A Typological Approach With Two Longitudinal Samples

Copyright 2000 by the American Psychological Association, Inc. 0022-0663100/$5.00 DOI: 10.1037110022-0663.92.1.171

Journal of Educational Psychology 2000, Vol. 92, NO.1,171 -190

Predicting Different Types of School Dropouts: A Typological Approach With Two Longitudinal Samples Michel Janosz, Marc Le Blanc, Bernard Boulence, and Richard E. Tremblay University of Montreal Despite evidence of the psychosocial heterogeneity of school dropouts, empirical studies have rarely directly addressed this issue. The general goal of this research was to explore the heuristic value of a typological approach for preventing and studying school dropout. The specific objectives were to build empirically a typology of dropouts based on individual school experience, to test the typology's reliability by replicating the classification with two different longitudinal samples, and to examine the typology's predictive and discriminant validity. The results led to a 4-type solution: Quiet, Disengaged, Low-Achiever, and Maladjusted dropouts. The results support the internal and external validity of the typology and highlight important different profiles with regard to personal and social risk factors. The discussion underscores the theoretical and clinical utility of a typological approach by assisting the study of the different paths in the etiology of school dropout and the adoption of a differential prevention strategy.

Forty years ago, Tesseneer and Tesseneer (1958) noted that the study of the dropout phenomenon and its causes is difficult because "the sarne factors may influence different pupils in different ways and even affect the same pupil in different ways at different times" (p. 143). Since that time, empirical studies have shown very consistently that adolescents who leave school before graduation are more likely to present behavioral, academic, social, and attitudinal vulnerabilities and to suffer from deprived or inadequate social and school environments (Bachman, Green, & Wirtanen, 1971; Cairns, Cairns, & Neckerman, 1989; Ekstrom, Goertz, Pollack, & Rock, 1986; Elliot & Voss, 1974; Fagan & Pabon, 1990; Janosz, Le Blanc, Boulerice, & Tremblay, 1997; Rumberger, 1983, 1987, 1995; Rumberger, Ghatak, Poulos, Ritter, & Dornbush, 1990; Steinberg, Elmen, & Mounts, 1989; Tesseneer & Tesseneer, 1958; Wehlage, Rutter, Smith, Lesko, & Fernandez, 1989). One conclusion that can be drawn from the accumulated empirical knowledge is that dropouts are not al1 alike. Data from numerous Michel Janosz and Marc Le Blanc, School of Psychoeducation, University of Montreal, Montreal, Quebec, Canada; Bernard Boulerice and Richard E. Tremblay, Research Unit on Children's Psychosocial Maladjustrnent, University of Montreal, Montreal, Quebec, Canada. This,research was supported by gants from the Social Sciences Research Council of Canada, the Conseil Québécois de la Recherche Sociale, the Department of the Solicitor General Canada, and the Fonds Pour la Formation des Chercheurs et l'Aide à la Recherche du Québec. We thank them all. We also thank Michel Fournier for help with data management. A preliminary version of this article was presented to the Society for Research on Adolescence in Boston, March 1995. Correspondence concerning this article should be addressed to Michel Janosz, School of Psychoeducation, University of Montreal, PO. Box 6128, Station Centre-Ville, Montreal, Quebec, Canada H3C 357. Electronic mail may be sent to janoszma psyced.umontreal.ca.

studies suggest that students who drop out of school display a wide variety of personal and social characteristics (Cairns et al., 1989; Elliot & Voss, 1974; Fagan & Pabon, 1990; Rumberger, 1983, 1987; Wehlage et al., 1989). Different reasons are cited by dropouts to explain their disengagement from school (Rumberger, 1987; Tidwell, 1988). Moreover, the effects of school dropout on criminality vary according to the reason for dropping out (Jarjoura, 1993, 1996). Risk factors for school dropout can be found in al1 spheres of children's social development and include personal, interpersonal, and contextual factors (e.g., poverty, cornmunity, school characteristics). Dropouts come from al1 socioeconomic and cultural backgrounds, although minority students fkom low socioeconomic status (SES) families appear to be particularly at risk (Rumberger, 1987; Wehlage et al., 1989). Studies using multivariate analyses have shown that behavioral problems (rebelliousness, delinquency, drug use), school failure (low grades, grade retention), low motivation, low cognitive abilities, and poor parental practices (supervision, support, school expectations) are factors that each uniquely contribute to the prediction of school dropout (Cairns et al., 1989; Elliot & Voss, 1974; Fagan & Pabon, 1990; Janosz et al., 1997; Rumberger, 1983, 1987, 1995; Wehlage et al., 1989). Furthermore, the ways in which students drop out of school vary: Some quit voluntarily, whereas others are pushed out (Elliot & Voss, 1974; Kronick & Hargis, 1990; Tinto, 1993). Thus, although some risk factors appear to be quite common among dropouts (i.e., poor school achievement, low SES), it is highly improbable that al1 dropouts have the same personal attributes and family, school, and social experiences and follow the sarne developmental pathway (Hargroves, 1987; Kronick & Hargis, 1990; Rumberger, 1987; Wehlage et al., 1989). Clearly, school dropouts do not form a homogeneous group. However, this heterogeneity per se does not appear to be a major issue in the literature, because most empirical studies treat their samples as homogeneous groups. It is not

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sufficient to merely state that it is common sense to talk of different types of dropout or that the diversity of risk factors underlies heterogeneity. Empirical research needs to verify these clinical observations and intuitions; it has to clarify the qualitative and quantitativeinterindividual differences regarding the psychological and social expenences among school dropouts. This specific issue has rarely been tackled directly and empirically. Some researchers acknowledge the diversity of school dropouts but do not take it into account when analyzing their data (Elliot & Voss, 1974). Others propose the use of typologies to address the dropout problem but do not empirically verify their classification(Kronick & Hargis, 1990). For example, Kronick and Hargis (1990) suggested a typology of dropouts that integrates personal characteristics, school experience, and the timing of school disengagement. They distinguished higher achieving students fiom lower achievers. High-Achiever Pushouts have good grades but are expelled from school because of problem behaviors; dropouts, however, are more likely to come from the low-achieving group. Within this category, Kronick and Hargis suggested three types of dropouts: Low-Achiever Pushouts, Quiet Dropouts, and In-School Dropouts. LowAchiever Pushouts are students who react to the frustration of repeated school failures with aggressiveness and rebellion. Their misbehavior results in disciplinary sanctions, and a vicious cycle begins that ends only when the student is expelled. The Quiet Dropouts, theoretically a frequent dropout type, also have a history of academic failure but, unlike Low-Achiever Pushouts, do not react with frustration and anger or manifest externalized behavior problems. Thus, they go unnoticed until they drop out. Finally, the In-School Dropouts are students who reach 12th grade but fail the final exams because of serious deficiencies in their knowledge. Kronick and Hargis have not tested their classification empirically; they did not discuss the differential role of important exogenous factors (i.e., family experience, socioeconomic situation, etc.), and they tend to confuse behavioral and cognitive characteristics with their consequences. Still, their typology has important heuristic value in that it proposes two major axes for distinguishing the school experience of potential dropouts: behavioral and academic adjustrnent, two well-known predictors of school dropout (see Janosz et al., 1997; Rumberger, 1987). One longitudinal study gives strong empirical support to the idea that the severity of school (academic) and personal problems (problem behavior) varies greatly among dropouts. Cairns et al. (1989) demonstrated that although students who cumulated academic failure and problem behavior had a higher probability of dropping out, they did not constitute the majority of dropouts. About 45% of dropouts were low achievers without problem behavior (50% for boys and 39% for girls); 45% showed high levels of aggressiveness, with or without academic failure (45% for boys, 46% for girls); and about 10% showed no signs of academic or behavioral problems (5% for boys, 15% for girls).

A Typological Approach We believe that empirical research has overlooked the issue of heterogeneity and that Our understanding of the

complex etiology of school dropout and its prevention would benefit fiom a typological approach. Unlike fields of study such as delinquency (Moffit, 1993; Quay, 1987), substance abuse (Anglin & Hser, 1990), and problem behavior in childhood (Pulkkinen & Tremblay, 1992), research on school dropout has seldom made use of typologies for understanding individual differences, highlighting different major developmental pathways, or guiding differential intervention (Brennan, 1987; Quay, 1987). Regarding intervention, one important characteristic of good programs is their ability to closely match their content and didactic methods to the specific needs, vulnerabilities, and strengths of the participants (Lipsey & Wilson, 1998; Mash, 1989; Wehlage et al., 1989). This match, which we term digerential intervention, is based on the premise that no single program can respond adequately to the needs of al1 the participants identified by a single label, such as dropout, delinquent, or drug addict (Le Blanc, 1990; Losel, 1996; McCord, 1990). In a thoughtful analysis of the efficacy of 14 schools for at-risk students, Wehlage et al. (1989) came to the conclusion that the positive effects of these schools were largely attributable to the match between the programs and the students' characteristics. This was possible, in part, because the schools carefully selected at-risk students who had characteristics that were specifically considered by the intervention program. A differential strategy can be developed and implemented over time using the experience of a specific program. A more systematic approach may be to use a typology of potential dropouts to plan a program according to each type's characteristics. However, to be useful for this purpose the selected typology must display good predictive, discriminative, and face validity (Brennan, 1987). To support a differential preventive approach, a reliable classification of potential dropouts is required. The classification scheme should help to not only predict dropouts but also to classi@ them into different types. In addition, to be clinically useful the typology must maximize intergroup differences and minimize intragroup differences. Furthermore, the characteristics distinguishing subgroups of students must be clinically significant, that is, offer school practitioners real intervention targets or modifiable risk factors that justify the differentiations among at-risk students. From a theoretical perspective, the construction and use of typologies can help clarify the different pathways that may lead to a particular outcome or the manner in which different risk and protective factors tend to interact or agglomerate within certain types of individuals or contexts (Brennan, 1987; Compas, Hinden, & Gerhardt, 1995). For instance, let us consider three interesting models of school dropout (Finn, 1989; Tinto, 1975, 1993; Wehlage et al., 1989).Al1 three describe school experience as the proximate determinant of school dropout and identi@ as the cornerStone of the disengagement process such factors as school participation (or involvement), academic performance, and school comrnitrnent (or membership, identification). But the expression of the relationships among these factors varies with each author. For example, Finn's (1989) mode1 focuses specifically on the interplay among participation in school activities, performance outcomes, and identification with school (belonging and valuing). Wehlage et al. (1989) also

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recognized the importance of these three elements; however, whereas they identified school achievement as an outcome of educational engagement (similar to participation for Finn) and school membership (similar to identification for Finn), Finn situated academic performance as a mediator of the influence of participation on identification. To varying degrees, al1 three models consider the educational environment as the determinant of the quality of school experience. The process leading to school dropout is never regarded as based solely on individuals' characteristics, and school educational practices always appear as major determinants of the quality of students' participation, achievement, and motivation. Furthermore, al1 the authors recognize the diversity of the dropout population but do not specifically address the extent of their theory's explanatory power.' In other words, is their theory valid for al1 types of dropouts, or only for certain types? Usually, theoretical models try to propose the most comprehensive explanatory structure possible. A way to test and compare the explanatory powers of different theories is to see how well they fit the characteristics of different subgroups of participants and how accurate they appear to be in describing the developmental trajectory of different types of participants. Typologies may validate the heuristic value of a theory or confirm the need for the development of a complementary model. Working with a t~pologyof dropouts may help highlight the complexity of the relationships between and ~rotectivefactors for a particular problem (Brennan, 1987; Compas et al., 1995).

Research Objectives and Hypotheses The general goal of our study was to explore the heuristic value of a typological approach for preventing and studying school dropout. Our first objective was to build empirically a typology of dropouts based on individual school experience characteristics. Many avenues exist for classifying dropouts. We chose to focus on school experience, for the following reasons. First, many studies have demonstrated that school factors are among the best predictors of school dropout (see Janosz et al., 1997). Academic performance, attitudes toward school (e.g., commitment, motivation), and misbehavior are al1 powerful individual predictors. For instance, Hess, Lyons, and Corsino (1989) predicted school dropout with 85% accuracy using third- and fourth-grade attendance and academic data. Janosz et al. (1997) obtained 80% accuracy using academic achievement and school commitment as predictors in high school. We hypothesized that a classification scheme that included good predictors of school dropout would have optimum predictive validity, a quality required for preventive action. Second, as reported earlier, many important theoretical models of school dropout use school factors as key elements (Finn, 1989; Tinto, 1993; Wehlage et al., 1989). We believed that a typology highlighting different profiles using these factors would increase the heuristic value of our approach, mainly by supporting a dialectical process between theory and empiricism: The theories could help us organize the findings of the empirical investigation and avoid the hazards of an atheoretical approach, and the empirical results could highlight distinct theoretical proposi-

tions or unexplained areas. Third, the literature supports the view that the quality of school experience among students who drop out can be quite heterogeneous, especially in terms of academic competence, behavioral adjustment, and school commitment (Cairns et al., 1989; Hackman & Dysinger, 1970; Kronick & Hargis, 1990; Wehlage et al., 1989). Fourth, by limiting our classification to school-related factors, we wanted to enhance its face validity and acceptance by school practitioners (Brennan, 1987). A typology of potential dropouts that concurs with the experience of teachers and school practitioners is more likely to be of practical use, especially if it emphasizes intervention targets that they can control and influence. Poverty, gender, or drug use may be good predictors of school dropout, but they do not offer as much "grip" for school intervention as motivation, achievement, or misbehavior. The second objective of the study was to test the typology's reliability by replicating the classification with two different longitudinal samples. The third objective was to test the typology's predictive validity.

Method Participants Our relied on secondary analyses of two longitudinal samples of high s~hooistudents. The same data set was used in a previous study to investigate the relative weight of school dropout predictors (Janosz et al., 1997). The first sample was representative of the student population on the Island of Montreal (public and private schools, al1 socioeconornic levels). This sample was established as part of a longitudinal study on delinquency begun in 1974 (see Biron, Caplan, & Le Blanc, 1975). Eight hundred twenty-five White, French-speaking adolescents in Grades 7-11 were recruited (see Le Blanc, 1996). Using the provincial data set of the Quebec Department of Education, we were able to identify the high school graduation status of 791 of the 825 students (438 males and 353 females). Dropouts are people who by the age of 22 have not completed the minimal requirements for a high school diploma. Using this definition, we identified 172 dropouts (100 boys and 72 girls), for a dropout rate of 21.7% (22.8% for boys and 20.4% for girls). The mean age when interviewed was 14.26 for the dropouts (14.2 for boys and 14.3 for girls) and 14.3 for graduates (14.2 for boys and 14.4 for girls). The second sample consisted of 797 White, French-speaking adolescents from Montreal (Grades 7-9) who were interviewed in 1985 as part of a longitudinal study on psychosocial adjustrnent in children from moderate- and low-SES families (Tremblay, Le Blanc, & Schwartzman, 1986). Of these 797 students, 791-(367 boys and 424 girls) could be identified as either dropouts or graduates. The number of dropouts was 335 (171 boys and 164 girls), for a dropout rate of 42.3% (46.6% for boys and 38.7% for girls), twice as high as for the first sample. The mean age at time of interview for the dropouts was 14.3 (14.4 for boys and 14.3 for girls) and, for graduates, 14.2 (14.2 for boys and 14.1 for girls). The distribution of boys and girls with regard to dropout status differed. Boys represented 55.4% of the total sample in 1974 but only 46.4% in 1985. Chi-square analyses showed that gender and graduation status were independent in 1974 but not in 1985. In the second

l However, Tinto did specify that his model focuses on voluntary college dropouts.

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sample, boys dropped out more often than girls, a finding consistent w$h data from the Quebec Department of Education (Ministère de 1'Education du Québec, 1991), which show a growing gap between graduation rates of boys and girls in Quebec. Also, as noted previously, SES differed in the two samples. The 1974 sample was a stratified random sample of the student population, and the 1985 sample had an overrepresentation of students from low-SES families. A 2 X 2 analysis of variance (ANOVA; Gender X Generation) revealed that the participants' SES was significantly lower in 1985, F(l, 1,573) = 107.09, p < -001.There was no effect of gender and no interaction. The difference in SES may explain the difference in the dropout rates because it is well documented that coming from a low-SES family increases the risk of dropping out (see Cairns et al., 1989; Fagan & Pabon, 1990; Rumberger, 1983, 1987).

Measures and Variables Measures of the adolescents' psychosocial adjustment constituted the independent variables. Al1 the 1974 and 1985 adolescents completed the self-administered Social Inventory Questionnaire (SIQ; Le Blanc, 1996) at least 1 year before dropping out (see Tremblay et al., 1986). This instrument was initially created to study the development of delinquency from a social control perspective (Hirshi, 1969; Le Blanc, 1997; Le Blanc & Fréchette, 1989). Thus, it includes multiple items to measure extemal behavioral problems (delinquency, drug use, conduct disorder) and constructs such as social bonding (attachment, involvement, commitment, beliefs) and constraints (sanctions, supervision), all within different social contexts (school, family, peers). The SIQ indicators appeared relevant to the study of school dropout because they refer to personal and social factors already mentioned in several theories and empirical studies of school dropout (Fagan & Pabon, 1990; Fim, 1989; Wehlage et al., 1989). Furthemore, a recent study showed the predictive value of the instrument's indicators regarding school dropout (Janosz et al., 1997). However, it is not our intention in this article to link our empirical investigation to a social control theory framework. Our aim bas to s t u d ~the (sch0019 famil~,and peers) and personal (use of leisure time, attitude toward conventional norms, behavioral problems) experiences of school dropouts using the available data. Because a shorter version of the SIQ (120 items) was used in 1985, only the variables included in both samples were considered for this study. The abridged SIQ focused on the participants' school and family experience, peer relationships, leisure activities, belief in conventional norms, and deviant behaviors. Students were asked to evaluate the frequency with which a specific behavior had occurred during the past 12 months or the intensity of a specific experience. The items were assessed on a 4-point scale (1 = never/ none, 2 = sometimedonce or twice, 3 = ofren/many times, 4 = alwaydvery ofren), then summed or averaged when more appropriate (i.e., averaging math and French scores to get the average grade score or computing the average of the mother's and father's scores to obtain a parental measure). In Appendix A we summarize the scales' characteristics (number of items, alpha) and give examples of items from each scale. Al1 scales, with the exception of number of years behind in school (grade retention), were standardized within each sample with a mean of O and a standard deviation of 1. Also, the scales were coded so that higher scores indicated more of the indicated construct. Concurrent, discriminant, and predictive validity, as well as reliability, were confirmed for the instrument's 33 scales based on an analysis of 6,604 boys and girls between the ages of 10 and 19 (see Le Blanc, 1996, for a more detailed description of the analyses). Because the number of items is not the same for each scale, adjusted alphas have been estimated on the

basis of a 12-item scale (see formula in Nunnally, 1967). This standardization procedure corrects the underestimation of the alpha for scales with a small number of items. The correction is based on Cronbach's alpha and estimates the alpha's value if the scale had 12 items.

School Experience Measures of school experience included school grades (average in French and mathematics); grade retention (i.e., number of years behind in school); commitment to schooling, that is, attitude toward schooling, self-evaluation of competence, importance of success, and educational aspirations; level of stress in school; disciplinary sanctions (suspension, expulsion); involvement in school and extracurricular activities; and school misbehavior.

Family Experience Farnily experience was assessed with general background and process variables. The general background variables included the average educational level of the mother and father; SES, which was a summary index of occupational prestige and economic dependency; and family disadvantage, an index of five indicators: family disruption, time of family disruption (i.e., recently or not), working mother, size of family, and frequency of moving. The process variables included the presence of potential negative models or parental problems through marital discord and parental alcohol consumption. Family process also referred to the quality of the participant's attachent to his or her parents (communication with parents, sharing of feelings, adolescent's identification with parents) and parental management practices, assessed in tems of supervision; punishment used by parents (quarreling, shouting, hitting, resûicting); and rules established to regulate homework, meals, friends, and whereabouts.

Peer Relationships Peer relationships were assessed by inquiring about the participants' number offrien&, the level of involvement with fiends (iee., time spent with friends discussing and sharing activities), the desire to be like one's best friend (identification), assurnption of the role of leader in one's peer group, and exposure to deviant peers.

Leisure Activities and Beliefs Our assessment of leisure activities examined how the adolescents spent their spare time. We assessed them on the following variables: allowance given, involvement in passive leisure activities (cinema, listening to music, etc.), active leisure activities (hobbies, sports, etc.), loitering, and time spent on a part-time job. Belief in conventional noms was measured with items on religious practice (going to church and attending religious classes).,items on adherence to deviant norms (theft, cheating, vandalism, drug use, truancy), and items on the participants' level of respect for authority figures (police).

Deviant Behaviors We assessed drug use with a 5-item scale measuring the frequency of use of alcohol, marijuana, and hard drugs in the past 12 months. Delinquency was assessed with a 21-item scale that measured participation in fighting, minor and major thefts, and vandalism. Participants also reported the number of times they had been arrested by the police.

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Data Analyses We conducted analyses in two major stages. First we used a cluster analysis technique to group school dropouts into different homogeneous groups according to their previous school experience. Although a legitimate issue in itself, we were not aiming to produce a classification of students but rather to maximize the ability to identify different kinds of dropouts. Students who graduated were thus excluded from cluster analyses. There are many classification methods, each with its own strengths and weaknesses (Brennan, 1987). We opted for a monothetic divise method called association analyses (MacNaughton-Smith, 1965). Association analyses produce simple and clear class definitions. They are sensitive to complex interactions between variables and are useful for prediction. New individuals can readily be assigned to the previousiy defined classification, and disjointed and nonoverlapping groups are produced (Brennan, 1987; MacNaughton-Smith, 1965). We concluded that this method was especially adapted for the clinical use we intended for the typology (e.g., differential intervention). Monothetic categories are defined by the repeated division of a sample on successive variables. The initial sample is divided into two subsamples by means of a splitting variable. Each subsampleis, in turn, divided by another splitting variable, and so on. This process continues until the groups become too small or no other variables can be considered splitting criteria. A variable becomes a splitting criterion because it is the one that shows the strongest relation (i.e., chi-square coefficient) with al1 other potential criteria. Hence, the first step in our association analyses was to dichotomize al1 school experience variables: school marks, grade retention, school cornmitment, involvement, sanctions, stress, misbehavior, and tniancy. The variables were divided on their median value or its nearest value. Al1 analyses were conducted separately for the two samples. To identify the splitting variables, we computed the strength of association (chi-square values) between al1 pairs of school variables in 2 X 2 contingency tables. For each variable we summed al1 the significant chi-square values in which it was involved (p < .05). The variable that cumulated the highest surn of chi-square, k i n g most strongly related to the others, became our splitting criterion. Once a variable was used as a splitting criterion, it was removed from subsequent analyses. The splitting of the sample ended when there were no more significant associations or when the number of participants was too small to conduct the chi-square analyses (n < 20). In the second stage of our analyses we investigated our capacity prospectively to predict the different types of dropouts. First we conducted a series of univariate logistic regression analyses (polychotomic)on both samples to determine which school, farnily, social, and persona1 variables were able to discriminate the different types of dropouts from the students who graduated. Next we conducted a multivariate analysis to identify the best prediction models for correctly screening the different types of dropouts. These models were developed on the 1985 sample and crossvalidated on the 1974 sample.

Results Construction of the Typology The results2 of the association analyses illustrated in Figure 1 were very consistent between the two samples and led to a five-group solution. Each criterion variable was identified with its chi-square sum at the level where it became a splitting variable. Dropouts could be classified

Sarnple of 1974

Average-High Commitment Average-Low

School misbehavior

Dropouts Z x2= 41.41 Very LOW Achievement High School rnisbehavior

C

Sample of 1985

No

n = 49

grade retention Z x2= 6.96

\L

n = 15

one or more grade retention

n = 124

Average-High Commitment Average-LOW Achievement

Dropouts

-

x2=

n = 20

Cornmitment 14.82

La;,.&.

School misbehavior

"""

n =21

Very

/I grade