dunn, 1997) was used to assess children's receptivevocabulary. The PPVT-III is an individually administered 204-itemtest designed to assess receptive vocabulary for individuals aged 2.5through adulthood. The PPVT-III was nationally standardized on astratified normative sample of 2000 children and adolescents, and anadditional 725 adults. Internal consistency is reported in terms ofSpearman–Brown split half reliability coefficients and range from .92to .98. Test–retest reliability is reported at .91–.93. Validity wasdemonstrated through correlations between the PPVT-III and theWechsler Verbal Intelligence Quotient, Performance IntelligenceQuotient, and Full Scale Intelligence Quotient (.82–.92); the KBIT(.62–.82); and Oral/Written Language Scales (.63–.83).

The Expressive One-Word Picture Vocabulary Test—RevisedThe Expressive One-Word Picture Vocabulary Test—Revised

(EOWPVT-R; Gardner, 1990) was used to assess expressive vocabulary.It is an individually administered 143-itemmeasure for children aged 2to 12 years. Internal consistency ranged from .84 to .92 with a medianreliability of .90. Concurrent validity was established through correla-tions with the Peabody Picture Vocabulary Test—Revised (PPVT-R;

Dunn & Dunn, 1981), the Receptive One-Word Picture Vocabulary Test(ROWPVT; Gardner, 1985) and the vocabulary subscales of theWechsler Preschool and Primary Scale of Intelligence—Revised(WPPSI-R; Wechsler, 1989).

Procedures

Data collectionThis study was part of a larger collaborative university research

partnership with an urban public school district Head Start program.Participants were recruited from a representative set of classrooms,randomly selected to represent the program's six geographic regions.The teachers in these classrooms volunteered to participate and wererecruited by the program's six Educational Coordinators. The same 46teachers completed assessments on the same children twice duringthe school year (ASPI in the fall and PIPPS, COR in the spring). In thefall, assessment packets containing the ASPI and a demographicquestionnaire for each participating child were distributed to each ofthe 46 participating teachers. The ASPI was collected by the programas part of a federal Head Start assessment requirement (PerformanceStandard, 1304.20; USDHHS, 1996). In partnership with the teachers,parental consent for child participation was sought (97% of theparents gave consent for their child to participate).

In the spring six months post Time 1 data collection, each of the 46teachers completed the PIPPS-T for all children and completed theCOR for five boys and five girls (randomly selected to participate).From each classroom six children (three boys and three girls) wererandomly selected and individually tested on the PPVT-III (Dunn &Dunn, 1997) and EOWPVT-R (Gardner, 1990). Trained doctoralstudents administered these individual assessments. This subset ofchildren was demographically comparable to the larger sample.

Information about children's special needs was collected incooperation with the program. In accordance with the Head StartPerformance Standards (USDHHS, 1996), children who receive servicesare classified with a specific disability based on comprehensiveprofessional assessments. A subgroup of 136 children independentlyreferred and found eligible for special needs services was identifiedthrough an archival file maintained by the program.

Data analysis

Multistage hierarchical person-profile cluster analysis withreplications and relocations as per McDermott (1998) was employedto identify distinct typologies of preschool classroom behaviorproblems. Each of the 829 individual profiles of the five ASPIbehavioral dimensions and three situational dimensions was sub-jected to a three-stage clustering process, with the goal to formclusters according to T score elevation, dispersion, and profile shape,such that each cluster retained maximum within-type homogeneityand between-type separation. First the 829 profiles were randomlyand equally assigned to six mutually exclusive blocks (five of 138and one of 139) and Ward's method was applied independently forthe profiles composing each block. For each block, the ideal numberof clusters was determined via multiple criteria: (a) atypicaldecrease in overall between-cluster variance R2 and increase inwithin-cluster variance with no reverse trend in subsequent steps(Ward, 1963), (b) simultaneous elevation of the pseudo-F statistic(Calinski & Harabasz, 1974) over pseudo-t2 statistic (Duda & Hart,1973), and (c) peak in Sarle's (1983) cubic clustering criterion >3.0.Pseudo F indicates separation among all clusters at the current step,whereas pseudo t2 indicates separation of the two clustersimmediately joined at the current step. Clusters derived from thesix independent first-stage analyses were pooled and subjected tosecond-stage clustering. A similarity matrix was constructed toimpart full first-stage history (cluster mean-profiles, radial anddispersion statistics, and within-cluster profile frequency), and

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Ward's method was reapplied, providing independent replications ofthe final cluster solution. Because agglomerative clustering providesno natural mechanism for relocating misplaced profiles, third-stageclustering applied divisive k-means iteration (Scheibler & Schneider,1985) to relocate misplaced profiles.

Selection criteria for second- and third-stage clustering wereidentical to first-stage clustering with the addition of several moreconservative stopping rules applied: (a) the average within-clusterhomogeneity coefficient, H̄ (Tryon & Bailey, 1970) must be >.60;(b) the average between-clusters similarity coefficient, r̄p (Cattell,1949), must be <.40; (c) each final cluster should have >50%replication rate as verified by absorption of first-stage cluster into thesame second- and third-stage cluster; and (d) the solutionmust makepsychological sense in terms of parsimonious coverage and therelevant literature.

Significant differences in child demographic variables and schoolreadiness outcomes were inspected among resultant profile types.Statistically significant deviations in the expected prevalence ofcategorical variables (sex, age years, and special education status)were determined based on the two-tailed tests of the standard errorof proportional differences (Ferguson & Takane, 1989) corrected formultiple pairwise comparisons by the Bonferroni method. One-wayANOVA using least square means with Tukey–Kramer post hoccomparison was used to examine differences with respect to age inmonths. To determine whether profile types differed across social andacademic outcomes, one-way ANOVA (EOWVT-R and PPVT-III) andMANOVA (PIPPS-T and COR) were employed using Tukey–Kramerpost hoc comparison.

Results

Typological analyses of preschool classroom behavior

Multistage hierarchical cluster analyses produced six distinctprofile types of classroom emotional and behavioral adjustment.Table 1 shows the prevalence, homogeneity and similarity coeffi-cients, and replication rates for the six profile types. The six typesreplicated an average of 89% over first- through third-stage clusteringand demonstrated adequate psychometric properties. Homogeneitycoefficients (H), which measure the internal cohesion (degree ofsimilarity of each child's profile among the other profiles within eachcluster), ranged from .75 to .92, with an overall average H of .83. An Hvalue of 1.0 would indicate that all children within a given clusterhave identical profiles. H decreases as the variability of profiles withina given type increases. An H of .0 would signify that the variability ofprofiles within a given type equals the variability with the entiresample. Calculations of external isolation indicated a high level ofseparation among profiles, with average similarity coefficients (rp)ranging from .00 to .33. Between-type similarity indicates the degreeof similarity between the mean attribute profile for a given type andthe mean profile of all other types. An rp of 1.0 would signify that themean attribute profile of a type was identical to that of another type.As rp decreases, the similarity between the average profile of a typeand all others decreases. Table 2 shows the mean t scores for eachprofile type and Fig. 1 presents a graphic display of the patterns ofclassroom behavioral adjustment comprising the six distinctiveprofile types.

Profile types

Type 1: Well adjusted. The largest percentage of the representativesample of Head Start children comprised Profile Type 1(25.7% of the sample). These children demonstrated lowlevels of problems (less than 1 or .5 SD below the mean)across the five behavioral (aggressive, inattentive/hyperac-tive, oppositional and withdrawn/low energy, socially reti-cent behavior) and the three situational (problems instructured learning, peer and teacher interactions) dimen-sions of classroom adjustment.

Type 2: Adjusted with some peer problems. This profile type (15.9%)was comprised of children with elevated (M = 54.9, SD =4.13) problems in peer interactions but otherwise low levelsof classroom adjustment problems.

Type 3: Mildly socially disengaged. This profile type (14.2%) wascomprised of children with elevated socially reticentbehavior (M = 55.3, SD = 5.67) and mildly elevatedproblems in teacher interactions (M = 53.6, SD = 6.47)but with average or below average levels of problems onother dimensions of classroom adjustment.

Type 4: Mildly socially disruptive. The second most prevalent profiletype (18.9%) was comprised of children who demonstrated

Table 1Prevalence, homogeneity, similarity, and replication rates for ASPI profile types.

Cluster(profile type)

Prevalence(%)

Within-typehomogeneity(H)a

Between-typesimilarity (rp)b

% replicabilityacross 3independent blocksc

Cluster 1 25.7 .92 .14 100.0Cluster 2 15.9 .83 .33 66.7Cluster 3 14.2 .85 .32 66.7Cluster 4 18.9 .82 .29 100.0Cluster 5 10.9 .75 .00 100.0Cluster 6 14.4 .79 .17 100.0

H̄ = .83d r̄p = .21e

Note. N = 829.a Within-type homogeneity reveals the degree of profile similarity among the

children comprising each type.b Between-type similarity indicates the degree of similarity between the mean

attribute profile for a given type and the mean profile of all other types.c Replicability of every final type was determined by assessing whether it was found

to exist within each of the first-stage cluster solutions. The percentage corresponds tothe number of first-stage solutions in which each final type was found to emerge.

d H̄ is the mean of the within-type homogeneity values and is an index of the overallhomogeneity of children's profiles within the final types.

e r̄p is the mean of the between-type similarity values and is an index of the overallsimilarity or dissimilarity found between the average profiles of the final types.

Table 2Mean ASPI t scores for the profile types.

Behavioral problems Situational problems

Profile type Aggressive Oppositional Inattent/hyper. Withdr/lowenergy

Sociallyreticent

Structuredlearning

Peerinteraction

Teacherinteraction

1. Well adjusted 43.3 44.1 43.9 45.7 43.5 40.6 41.2 42.62. Some peer problems 49.8 50.4 48.3 46.2 44.1 47.0 54.9 47.03. Mildly socially disengaged 44.5 44.8 45.2 50.5 55.3 51.9 43.2 53.64. Mildly socially disruptive 57.7 55.6 56.7 46.2 46.3 57.5 63.2 54.75. Extremely socially disruptive 63.3 60.9 62.4 52.9 55.2 68.7 70.0 65.56. Extremely disengaged 47.3 49.0 47.8 62.6 61.8 64.0 53.3 63.0

Note. N = 829; n = 213, 132, 118, 157, 90, 119 for Profile Types 1–6, respectively. Values are mean T scores (M = 50, SD = 10) based on area conversion of raw factor score totalsderived from the standardization sample. T scores one standard deviation above the mean are italicized and in boldface type. T scores .5 SD's above the mean are italicized. Inattent/hyper. = inattention/hyperactive. Withdr/low energy = withdrawn/low energy.

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elevated overactive behavior problems, including aggressive(M = 57.7, SD = 5.24), oppositional (M = 55.6, SD = 6.83)and inattentive/hyperactive behavior (M=56.7, SD=6.04),elevated problems in structured learning (M = 57.5, SD =4.98) and teacher interactions (M = 54.7, SD = 6.06), andhigh problems in peer interactions (M = 63.2, SD = 6.01).

Type 5: Extremely socially disruptive. The least prevalent profile type(10.9%) was comprised of children with extremely highoveractive problems (aggressive, oppositional and inatten-tive/hyperactive behavior, with Ms = 63.3 (SD = 4.90), 60.9(SD = 6.91), and 62.4 (SD = 6.62), respectively, extremelyhigh problems in peer interaction (Ms = 70.0, SD = 5.97)and extreme problems in structured learning and teacherinteractions (Ms = 68.7, SD = 5.86 and 65.5, SD = 7.00,respectively).

Type 6: Extremely socially and academically disengaged. This profiletype (14.4%) was comprised of children with extremely highunderactive problems (withdrawn/low energy, sociallyreticent behavior with Ms = 62.6 (SD = 5.54) and 61.8(SD=5.10), respectively) and problems in structured learningsituations and teacher interactions (M = 64.0, SD = 6.91 andM = 63.0, SD= 7.19, respectively).

Significant child demographic differences among profile types

Significant divergence in expected profile type prevalence of sex,age, and special education status in comparison to the larger sampleare displayed in Table 3. Significant differences among types in age (inmonths) are also displayed. No statistically significant departuresfrom sample expectancy were found in the percentage of girls or boyswithin profile types. When age was examined, well adjusted (Type 1)encompassed older children and significantly less three-year-oldchildren than sample expectancy (p < .05). Extremely sociallydisruptive (Type 5) and extremely socially and academically disengaged(Type 6) encompassed significantly younger children than all otherprofile types. Type 6 consisted of a significantly lower percentage thansample expectancy of five-year-old children (p < .05) and a sig-nificantly higher percentage of three-year-old children (p< .0001). Interms of special needs status, mildly socially disengaged (Type 3)comprised a significantly lower percentage of identified special needschildren (6.8%). Mildly socially disruptive (Type 4) was comprised of a

significantly higher percentage of children with mental healthdisability (3.2%) and extremely socially disruptive (Type 5) wascomprised of significantly higher prevalence of children withdevelopmental disability (6.8%).

Differential relations to social and academic outcomes

MANOVA models examining differences among profile types onpeer social and classroom learning outcomes were significant;however ANOVA models examining receptive and expressive lan-guage skills were not. Table 4 displays significant differences amongtypes on peer social and classroom learning outcomes. Tukey–Kramer's post hoc comparisons indicated that children comprisingwell adjusted (Type 1) consistently demonstrated the highestinteractive peer play, lowest disruptive and disconnected peer playproblems, and the highest cognitive, social engagement and coordi-nated movement skills. Adjusted with some peer problems (Type 2)demonstrated the second highest interactive play and classroomlearning competencies; in this group, disruptive play was within theaverage range (M=49.1, SD=8.13) but higher than other types withthe exception of mildly socially disruptive (Type 4) and extremelysocially disruptive (Type 5). Children comprising mildly sociallydisengaged (Type 3) demonstrated lower social engagement, cogni-tive, coordinated movement, and interactive peer play skills thanTypes 1 and 2, and higher disconnected peer play in comparison toother more adjusted types.Mildly socially disruptive (Type 4) childrendisplayed high disruptive peer play problems (M = 55.0, SD = 8.29)and classroom learning competencies below themean although not aslow as in Type 3. Extremely socially disruptive (Type 5) childrenexhibited the highest disruptive peer play. Type 5 and Type 6(extremely socially and academically disengaged) children demonstrat-ed the lowest cognitive, coordinated movement, and interactive playskills. Children in Type 6 displayed the lowest social engagement andthe highest disconnected play of all profile types.

Discussion

Addressing the mental health needs of low-income children is anational priority. In response to this need, the present study employeda person-centered approach to identify an empirically-based typologyof classroom emotional and behavioral adjustment for children living

Fig. 1. Mean T scores for behavioral and situational adjustment across the six profile types.

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in urban poverty. This study extends the knowledge base in severalimportant ways. First, the study empirically identified distinctiveprofile types of classroom emotional and behavioral adjustment for arepresentative sample of Head Start children within a large, urbanpublic school district program. The study used a contextually anddevelopmentally appropriate assessment based on teacher observa-tions of children's adjustment across andwithin naturalistic preschoolsituations. This provides a comprehensive person-centered under-standing of early patterns of “what” type of behavioral problems co-occur with situational problems—“where” problems emerge withinthe context of classroom learning and social demands. Second, thestudy identified unique associations between profile types and childdemographics. Third, the study examined relations between profiletypes and a comprehensive set of school readiness outcomes.

As per McWayne, Fantuzzo, and McDermott (2004) and Mendez,Fantuzzo, and Cicchetti (2002) the present study extends priorvariable-centered research, providing a more comprehensive anddifferentiated picture of classroom behavioral problems for apopulation of preschool children living in urban poverty. Hierarchicalcluster analyses identified six distinct, psychometrically sound typesof classroom emotional and behavioral adjustment. This typologyreflects considerable heterogeneity in preschool emotional andbehavioral adjustment among low-income children. Previous re-search employing a variable-centered approach with low-income,preschool children identified an overall prevalence rate of 13% ofchildren exhibiting at least one behavioral problem (defined as 1.5SD's above the M on any one of the ASPI behavioral dimensions)(Fantuzzo, Bulotsky, et al., 2003; Fantuzzo, McWayne, et al., 2003).

Table 3Significant differences in child demographic characteristics among profile types.

Profile typec

Overall 1 2 3 4 5 6

Child characteristics (N = 829) (n = 213) (n = 132) (n = 118) (n = 157) (n = 90) (n = 119) Significant differencesa,b

AgeMean age (years) 4.4 4.5 4.5 4.3 4.5 4.29 4.11 1,2,3,4 > 6 and 1,2 > 5(SD) (.58) (.58) (.51) (.60) (.55) (.59) (.56)

Age in years (%)Three 24.5 18.3* 15.9 28.0 17.2 32.2 44.5**** 1 (lower), 6 (higher)Four 61.3 63.9 67.4 59.3 65.6 58.9 47.9Five 14.4 17.8 16.7 12.7 17.2 8.8 7.6* 6 (lower)

Sex (%)Boys 47.8 39.4 50.8 44.9 58.0 61.1 38.7Girls 52.3 60.6 49.2 55.1 42.0 38.9 61.3

Special education status (%)Has IEP 13.8 10.0 19.7 6.8* 19.1 15.6 11.8 3 (lower)

Specific disabilityDevelopmental dis. 2.8 1.4 3.8 1.7 3.8 6.8* .8 5 (higher)Mental health 1.2 .0 1.5 .9 3.2* 2.2 .0 4 (higher)Other disability 1.3 .9 .8* .0 1.9 2.2 2.5 2 (lower)Speech and language 8.4 8.0 13.6 4.2 10.2 4.4 8.4

a Statistically significant differences between profiles (p < .05) with respect to age were determined based on Tukey–Kramer post hoc comparison corrected for simultaneousstatistical contrasts.

b Statistically significant differences between profiles (*p < .05, ****p < .0001) with respect to categorical variables (sex, age, special education status) were determined based onthe standard error of proportional differences corrected for simultaneous statistical contrasts by the Bonferroni method.

c Profile Type 1 = well adjusted, Profile Type 2 = some peer problems, Profile Type 3 = mildly socially disengaged, Profile Type 4 = mildly socially disruptive, Profile Type 5 =extremely socially disruptive, Profile Type 6 = extremely disengaged.

Table 4Significant differences in classroom learning and peer social competence among profile types.

Profile typeb

1 2 3 4 5 6

Spring outcome (n = 213) (n = 132) (n = 118) (n = 157) (n = 90) (n = 119) Significant differences across typesa

Classroom learning (COR)Social engagement 54.3 53.1 46.7 48.3 45.0 39.4 1,2 > 3,4,5,6 and 4,3,5 > 6Cognitive skills 53.6 51.8 48.4 49.2 43.8 41.4 1 > 3,4,5,6 and 2,4 > 5,6 and 3 > 6Coordinated movement 52.9 52.4 46.7 48.9 45.4 40.7 1 > 3,4,5,6 and 2 > 3,5,6 and 3,4 > 6

Peer social (PIPPS-T)Interactive play 56.1 51.9 48.4 19.2 44.9 43.4 1 > 2,3,4,5,6 and 2 > 3,5,6 and 3 > 6, 4 > 5,6Disruptive play 42.0 49.1 44.1 55.0 58.5 44.4 1 < 2,4,5 and 3,2 < 4,5 and 6,3 < 2 and 4 < 5 and 6 < 4,5Disconnected play 37.0 44.0 49.0 47.8 53.9 57.2 1 < 2,3,4,5,6 and 2 < 5,6 and 3 < 6 and 4 < 5,6

EOWVT-RExpressive vocabulary 83.2 78.1 75.6 77.9 76.0 76.0

PPVT-IIIReceptive vocabulary 88.2 87.9 88.2 88.5 86.4 80.8

Note. n = 486, 738, 121, 118 for COR, PIPPS-T, EOWVT-R, PPVT-R respectively. COR, Child Observation Record—Revised. PIPPS-T, Penn Interactive Peer Play Scale—Teacher version.EOWVT-R, Expressive One-word Vocabulary Test—Revised. PPVT-III, Peabody Picture Vocabulary Test—Third edition.

a Statistically significant differences between profiles (p < .05) with respect to classroom learning competence, interactive peer play, expressive and receptive vocabularyoutcomes were determined based on Tukey–Kramer post hoc comparison corrected for simultaneous statistical contrasts.

b Profile Type 1 = well adjusted, Profile Type 2 = some peer problems, Profile Type 3 = mildly socially disengaged, Profile Type 4 = mildly socially disruptive, Profile Type 5 =extremely socially disruptive, Profile Type 6 = extremely disengaged.

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The present study extends this research by providing a richerunderstanding of the nature and prevalence of distinct patterns ofadjustment problems for this vulnerable population. The studyidentified more than 25% of children (comprising Types 5 and 6)with significant patterns of problem behavior occurring acrossmultiple social and learning situations; as well, 33% of childrenexhibitedmild classroom adjustment problems (Types 3 and 4). Theseprevalence rates are consistent with those found in other studies oflow-income children (Barbarin, 2007; Feil et al., 2005; Qi & Kaiser,2003) and with national studies of kindergarten teachers whodescribe 18% of the children in their classrooms with seriousadjustment problems and 30% with milder adjustment problems(Rimm-Kaufman et al., 2000).

The most prevalent type (well adjusted) was comprised of childrendemonstrating low levels of classroom behavioral and situationalproblems. Children comprising this resilient type were significantlyolder and demonstrated the highest peer social and classroomlearning outcomes. A second group of children comprising Type 2(some peer problems) also were characterized by low behavioral andsituational problems and demonstrated high interactive play andclassroom learning competencies; however children in this type weredistinguished by higher problems in peer situations and some degreeof disruptive peer play (although within the average range). Twoprofile types were characterized by socially disruptive behavior acrosspeer interactions and structured learning situations (Types 3 and 5).Two types (Types 3 and 6) were characterized by socially andacademically disengaged behavior (withdrawn behavior and pro-blems in structured learning and teacher interactions). Types 5 and 6were distinguished by extremely high levels of behavioral andsituational problems and an overrepresentation of younger childrenand children identified to receive services for special needs. Childrenin Type 5 (extremely socially disruptive) demonstrated notably thehighest levels of disruptive peer play at the end of the year and lowerclassroom learning competence compared to children in moreadjusted types. The findings with regard to patterns of sociallydisruptive behavior are supported by previous variable-centeredresearch in Head Start indicating that aggressive and oppositionalbehavior problems, as well as problems in peer interactions predictsocially disruptive play (Bulotsky-Shearer et al., 2008; Fantuzzo,Bulotsky, et al., 2003; Fantuzzo, McWayne, et al., 2003). Other studiesof preschool children in general link the co-occurrence of overactiveproblems (e.g., attention-deficit/hyperactivity and conduct disorder)with greater risk for social maladjustment (Campbell, 2002).

Children in Type 6 (extremely socially disengaged) demonstratedthe lowest interactive peer play, cognitive, social engagement andcoordinatedmovement skills, and the highest disconnected peer play.Of note, the average level of disconnected play in this group ofchildren was 2 SD's above those in the adjusted group. One mayhypothesize that this subgroup of children with markedly lowlearning and social outcomes were the most at-risk subgroup interms of not acquiring fundamental learning and social skills toprepare them for kindergarten. This is concerning given research inHead Start indicating that children with underactive needs are sys-tematically under identified by early childhood programs (Fantuzzo,Bulotsky, et al., 2003; Fantuzzo, McWayne, et al., 2003). This studyprovides further evidence that we need to attend to the needs of thisvulnerable subgroup of children who are disengaged from formativeclassroom social and learning experiences. In addition, since thisprofile typewas overrepresented by younger children it could reflect adevelopmental phenomenon whereby at least early in the Head Startyear, younger children within the classroom are more likely todemonstrate underactive behavior problems and difficulties ‘gettingengaged’within structured learning situations. Younger children newto the classroom environment may need additional adjustment timeto learn and practice the behavioral skills necessary to initiateengagement early in the year. Future studies can identify distinctions

between those children who stay in or move out of this profile typeand into a more adjusted type across the school year. It would becritical to follow this group over time as they transition intoelementary school, to examine their social adjustment and academicachievement trajectories.

Study findings extend previous research in Head Start indicatingthat underactive problems such as withdrawn or socially reticentbehavior within the classroom differentially predict poor learningoutcomes and disconnection from peers (Fantuzzo, Bulotsky, et al.,2003; Fantuzzo, McWayne, et al., 2003; Lutz et al., 2002) and thatproblems in structured learning and teacher interactions uniquelypredicted lower classroom learning outcomes (Bulotsky-Sheareret al., 2008). While there is less research evidence for the predictiverelations between underactive problems and academic outcomes,findings from a recent longitudinal study of the NICHD cohortsuggests that while both initial levels of early externalizing andinternalizing behavior problems predicted lower academic outcomesin first grade, only increases in internalizing behaviors from preschoolover time predicted lower first grade outcomes (Bub, McCartney, &Willet, 2007).

Limitations and directions for future research

The present study is qualified by the nature of the sample and bythe measures used. In this study, an empirically-based typology ofbehavior problems was identified for a predominantly African-American, English-speaking urban Head Start population in theNortheast. Future research is needed to investigate the generalizabil-ity of these profile types to other ethnic and linguistic groups ofchildren across other geographic regions. As well, while the presentstudy identified psychometrically sound profile types that replicatedadequately within the sample, future studies can extend the externalvalidity of present findings by cross-validating the typologies withindependent samples of Head Start children. Independent clusteranalyses can be conducted to determinewhether the present typologyreplicates with a larger cohort of Head Start children. Multivariatediscriminant function analyses also can be conducted to determinethe accuracy (sensitivity and specificity) of the identified profile typesto classify children at a population-based level (McDermott, 1982;Tatsuoka, 1970).

In this study, a behavioral typology was derived based on teacherobservations of children's behavior across and within the demands ofroutine, preschool situations. A strength of this study was that itspecifically targeted teachers as the most appropriate source foraccurate observations of children's behavior within the classroomcontext (McDermott, 1986, 1993) using an assessment specificallydeveloped for use with low-income, diverse preschool populations(Rogers, 1998; USDHHS, 2002). However, to substantiate findingsfuture studies can incorporate assessments of children's behaviorfrom additional sources (e.g. parents, teacher assistants, or indepen-dent raters) and across different contexts (e.g. home) (AmericanPsychological Association, 1999; Lidz, 2003; Nuttal, Romero, &Kalesnik, 1999). It would also be important for future studies toincorporate other indicators of school readiness as recommended bythe National Education Goals Panel (NEGP, 1999). Contrary to ourhypotheses we did not find any significant associations betweenderived profile types and assessments of expressive and receptivevocabulary. There was a nonsignificant trend in mean vocabularyscores across the groups in the expected direction (e.g., Profile Type 1“well adjusted” had higher means on both vocabulary measures whileProfile Types 5 and 6 “extremely socially disruptive” and “extremelydisengaged” had the lowestmeans on both vocabularymeasures). It isquite possible that given that the vocabulary measures wereadministered to a smaller subsample (n's 142 and 118, for receptiveand expressive vocabulary respectively) power to detect sufficientdifferences might have been reduced across the six groups. Future

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studies can provide additional external validation for profile types byemploying additional direct assessments of vocabulary with largersamples, as well as approaches toward learning, and measures ofcognition such as emergent numeracy or literacy skills (NEGP, 1999).

The present study sought to identify an empirically-basedtypology of classroom emotional and behavioral adjustment early inthe year predictive of social and academic outcomes at the end ofpreschool. DiStefano, Kamphaus, Horne, and Winsor (2003) suggestthat if a typological classification is to be useful for research andpractice purposes, that it must reliably differentiate children byetiology (Cantwell, 1996), risk or adjustment status (Kagan, 1997), ordevelopmental pathway or course (Richters, 1997). Surprisingly, wefound that special needs classification did not neatly map onto theidentified profile types. We would have liked to see that childrenparticularly those within the extremely disengaged or disruptiveprofile types, would be differentiated by special needs status. The onlyprofile type differentiated by special needs classification was themildly socially disengaged group. One hypothesis is that childrenwithin the mildly disengaged group demonstrated greater develop-mental delay and thus were statistically more likely to be classifiedwith special needs. Unfortunately, one limitation of using specialneeds classification as a variable for validating the profile types is thatwithin early childhood educational programs special needs status isnot a consistently reliable indicator. Recent research within urbanHead Start programs suggests that programmatic biases exist in thedesignation of special needs status (Fantuzzo et al., 1999). Thesebiases stem from several barriers to equitable and timely identifica-tion, particularly for children demonstrating mental health orbehavioral special needs. Within early childhood programs, whilethere are increasing amounts of children exhibiting behavioral needs,many teachers lack training and support; and early interventionassessment and intervention services are limited or take too long forfamilies to access (Cooper et al., 2008; Fantuzzo et al., 1999). Indeed,studies in Head Start indicate that children with externalizingbehavior problems are overidentified and those exhibiting with-drawn/internalizing problems are underidentified (Fantuzzo,Bulotsky, et al., 2003; Fantuzzo, McWayne, et al., 2003). Futurestudies can extend the present study by examining the predictivevalidity of the profile types longitudinally across the transition intoelementary school. Associations with indicators of social adjustmentand academic achievement in elementary school can be examined todetermine which profile types differentially predict school adjust-ment and performance, informing our understanding of the influenceof early patterns of behavioral adjustment on future social andacademic trajectories.

In addition, due to the complex nature of the multistagehierarchical megaclustering technique, we were unable to accountstatistically for the potential influence of children nestedwithin the 46Head Start classrooms. This is an empirical question beyond the scopeof the present study. Future studies can however examine the relation-ship between the ASPI typologies and classroom-level variables, suchas classroom quality and teacher characteristics on children'sclassroom behavioral functioning. A growing body of research in factsuggests that classroom emotional and instructional support andpositive teacher-child interactions influence children's ability tonegotiate the challenges of classroom learning and social situations(Howes et al., 2008; Mashburn et al., 2008; Rimm-Kaufman, LaParo,Downer & Pianta, 2005).

Further, it should be noted that the purpose of our study was toempirically identify a behavioral problem typology for low-incomechildren. We hope that the ASPI typology will be used as a startingplace to facilitate equitable and timely early identification of theseproblem behaviors within early educational systems serving low-income children. Nonetheless, future studies can extend the utility ofthe present findings using measures that tap into adaptive orprosocial behavior. This would provide a picture of strengths that

children exhibit within the classroom rather than problem behavior,and could provide skills with which to build upon in a strength-basedapproach (McWayne & Cheung, 2009).

Finally, research indicates that classroom behavioral problemsoften emerge as a function of repeated exposure to multiplecontextual risk factors associated with poverty such as maternaldepression, unemployment, domestic and neighborhood violence(Campbell, 2002; Garbarino, 1995; Rutter, 2009). Consistent with awhole child approach, future studies can incorporate multidimen-sional measures of family, community, and neighborhood risk tofurther understand the etiology of extremely problematic patterns ofclassroom behavioral adjustment.

Implications for policy and practice

There is significant national concern for the social–emotionaldevelopment of young children, particularly those living in urbanpoverty at greatest risk for social and academic difficulties. The presentstudy is responsive to national priorities that call for the expansion ofavailable developmentally- and contextually-appropriate assessmenttools to inform interventions within naturalistic contexts (McLearn,Knitzer, & Carter, 2007; USDHHS, 2001). The current study confirmsthat early in the school year, Head Start children are displaying a highprevalence of multiple co-occurring behavioral and situationalproblems—and makes visible children who otherwise might beinvisible within early childhood programs. The study also identifiedpatterns of behavioral adjustment differentially associated with socialand learning outcomes. These provide a picture of mutable behaviorsobserved within the context of routine social and learning demands;this information can be applieddirectly to inform targeted, intentional,and differentiated classroom-based intervention within early child-hood programs.

There are several direct practical applications to early childhoodprograms currently serving low-income children. In many under-resourced programs and communities, referrals for psychologicalevaluations through preschool early intervention may take manymonths and access to individual psychological service providers whowork with young children is limited (Cooper et al., 2008). Manyprograms also lack the internal resources to support teachers to provideintervention within the classroom that could prevent children'sexpulsion from the program or placement in a self-contained specialeducation classroom (Gilliam, 2005).

The profile types identified in the present study can provideteachers and professional staff within early childhood programs withtools they need to identify and to address children's behavioral needsdirectly within the classroom until, or if needed, other services can beaccessed. With the support of program staff and parents, earlychildhood educators can use these typologies as a guide to identifychildren within their classroom displaying similar types of behaviorpatterns and strategically tailor classroom-based interventions tomeet children's unique social and educational needs within specificclassroom social or learning contexts. Certain subgroups of childrenfor example those in the extremely socially and academically disen-gaged type, may need intensive assistance by the teacher to support“getting involved in” classroom activities; they may need morescaffolded instruction to increase opportunities for success, and thusfoster and build feelings of competence within more structuredlearning and peer-mediated learning activities (like games and play).On the other hand, children demonstrating high levels of overactiveand socially disruptive behavior within the classroommay need moresupport and specific strategies to regulate or manage their emotions,activity or attention level, and promote frustration toleranceparticularly during peer interactions and other peer-mediatedlearning activities (Fantuzzo, Gadsden, McDermott, & Frye, 2003).Children with more extreme patterns of behavioral and situationalneeds, may need more intensive emotional and instructional support

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while those demonstrating less extreme patterns, may need lessintensive support.

Special needs coordinators, educational coordinators, parents, andmental health professionals can support teachers to address specifictypes of behavior problems occurring within key classroom contexts.At a programmatic level, the empirically derived ASPI profile types canbe used to identify the most prevalent pattern of behavioral needsoverall as well as across individual classrooms. Classroom manage-ment, intervention strategies, and integrative curricula can begenerated and shared programmatically to meet these needs, andprofessional development efforts targeted to classrooms and teachersin greatest need of support.

Acknowledgements

This study was supported by a Head Start Graduate StudentResearch Scholars grant to the first author. Special thanks go to ourcollaborators at Prekindergarten Head Start in the School District ofPhiladelphia: Director Jennifer Plumer Davis, Assistant Director DavidSilbermann, Special Needs Coordinator Samuel Mosca, Dr. StephanieChilds, Education Coordinators, and Head Start teachers and teacherassistants.

Appendix A

ASPI directions and sample classroom situations with behavioraldescriptions

Directions: After each question, there are several descriptions ofbehaviors children may display. Fill in the circle beside anydescription that fits the child's behavior over the past month. Foreach question, mark as many descriptions as apply to the child. If nodescriptions apply, then do not fill in any circles for that question.

A.1. Sample 1: How does this child cope with new learning tasks?

• Has a happy-go-lucky attitude to every problem• Charges in without taking time to think or follow instructions• Approaches new tasks with caution, but tries• Won't even attempt it if he/she senses a difficulty• Likes the challenge of something difficult• Cannot work up the energy to face anything new.

A.2. Sample 2: How is this child at free play/individual choice?

• Engages in appropriate activities• Rather loud but not disruptive• Is too timid to join in• Disturbs others' fun• Wants to dominate and have his/her own way• Starts fights and rough play• Needs teacher assistance to get involved• Usually plays by him/herself• Moves quickly from one activity to another.

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