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
172
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
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
173
DROPOUTTYPOLOGY
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.
174
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
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.
175
DROPOUT TYPOLOGY
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
<
retention
LOW
n=25
n = 53
Z ~ 2 =17.17
\
One or more
grade retention
n=79
Figure 1. Divise clustering: sum of chi-square of the criterion
variables and the participants' distribution according to the final
grouping.
first according to their level of school misbehavior: high
versus average-low. The number of participants was not
identical between these two groups because the split could
not be made at the exact median. The 1974 group was
divided at the 60th percentile, and the 1985 group was
divided at the 57th percentile. Students who did not show
signs of school problem behavior could be classified according to their level of commitment to school and their
achievement scores. Here, a group of students emerged who
reported an average-high level of school commitment.
Furthermore, within the group of poorly committed participants, we were able to distinguish those who had very low
achievement scores from those whose scores were average.
The subgroup of students who showed problem behavior
could be characterized by their grade level: those who
cumulated grade retentions and those who were in an
age-appropriate class. We decided to merge these two
groups, for two reasons. First, keeping only 15 participants
in the 1974 grade retention category would have affected our
statistical power, as we would have had to run the analyses
on a typology with five groups. Second, we compared these
2The length of the computations and the number of matrices
needed to do the association analysis preclude their inclusion in this
article; however, they may be obtained on request from Michel
Janosz.
176
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
Table 1
Distribution of Dropouts According to Gender and Sample
Dropouts
Dropout types
Male
Female
Total
57
(37.3)
10
(6.5)
13
(8.5)
73
(47.7)
67
(45.3)
10
(6.8)
12
(8.1)
59
(39.9)
124
(4 1.2)
20
(6.6)
23
(8.3)
132
(43.9)
153
148
30 1
1974 sample
Quiet
Disengaged
Low achiever
Maladjusted
36
(37.1)
9
(9.3)
11
(11.3)
41
(42.3)
1985 sample
Quiet
Disengaged
Low achiever
Maladjusted
Total
Note.
Numbers in parentheses indicate the tolu- percentages.
two subgroups on al1 measures and found that, except for
grade retention, there were only (few) marginal differences,
and they were not stable between the 1974 and 1985
samples.
Thus, the final typology was based on three school axes:
behavioral maladjustment, commitment, and achievement.
The interactions among these factors revealed four types of
dropouts. The means and variance of al1 variables for the
different types of dropouts and for the graduates are
presented in Appendix B. We proposed four labels to
differentiate the four types of dropouts after a careful
examination of the school variable means3 Table 1 shows
the distribution of the dropout types according to gender for
each sample. The association analyses were successful in
classifying 96.5% of the 1974 group dropouts and 89.9% of
the 1985 group dropouts. We begin by presenting an
operational and conceptual definition of the dropout types,
with reference to aspects of their school experience and
definitions of dropout types proposed by other authors.
sanctions, and held positive views about school attendance.
Of al1 the dropouts,- they showed the most positive school
profile, and the results appeared quite stable between the two
samples. On a conceptual level this group showed very
strong similarities with Kronick and Hargis's (1990) Quiet
dropouts, who were described as having few external
problems and poor school performance. According to this
description, these youths do not react openly to their
difficulties at school, do not misbehave, and generally go
unnoticed until they choose to leave school. In fact, Quiet
dropouts look fairly similar to the average Graduate except
for their achievement. Thus, we borrowed from Kronick and
Hargis the label Quiet, because their clinical description
seemed to fit Our empirical description quite well. As
indicated in Table 1, Quiet dropouts account for 38% (37%
of boys, 39% of girls) of the dropouts in the 1974 cohort and
41% (37% of boys, 45% of girls) of the 1985 cohort.
Disengaged Dropouts
Operationally, these dropouts show (a) an average-low
level of school misbehavior, (b) low commitment to school,
and (c) average performance with respect to grades. In fact,
al1 dropouts except Quiets show a weak commitment to
like
few g
schoO1. ~
~ dro~outs
~
~ schoO1, have
~
educational aspirations, care little about school grades, and
feel that they Ge less competent than other studenis. In short,
they do not recognize the importance of education in their
lives and accord it little value. Like the Quiets, their school
performance is superior to that of the other two types, the
Maladjusted dropouts and the Low Achievers. They actually
show slightly higher grades than the Quiets. Disengaged
dropouts do not misbehave as much as the Maladjusted
dropouts do, but they are more troublesome than Quiets and
receive more disciplinary sanctions. These results are stable
for the two cohorts. Conceptually, this group resembles
Kronick and Hargis's (1990) "Pushouts" who, although
functional as students, expressed fnistration with school and
were rebellious and undisciplined. It is worth noting that
Disengaged dropouts obtained surprisingly good achievement scores, considering their level of disengagement and
low school involvement. As indicated in Table 2, they are a
smaller group than the Quiets, comprising just 11% (9% of
males, 13% of females) of dropouts in the 1974 cohort and
7% (6% of males, 7% of females) in the 1985 cohort.
Quiet Dropouts
Low-Achiever Dropouts
On an operational level, these adolescents are dropouts
show (a) no evidence of school misbehavior and @)
moderate or high levels of commitment to education in
general. Although their achievement scores were not used to
select them, their school performance tended to be lower
than that of Graduates and Disengaged dropouts, although
higher than that of Low Achievers and Maladjusted dropouts. Consistent with this profile, they appeared to be
involved in school activities, did not experience disciplinary
This group is 'perationally defined as showing (a) weak
commitment to education, (b) average-low levels of dl001
misbehavior and, unlike the Disengaged dropouts, (c) very
poor school performance (below the passing grade of 60%).
Low Achievers are distinct in their inability to fulfill the
Statistical support for these descriptions is provided by the
results of the logistic regression analyses presented in Tables 2
and 3.
~
Table 2
Predicted Odds Ratios for DifSerent Types of Dropouts (Univariate Estimates): 1985 and 1974 Samples
1985
Predictor
Q
D
1974
LA
M
Q
D
Likelihood ratio
LA
M
1985; x2(4, N
=
757)
1974; x2(4,N
= 785)
School experience
Achievementa
Commitmenta
School misbehaviora
Grade retentiona
Involvement
Stress
Disciplinary sanctions
Beliefs versus truancy
Family background
Structure
Socioeconomic status
Family disadvantage
Parents' education
Process
Parents' alcohol use
Marital conflict
Attachment
Supervision
Punishment
Rules
Rebelliousness
Lifestyle
Peer relationships
No. friends
Involvement wlfriends
Leadership
Identification
Deviant friends
Leisure
Allowance
Passive leisures
Active leisures
Loitering
Part-time job
Beliefs
Respect for authorities
Religious practice
Conventional noms
Deviant behavior
Drug use
Delinquency
Arrests
Note. Table values are estimates after controlling for gender, age, and time lag between year of testing and year of dropping out. Significant interactions with gender are discussed in
the text (1985: number of friends and leadership, 1974: grade retention, punishment, involvement with friends, passive leisures, delinquency). Q = Quiet; D = Disengaged; LA =
Low Achiever; M = Maladjusted.
"Criterion variable used to construct the typology.
- f p < .10 (marginally significant). * p < .05. **p < .01. ***p < . m l .
F
4
4
178
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
minimal requirements needed to pass their courses. The
difficulties of the Low Achievers, compared to those of
Disengaged and Maladjusted dropouts, appear to center on
leaming and grades. In fact, their behavior at school was
comparable to that of graduates and appears better than that
of the Maladjusted dropouts. Consequently, they tended to
receive fewer sanctions than the latter group. Low-Achiever
dropouts do not fit any of Kronick and Hargis's (1990)
categones. They are also a small group, comprising only
13% (11% of males, 15% of females) of the dropouts in the
1974 cohort and 8% (9% of males, 8% of females) of the
1985 cohort.
Maladjusted Dropouts
Dropouts of this type were selected primarily because of
their high level of school misbehavior. In addition to this
behavioral profile, Maladjusted dropouts also showed (a)
poor school performance and (b) a weak commitment to
education. Thus, they have difficulties at al1 academic,
behavioral, and motivational levels. Their grades and commitment to school are inferior to those of the Quiets, but
what really sets them apart is their poor behavior. It is
therefore not surprising that this group was found to invest
little in school life, receive numerous sanctions, and approve
of school truancy. In short, the Maladjusted dropouts have
the most negative school profile of the four dropout types,
owing not only to the variety of difficulties experienced at
school but also the severity of these difficulties. This group
compares to Kronick and Hargis's (1990) "Low-Achiever
Pushouts." This type comprised a large portion of the
dropout population: 39% (42% of males, 33% of females) of
the dropouts in the 1974 cohort and 44% (48% of males,
40% of females) in the 1985 cohort.
In summary, the classificationsengendered by the association analyses indicate that dropouts can be distinguished
from one another with respect to the intensity and nature of
their school difficulties. Two groups show clearly different
profiles. At one extreme are dropouts who, other than having
low grades, resemble most future graduates (the Quiets). Of
the four types, their school experience is the least negative.
At the other extreme is a group of dropouts with severe
school behavioral and academic difficulties, the Maladjusted
dropouts. The Quiets and the Maladjusted dropouts make up
the majority of the dropout population, accounting for 76%
of dropouts in 1974 and 85% in 1985. Two smaller groups
fa11 between the two extremes in terms of school difficulties.
The Disengaged dropouts are characterized by disinterest in
school but an ability to make the grade with little effort or
motivation. The Low Achievers are distinguishable by
severe difficulties in their school performance. Although the
data in Table 1 reveal a slight tendency for boys to be more
numerous in the Maladjusted group and girls to be more
numerous in the Quiet group, chi-square analyses failed to
show significant relationships between gender and dropout
type, Pearson x2(3, N = 301) = 2.25, p = .52, in 1985;
Pearson x2(3, N = 166) = 1.72, p = .63, in 1974. We next
turned to differentiating these different types of dropouts
from the graduating students.
Predicting Dzfferent Types of Dropouts
Univariate Analyses
We conducted polychotomic logistic regressions to investigate the predictive value of different school, family, peer,
social, and behavioral factors. Table 2 shows the predicted
odds ratios (ORS)of al1 independent variables for each type
of dropout. Each OR indicates the relative odds of being a
specific type of dropout versus a graduating student due to a
one-unit change in the independent variable. An OR greater
than 1 indicates that the probability of being in that category
increases with the value of the predictor. For example, an
OR of 1.2 indicates that for each increment of 1 in the
independent variable, the probability or risk increases by
20%. Conversely, when an OR is lower than 1, the relationship with the predictor is negative. This table also shows the
likelihood ratio chi-square and its level of significance. This
is an indicator of the goodness of fit of the predictor: High
chi-square values and low p values indicate a well-fitting
model.
The results were controlled for gender, age, and the time
lag between the year of the interview and the year of
dropout. Although there were gender differences in the 1985
dropout rate, gender alone was not a significant predictor for
discriminating between different types of dropouts and
graduating students. Nevertheless, we tested the interaction
of al1 predictors with gender, Age and time lag were
significant predictors. More precisely, older students were
more likely to be Maladjusted dropouts (1974: OR = 1.32,
df = 4, p < .05; 1985: OR = 1.43, df = 4, p < .001), and
younger students tended to be Quiet dropouts in 1974
(OR = 0.72, df = 4, p < .05). This age effect reflected on
the time-lag effect. In 1974 the time lag was greater for
Quiets than for Graduates (OR = 1.35, df = 4, p < .05) but
did not vary for the other types. In 1985, however, al1 types
of dropouts showed a significantly shorter time lag than
graduating students (likelihood ratio = 140.26, df = 4,
p < .001), the shortest time-lag being for the Maladjusted
dropouts (see Appendix B for a representation of the
variance among groups).
School experience. The four variables used to construct
the typology served not only as good discriminant variables
among dropouts but also as good predictors. The patterns of
prediction support empirically the previous qualitative descriptions. Quiets shared with the other dropouts such risk
factors as low achievement scores, grade retention, and
stress. However, contrarily to other dropouts, they could not
be distinguished from Graduates on their commitment and
involvement in school, disciplinary sanctions, or beliefs
regarding truancy. In fact, reporting less misbehavior than
Graduates was a characteristic of Quiet dropouts. Low
school cornmitment, grade retention, poor school involvement, feeling stressed in school, and being punished without
reporting high levels of misbehavior increased the risk of
DROPOUT T'YPOLOGY
dropping out as a Disengaged dropout. Low achievement
was also a significant risk factor, but only in 1985, and did
not have the same predictive strength as for the other
dropouts, especially for Low Achievers. Low achievement
scores, low commitment, grade retention, and stress were
strong predictors for Low Achievers. Grade retention was a
better predictor of Low Achiever girls (OR = 2.94, df = 4,
p < .001) than boys (OR = 1.28, df = 4, p = .48) in 1974.
Despite their academic failure, negative feelings in school,
and lack of commitment, the Low Achievers did not behave
worse than Graduates. The Maladjusted dropouts were the
only dropouts who cumulated al1 the school risk factors.
Academic failure, disengagement, and behavioral problems
characterized Maladjusted dropouts as compared to Graduates. These results appeared quite robust between the two
samples. It is important to mention that the smaller number
of participants in the Disengaged and Low Achiever categories affected our statistical power, making it more difficult to
reach statistical significance.
Farnily experience. Lower SES, lower parental educational levels, and more structural disadvantages (e.g., singlemother family, numerous children, fiequent moving) appeared to be shared risk factors among al1 dropouts. The
level of significance varied a little between the two samples,
but the direction and strength of the coefficient stayed within
the same range. Parental educational level was the only
predictor to be significant for al1 four types of dropouts in the
two samples, and it appeared especially important for Low
Achievers.
With regard to family processes, our results indicated that
parental alcohol use and conflict were not good overall
predictors, although there was a stable tendency for Low
Achievers to report more alcohol use by parents. The results
for Quiet and Maladjusted dropouts showed opposite patterns. Quiets were predicted by high levels of family
attachment and supervision, a greater number of rules, and
lower rebelliousness (significant only for 1985 dropouts).
Conversely, siudents who left school as Maladjusted dropouts were more likely to report low levels of attachment,
supervision, and rules, and high levels of rebelliousness.
Results for the Disengaged dropouts and Low Achievers
were less clear and stable between samples. In 1985 lower
attachment and supervision were the best predictors for the
Disengaged dropouts, and a large number of family rules
was the best predictor for Low Achievers. In 1974, only a
large number of rules and rebelliousness were significant for
Disengaged dropouts.
Peer relationships. In both cohorts, siudents classified
as Maladjusted dropouts reported having more friends than
the Graduates. They spent more time with friends but
assumed less leadership in their peer group. Moreover, their
friends displayed more deviant behaviors. A larger number
of friends was also a good predictor for Quiets, as well as
less leadership, but in 1985 only. Number of friends and
deviant friends were significant predictors for Disengaged
dropouts in 1985 but not in 1974. In 1974,however, the level
of involvement with friends was a strong characteristic of
the Disengaged dropouts compared to the Graduates. Having a lot of deviant friends appeared to be a good predictor
for Low Achievers. The ORS indicated a trend for Low
Achievers to perceive themselves less as leaders in their
groups. It is interesting that the results indicated. two significant interactions with gender in 1985 (number of friends
and leadership) and one in 1974 (involvement with friends).
The analyses showed that in 1985 reporting more friends
was a predictor for Low Achiever boys (OR = 1.60, df = 4,
p = .07) but not for girls (OR = 0.07, df = 4, p = .04).
Furthemore, Low Achiever boys reported being leaders in
their group (OR = 2.11, df = 4 , p = .002), but the girls did
not (OR = 0.73, df = 4, p = .35). A similar pattern could be
observed for Maladjusted girls (OR = 0.70, df = 4,p = .04)
and boys (OR = 1.11, df = 4, p = .50). The results indicated that in 1974 higher peer involvement was predictive
for Disengaged (OR = 5.30, df = 4, p = .003) and Low
Achiever boys (OR = 1.81, df = 4, p = .08) but not for girls
(Disengaged: OR = 1.31, df = 4, p = .47; Low Achiever:
OR = 0.60, df = 4, p = .14). These results suggest that peer
relationships are clearer risk factors for Low Achiever and
Maladjusted boys than girls.
Leisure activities. Regarding leisure activities, the results showed that al1 types of dropouts engaged in less
active, more passive leisure activities, although significant
results were limited mainly to Quiet and Maladjusted
dropouts. A significant interaction with gender indicated that
passive activities were more characteristic of Quiet girls
(OR = 1.73, df = 4,p = .01) than boys (OR = 0.87, df = 4,
p = .43). Students who spent time loitering (in 1985), had
plenty of allowance (1974), or worked many hours at a
part-time job were more at risk of becoming Maladjusted
dropouts. Involvement in a part-time job was also a risk
factor for Low Achievers in 1985.
Beliefs. The three measures pertaining to normative
beliefs were also relevant for discriminating between graduating students and the different types of dropouts. Again, the
results for Quiets indicated a different pattern of prediction
than for other dropouts. Quiets were not different fiom
Graduates with respect to religious practices or belief in
conventional noms. However, they did report more respect
for authority. It is not surprising that the Maladjusted
dropouts adhered more to deviant noms, showed less
respect for authority (1985), and were less religious than
Graduates. Disengaged and Low Achiever dropouts shared
with the Maladjusted dropouts less adherence to conventional values, although the results showed contradictory
trends between samples for Low Achievers.
Deviant behaviors. Self-reported dmg use, delinquency,
and number of arrests clearly predicted Maladjusted dropouts. Results for the Disengaged dropouts indicated a similar
pattern of predictors. However, only the number of arrests
was significant in 1974, and high delinquency was a good
predictor for boys (OR = 1.75, df = 4, p = .04) but not for
girls (OR = 0.12, df = 4, p = .07) in 1974. Although Quiets
tended to report less delinquent activity than Graduates,
more of them had been arrested, as was also the case with
180
Table 3
Predicted O&
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
Ratiosfor DlfSerent Types of Dmpouts (MultivariQteEstimtes): Stepwise Logistic Regression-1985 Sample
School baseline predictors
ste~
Baseline model
Quiets
Disengaged
Underachievers
Maladjusted
step 2
Quiets
Disengaged
Underachievers
Maladjusted
step 3
pets
Disengaged
Underachievers
Maladjusted
step 4
Quiets
Disengaged
Underachievers
Maladjusted
Step 5
Quiets
Disengaged
Underachievers
Maladjusted
step 6
Quiets
Disengaged
Underachievers
Maladjusted
step 7
Quiets
Disengaged
Underachievers
Maladjusted
Achiev.
Commit.
Misbeh.
Family structure
Retent.
Disadvant.
Educat.
School other
Stress
Involvem.
Note. Table values are estimates after controlling for gender and age. Achiev. = achievement; Commit. = commitrnent; Misbeh. =
volvement with friends;Attach. = attachent; LL = log likelihood.
t p < .10 (marginally significant). * p < .05. **p < .01. ***p < .W1.
Low Achievers (1985). Again, Low Achievers reported
some contradictory results. In 1985 they tended to be more
delinquent than Graduates, but in 1974 they reported less
delinquency. This result is consistent with the change in
conventional beliefs we saw earlier.
In 1985, of the 34 independent variables, 15 discriminated
Quiet dropouts from Graduates (10 in 1974), 12 discriminated Disengaged dropouts (11 in 1974), 12 discriminated
Low Achievers (9 in 1974), and 28 discriminated Maladjusted dropouts (24 in 1974). The Maladjusted type was
clearly the easiest one to predict with the variables used in
this study. We assessed the replication of the predictive
patterns by computing the percentage of agreement between
the two samples. Agreement by omission is agreement in
which one variable is not a significant predictor in either
sample. Agreement by commission is when a variable is a
significant predictor in both samples (in the same direction)
or significant in one and marginally significant in the other.
The total percentage of agreements for Quiets was 73%
(32% by commission and 41% by omission); for the
Disengaged, 62% (21% by commission and 41% by omission); for Low Achievers, 71% (24% by commission and
47% by omission); and for the Maladjusted, 77% (68% by
commission and 9% by omission). Our last series of
analyses concemed the capacity of these predictors to
accurately screen the different types of dropouts.
Multivariate Analyses and Power of ClassiJication
We conducted a stepwise polychotomic logistical regression (PLR) on the 1985 sample to determine the best sets of
predictors for the different types of dropouts. In this
sequential analysis we decided to enter at the first step the
variables used to constnict the typology. We wanted to
determine whether, for accurate screening of the different
types of dropouts, it was necessary to consider other
variables. After forcing the entry of this first set of predictors
(the baseline prediction model), we determined empirically
the order of entry of the other sets of predictors. We grouped
DROPOUT TYPOLOGY
Peer relationships
No. friends
Leader
Family processes
Rules
school misbehavior; Retent.
=
Attachm.
Beliefs
Deviancy
(arrest)
grade retention; Disadvant.
Respect
=
+
Likelihood
ratio (df)
family disadvantage; Educat.
al1 other predictors into Our seven psychosocial dimensions:
other school variables, family structure, family processes,
peer relationships, leisure activities, beliefs, and deviant
behaviors. Then we tested separately the improvement in
prediction for each group of predictors. In other words,
family structure variables were added to the baseline model,
and we calculated the goodness-of-fit index (the likelihood
ratio chi-square), the improvement in log likelihood (similar
to the F-change test in multiple regression), and the percentage of correct classification of this additive model. We
compared these statistics among al1 possibilities (i.e.,
baseline family structure vs. baseline family processes
vs. baseline + peer relationships, etc.), and the dimension
that most improved the prediction was added to the model.
We repeated this procedure with the remaining predictors
until there was no further significant improvement in
prediction. Table 3 shows the results of this analysis.
This analytic procedure was not guided theoretically
because the issue of prediction and screening is mainly an
empirical question. We did not investigate the causal links
+
Religion
=
Improvement
in LL (df)
% correct
classified
parents' education; Involvem.
=
in-
among al1 the dimensions. Had modeling causal relationships been the focus of this study, we would have determined theoretically the rank of the predictors (Le., Erom
distal to proximal factors; see Rumberger, 1995) or chosen
another analytic strategy (e.g., structural equation modeling). Moreover, this analysis was conducted only on the
more recent sample (1985) in order to test the validity of the
prediction models on a distinct sample (1974). This strategy
is much more stringent than the split halves procedure
usually done to validate the predictive efficiency of a
particular model (Farrington, 1987).
Only the ORS of significant predictors within each
psychosocial dimension are reported in Table 3. In Step 2,
for example, SES, family disadvantage, and parental educational levels were al1 initially included for the family
structure dimension (see Table 2 for the list of variables1
dimensions). However, SES was no longer predictive once
the variability explained by family disadvantage and parental educational level was taken into account.
The results for the baseline model reinforced the different
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
Table 4
Percentage of Correct ClassiJica'tionand Improvement Over Chancefor the Prediction
of Different Types of Dropouts According to Several Prediction Models
1985 sample
1974 sample
N correctly % correctly Improvement N correctly % correctly Improvement
Type of
studentf dropout classified
classified over chance classified classified over chance
Graduates
Baseline
Step 2
Step 3
Step 4
Step 5
step 6
step 7
Quiet
Baseline
Step 2
Step 3
Step 4
step 5
Step 6
step 7
Disengaged
Baseline
Step 2
Step 3
step 4
Step 5
Step 6
step 7
Low Achievers
Baseline
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Maladjusted
B aseline
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Total
Baseline
Step 2
Step 3
Step 4
step 5
Step 6
Step 7
school profiles of the typology and demonstrated the predictive efficiency of the criterion variables. As demonstrated by
the strong stability of the ORSfrom one step to another, the
addition of subsequent predictors never mediated their direct
predictive value. Furthemore, the addition of 11 predictors
in the final model (Step 7) improved the overall classification percentage by only 5.7%. The results from the stepwise
procedure indicated that family structure variables were the
best set of predictors to improve our baseline model,
followed by other school variables (stress and school
involvement), peer relationships (number of friends and
leadership), family processes (number of rules and attachment), number of arrests, and conventional beliefs (respect
for authority and religious practice). Leisure variables did
not contribute to the prediction after beliefs variables were
included.
The predictive efficiency of these models can best be
appreciated by examining Table 4, which indicates the
percentage of correct predictions for the four types of
dropouts and the graduates according to each of the seven
prediction models. Using a statistical prediction equation
based on the logistic regression coefficients (e.g., statistical
183
DROPOUT TYPOLOGY
bootstrapping procedure; Gottfiedson, 1987), we calculated
for every participant five probability scores, one for each
possible outcome (Graduates, Quiet, Disengaged, Low
. ~ also classified the
Achiever, and Maladjusted d r o p ~ u t s )We
participants into the group for which they received the
highest probability, and we computed the extent to which the
prediction models improved on what would have been
predicted by chance alone. Following Menard's (1995)
recommendations, we estimated the expected number of
correct predictions by chance for each category of outcome.
We then calculated the percentage of improvement by
subtracting the expected observations fiom the predicted
observations divided by the number of predicted observations. To predict the outcomes in 1974, we used the
predictors and coefficients identified in the 1985 sample.
The baseline model including the four criterion variables
was able to predict correctly 87.5% of the Graduates (81.5%
in 1974), 38% of the Quiets (27% in 1974), 5% of the
Disengaged dropouts (5.6% in 1974), 48% of the Low
Achievers (24% in 1974), and 68% of the Maladjusted
dropouts (75% in 1974). The percentage of improvement
over chance (IOC) was generally higher than 50% for al1
dropouts in both samples (47% for Disengaged dropouts in
1985) and much higher for the full additive model. For
Graduates, the addition of predictors improved the predictive efficiency of the baseline model in 1985 by only 2% and
not at al1 in 1974. The IOC was about 30% in 1985 but only
3% in 1974. In fact, the IOC was negative for the full
prediction rnodel (Step 7). This small improvement is
directly influenced by the base rate of Graduates (80% in
1974), which makes it difficult to improve the chance-alone
prediction (Fanington, 1987). In 1985, correct predictions of
Quiet dropouts increased by 13% from baseline (38%) to the
full prediction model (5 1%). However, the IOC varied fiom
70% to 80% in 1974, compared to 57%-68% in 1985. This
increase was similar in 1974, although the percentage of
correct classifications was 10% lower than in 1985. Parental
education level, family disadvantage, and having been
arrested particularly improved the percentage of correct
classification in 1985 and in 1974. Our prediction rnodels
were not as successful for Disengaged dropouts. In 1985 the
best prediction models included family structure, peer
relationships, and family process variables, for 25% of
correctly classified participants with an IOC of 89%. In 1974
the best prediction was given by the additive model with
family structure and other school variables, for 16.7% of
correct classification and an IOC of 86%. The high percentage of IOC despite the small number of predicted participants (4 or 5 in 1985 and 2 or 3 in 1974) can be explained by
the low base rate of Disengaged dropouts (3% in 1985 and
2.3% in 1974). The full prediction model increased correct
classification by 18% over the baseline model in 1985 (76%
vs. 48%) and by 19% in 1974 (43% vs. 24%), with an IOC
around and over 90%. The inclusion of stress and school
involvement markedly improved correct predictions in 1985
(from 44% to 72%). In 1974 the inclusion of belief scores
(respect for authority and religious practice) most improved
the prediction (from 24% to 43%). Finally, the baseline
prediction model was most effective for Maladjusted dropouts, both in 1985 (68%) and in 1974 (75%), with an IOC of
74% in 1985 and 89% in 1974. The addition of predictors
increased correct classifications by only 7% in 1985, with
parental education level being most responsible for that
increase. In 1974 the inclusion of level of respect for
authority improved correct predictions by 6%.
Discussion
The general goal of this study was to explore the heuristic
value of a typological approach for preventing and studying
school dropout. We argued that developing a typology of
dropouts would help us disentangle the school and psychosocial heterogeneity of this population, which would increase
Our understanding of the etiology of school dropout and
support the development of differential prevention strategies. The findings of our study fulfilled these expectations.
The clinical and theoretical usefulness and implications of
the typology developed in this study are closely linked to the
validity tests we conducted, and thus we discuss them
simultaneously.
A Typology Based on School Experience
We do not know of any other study that has investigated
empirically the school dropout phenomenon with a typological approach. Although this study was not a verification of
the clinically based classification of dropouts proposed by
Kronick and Hargis (1990), it certainly supported several of
the classification's assumptions regarding the heterogeneity
in dropouts' school experiences. As for many other studies,
the results of this research indicated that students who do not
graduate encounter more difficulties in meeting and adjusting to school demands and requirements (Janosz et al., 1997;
Rumberger, 1987, 1995; Wehlage et al., 1989). However, in
developing a typology and verifying its validity we found
that these difficulties varied in nature and intensity dong
three major axes: achievement, behavior, and school commitment. Previous researchers have noted heterogeneity with
respect to school achievement and misbehavior (Cairns et
al., 1989; Elliot & Voss, 1974; Kronick & Hargis, 1990), but
the discriminative strength of commitrnent is a distinct
empirical contribution of this study and raises questions,
which we address below, about the role of cornmitment in
school dropout theories (Finn, 1989; Wehlage et al., 1989).
Our typology appeared to be quite reliable. First, the
results of the association analyses converged to the same
classification scheme in the two samples. Considering that
one weakness of association analyses is their instability and
sensitivity to sampling variations (Brennan, 1987), we can
interpret this replication as a strong indication of reliability.
Second, the coverage of the dropout sample by the classification was very good (90% and over), and the distribution of
the different types was very similar in both samples, with the
majority of dropouts displaying a Quiet or a Maladjusted
The probability of dropping out was computed as follows: P =
exp [(xlyl + X ~ +Zx3y3 + x4y4 . . .) a], where P is the
probability of dropping out (between O and l), x is the individual
score on a predictor, and y is the standardized logit coefficient of
that predictor according to a category of student.
+
+
184
JANOSZ, LE BLANC, BOUIAERICE.AND TREMBLAY
profile. Third, the univariate analyses showed strong consistency in the nature and direction of the predictors (70%
overall) between the two samples. We note that, although
rare, there were some important changes in direction and
strength in predictors for the Disengaged dropouts (family
rules) and .the Low Achiever dropouts (conventional noms
and delinquency). The limited number of such radical
differences between the samples makes it difficult to interpret whether these are real changes or biases attributable to
the small number of participants.
Finally, in both samples, Maladjusted and Low Achiever
dropouts were the most accurately predicted, followed by
the Quiet dropouts. Disengaged dropouts were the most
difficult types to predict in both samples. The investigation
of the percentage of accurate prediction for each type of
dropouts enabled us to conclude that the baseline prediction
model was sufficient to identify accurately future graduate
students and Maladjusted dropouts. The additions of other
school and social variables did not increase markedly our
prediction effectiveness.
Future tests on the reliability of this classification should
include the use of a different clustering technique to see
whether a similar grouping would result. The stability of the
classification also needs to be verified.
A major finding of this study was that the nature and
intensity of school difficulties were sufficiently diverse that
the adolescents' social experiences varied accordingly. This
homogeneity was especially notable for the two most
numerous dropout types: Quiet and Maladjusted. Maladjusted dropouts are students at risk for multiple problems in
addition to school dropout. Not only do they encounter
significant academic and behavioral difficulties at school,
but also every social dimension of their life is affected. They
come from disadvantaged families with poor management
practices, are highly involved with deviant peers, do not
adhere to conventional beliefs, engage in mainly passive
leisure activities, and display varied deviant behavior. Their
profile fits the general deviance syndrome described by
Jessor and Jessor (1977) and, because of the nature, intensity, and diversity of the risk factors associated with it,
Maladjusted dropouts can be considered at risk for serious
social maladjustment (Loeber & Fanington, 1998).
Quiet dropouts show a very different profile. These
students are more likely to drop out of school because they
experience greater academic difficulties, but they also display more commitment to school and fewer behavioral
problems than the average graduating student. Like the other
dropouts, they come from more deprived families, but on
some family processes they do better than the Graduates,
reporting more family attachment, more supervision and
rules, and less rebelliousness. They have more friends, but
the fiiends are not deviant. They report more positive beliefs
regarding the police, although more of them than of
Graduates have been arrested. On the whole, their vulnerabilities are specifically limited to school failure and a deprived
family environment. Contrary to Maladjusted dropouts, their
family functioning and peer network do not appear to foster
problem behaviors. For Disengaged and Low Achiever
dropouts, such homogeneity is less evident. As stated earlier,
the small sample sizes of these two groups reduced our
capacity to detect significant differences during analyses.
However, some trends were observable. Disengaged dropouts seem to experience the same social risk factors as the
Maladjusted dropouts but with less intensity and with the
notable differences of not cumulating academic failures and
displaying less school misbehavior. As for Low Achievers,
they appear to experience more diverse risk factors than the
Quiets but fewer than Maladjusted dropouts. They do not
display externalized problem behaviors, although their school
failure and lack of motivation are very significant.
It should be noted that the labeling of the different types of
dropout was chosen to highlight a dominant characteristic of
each profile in this study and do not refer to other labels used
by other authors, unless stated otherwise (e.g., the Quiets
from Kronick & Hargis, 1990). Furthermore, we do not
suggest, for clinical use, labels with negative connotations.
Theoretical Implications
The school and social profiles of our four dropout types
generate far more questions than answers. In this study we
confirmed the heterogeneity of the dropout school experience with regard to achievement, behavioral adjustment, and
commitment to schooling. Although these are individual
factors, it should be kept in mind that they are not solely
tributaries of individual attributes. We know, for example,
that cultural differences affect school success (Wehlage et
al., 1989). Poor students are often taught skills, attitudes,
behaviors, and aspirations that are not valued by the
dominant school culture, and the resulting lack of congruence negatively affects adjustment to school (Gleeson, 1992;
Tinto, 1993; Wehlage et al., 1989). The family environment,
through its structural aspects and educational practices
(DeBaryshe, Patterson, & Capaldi, 1993; Le Blanc, Vallière~,& McDuff, 1992; McCombs & Forehand, 1989;
Rumberger, 1995; Steinberg, Blinde, & Chang, 1984); the
nature and quality of peer relationships (Hymel, Comfort,
Schonert-Reichl, & McDougall, 1996); the nature and
quality of school organizations (Bryk & Thum, 1989;
Rurnberger, 1995); and teacher practices (Abbott et al.,
1998; Durlak, 1995) are al1 important contextual factors that
interact with individual characteristics to account for the
quality of school experience.
The qualitative variations in the school and social experiences of dropouts show the importance of discontinuity and
heterogeneity in maladaptive development in conjunction
with different constellations of risk factors (Compas et al.,
1995; McCord, 1990; Moffit, 1993). Current theories of
school dropout do not ignore these variations, but they lack
specificity in operationalizing heterogeneous pathways to
school dropout and integrating the influence of other socialization agents (Finn, 1989; Tinto, 1993; Wehlage et al.,
1989). The characteristics of Disengaged and Quiet dropouts, for example, challenge the theoretical models put
forward by Finn (1989) and Wehlage et al. (1989). In Finn's
model, school valuing is directly influenced by school
performance, but the very low comrnitment and average
performance of the Disengaged dropouts do not conform to
DROPOUT TYPOLOGY
this prediction. The Quiets' high commitment and low
achievement do not fit the negative recursive interactions
between performance and motivation predicted in these
theoretical models. A more direct test of the explanatory
power of these models would be to conduct structural
equation analyses and observe how well the same mode1 can
explain the trajectory of different types of dropouts. Obviously, a systematic test of these valuable theories would
require a much more detailed study, including variables to
assess important constructs that were not investigated here
(Le., school belonging and membership, school practices,
etc.). We note, nevertheless, that these theoretical models do
not consider the role of other socialization agents, in
particular, family and peers, as they interact with school and
individual factors. We believe that only by integrating the
role of these three spheres of socialization and their interactions with individual attributes into a comprehensive theory,
although a tremendous challenge, will we be able to describe
the multiple pathways that lead to school dropout. The
works of Steinberg and Darling (1994), DeBaryshe et al.
(1993), Le Blanc (1997), Catalano and Hawkins (1996),
Hymel et al. (1996), and Eccles and Midgley (1989) provide
useful ideas for such an undertaking.
Finally, our description and understanding of the four
types of dropouts is still very incomplete; we have viewed
only the tip of the iceberg. Our study originated with
research on delinquency and problem behaviors (Le Blanc,
1997) and was not specifically designed to study the etiology
of school dropout. At this stage of our research we can offer
only speculative answers conceming the etiology of the
different dropout types, and we have more questions than
answers. The data used in this study lacked information on
processes closely related to school success. For example, we
could not explore family practices with regard to schooling
(i.e., school involvement, commitment, and aspirations of
parents), school involvement and commitment of friends,
participants' perceptions of teachers and school practices,
their reasons for dropping out, their sense of belonging,
cultural beliefs about schooling, and so on. We do not
believe that it is possible to understand students' developmenta1 pathways without considering the interactional processes
involved in schooling (Eccles & Midgley, 1989). Integrating
information about the school itself, such as climate, curriculum, teaching strategies, and so on, would prove useful in
determining whether certain types of dropouts are more
affected than others by contextual factors.
Widening our investigation to include better focused
variables would help us explain more accurately the trajectories of Quiet, Disengaged, and Low Achiever dropouts in
particular. It would help us examine more closely the
heterogeneity of Quiet dropouts, about which we have some
doubts. In particular, we suspect that some adolescents in
this group of dropouts experience different levels of internalizing problems (see Farrington, 1995). Additionally, measures of cognitive abilities, leaming strategies, motivation,
and intemalized problem behavior would increase our
understanding of the dropout types, especially the Disengaged and the Low Achievers.
Clinical Implications
Many authors have supported the relevance of a differential approach for the prevention of social maladjustment
(McCord, 1990; Quay, 1987) and school dropout (Wehlage
et al., 1989). The methodology to support this approach has
not received a lot of attention, however. One expected utility
of a typology is to lay the fondations of a differential
prevention program by forming groups of different kinds of
potential dropouts. To be useful for school practitioners, a
typology must highlight significant differences between
subgroups with distinct needs, vulnerabilities, and strengths
that are unlikely to be met by a single prevention program.
The priorities of the interventions, such as increasing
achievement, motivation, social skills, parental involvement, and support, should vary with the type of dropout
concemed. There is still too much to learn about the types of
dropouts from this study, and specific prescriptions for each
type are not advisable at this time. However, general
recommendations can be made on the basis of the school
characteristics of the different types.
Preventive actions toward Quiet potential dropouts should
focus principally on improving achievement. These students
are stilb motivated in regard to school, and they should
respond positively to school proactive measures. This strength
should not be ignored. Students classified as Disengaged,
Low Achiever, or Maladjusted dropouts are much less
committed to school, and this disposition needs to be
addressed in addition to other difficulties. Although this
hypothesis needs to be tested, we believe that students
classified as Disengaged have good cognitive abilities.
Increasing performance should not be so difficult if motivation increases, which is probably the biggest challenge with
this kind of student. We hypothesize that, contrary to
Disengaged, Low Achievers have some leaming difficulties;
thus, intervention programs for these youths should address
both problems with intensive, highly efficient teaching
strategies. Because of their numerous social vulnerabilities
and extemalized problem behaviors, students identified as
potential Maladjusted dropouts should benefit from highly
structured learning environments. In addition to increasing
school performance and commitment, interventions should
aim at increasing social and survival skills, because they
display very similar profiles to delinquent adolescents.
Specific prescriptions for how to increase the school
performance and commitment of these at-risk students is not
clear yet because the causes for these weaknesses have not
been demonstrated but, more important, because they probably vary between the different types: cultural gap? lack of
family support? intellectual or biological vulnerabilities?
Whatever the specific causes for the academic failures and
school disengagement, the interventions will need to take
into account the contextual learning impediments (Wehlage
et al., 1989).
The factors used to categorize the students must be
clinically significant, pinpointing specific needs or intervention strategies that can be realistically addressed. A clinical
strength of the typology of this study is that it builds on
malleable factors for school practitioners. Academic perfor-
186
JANOSZ,LE BLANC,BOULERICE,AND TREMBLAY
mance, motivation, and school behavior are al1 outcomes
that are directly influenced by school and teaching practices
(teaching methods, class management practices, etc.) that
are directly under the control of school practitioners (Abbott
et al., 1998; Durlak, 1995).
To be useful for practitioners, the typology must have a
good face value, and the assignment procedure must predict
real potential dropouts. Finally, the classification must be
reliable. One must feel confident that similar individuals
from different samples can be accurately classified using the
same rules. Our findings demonstrate the possibility of
building a typology with these attributes, although more
validation work remains to be done. For example, the
typology's reliability needs to be tested on a more culturally
diverse population. Because this typology was developed
and tested on a French Canadian sample of students, future
research should explore the impact of cultural factors on the
psychosocial heterogeneity of school dropouts, within and
between different ethnic groups. In this regard, some preliminary analyses using a longitudinal multiethnic sample of
predominantly low-SES American students have shown that
97% of the dropouts could be classified by the typology. The
results replicated the distribution of the different dropout
types quite closely, although there were fewer Quiets (28%
of the dropouts) and more Maladjusted dropouts (50%;
Janosz, Catalano, & Hawkins, 1995). The naming and face
value of the classification need to be rigorously tested with
teachers and school practitioners, although the feedback we
have received so far from our ongoing work in high schools
has been very positive.
In addition, we need to study in greater depth the
significance of gender differences, which were very small in
our study. This finding may refiect the nature of our data,
which focused on externalized problem behaviors and
related risk factors. Nevertheless, recent work has shown
that gender is much less significant for predicting school
dropout when multiple school and social factors are taken
into account (Janosz et al., 1997;Rumberger, 1995). A larger
scale replication study, with more school-relevant variables,
may help us disentangle the impact of gender in the
typology. Finally, although overall our typology showed a
very good capacity for dropout prediction, with an accuracy
much better than chance, there is still room for improvement, especially in predicting Disengaged and Quiet dropouts.
Conclusion
The typology we developed in our study is clearly not the
only valid way of classiQing potential dropouts. The same
clustering method applied to another sample, or the same
sample used with another clustering method, or with other
variables, may well have yielded different results. However,
we believe that certain recurrent empirical findings show
that school performance and behavior are major dimensions
dong which dropouts differ.
This study demonstrated the theoretical and clinical
importance of considering the heterogeneity of the school
dropout population. Although the classification that emerged
from our analyses possesses a validity of its own, the results
of this study make a stronger case for the need to study the
etiologies of school dropout and to prevent different types of
school dropout. Lumping al1 dropouts into a single sample
conceals important relationships or characteristics and can
lead to erroneous conclusions about the dropout phenomenon.
References
Abbott, R. D., O'Donnell, J., Hawkins, J. D., Hiil, K. G.,
Kosterman, R., & Catalano, R. F. (1998). Changing teaching
practices to promote achievement and bonding to school.
American Journal of Orthopsychiatry,68, 542-552.
Anglin, M. D., & Hser, V.-1. (1990). Treatrnent of drug abuse. In M.
Tonry & J. Q. Wilson (Eds.), Drugs and crime (Vol. 13, pp.
393459). Chicago: University of Chicago Press.
Bachrnan, J. G., Green, S., & Wirtanen, 1. D. (1971). Dropping out:
Problem or symptom? Ann Arbor: Institute for Social Research,
University of Michigan.
Biron, L., Caplan, A., & Le Blanc, M. (1975). La construction de
l'échantillon, la cueillette des données et leur préparation
[Sample construction and data collection of the longitudinal
study]. Montréal, Quebec, Canada: Université de Montréal,
Groupe de Recherche sur l'Inadaptation Juvénile.
Brennan, T. (1987). Classification: An overview of selected meîhodological issues. In M. Gottfredson & M. Tonry (Eds.),
Prediction and classijîcation (Vol. 9, pp. 201-248). Chicago:
University of Chicago Press.
Bryk, A. S., & Thum, Y. M. (1989). The effects of school
organization on dropping out: An explanatory investigation.
American Educational Research Journal, 26, 353-383.
Cairns, R. B., Cairns, B. D., & Neckerman, H. J. (1989). Early
school dropout: Configurations and determinants. Child Development, 60, 1437-1452.
Catalano, R. F., & Hawkins, J. D. (1996). The social development
model: A theory of antisocial behavior. In J. D. Hawkins (Ed.),
Delinquency and crime: Current theories (pp. 149-197). New
York: Cambridge University Press.
Compas, B. E., Hinden, B. R., & Gerhardt, C. A. (1995).
Adolescent development: Pathways and processes of risk and
resilience. Annual Review of Psychology, 46, 265-293.
DeBaryshe, B. D., Patterson, G. R., & Capaldi, D. M. (1993). A
performance model for academic achievement in early adolescent boys. Developmental Psychology, 29,795-804.
Durlak, J . A. (1995). School-based prevention programs for
children and adolescents. Thousand Oaks, CA: Sage.
Eccles, J. S., & Midgley, C. (1989). Stagelenvironments fit:
Developmentally appropriate classrooms for early adolescents.
In R. E. Ames & C. Ames (Eds.), Research on motivation in
education (Vol. 3, pp. 139-1 86). New York: Academic Press.
Ekstrom, R. B., Goertz, M. E., Pollack, J. M., & Rock, D. A.
(1986). Who drops out of high school and why? Findings of a
national study. Teachrs College Records, 87, 357-373.
Elliot, D. S., & Voss, H. L. (1974). Delinquency and dropout.
Lexington, MA: Heath-Lexington.
Fagan, J., & Pabon, E. (1990). Conûibutions of delinquency and
substance use to school dropout arnong inner-city youîh. Youth &
Society, 21, 306-354.
Farrington, D. P. (1987). Predicting individual crime rates. In M.
Gottfredson & M. Tonry (Eds.), Prediction and classijîcation
(Vol. 9, pp. 53-101). Chicago: University of Chicago Press.
Farrington, D. (1995). Later life outcome of truants in îhe
Cambridge study. In 1. Berg & J. Nursten (Eds.), Unwillingly to
school(4th ed., pp. 96-1 18). London: Gaskell.
Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117-1 42.
Gleeson, D. (1992). School attendance and truancy: A sociohistorical account. Sociological Review, 40, 437490.
DROPOUT TYPOLOGY
Gottfredson, S. D. (1987). Prediction: An overview of selected
methodological issues. In M. Gottfredson & M. Tonry (Eds.),
Prediction and classification (Vol. 9, pp. 21-51). Chicago:
University of Chicago Press.
Hackrnan, R., & Dysinger, W. S. (1970). Cornmitment to college as
a factor in student attrition. Sociology of Education, 43, 31 1324.
Hargroves, J. (1987). The Boston compact. Education and Urban
Society, 19, 303-3 10.
Hess, G. A., Jr., Lyons, A., & Corsino, L. (1989). Against the odds:
The early identification of dropouts. Chicago: Chicago Panel on
Public School Policy and Finance.
Hirshi, T. (1969). Causes of delinquency. Berkeley: University of
Califomia Press.
Hymel, S., Comfort, C., Schonert-Reichl, K., & McDougall, P.
(1996). Academic failure and school dropout: The influence of
peers. In J. Juvoven & K. R. Wentzel (Eds.), Social motivation:
Understandingchildren 5.school adjustment (pp. 3 13-345). New
York: Cambridge Uqiversity Press.
Janosz, M. (1995). Etude longitudinale sur la prédiction de
l'abandon scolaire, l'hétérogénéité des décrocheurs et
l'intervention differentielle [A longitudinal study on the prediction of school dropout, the heterogeneity of dropouts, and
differential prevention]. Unpublished doctoral dissertation, University of Montreal, Montreal, Quebec, Canada.
Janosz, M., Catalano, R. F., & Hawkins, J. D. (1995, March).
Cross-cultural validation of a school dropout typology: Implicationfor research and intervention. Paper presented at the Annual
Meeting of the American Society of Criminlogy, Boston.
Janosz, M., Le Blanc, M., Boulerice, B., & Tremblay, R. E. (1997).
Disentangling the weight of school dropout predictors: A test on
two longitudinal samples. Journal of Youth and Adolescence, 26,
733-759.
Jarjoura, G. R. (1993). Does dropping out of school enhance
delinquency involvement? Results from a large-scale national
probability sample. Criminology,31, 149-17 1.
Jarjoura, G. R. (1996). The conditional effect of social class on the
dropout-delinquency relationship. Journal of Research in Crime
and Delinquency, 33, 232-255.
Jessor, R., & Jessor, S. (1977). Problem behavior andpsychosocial
development. New York: Academic Press.
Kronick, R. F., & Hargis, C. H. (1990). Who drops out and
why-And the recommended action. Springfield, IL: Charles C.
Thomas.
Le Blanc, M. (1990). The educator working with delinquents:
A generalist or a specialist? Revue Canadienne de PsychoEducation, 19, 85-100.
Le Blanc, M. (1996). Manuel des mesures de l'adaptation sociale
et personnelle pour les adolescents québécois [The social and
persona1 inventory questionnaire manual] (2nd ed.). Montréal,
Quebec, Canada: Groupe de Recherche sur les Adolescents en
Difficulté, Université de Montréal.
Le Blanc, M. (1997). A generic control theory of the criminal
phenomenon, the structural and the dynamical statements of an
integrative multilayered control theory. In T. P. Thornberry (Ed.),
Advances in theoretical criminology: Vol. 7. Developmental
theories of crime and delinquency (pp. 215-286). New Brunswick, NJ: Transaction.
Le Blanc, M., & Fréchette, M. (1989). Male criminal activity,from
childhood through youth: Multilevel and developmental perspectives. New York: Springer-Verlag.
Le Blanc, M., Vallières, E., & McDuff, P. (1992). Adolescents'
school experiences and self-reported offending: An empirical
elaboration of an interactional and developmental school social
187
control theory. International Journal of Adolescence and Youth,
3, 197-247.
Lipsey, M. W., & Wilson, D. B. (1998). Effective intervention for
senous juvenile offenders: A synthesis of research. In R. Loeber
& D. P. Farrington (Eds.), Serious and violent juvenile offenders:
Risk factors and successful interventions (pp. 3 13-345). Thousands Oaks, CA: Sage.
Loeber, R., & Farrington, D. P. (1998). Serious and violentjuvenile
offenders: Risk factors and successful interventions. Thousands
Oaks, CA: Sage.
Losel, F. (1996). Working with young offenders: The impact of
meta-analyses. In C. R. Hollin & K. Howells (Eds.), Clinical
approaches to working with young offenders (pp. 57-84).
Chichester, England: Wiley.
MacNaughton-Smith,P. (1965). Some statistical and other numerical techniques for classifying individuals. London: H. M.
Stationery Office.
Mash, E. J. (1989). Treatment of child and family disturbance: A
behavioral-systems perspective. In E. J. Mash & R. A. Barkeley
(Eds.), Treatment of childhood disorders (pp. 3-36). New York:
Guilford Press.
McCombs, A., & Forehand, R. (1989). Adolescent school performance following parental divorce: Are there farnily factors that
can enhance success? Adolescence, 24, 87 1-880.
McCord, J. (1990). Problem behaviors. In S. Feldman & G. Elliott
(Eds.), At the threshold: The developing adolescent (pp. 414430). Cambridge, MA: Harvard University Press.
Menard, S. (1995). Applied logistic regression analysis. Thousand
Oaks, CA: Sage.
Ministère de 1'Education du Québec. (1991). Indicateurs sur la
situation de l'enseignementprimaire et secondaire [Indicatorsof
the situation of primary and secondary schools]. Quebec,
Quebec, Canada: Author.
Moffit, T. E. (1993). Adolescence-lirnited and life-course persistent
antisocial behavior: A developmental taxonomy. Psychological
Review, 100, 674-70 1.
Nunnally, J. C. (1967). Psychometnc theory. Toronto, Ontario,
Canada: McGraw-Hill.
Pulkkinen, L., & Tremblay, R. E. (1992). Patterns of boys' social
adjustrnent in two cultures and at different ages: A longitudinal
perspective. International Journal of Behavioral Development,
15,527-553.
Quay, H. C. (1987). Patterns of delinquent behavior. In H. C. Quay
(Ed.), Handbook of juvenile delinquency (pp. 118-1 38). New
York: Wiley.
Rumberger, R. W. (1983). Dropping out of high school: The
influence of race, sex, and family background. American Educational Research Journal, 20, 199-220.
Rumberger, R. W. (1987). High school dropouts: A review of issues
and evidence. Review of Educational Research, 57, 101-121.
Rumberger, R. W. (1995). Dropping out of middle school: A
multilevel analysis of students and schools. American Educational Research Journal, 32, 583-625.
Rumberger, R. W., Ghatak, R., Poulos, G., Ritter, P., & Dornbush,
S. M. (1990). Family influences on dropout behavior in one
Califomia high school. Sociology of Education, 63, 283-299.
Steinberg, L., Blinde, L., & Chang, K. (1984). Dropping out among
language minonty youîh. Review of Educational Research, 54,
113-132.
Steinberg, L., & Darling, N. (1994). The broader context of social
influence in adolescence. In R. K. Silberstein & E. Todt (Eds.),
Adolescence in context: The interplay of family, school, peers,
and work in adjustment (pp. 2545). New York: Springer-Verlag.
Steinberg, L., Elmen, J. D., & Mounts, N. S. (1989). Authoritative
188
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
parenting, psychosocial maturity, and academic success among
adolescents. Child Development, 60, 1424-1 436.
Tesseneer, R. A., & Tesseneer, L. M. (1958). Review of the
literature on school dropouts. National Association of Secondary
School Principals Bulletin, 42, 141-153.
Tidwell, R. (1988). Dropouts speak out: Qualitative data on early
school departures. Adolescence, 23, 939-954.
Tinto, V. (1975). Dropout from higher education: A theoretical
synthesis of recent research. Review of Educational Research,
45, 89- 125.
Tinto, V. (1993). Leaving college: Rethinking the causes and cure
of student attrition (2nd ed.). Chicago: University of Chicago
Press.
Tremblay, R. E., Le Blanc, M., & Schwartzman, A. E. (1986). La
c9nduite délinquante des adolescents de Montréal (1974-1985):
Etude descriptive et prédictive [Delinquent behavior of adolescents from Montreal (1974-1985): A descriptive and predictive
study]. Montréal, Quebec, Canada: Ecole de Psycho-Education,
Université de Montréal.
Wehlage, G. G., Rutter, R. A., Smith, G. A., Lesko, N., &
Fernandez, R. R. (1989). Reducing the risks: Schools as
communities of support. New York: Falmer Press.
Appendix A
Scales and Variables of the Social Inventory Questionnaire
Scalelvariable
School experience
Achievement scores
Commitment
Misbehavior
Stress
Disciplinary sanctions
Involvement
Truancy (beliefs)
Family experience
Background
Socioeconomic status
(surnrnary index)
Family disadvantage
(surnmary index)
Parents' education
Processes
Parents' alcohol use
Marital conflict
Attachment
Supervision
Punishments
Family rebelliousness
Peer relationships
No. friends
Involvement wlfriends
Leadership
Identification
Deviant friends
Leisure
Allowance
Passive leisures
Active leisures
Loitering
Part-time job
Beliefs
Conventional noms
No.
items
or
Sample item
On average, what are your grades in French this year?
Do you like school?
How important is it for you to have good grades?
Have you voluntariiy disturbed the class; been impolite wlteacher; been cheating during exams?
Do you feel nervous and tense, stressed in school?
Have you been sent out of class this year?
Have you been suspended from school this year?
Usually, how many hours a day do you spend on your schoolwork at school and at home?
Do you participate in extra-curricular cultural activities?
What do you think of kids of your age who miss school without an excuse?
A measure of occupational prestige-motherlfather
Unemployment and welfare of fatherlmother
How many times have you moved in your life?
How many brothers and sisters do you have?
Approximately how many years did your motherlfather go to school?
Can you Say that your motherlfather never drinks alcohol?
(Even if they're not living together) do your parents fight in front of you?
Do you share thoughts and feelings with your parents?
Do your parents understand what you think or feel?
Would you like to have the qualities and failings of your parents; do you feel rejected by
your parents?
Do your parents know who you are with when you are not home?
Do your parents punish you by calling you names?
Do your parents punish you by hitting you?
1s there a rule at home regarding a curfew?
1s there a rule at home regarding TV watching?
Did you refuse to comply to parent orders; have you run away from home for more than 24
hours?
How many best friends do you have?
How many hourslwk. do you share activities wlfriends?
Are you the kind of person who tells the others what to do in group?
Would you like to have the qualities and failings of your best friend?
How many of your friends have been arrested by the police?
Could your best friends be in trouble wlpolice for îhings they've done?
How much money do your parents give you each week?
How many hours a day do you listen to music?
How often do you go to the movies?
How many hourslwk. do you practice a sport?
How many hourslwk. are you involved in a specific hobby?
How many hourslwk. do you do nothing, be idle?
How many hourslwk. do you work after school and on weekends for money?
What do you think of kids of your age who voluntarily break things of others?
What do you think of kids of your age who run away from home for more than 24 hours?
189
DROPOUT TYPOLOGY
Appendix A
Scalelvariable
Beliefs (continued)
Religious practice
Respect authorities
Problem behaviors
Drug use
Delinquency
No.
items
Sample item
a
2
2
.70 Do you go to church?
.88 Do you have a lot of respect for and confidence in the police?
5
.90 During the past year, have you used marijuana, pot (a joint)?
During the past year, have you taken hard drugs?
.82 During the past year, did you fight with a weapon?
During the past year, have you deliberately broken something that wasn't yours?
During the past year, have you been arrested by the police?
21
Arrests
(continued)
1
Note. Several scales include only one or two items. Though they may appear lirnited in their ability to represent different constructs, they
were included in this study for two reasons. First, these items and scales have shown strong and stable discrirninating value between
delinquents and nondelinquents and between dropouts and nondropouts (Janosz, 1995). Second, we tested systematicallythe linearity of the
relationships between al1 scales, including those with one and two items, and the graduation status.
Appendix B
Means and Standard Deviations (2 Scores) for Graduates and Types of Dropouts
Graduatesa
Independent variable
M
SD
Quietsb
M
SD
Disengagedc
M
1974 sample
Age (years)
No. years before dropout
School experience
Comrnitmentf
Achievementf
Grade retentionf
School misbehaviorf
Involvement
Stress
Disciplinary sanctions
Beliefs versus tniancy
Family background
Structure
Socioeconomic status
Family disadvantage
Parents' education
Process
Parents' alcohol use
Marital conflict
Attachment
Supervision
Punishrnents
Rules
Rebelliousness
Lifestyle
Peer relationships
No. friends
Involvement wlfriends
Leadership
Identification
Deviant friends
Leisure
Allowance
Passive leisures
Active leisures
Loitering
Part-time job
(Appendix continues)
SD
Low
achieversd
Maladjustede
M
M
SD
SD
JANOSZ, LE BLANC, BOULERICE, AND TREMBLAY
Appendix B
Graduatesa
Independent variable
Beliefs
Respect for authorities
Religious practice
Conventional noms
Deviant behavior
h g use
Delinquency
Arrests
(continued)
Quietsb
Disengagedc
Low
achieversd
Maladjustede
M
SD
M
SD
M
SD
M
SD
M
SD
0.03
0.05
-0.05
1.00
0.99
0.98
-0.52
0.07
-0.14
0.84
0.99
1.12
0.14
-0.41
0.39
1.05
0.65
1.03
-0.31
-0.18
-0.45
0.91
0.86
0.72
0.29
-0.43
0.57
1.01
0.88
0.87
-0.04
-0.04
-0.09
0.97
0.94
0.65
-0.21
-0.16
0.23
0.75
0.96
1.68
0.13
0.11
-0.03
1.02
1.41
0.70
0.07
-0.37
0.09
0.97
0.57
0.89
0.58
0.59
0.64
1.30
1.36
2.15
1985 sample
Age (years)
No. years before dropout
School experience
Commitmentf
Achievementf
Grade retentionf
School rnisbehaviorf
Involvement
Stress
Disciplinary sanctions
Beliefs versus truancy
Family background
Structure
Socioeconomic status
Family disadvantage
Parents' education
Process
Parents' alcohol use
Marital conflict
Attachment
Supervision
Punishrnents
Rules
Rebelliousness
Lifestyle
Peer relationships
No. friends
Involvement wlfriends
Leadership
Identification
Deviant friends
Leisure
Allowance
Passive leisures
Active leisures
Loitering
Part-timejob
Beliefs
Respect for authorities
Religious practice
Conventional norms
Deviant behavior
h g use
Delinquency
Arrests
"n = 619 for 1974 sample and 456 for 1985 sample. bn = 63 for 1974 sample and 124 for 1985 sample. "n = 18 for 1974 sample and 20
for 1985 sample. dn = 21 for 1974 sample and 25 for 1985 sample. en = 64 for 1985 sample and 132 for 1985 sample. 'Criterion
variable used to construct the typology.
Received June 9, 1997
Revision received March 1, 1999
Accepted June 1,1999