Latent profile analysis of sixth graders based on teacher ratings: Association with school dropout.

School Psychology Quarterly 2015, Vol. 30, No. 4, 577–592

© 2014 American Psychological Association 1045-3830/15/$12.00 http://dx.doi.org/10.1037/spq0000107

Latent Profile Analysis of Sixth Graders Based on Teacher Ratings: Association With School Dropout Pamela Orpinas, Katherine Raczynski, Jaclyn Wetherington Peters, and Laura Colman

Deborah Bandalos James Madison University

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Georgia The goal of this study was to identify meaningful groups of sixth graders with common characteristics based on teacher ratings of assets and maladaptive behaviors, describe dropout rates for each group, and examine the validity of these groups using students’ self-reports. The sample consisted of racially diverse students (n ⫽ 675) attending sixth grade in public schools in Northeast Georgia. The majority of the sample was randomly selected; a smaller group was identified by teachers as high risk for aggression. Based on teacher ratings of externalizing behaviors, internalizing problems, academic skills, leadership, and social assets, latent profile analysis yielded 7 classes that can be displayed along a continuum: Well-Adapted, Average, Average-Social Skills Deficit, Internalizing, Externalizing, Disruptive Behavior with School Problems, and Severe Problems. Dropout rate was lowest for the Well-adapted class (4%) and highest for the Severe Problems class (58%). However, students in the Average-Social Skills Deficit class did not follow the continuum, with a large proportion of students who abandoned high school (29%). The proportion of students identified by teachers as high in aggression consistently increased across the continuum from none in the Well-Adapted class to 84% in the Severe Problems class. Students’ self-reports were generally consistent with the latent profile classes. Students in the Well-Adapted class reported low aggression, drug use, and delinquency, and high life satisfaction; self-reports went in the opposite direction for the Disruptive Behaviors with School Problems class. Results highlight the importance of early interventions to improve academic performance, reduce externalizing behaviors, and enhance social assets. Keywords: dropout, externalizing behaviors, latent profile analysis, social assets, teacher rating

Given the countless hours children spend in school, educators are often the first to identify

This article was published Online First December 22, 2014. Pamela Orpinas, Department of Health Promotion and Behavior, College of Public Health, University of Georgia; Katherine Raczynski, College of Education, Department of Educational Psychology, University of Georgia; Jaclyn Wetherington Peters and Laura Colman, Department of Health Promotion and Behavior, College of Public Health, University of Georgia; Deborah Bandalos, Department of Graduate Psychology, James Madison University. This study was funded by the National Center for Injury Prevention and Control, Centers for Disease Control and Prevention (CDC) Cooperative Agreement U81/CCU417778, and research grants R01 CE001397 and R49 CE000562. The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the funder. Correspondence concerning this article should be addressed to Pamela Orpinas, Department of Health Promotion and Behavior, College of Public Health, University of Georgia, Athens, GA 30602. E-mail: [email protected]

students’ behavioral, academic, and emotional problems. Many of these problems have consequences that last a lifetime and may be difficult to overcome, such as school dropout (O’Connell, Boat, & Warner, 2009). To confront the conditions that lead to such problems, prevention efforts must start early. Most importantly, a nuanced understanding of groups of students who have similar assets, behavioral and emotional problems, and academic difficulties can inform targeted prevention programs. The goal of this study is to identify meaningful groups of sixth graders with common characteristics based on teacher ratings of assets and maladaptive behaviors, describe dropout rates for each group, and cross-validate the results using students’ self–reports. In the following sections, we describe the risk and protective factors associated with high school dropout, discuss the dimensional classification of youth using the Behavior Assessment System for Chil-

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ORPINAS, RACZYNSKI, PETERS, COLMAN, AND BANDALOS

dren – Teacher Rating Scales for Adolescents (BASC), and examine the association between teacher ratings and student self-reports.


High School Dropout In the United States, an unacceptably high number of students drop out of school (Aud et al., 2013). Dropping out of school is associated with negative outcomes that are damaging to individuals and society at large, including lower income over the lifetime, difficulty finding steady employment, reduced opportunities for advancement, worse health outcomes, lower life expectancy, higher rates of incarceration, and more demand on social services (Freudenberg & Ruglis, 2007; Hummer & Hernandez, 2013; Rumberger, 2011). High school dropout, like many adverse outcomes, is a complex phenomenon. No single pathway leads to dropping out of school. Rather, adolescents who drop out have different family characteristics, school experiences, and behavioral and emotional problems that contribute to noncompletion (Alexander, Entwisle, & Kabbani, 2001; Barry & Reschly, 2012; Rumberger, 2011). Dropout is influenced by multiple levels of the ecological model (Bronfenbrenner, 1979). Further, dropout is associated with family and neighborhood poverty, thus it could be seen as an indicator of societal inequities (De Witte, Cabus, Thyssen, Groot, & van den Brink, 2013). At the household level, family turmoil and authoritarian and neglectful parenting are risk factors for abandoning school (Blondal & Adalbjarnardottir, 2009; Ensminger & Slusarcick, 1992; Lessard et al., 2008). Educators have little control of the child’s neighborhood or family situation, but they have some influence over the child’s behavior at school. School psychologists, counselors, teachers, and other school professionals can observe students’ behavioral, emotional, and academic assets and liabilities and develop a plan to solve possible problems. Frequent goals of prevention programs are to decrease behavioral problems and increase academic achievement (Carter & Horner, 2009). Teacher behavioral evaluations using national standardized rating scales, such as the Behavior Assessment System for Children – Teacher Rating Scales for adolescents (BASC) (Reynolds & Kamphaus, 1992), allow psychologists to compare results to national

norms and to evaluate complex patterns of areas of risk and assets. Thus, this multidimensional measure can be a tool that school personnel can use to identify students who need additional help or specific interventions. Predictors of High School Dropout Externalizing Problems Numerous studies point to externalizing behavioral problems, such as fighting with peers or engaging in delinquent behaviors, as an important risk factor for school dropout (Fortin, Marcotte, Potvin, Royer, & Joly, 2006; Newcomb et al., 2002). Externalizing behavior is frequently seen as part of a problem behavior syndrome (Jessor et al., 2003), which may include fighting with peers, hurting others emotionally, using alcohol and drugs, participating in a peer subculture of partying, skipping school, and having precocious sexual intercourse, which may result in an unwanted pregnancy. None of these behaviors support academic achievement. School personnel need appropriate tools to identify students with externalizing problems; the BASC is one such well-validated tool. The BASC assesses three types of externalizing problems (Reynolds & Kamphaus, 1992). The aggression scale measures hurting others emotionally or physically. The hyperactivity scale assesses two components of Attention Deficit Hyperactivity Disorder (ADHD): being overly active and acting without thinking. The conduct problems scale measures antisocial behaviors, delinquency, and socially deviant behaviors, such as cheating, being truant, lying, running away from home, and using alcohol and drugs. Common characteristics of these three scales are the disruptive nature of the behaviors, being unresponsive to adult direction, and having problems with peers. Internalizing Problems In addition to externalizing behaviors, researchers have consistently associated internalizing problems—such as anxiety, depression, and neuroticism—with increased risk of dropout (Fortin et al., 2006; Janosz, LeBlanc, Boulerice, & Tremblay, 1997; Marcotte, Fortin, Royer, Potvin, & Leclerc, 2001). The BASC measures three types of internalizing problems


LATENT PROFILE ANALYSIS BASED ON TEACHER RATINGS

(Reynolds & Kamphaus, 1992). The anxiety scale measures excessive worry, fears, and selfdeprecation. The depression scale measures dysphoric mood and withdrawal from others. The somatization scale measures frequent verbalization of somatic complaints. Although somatization has not been directly linked to dropout, extensive research supports the link between psychosomatic problems and peer victimization (Gini & Pozzoli, 2013; Nixon, Linkie, Coleman, & Fitch, 2011), and victimization is associated with depression and poor mental health (Fekkes, Pijpers, Fredriks, Vogels, & Verloove-Vanhorick, 2006; Forero, McLellan, Rissel, & Bauman, 1999). Thus, somatization can be a symptom of school or family problems that may result in depression, anxiety, and school avoidance (Dube & Orpinas, 2009). School Problems A third group of problems associated with abandoning school are academic problems, such as low academic achievement, learning problems, and generally poor academic competence. Conversely, academic achievement and educational aspirations protect students from dropping out (Kasen, Cohen, & Brook, 1998; Newcomb et al., 2002). Some scholars have indicated that grade retention is a better predictor of abandoning school (Jimerson, Anderson, & Whipple, 2002). However, grade retention often reflects an accumulation of learning and behavioral problems. The BASC has two measures of school problems (Reynolds & Kamphaus, 1992). The attention problems scale measures inability to concentrate on tasks. The learning problems scale assesses problems in multiple domains such as reading, writing, math, and more generally difficulty in testing. Adaptive Skills Not surprisingly, adolescents who have academic, social, and personal assets are less likely to abandon school. The BASC includes three measures of adaptive skills (Reynolds & Kamphaus, 1992). The leadership scale measures competencies for good school adaptation, such as joining extracurricular activities, being creative, making decisions easily, and being good at getting people to work together. The perception of being a leader at school and being en-

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gaged in school has been associated with decreased risk of dropping out (Archambault, Janosz, Fallu, & Pagani, 2009; Janosz et al., 1997). The social skills scale measures behaviors such as showing interest in others, being polite, admitting mistakes, volunteering, and encouraging others to do their best. The study skills scale measures achievement motivation and organizational skills, such as using the school library, taking notes, working for extra credit, and being organized. Research supports that having close friends, being connected to school, and valuing school protects against aggression, depression, and school dropout (Orpinas & Horne, 2006, 2014; Resnick, Ireland, & Borowsky, 2004). Combined Impact of Risk and Protective Factors Researchers have examined different configurations of risks and assets to explain dropout. Cairns, Cairns, and Neckerman (1989) identified subgroups based on behavioral, academic, and demographic variables. Three clusters had the highest dropout rates: (a) high aggression and low academic achievement, (b) high aggression and average academic achievement, and (c) average aggression and low academic achievement. Janosz, Le Blanc, Boulerice, and Tremblay (2000) examined grades, behavioral problems, and commitment to school to create four clusters of dropouts: disengaged, low achievers, quiet, and maladjusted. The quiet group (good behavior, regular commitment, but low grades) and the maladjusted group (bad behavior, low commitment, and low grades) accounted for the large majority of dropouts. Fortin and colleagues (2006) using personal, family, and school characteristics also identified four clusters of seventh graders at-risk of dropping out: antisocial covert behavior (i.e., lying, petty thefts, chaotic family life, depression); uninterested in school (i.e., no behavioral problems, bored in school); depressive (i.e., high depression, regular grades, no behavioral problems); and school and social adjustment problems (i.e., low grades, multiple behavioral problems). This latter group is similar to the maladjusted group identified by Janosz and colleagues. These typology analyses underscore the interplay among risk factors and


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the utility of identifying disparate groups using multivariate methods. High school dropout is frequently the culmination of a process that started much earlier in the student’s academic career. Although some researchers have examined risk factors as early as first grade (Alexander, Entwisle, & Horsey, 1997), middle school is a fundamental developmental stage for identifying students at risk for dropping out. Most middle schoolers in the United States start sixth grade in a new, larger school. They are going through the turbulent years of early adolescence, have the highest levels of peer aggression and bullying, and may start showing signs of disengagement (Nansel et al., 2001; Orpinas & Horne, 2006). An examination of a large sample of sixth graders showed that poor attendance, misbehaviors, and lower academic achievement predicted 60% of high school dropout (Balfanz, Herzog, & Mac Iver, 2007). In a small sample followed for 12 years, Bowers (2010) identified early middle school as a pivotal time for prediction. Progress in reducing drop out has been challenging, and a more nuanced understanding of characteristics of behavioral problems and assets is needed. Students with different levels of risk and protective factors may need different preventive interventions. For example, students who lack study skills may require a different type of support than students who lack study skills and display behavior problems. The identification of students with similar profiles in sixth grade could inform efforts to provide targeted interventions well before the decision to drop out is made. Dimensional Classification Using the BASC Classification approaches may be categorical or dimensional (Cantwell, 1996). Under a categorical system, people are classified by the absence or presence of a diagnosable disorder, such as Attention Deficit Hyperactivity Disorder. In the dimensional approach, behaviors are classified on a continuum from adaptive to maladaptive. This strategy may help identify people who do not meet the threshold of diagnosis, but exhibit problematic behavior, and may help identify groups with similar patterns of behaviors across domains (i.e., comorbidity; Cantwell, 1996). To fully understand a child’s behavior, it is necessary to examine multiple

dimensions simultaneously (Kamphaus, DiStefano, & Lease, 2003). Using person-centered approaches to data analysis, researchers can classify individuals into homogenous groups, describe the characteristics of each group, and investigate differences across groups. Latent profile analysis (LPA) is one approach to group individuals based on multiple variables simultaneously, creating different “profiles.” These profiles may vary greatly, with unique combinations of high and low scores across components of the profile. Therefore, LPA can assist researchers and educators in identifying subgroups of people with distinct patterns of risk and protective factors. We used LPA to identify groups of sixth graders with similar profiles, based on teacher ratings using the BASC. This dimensional classification can provide a nuanced understanding of the strengths and challenges of student subgroups. Based on these results, school personnel and mental health service providers could offer a prevention-oriented approach that tailors interventions to students’ levels of risk (Baker, Kamphaus, Horne, & Winsor, 2006). By describing groups of students at different levels of risk for school dropout, this study can assist school administrators in allocating resources toward dropout prevention more efficiently. Because LPA is an exploratory method, the number of latent classes is not known a priori. Research, theory, and interpretability of results guide the selection of the final class solution. Previous studies based on teacher ratings of the BASC using cluster analysis methodology have identified seven clusters of students. These results are most relevant to the current study because the researchers used essentially the same variables to create subgroups. Examining externalizing problem behaviors, internalizing difficulties, school functioning, and assets simultaneously in a national sample, researchers identified the following distinct clusters of elementary schoolchildren: well adapted, average, learning disorder, physical complaints/worry, mildly disruptive, disruptive behavior disorder, and severe psychopathology (Kamphaus, Huberty, DiStefano, & Petoskey, 1997). A sevencluster solution has been found in another sample of elementary schoolchildren and in one study of sixth graders (DiStefano, Kamphaus, Horne, & Windsor, 2003; Kim, Kamphaus, & Baker, 2006; Kim et al., 2010).



Teacher Ratings Versus Student Self-Reports One strategy to collect validity evidence of these profiles is to examine their association with theoretically relevant external measures. In this study, we compared the LPA profiles based on teacher ratings with student self-reports of relevant behaviors, that is, physical and relational aggression, drug use, delinquency, and life satisfaction. It is important to note that the association of ratings by different informants has been generally modest, particularly teacher ratings and student self-reports (Achenbach, McConaughy, & Howell, 1987; Løhre, Lydersen, Paulsen, Maehle, & Vatten, 2011; van der Ende, Verhulst, & Tiemeier, 2012). Five decades of research have highlighted these discrepancies, as measured by correlations, level of agreement, and mean difference between measures (Achenbach, 2011; De Los Reyes, 2013). This study provides a different examination of this area of research, by examining consistency between teachers and students within each profile. We did not expect teacher ratings and student self-reports to be perfectly concordant but anticipated that they would be related in predictable ways if the LPA profiles were supported. Current Study This study had three objectives. The first objective was to identify meaningful latent profiles of sixth graders based on teacher ratings of externalizing behaviors, internalizing problems, academic skills, leadership, and social assets. Based on prior research, we expected to identify six to eight groups of students. The second objective was to report the proportion of students who dropped out of school in high school by group. We hypothesized that students in groups with more behavioral and emotional problems would be more likely to drop out of school. The third objective was to investigate the validity of these teacher-reported latent profiles by examining differences in self-reported behavior at sixth grade. We expected that student-reported behavior would vary across classes in predictable ways (e.g., students in classes with high externalizing behaviors would self-report more aggressive behaviors than students in other groups.)

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Our study expands prior research in several ways. First, we identified students with similar characteristics using LPA instead of cluster analysis. Cluster analysis is a numerical (as opposed to a model-based) technique, and the variables used to estimate classes are measured at the observed, rather than the latent level (DiStefano & Kamphaus, 2006). Second, we used two samples: a random sample and a sample identified by teachers as high-risk for aggression. Third, we cross-validated groups identified based on teachers’ ratings with students’ self-reports. Lastly, because our data are part of a 7-year longitudinal study, we identified students who dropped out of high school. Method Design, Setting, and Sample This study used sixth-grade data of students participating in the Healthy Teens Longitudinal Study, as well as information about high school dropout. One of the goals of Healthy Teens is to identify predictors of high school dropout. We collected annual data from Grades 6 to 12 from a cohort of students attending schools in Northeast Georgia. The sample was originally identified as part of the Multisite Violence Prevention Project (Multisite Violence Prevention Project, 2004), and we followed the participants from Georgia into high school. In the spring of every year, students completed a self-reported survey, and teachers completed the BASC. In Grade 6 (spring of 2002), students attended one of nine middle schools located in six school districts. Schools were diverse, representing a wide range of characteristics of students (proportion of White students ranged by school from 13% to 90%, consistent with the racial distribution of participating schools), family socioeconomic status (at the school level, percent qualifying for free or reduced-price lunches ranged between 19% and 73%), and urban and rural location of the schools. Of the 1,070 students invited to participate in sixth grade, 839 (78%) accepted. Because the present study examines high school dropout, we only included those students who reconsented in high school (n ⫽ 689; 82%). We excluded from the current analysis 14 records of students who did not have a sixth-grade teacher assessment. Thus, the final sample consisted of 675


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students (47% girls; 47% White, 38% Black, 11% Latino; M ⫽ 11.8 years; age range, 11 to 14 years). In Grade 6, two samples were selected: a large random sample of sixth graders (n ⫽ 612) and a smaller sample of children who were nominated by teachers for being more aggressive than their peers, as well as influential (n ⫽ 170). Two core sixth grade teachers from each school nominated students who were most aggressive (i.e., encourages students to fight, intimidates others, gets angry easily, and gets into physical fights), and then rated them on social influence (i.e., others try to imitate, is respected). These two samples overlap: 107 students were both randomly selected and identified by teachers as high risk for aggression. Procedures The university’s Institutional Review Board approved all the research procedures. We obtained parent permission, student assent, and teacher consent for participating in the study. Student surveys and teacher ratings for the current study were collected in the spring of sixth grade in 2002. Students answered a computerassisted survey interview, reading the questions on the computer and listening via headphones. Students received a small gift for their participation (e.g., a pen). Trained research assistants proctored the data collection. For each student, one academic teacher who knew the student well rated the student’s behavior. Teachers received a small monetary incentive for completing the rating scale. Research assistants went to the neighborhoods of students who had dropped out of school to surveyed them. Measures Behavior Assessment System for ChildrenTeacher Rating Scales for adolescents (BASC). Teachers rated sixth graders using the BASC (Reynolds & Kamphaus, 1992). In this study, we used 11 scales that assess externalizing problems (aggression, hyperactivity, conduct problems), internalizing problems (anxiety, depression, somatization), school problems (attention problems, learning problems), and adaptive skills (leadership, social skills, study skills). The BASC is a nationally normed, standardized behavior rating scale, which has been normed for adolescents. Response categories range from Never (0) to Al-

most Always (3). Teachers are not instructed on a time frame for their responses. A t score, with a mean of 50 and standard deviation of 10, was calculated for each subscale based on the mean of the scale’s items and the norm-group mean and standard deviation provided in the BASC manual. Physical aggression, relational aggression, drug use, and delinquent behavior. Four scales from the Problem Behavior Frequency Scales assessed self-reported frequency of physical aggression, relational aggression, drug use, and delinquent behavior (Farrell, Kung, White, & Valois, 2000). The physical aggression subscale (7 items, ␣ ⫽ .83) measures threats and actual use of behavior that could harm others (e.g., Hit or slapped another kid). The relational aggression subscale (6 items, ␣ ⫽ .79) measures behaviors intended to damage peer relationships (e.g., Spread a false rumor about someone). The drug use scale (6 items, ␣ ⫽ .77) measures frequency of using alcohol, tobacco, and marijuana. The delinquent behavior subscale (8 items, ␣ ⫽ .71) measures students’ frequency of delinquent behaviors such as cheating, truancy, shoplifting, and damaging property. Students reported the number of times they engaged in each behavior over the 30 days before the survey. Response categories ranged between never (1) and 20 or more times (6). Scales scores were calculated by averaging the items; thus, all scale scores range from 1 to 6, with higher scores indicating more problem behavior. Life satisfaction. The Life Satisfaction Scale (6 items, ␣ ⫽ .82) (Valois, Paxton, Zullig, & Huebner, 2006) measures perceived life satisfaction across multiple domains (e.g., school, home, friends). Response categories ranged from terrible (1) to delighted (7). Scale scores were averaged; thus, all scale scores range from 1 to 7, with high scores indicating more life satisfaction. School dropout. Participants were defined as dropouts if they were not enrolled in private or public school in the spring of Grade 12 and had not obtained a high school diploma. We defined dropout based on students’ self-report (i.e., indicated they were not attending high school and had not graduated) and examination of school records. Of the total sample, 18% (n ⫽ 123) dropped out of school. We could not verify the dropout status for a small portion of the sample (3%, n ⫽ 22).



Analyses Latent profile analysis. We used LPA to identify groups with different configurations of risks and assets. The goal of this approach is to identify groups of individuals who are similar to each other but different from people in other groups (Magidson & Vermunt, 2002; Muthén & Muthén, 2007). As a person-centered approach, the emphasis of LPA is on grouping individuals with similar characteristics rather than describing relations among variables, such as would be done if correlations were examined (i.e., the variable-centered approach). LPA is conceptually akin to cluster analysis, which groups individuals based on observed responses by maximizing the differences among clusters and minimizing the differences of individuals within clusters. LPA has several advantages; particularly, it is a model-based technique that classifies individuals based on likelihoods. A probability is provided for each individual and each group. All analyses were conducted using Mplus, version 6 (Muthén & Muthén, 2007). We used MLR, a maximum likelihood estimator with robust standard errors that does not assume normal distribution of the data. The means of the latent class indicators were allowed to vary across classes and indicators. To avoid convergence problems, variances were constrained to be equal across classes and covariances among latent class indicators were set at zero. To avoid model convergence problems caused by local maxima, we specified 500 random sets of starting values with 20 optimizations. Model selection. In exploratory applications of LPA, the number of latent groups is not known a priori. Researchers compare models with increasing numbers of latent groups to find the best fit. No single criterion exists to determine the best solution. Rather, models are compared based on interpretability, theory, and statistical criteria (Marsh, Lüdtke, Trautwein, & Morin, 2009; Pastor & Gagné, 2013). The optimal solution adequately accounts for the complexity of the data using the fewest latent groups (DiStefano & Kamphaus, 2006; Samuelsen & Raczynski, 2013). Using the 11 scales from the teacher-rated BASC as latent class indicators, we fit a series of models with the number of classes ranging

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from one to nine. Based on prior research on typologies using the BASC (DiStefano et al., 2003; Kamphaus et al., 1997; Kim et al., 2006), we expected to find between six and eight meaningful subgroups. We compared solutions on the interpretability, distinctiveness, and size of the estimated latent groups and referred to a measure of classification utility (i.e., entropy); these scores range between 0 and 1, with higher scores indicating clearer delineation between classes. We also consulted recommended statistical indicators of fit. These included two indexes based on information criteria—the Bayesian Information Criterion (BIC) and the sample-size adjusted BIC (SABIC)—and two indexes based on the likelihood ratio test—the Lo-Mendell-Rubin likelihood ratio test (LMR) and the bootstrap likelihood ratio test (BLRT) (Nylund, Asparouhov, & Muthén, 2007; Pastor & Gagné, 2013). For the BIC and SABIC, lower values are preferable. For the LMR and the BLRT, significant values support the tested model over a model with one fewer classes. Researchers have found that when the indexes indicated the incorrect number of classes, they were much more likely to overestimate than to underestimate the number of classes, which was especially true for the LMR (Nylund et al., 2007). After selecting the final model, we described each class. We used posterior probabilities to classify students into their most likely group, and described the proportion of students within each class based on sex, race, dropout, and sample type (randomly selected vs. high risk for aggression). We also used the AUXILIARY function in Mplus to compare groups on sex, race, and dropout. This approach provides a chi-square test of the equality of means across classes. An advantage of this technique is that it takes into account the uncertainty in classifying an individual in a group, rather than simply examining proportions of individuals based on their most likely group. External validation. Collecting validity evidence is an important way to enhance confidence in the selected solution. A recommended strategy for demonstrating evidence of validity is to show that groups are related to other associated variables in in predictable ways (Marsh et al., 2009; Pastor & Gagné, 2013). Because groups were determined by teacher rating, we compared students’ self-ratings on


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related variables across groups: physical and relational aggression, drug use, delinquency, and life satisfaction. Problem behavior theory guided the selection of self-reported variables (Jessor & Jessor, 1997; Jessor et al., 2003). This theory posits that problem behaviors— behaviors that conventional society defines as undesirable or unhealthy—tend to covary, creating a problem behavior syndrome. Thus, aggression against peers, illicit drug use, and delinquent behaviors will be interrelated. In addition, problem behavior syndrome has been associated with low life satisfaction (Proctor, Linley, & Maltby, 2009; Valois et al., 2006). Although the questions in the student survey were different from the teacher ratings, we expected that students classified in groups with more externalizing problems would report more aggression, drug use, and delinquent behaviors. In addition, we expected that groups with more internalizing problems would report lower life satisfaction. To investigate the relation between latent classes and student self-report, we used the AUXILIARY function in Mplus. We reported the means for each class on the five selfreported scales and the results of the omnibus chi-square test.

Results Model Selection To select the best model, first we examined the fit statistics of the latent profiles and second we evaluated the interpretability of classes. We estimated latent profiles up to a 9-class solution. We stopped at nine classes because that solution included a very small class (n ⫽ 9) that had no practical significance. Table 1 presents the fit indices and entropy values for each solution. Three fit statistics—BIC, SABIC, and BLRT— supported models with more classes. In every case, the BIC and SABIC decreased as the number of classes increased, although the improvement between 7 and 8 classes and between 8 and 9 classes was very small. Similarly, a significant BLRT indicated that in each case adding a class improved fit. The results of the LMR were contradictory. The LMR indicated that the fit of the 2-class model was improved over the 1-class model at ␣ ⫽ .10. For all other models, p values were nonsignificant, indicating that fit did not improve as classes were added. With regard to classification utility, entropy values were similar, with all values ranging from .88 to .91.

Table 1 Fit Indices and Entropy With 2 to 9 Latent Profile Groups Number of classes

# of parameters

1 2 3 4 5 6 7 8 9 Interpretation

22 34 46 58 70 82 94 106 118

BIC (⌬BIC)

SABIC (⌬SABIC)

LMR p value

BLRT p value

55560 (4568) 53298 (2306) 52214 (1222) 51854 (862) 51593 (601) 51393 (401) 51210 (218) 51096 (104) 50992 (0) Lower values are better

55490 (4873) 53190 (2573) 52068 (1451) 51669 (1052) 51371 (754) 51133 (516) 50912 (296) 50759 (142) 50617 (0) Lower values are better

— .07 N.S. N.S. N.S. N.S. N.S. N.S. N.S. Significant values support the tested model over a model with one fewer class

— ⬎.001 ⬎.001 ⬎.001 ⬎.001 ⬎.001 ⬎.001 ⬎.001 ⬎.001 Significant values support the tested model over a model with one fewer class

Entropy — 0.886 0.899 0.892 0.908 0.887 0.893 0.885 0.893 Higher values are typically better

Note. BIC ⫽ Bayesian Information Criterion; SABIC ⫽ Sample size-adjusted BIC; LMR ⫽ Lo-Mendell-Rubin likelihood ratio test; BLRT ⫽ Bootstrap likelihood ratio test. ⌬BIC ⫽ Difference between the lowest Bayesian Information Criterion value estimated (i.e., the 9 class model) and the BIC estimated for the profile group. ⌬SABIC ⫽ Difference between the lowest sample-size adjusted BIC value estimated (i.e., the 9 class model) and SABIC estimated for the profile group.



Following the statistical analyses, we examined the 7 and 8 class solutions for the interpretability and distinctiveness of the subgroups. We identified the 7-class model as the most informative for understanding child development (see Table 2). This model had classes with clearly distinguishable characteristics, and it was most consistent with previous research (Kamphaus et al., 1997). The 8-class solution was more difficult to interpret. The biggest difference was that it included two “average” groups (one with average scores around 50 and the other with average scores around 45), but with no scores at least one standard deviation above or below the mean. We also considered the fact that the likelihood ratio based on indexes tend to overestimate the number of classes (Nylund et al., 2007).

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Class Descriptions and Association With Dropout The Well-Adapted class (21% of the sample) consisted of students whose BASC scores reflected desirable and well-adjusted behaviors across all domains. Students’ scores on the externalizing and internalizing scales were consistently better than the normative average. The scores on the school problems scales were 1 standard deviation (SD) below the normative average and those on the adaptive skills subscales were nearly 1 SD above the normative average. This group had a higher proportion of girls (68%) and White students (67%); none were from the high risk for aggression sample. Only 3.6% dropped out of high school.

Table 2 t Scores for the Seven-Class Solution of the Teacher Rating Scales for Adolescents From the BASC and Characteristics of the Sample (n ⫽ 675)

Scale Externalizing Aggression Hyperactivity Conduct Internalizing Anxiety Depression Somatization School problems Attention Learning problems Adaptive skills Leadership Social skills Study skills % of Class Boys Black White Dropout High risk aggressiona Randoma

Well-adapted Average (n ⫽ 145) (n ⫽ 170) Mean Mean

Average, social Disruptive Severe skills deficit Internalizing Externalizing behavior ⫹ SP problems (n ⫽ 105) (n ⫽ 57) (n ⫽ 117) (n ⫽ 62) (n ⫽ 19) Mean Mean Mean Mean Mean

44.96 42.22 44.72

47.79 47.20 46.58

47.96 47.35 47.90

58.32 56.25 52.26

63.30 61.06 53.40

71.66 67.46 64.45

82.76 72.93 79.67

41.06 44.26 47.20

44.31 45.20 46.77

46.06 45.98 47.07

61.54 59.03 68.88

46.41 47.88 48.40

53.39 52.43 57.14

63.20 76.00 63.69

39.61

47.19

55.22

56.87

57.74

68.58

68.91

40.67

46.36

55.84

55.70

53.45

66.29

64.11

58.19 59.10 62.26

47.01 50.23 50.33

36.65 41.12 39.04

43.98 46.39 45.93

44.01 43.42 42.95

38.67 40.48 36.58

41.21 39.64 37.24

32.4% 20.0% 66.9% 3.6%

48.8% 31.2% 48.8% 9.0%

60.0% 32.4% 48.6% 29.3%

43.9% 38.6% 50.9% 19.2%

69.2% 56.4% 29.9% 24.1%

77.4% 62.9% 27.4% 44.1%

57.9% 52.6% 26.3% 57.9%

0.0% 100.0%

11.2% 94.1%

19.0% 92.4%

35.1% 89.5%

43.6% 87.2%

71.0% 75.8%

84.2% 52.6%

Note. SP ⫽ School problems. Bold-face type indicate scores at least one SD above (60 or higher) or below (40 or lower) the normative average. a High risk for aggression includes all students selected by teachers as at high risk for aggression, including those who were also randomly selected. Random includes all students randomly selected, including those who were selected by teachers as high risk for aggression.


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The Average class (25%) had scores close to the national norm on all scales. The group was evenly distributed by sex; 9% dropped out of school. The Average, Social Skills Deficits class (16%) had average scores on externalizing, internalizing, and school problems scales. However, mean scores on the adaptive skills scales were approximately 1 SD below the normative average. This class had more boys (60%) than girls, and more than a quarter (29%) dropped out of school. The Internalizing class (8%) was characterized by scores 1 SD above (i.e., worse than) the normative average on all Internalizing scales. A third of this group (35%) had been nominated by teachers as high risk for aggression, and 19% dropped out of school. The Externalizing class (17%) had mean scores 1 SD above the normative mean on Aggression and Hyperactivity. This group had a high proportion of boys (69%) and Black students (56%). Almost half of this group (44%) was nominated for the high risk for aggression sample, and one quarter (24%) dropped out of high school. The Disruptive Behavior with School Problems class (9%) had mean scores between 1 and 2 SD above the normative average on externalizing and school problem scales. Additionally, their mean scores on the adaptive skills were approximately 1 SD below the normative mean. This group had the highest proportion of boys (77%), and a large proportion of Black students (63%). More than two thirds of this group (71%) was nominated for the high risk for aggression sample, and 44% dropped out of high school. The Severe Problems class (3%) had mean scores 1 to 3 SD worse than the normative mean on almost all scales. This group had slightly more boys (56%). Almost all students in this group were nominated for the high risk for aggression sample (84%), and more than half (58%) dropped out of high school. In the final step of the analysis, we used the AUXILIARY function in Mplus to compare groups on sex, race, and dropout status. The overall tests were significant for sex, ␹2(6) ⫽ 60.93, p ⬍ .001, and White versus non-White, ␹2(6) ⫽ 54.84, p ⬍ .001, indicating that the latent classes differed in the proportions of boys and girls and of Black and White students in

each. In addition, the latent classes differed in the proportions of students who dropped out, ␹2(6) ⫽ 86.91, p ⬍ .001. Comparison of Teacher-Rated LPA and Student Self-Reports Table 3 displays the mean scores of sixth graders’ self-reports on physical aggression, relational aggression, drug use, delinquency, and life satisfaction for each of the seven classes. The omnibus chi-square test indicated the seven classes differed significantly on each selfreported variable; however, not all pair comparisons showed statistically significant differences. Overall, the lowest mean scores in aggression, drug use, and delinquency, as well as the highest mean scores for life satisfaction, were found in the Well-Adapted class, followed by the Average class. The Well-Adapted class scored significantly better than all other classes. On the other end, the highest mean scores in aggression, drug use, and delinquency, as well as the lowest mean score in life satisfaction, were found in the Disruptive Behavior with School Problems and Severe Problems classes; these two classes scored significantly worse than most other classes. Discussion The goal of this study was to examine whether teacher ratings can be used to identify meaningful groups or latent classes of students. The present study expands scientific understanding of child development and supports previous research findings. We used LPA to identify the classes and applied several strategies to further understand and characterize them: (a) examination of the distribution of the classes by type of sample: high risk for aggression and randomly selected; (b) identification of the proportion of each class that dropped out in high school; and (c) description of the association of these teacher-defined latent classes with students’ self-reports. We identified seven distinct classes of students that could be displayed along a continuum based on assets and problems. On one end, the Well-Adapted students were those with low externalizing, internalizing and school problems, paired with high leadership, social skills, and study skills. On the other end, two classes—

33.59 (p ⬍ .001) 4.46 4.71 5.49 5.42 5.25 5.93 Life satisfaction

5.65

1.49 1.04 1.10 Relational aggression Drug use Delinquency

1.63 1.08 1.19

1.48 1.17 1.25

1.58 1.10 1.17

1.75 1.15 1.30

1.82 1.33 1.45

1.97 1.31 1.32

18.38 (p ⫽ .005) 21.44 (p ⫽ .002) 32.70 (p ⬍ .001)

Class comparisons

1 ⬍ 2–7; 2, 3 ⬍ 5–6; 4⬍5 1, 3 ⬍ 5–7 1 ⬍ 3, 5–6; 2, 4 ⬍ 6; 1 ⬍ 2–3, 5–7; 2, 4 ⬍ 5–6; 3 ⬍ 6; 1 ⬎ 2–7; 2 ⬎ 3, 6; 4, 5⬎6 40.77 (p ⬍ .001) 1.35 Physical aggression

1.54

1.55

1.61

1.79

1.95

2.06

Overall ␹2 Severe problems (7) Disruptive behavior ⫹ SP (6) Externalizing (5) Internalizing (4) Average, social skills deficit (3) Average (2) Well-adapted (1) Self-reported measures

Table 3 Mean Scores on Student Self-Reported Measures by Class (n ⫽ 675)



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Disruptive Behavior with School Problems and Severe Problems—were rated very high in externalizing and school problems, as well as low in adaptive skills. In the middle of this continuum, we identified the Average class, with most scores close to the normative average; the Average, Social Skills Deficit characterized by average scores in all problem scales, but low scores in the adaptive skills; and the Internalizing and Externalizing classes. Other studies have also found seven clusters of students (Kamphaus et al., 1997; Kim et al., 2010). Kamphaus and colleagues, in a national sample of elementary schoolchildren, identified seven groups; four of them were very similar to those in this study: Well-Adapted, Average, Disruptive Behavior, and Severe Psychopathology. Most importantly, students in these classes have different needs that may require distinct prevention approaches. We used three indicators to further characterize the latent classes. First, we examined the proportion of students identified by teachers as high risk for aggression. Not surprisingly, none of the students in the Well-Adapted class were identified, and the proportion of students identified by teachers as high risk for aggression increased across the continuum. Second, in general, the prevalence of dropout increased as the number and intensity of problems increased. Previous studies have identified four clusters of adolescents with differing risk of dropping out (Cairns et al., 1989; Fortin et al., 2006; Janosz et al., 2000). Similar to these studies, our results showed that different compositions of problem behaviors, learning difficulties, internalizing challenges, and low assets were related to dropping out. However, the seven classes resulting from this study provides a more nuanced classification of problem behaviors than the previous four-group classification. As in prior research, students who exhibited aggressive behaviors and academic problems were particularly at risk for abandoning schools. A heartening finding was that not all students in classes with the most challenging behavioral, emotional, and academic problems dropped out of school. More research is needed to understand what differentiates dropouts from nondropouts in these classes with multiple problems.


588


An unexpected finding was the high dropout rate (29%) of students in the Average, Social Skills Deficit group, a class that did not exhibit externalizing problems. These students scored a half SD above the mean on school problems and one SD below in adaptive skills but were otherwise average in terms of internalizing, externalizing, and other problem behaviors. Low social skills may be linked to having fewer friends, increased peer victimization, and lower connectedness to school (Fox & Boulton, 2006). In addition, these students had lower scores in Life Satisfaction than the Well-Adapted, Average, Internalizing, and Externalizing groups. It is possible that the lack of social skills and leadership are symptoms of depression. Also, this class included more boys than girls, which may reflect developmental differences or a need for more gender-specific skills training. These students may potentially receive less attention in school because they do not score high on externalizing behaviors. School psychologists should provide support to those students who exhibit low leadership, poor social skills, and unsuccessful study skills. Third, students’ self-reports were generally consistent with the seven latent profile classes derived from teacher ratings. As expected, the “best” scores (that is, lowest means scores in aggression, drug use, and delinquency, and highest scores in life satisfaction) were in the Well-Adapted class. However, some students in this class did report some acts of aggression; these students may defy the popular stereotype of aggressive students as having poor social skills and academic outcomes (Sutton, Smith, & Swettenham, 1999). On the other side of the continuum, students in the Disruptive Behavior with School Problems and in the Severe Problems classes had the worst means. One unexpected finding was the Internalizing group. Teachers rates these with students very high in somatization and anxiety, but students selfreported life satisfaction was very similar to the Externalizing group. It is possible that teachers have more difficulty rating internalizing problems, as they are not as visible as aggression or academic difficulties. Teachers may need specific training to recognize mental health problems.

Study Strengths and Limitations The study has several strengths. First, its longitudinal design allowed the evaluation of high school dropout 5 to 7 years after the sixth-grade teacher evaluation. Second, to assess student risk of dropping out, we used teacher ratings based on a nationally normed measure. It is logistically easier for school administrators use teacher ratings than student surveys, as students may be less trustworthy reporters when they know that their survey responses on sensitive topics will be reviewed by school personnel. The BASC has several advantages. It is a nationally normed scale that is commonly used across the country, takes 10 to 15 minutes to complete, and does not require specialized training to complete. It includes dimensions traditionally linked to high school dropout (i.e., externalizing, internalizing, and academic problems), and also measures social and academic assets. The results highlight the importance of evaluating adaptive skills to identify subgroups of students. Third, in addition to the random sample, this study included a second sample of aggressive students. Oversampling students with aggression helped to identify the smaller Severe Problems class of students. Fourth, we cross-validated the results with students selfreport of related variables. Fifth, we used LPA, which has technical advantages over cluster analysis. Findings should be interpreted in light of some limitations. All statistical results are sample dependent, and LPA results are no exception. In particular, results become less stable as the number of latent classes increase (Nylund et al., 2007). This is one reason that we did not choose the 8-class solution. Although instability can be an issue with LPA, we note that previous studies have also identified seven groups. It is possible that the same teacher identified the student as high risk for aggression and later completed the rating. However, in early fall semester, the average score of three teachers were used to identify these students. For the spring rating, one teacher (not necessarily the same) completed the rating. Although the students were grouped in distinct classes and had similar scores, there still may be heterogeneity within these groups (Carter & Horner, 2009; McIntosh, Campbell, Carter, & Rossetto Dickey, 2009). The teacher ratings do not iden-



tify possible antecedents to the behavior, such as family conflict or neighborhood poverty, which are also fundamental to plan an intervention (De Witte et al., 2013). Teachers’ evaluations of internalizing problems may be less reliable than others, as many of these problems are less observable. The sample of students selected as high risk for aggression were also identified by teachers as influential among peers; it is possible that other aggressive students may have a different, possibly higher, risk for dropping out of school. The original sample was selected as part of a violence prevention program but the overall impact of the program was low and inconsistent (Simon et al., 2009). Given the small sample size (n ⫽ 19) of the Severe Problems class, results from this class should be interpreted with caution until replicated in an independent sample. Of note, in the national sample of elementary school children, Kamphaus et al. (1997) identified a cluster named ‘Severe Psychopathology’ that comprised 4% of their sample, which is similar to the present study. Implications for School Psychologists The findings have several implications for research and practice. First, our study adds to the existing literature on the importance of early warning signs associated with future high school dropout. Routine screening using a multidimensional assessment instrument may help middle school psychologist identify students in need of extra support. This study supports the implementation of behavioral Response to Intervention to identify and intervene early with students with a diverse combination of assets and problems (Gresham, 2007; Saeki et al., 2011). Based on the study’s results, screeners that include behavioral, emotional, academic, and prosocial components would be most helpful to identify children at risk of high school dropout. The study highlights the heterogeneity of paths that may lead to high school dropout. Not surprisingly, school problems and externalizing behaviors—particularly the combination of both—were associated with the highest risk of abandoning school. Theory and research on adolescent development highlight the interconnection among different dimensions that may affect academic outcomes, such as failure in one dimension (e.g., high aggression) may increase

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likelihood of failure in other dimensions (e.g., social competence) (Masten & Cicchetti, 2010). Thus, for some groups of students, programs that address a single dimension, such as a violence prevention programs, may not be enough to prevent dropout. Second, even though the findings suggest that subgroups of students may need targeted interventions, students with similar behavioral characteristics may differ in the antecedents and consequences that maintain the undesirable behaviors. For example, school psychologists should investigate whether externalizing behaviors are maintained by a desire to gain attention, maintain power, seek revenge, escape academic tasks or social interactions, obtain other rewards, or avoid feeling inadequate (Carter & Horner, 2009; McIntosh et al., 2009; Orpinas & Horne, 2006). Internalizing problems may be the result of family conflicts or peer bullying (Orpinas & Horne, 2006). This analysis will be important to identify the best intervention. Third, school psychologists should pay close attention to students who show poor study skills, social skills, and leadership skills, in the absence of other behavioral problems. More research is needed to examine whether these low assets are a sign of depression. Early interventions are needed not only to solve the immediate problem but to curtail possible longterm consequences. Finally, it is important that school personnel maintain positive expectations of students and meet their educational needs. Even from the most seemingly at-risk group, not all students dropped out. More research is needed to understand what protected students with multiple early problem behaviors from abandoning school. References Achenbach, T. M. (2011). Commentary: Definitely more than measurement error: But how should we understand and deal with informant discrepancies? Journal of Clinical Child and Adolescent Psychology, 40, 80 – 86. http://dx.doi.org/10.1080/ 15374416.2011.533416 Achenbach, T. M., McConaughy, S. H., & Howell, C. T. (1987). Child/adolescent behavioral and emotional problems: Implications of crossinformant correlations for situational specificity. Psychological Bulletin, 101, 213–232. http://dx .doi.org/10.1037/0033-2909.101.2.213


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