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Substance use risk profiles and associations with early substance
use in adolescence
Monique Malmberg • Geertjan Overbeek •
Karin Monshouwer • Jeroen Lammers •
Wilma A. M. Vollebergh • Rutger C. M. E. Engels
Received: November 9, 2009 / Accepted: June 30, 2010 / Published online: July 13, 20
10
� The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract We examined whether anxiety sensitivity,
hopelessness, sensation seeking, and impulsivity (i.e.,
revised version of the Substance Use Risk Profile Scale)
would be related to the lifetime prevalence and age of onset
of alcohol, tobacco, and cannabis use, and to polydrug use
in early adolescence. Baseline data of a broader effec-
tiveness study were used from 3,783 early adolescents aged
11–15 years. Structural equation models showed that
hopelessness and sensation seeking were indicative of ever-
used alcohol, tobacco or cannabis and for the use of more
than one substance. Furthermore, individuals with higher
levels of hopelessness had a higher chance of starting to
use alcohol or cannabis at an earlier age, but highly anxiety
sensitive individuals were less likely to start using alcohol
use at a younger age. Conclusively, early adolescents who
report higher levels of hopelessness and sensation seeking
seem to be at higher risk for an early onset of substance use
and poly sub
stance use.
Keywords Alcohol use � Tobacco use � Cannabis use �
Personality � Early adolescence
Introduction
Dutch adolescents are one of the leaders in terms of
drinking frequency and binge drinking in Europe and they
usually start drinking in early adolescence (Hibell et al.
2009). Also, their use of tobacco and cannabis increases
rapidly during this period (Monshouwer et al. 2008). This
is disturbing in that early initiation of substance use has
many detrimental consequences, like distortion of brain
development (e.g., Tapert et al. 2002) and elevated risk for
later dependence and misuse (e.g., Andersen et al. 2003).
Further, early initiation increases the likelihood of poly
substance use (Ellickson et al. 2003) that, in turn, leads to
more damaging health effects (Feigelman et al. 1998).
Thus, identifying risk profiles of early adolescent girls and
boys is of crucial importance, because it may facilitate
adequate prevention efforts targeted at youths who are at
risk for an early onset of substance use or abuse (e.g.,
Conrod et al. 2008, 2010).
It is well known that personality is associated with
substance use (e.g., Flory et al. 2002) and in general, per-
sonality dimensions involving neurotic tendencies or defi-
cits in behavioral inhibition are found to best predict
substance (mis)use (e.g., Barrett et al. 1998; Cloninger
et al. 1991). Furthermore, personality dimensions con-
cerning specific, rather than general personality disposi-
tions are of most interest for substance related behaviors
(Caspi et al. 1996; Comeau et al. 2001; Jackson and Sher
2003; Woicik et al. 2009). One instrument that specifically
taps specific personality dimensions involving neurotic
tendencies and inhibition deficits is the Substance Use Risk
M. Malmberg (&) � R. C. M. E. Engels
Behavioural Science Institute, Radboud University Nijmegen,
P.O. Box 9104, 6500 HE Nijmegen,
The Netherlands
e-mail: m.malmberg@pwo.ru.nl
G. Overbeek
Developmental Psychology, Utrecht University, Utrecht,
The Netherlands
K. Monshouwer � J. Lammers
Trimbos Institute (Netherlands Institute of Mental Health
and Addiction), Utrecht, The Netherlands
K. Monshouwer � W. A. M. Vollebergh
Department of Interdisciplinary Social Science,
Utrecht University, Utrecht, The Netherlands
123
J Behav Med (2010) 33:474–48
5
DOI 10.1007/s10865-010-9278-4
Profile Scale (SURPS; Woicik et al. 2009). This instrument
measures four distinct and independent personality traits
(i.e., anxiety sensitivity, hopelessness, sensation seeking,
and impulsivity) that are hypothesized and actually ap-
peared to be related to high and problematic substance use
behaviors (Conrod et al. 1998; Jackson and Sher 2003;
Pulkkinen and Pitkänen 1994; Shall et al. 1992; Sher et al.
2000; Stewart et al. 1995) and other risk behaviors (e.g.,
delinquency; Woicik
et al. 2009).
The first trait (i.e., anxiety sensitivity) refers to the fear
of symptoms of psychical arousal (e.g., feeling dizzy or
faint; Reis et al. 1986) and the second (i.e., hopelessness) is
identified as a risk factor for the development of depression
(Joiner 2001). Both anxiety sensitivity and hopelessness
relate to increased levels of drinking and problem drinking
(Stewart et al. 1995; Conrod et al. 1998). The third trait
(i.e., impulsivity) involves difficulties in the regulation
(controlling) of behavioral responses (Spoont 1992) and is
related to an increased risk for early alcohol and drug
(mis)use (Pulkkinen and Pitkänen 1994). Finally, the fourth
trait (i.e., sensation seeking) is characterized by the desire
for intense and novel experiences (Zuckerman 1994) and
sensation seekers have been found to drink more and to be
more at risk for heavy alcohol use (Shall et al. 1992; Sher
et al. 2000). The four SURPS’ personality traits are based
on extended personality measures (e.g., ASI; Peterson and
Reiss 1992) and show stronger associations with these
measures than with scales measuring broader dimensions
of personality (e.g., NEO-FFI; Costa and McCrae 1992).
Sensation seeking is, for instance, related to measures of
openness and extraversion, but is more strongly related to
scales measuring venturesomeness (Eysenck and Eysenck
1978; Woicik et al. 2009).
The SURPS personality traits show some overlap with
traits of temperament (TCI; Cloninger 1998). Novelty
seeking, for example, concerns the tendency to actively
respond to new stimuli and thus reflects elements of
impulsivity and sensation seeking. Further, the SURPS
personality traits are relevant for more neuropsychological
orientations. Different reinforcement processes are as-
sumed to mediate the relationship between the SURPS
personality traits and substance use in that the personality
traits are susceptible to different types of reinforcement
(e.g., Brunelle et al. 2004; Conrod et al. 1998). Individuals
with high levels of anxiety sensitivity or hopelessness are
more sensitive for the negative reinforcement processes of
substance use (i.e., the ability of substances to relieve
negative affective states). Individuals who score high on
sensation seeking and impulsivity on the other hand are
more sensitive for the positive reinforcement processes of
substance use (i.e., the positive hedonic effects of a sub-
stance).
According to Carver et al. (2009) these processes are
even more apparent in case of low serotonergic function.
It is argued that individual differences in serotonergic
function are important for personality dispositions in that
individuals with low serotonergic function are especially
susceptible for (affective) cues of the moment (Spoont
1992), like reinforcement processes. In accordance, low
serotonergic function is related to personality dispositions
as sensation seeking, impulsivity, and depression (Carver
et al. 2009). Considering the possible contribution of the
SURPS to many different fields (e.g., neuropsychology),
the fact that a more clinical orientation (i.e., the use of
more clinical instruments like the TCI) seems less obvi-
ous for early adolescents who are in the beginning stage
of substance use, and bearing in mind that specific rather
than general personality traits are most interesting, the
SURPS is a potentially important measurement for
examining the role of personality on substance use
behaviors.
Recall that the SURPS-based personality profiles are
useful in identifying individuals who are at risk for
alcohol use and alcohol-related problems in already
using samples. However, to our knowledge no previous
study examined whether these personality profiles are
indicative of an early onset of alcohol, tobacco, canna-
bis, and poly substance use. This is unfortunate, because
on the one hand early initiation is one of the strongest
identified risk factors for alcohol (De Wit et al. 2000),
tobacco (Breslau et al. 1993), and cannabis problems
(Chen et al. 2005) in later life. Further, poly substance
use in adolescence is a significant predictor of poly
substance use in adulthood (Galaif and Newcomb 1999).
On the other hand, the developmental role of personality
dispositions is important. The lower order personality
dispositions might be overruled by higher order systems
(i.e., rational or cognitive), but only if and once the
capacity for behavioral control develops (i.e., through
maturation of the pre-frontal cortex; Carver et al. 2009).
Thus, one might argue that especially early adolescents
are vulnerable for these lower order personality predis-
positions. To conclude, focusing on early onset of sub-
stance use in early adolescence, and identifying the
specific personality profiles related to these risk behav-
iors, might help us to identify youngsters at an early age
who are at risk for developing future substance misuse
patterns.
The present study examines a SURPS-based, four-factor
personality model in relation to early onset substance use
and poly substance use. A total of 3,783 adolescents in the
ages of 11–15 participated in the first wave of the ongoing
Healthy School and Drugs (HSD) effectiveness study in
which they filled out a digital questionnaire. Participants
J Behav Med (2010) 33:474–485 475
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3
answered questions about alcohol, tobacco, and cannabis
use and their personality traits. Based on previous
research on personality, we expected to find strongest
associations with substance use for sensation seeking.
Specifically, we hypothesize sensation seekers to have an
increased risk for an early initiation of alcohol, tobacco,
and cannabis use. Hence, we expected to find that anxiety
sensitive adolescents have an increased risk for an early
onset of alcohol use, adolescents reporting higher levels
of hopelessness to have an increased risk for an early
onset of alcohol and tobacco use, and impulsive adoles-
cents to have an increased risk for an early onset of
alcohol and cannabis use. Following these expectations
we also expected to find associations between the SURPS
personality profiles and poly substance use. However,
considering the lack of knowledge so far in adolescence,
no concrete expectations were formulated on poly sub-
stance use.
Method
Sample and procedure
The cross-sectional data for this study were collected as
part of a broader effectiveness study on a national school
prevention program ‘‘The Healthy school and drugs.’’ A
total of 23 schools were included from seven regions in
The Netherlands. We visited participating schools and
during these visits we provided further information about
the research project. In collaboration with the schools’
headmasters, we informed the students’ parents about the
goals of the study by a letter in which parents were also
explained they could refuse participation of their child in
the study. Approval for the design and data collection
procedures was obtained from the ethic committee of the
Radboud University Nijmegen. All data were collected
between January and March 2009. All first grade students
independently filled out a digital questionnaire during
school hours in the presence of a teacher and a
research
assistant. The questionnaires were counterbalanced on
alcohol, tobacco, and cannabis, thus six different versions
were administrated.
In total, 3,783 first-grade students took part in the study
of whom 231 (6.1%) were absent (i.e., illness) during data-
collection and three participants were declined participa-
tion by their parents. The total sample included 1,856 boys
(49.1%) and 31.5% (n = 1,192) of all participants pursued
lower secondary vocational education, 46.6% (n = 1,764)
pursued pre-university education, and 21.9% (n = 827) of
the students pursued a mixed educational program. Of the
participants who completed the questionnaire 3,375 par-
ticipants (96.2%) were of Dutch ethnic origin. Students
ranged in age from 11 to 15 years (M = 13.01, SD = .49).
For the question on lifetime prevalence of alcohol use,
2,103 (59.9%) reported to have at least once used alcohol
in the past. With regard to smoking, 768 (22.1%) partici-
pants had ever smoked, and with regard to cannabis 75
(2.1%) participants reported to have at least once used
cannabis. Finally, 670 (19.6%) stated that they already had
tried more than one substance.
Measures
Personality profiles
The Substance Use Risk Profile Scale (SURPS; Woicik
et al. 2009) distinguishes four personality dimensions,
namely anxiety sensitivity, hopelessness, sensation seek-
ing, and impulsivity. Each dimension was assessed using
five to seven items that could be answered on a 4-point
scale, ranging from 1 = ‘strongly agree’ to 4 = ‘strongly
disagree.’ Anxiety sensitivity refers to the fear for physical
arousal and an example item is: ‘It’s frightening to feel
dizzy or faint.’ Hopelessness concerns negative thinking
which might lead to depression proneness and ‘I feel that
I’m a failure’ is an example item. Sensation seeking is
characterized by wanting to try out new things and an
example of such an item is ‘I like doing things that frighten
me a little.’ Finally impulsivity refers to having difficulties
in controlling behavioral responses, and ‘I usually act
without stopping to think’ is an example item. Factor
structure, internal consistency and test–retest reliability, as
well as construct, convergent, and discriminant validity of
this instrument were shown to be adequate in studies
among college students and adult samples (e.g., Krank
et al. submitted). Because the instrument was translated in
Dutch and used for the first time the factor structure was
examined using Exploratory Factor Analysis (EFA) on a
randomly selected sample that consisted of the first half of
the original sample using Mplus (Muthén and Muthén
1998–2007). The Weighted Least Square parameter esti-
mator with Mean- and Variance adjusted chi-square test
statistic (WLSMV) was used because the metric of the
items is more ordered categorical than interval level. The
sample was randomly divided into two subsamples. Two
items were removed. The first item (i.e., I feel that I’m a
failure) had substantial loadings (.38 and .42, respectively)
on the factors anxiety sensitivity and hopelessness. The
second item (i.e., I feel I have to be manipulative to get
what I want) showed an almost zero loading on the factor
impulsiveness. A Confirmatory Factor Analysis (CFA) was
performed on the remaining 21 SURPS items on the other
half of the sample and confirmed the four-factor structure
of the SURPS. The final model had a satisfactory fit to
the data (v2 (54) = 611.315, P \ .001, RMSEA = .055,
476 J Behav Med (2010) 33:474–485
1
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CFI = .943). Cronbach’s alphas were .69 for anxiety
sensitivity (factor loadings between .42 and .72), .85 for
hopelessness (loadings between .72 and .96), .68 for sen-
sation seeking (loadings between .38 and .72), and .6
7
for impulsivity (loadings between .48 and .72). These
reliability estimates converge with those from previous
research (e.g., Jaffee and D’Zurilla 2009) and are satis-
factory for short scales (Loewenthal 1996).
Substance use
We assessed adolescents’ alcohol use in terms of lifetime
prevalence, or whether participants had ever consumed
alcohol in their life. Lifetime prevalence was measured by
asking: ‘‘Have you ever drunk alcohol?’’ Participants could
answer this question with yes (=1) or no (=0). To determine
the age of onset of participants’ alcohol use we asked how
old they were when they had first drunk alcohol (Kuntsche
et al. 2009).
Lifetime prevalence of tobacco use was measured by a
single item on a 9-point scale ranging from 1 = ‘I never
smoked, not even a puff’ to 9 = ‘I smoke at least once a
day’ (Kremers et al. 2001). To tap lifetime prevalence of
smoking, adolescents who responded in the categories 2–
9
were categorized as tried smoking before (=1), and the
adolescents who responded in category 1 were categorized
as never tried smoking (=0) following Kremers (2002). In
order to assess age of onset, participants who had ever
smoked were asked how old they were when they smoked
their first puff.
We assessed the lifetime prevalence of cannabis use
through a single item, namely: ‘‘Have you ever used can-
nabis?’’ (Monshouwer et al. 2005). Participants could an-
swer with yes (=1) or no (=0). Subsequently, participants
who ever used cannabis were asked how old they were
when they first used cannabis.
Finally, poly substance use was operationalized by the
use of more than one substance, regardless of the combi-
nation or amount of substances used. A new variable was
created in which all adolescents who used more than
one substance were categorized as poly substance users
(=1) and all other adolescents as non-poly substance users
(=0).
Strategy of analyses
First, descriptive analyses and Pearson correlations of age
of onset of alcohol, tobacco, and cannabis use and the
personality profiles (i.e., anxiety sensitivity, hopelessness,
sensation seeking, and impulsivity) were calculated
between model variables. Second, to investigate whether
participants’ sex and educational level should be specified
as covariates in the model, a MANOVA was conducted to
compare responses on the SURPS personality profiles be-
tween males and females and between different educational
levels. Another MANOVA was carried out to investigate
sex and educational differences on substance use. Also,
separate ANOVA’s were conducted to examine sex and
educational level differences on age of onset of alcohol,
tobacco, and cannabis use. The effect sizes (i.e., partial eta
squared) are reported for the analyses of variance. With
respect to the effect size, values around .02 are considered
small effects, values around .15 medium effects, and values
around .35 large effects (Cohen 1992). Post-hoc tests with
Bonferroni corrections were carried out to investigate the
significant differences in educational level on the outcome
variables.
Next, to investigate the relationships between person-
ality profiles and lifetime prevalence of alcohol, tobacco,
and cannabis use, we specified and tested a first model
(see Fig. 1) with structural equation modeling (SEM) in
Mplus (Muthén and Muthén 1998–2007). In this model,
lifetime prevalences of alcohol, tobacco, and cannabis
were included as observed variables and personality
profiles were added as latent constructs, with separate
scale items as indicators. Sex and educational level were
specified as covariates in the model. We used the
weighted least square method (WLSMV) to estimate
parameters in the model. The Chi-square and the p-value,
the Comparative Fit Index (CFI: Bentler 1989), and the
Anxiety
Sensitivity
Hopeless-
ness
Sensation
Seeking
Alcohol
use
Tobacco
use
Cannabis
use
8
10
2
1
18
1
4
6
3
23
20
13
7
4
1
19
1
2
9
.43
-.08*
.65
.73
.68
.57
.73
.73
.85
.73
.96
.77
.55
.60
.71
.54
.66
Impulsivity
15
5
2
11
.48
.59
.73
.57
.16
-.01
-.05
-.04
-.09
.30*
.42***
.43***
.31***
.36***
.21**
16 .36
Fig. 1 Standardized estimates of associations between SURPS
personality profiles and lifetime prevalence of substance use
(n = 3,783). * P \ .05,
** P \ .01, *** P \ .001
J Behav Med (2010) 33:474–485 477
123
Root Mean Square Error of Approximation (RMSEA:
Steiger 1990) were used to assess the goodness of fit of
the model. With respect to the CFI, values above .90
indicate an acceptable fit and values above .95 signify an
excellent fit to the data. Concerning the RMSEA, values
below .08 point to an acceptable fit and values below .05
indicate a good fit of the model to the data (Hu and
Bentler 1999). The explained variance was used as a
measure of effect size. Values around 2% are considered
small, values around 15% medium, and values around
35% are considered large effects (Cohen 1992). The data
have a multilevel structure (i.e., data of individual stu-
dents are nested within classes), which means that apart
from differences between individuals, average substance
use levels across classes may vary as well. In particular,
participants within certain classes may be more similar
to each other due to specific influence and selection
processes (Kuntsche et al. 2008); classmates in our tar-
get group might influence each other in such a way that
their substance using behaviors become more similar.
This means that individual respondents are not inde-
pendent within classes. As a consequence the standard
errors of the parameter estimates are biased leading to
incorrect decisions about the significance of parameter
estimates. The COMPLEX procedure in Mplus is used
to correct for dependency of the data, which results
in unbiased standard errors (cf Kuntsche and Jordan
2006).
To investigate the relationship between personality
profiles and age of onset of alcohol, tobacco, and can-
nabis use only substance users were included in the
subsequent analyses (e.g., only those who already drank
alcohol were included in the analysis to see whether
personality profiles were related to the age of onset of
alcohol use). The personality profiles were again included
in the model as latent constructs and the age of onset as
an observed variable. Identical statistical procedures were
used as in the former model. Finally, to investigate the
relationship between personality profiles and poly sub-
stance use two variables were created in the dataset, one
for mono use and one for poly use. All participants that
only used one substance were assigned a score ‘1’ and all
others were assigned ‘0’ in the mono variable. For poly
substance use, all participants who reported having used
two or three substances were assigned ‘1’ and all others
were assigned ‘0’. Based on this information, we esti-
mated the two models with the same procedures as the
other models in Mplus.
Results
Descriptive analyses
Table 1 presents the means and standard deviations of the
SURPS’ personality profiles and age of onset examined in
the present study, separately for educational level and sex.
For Pearson correlations of the model variables we refer to
‘‘Appendix’’. A MANOVA was conducted to examine
whether personality profiles would significantly differ
across sex and educational level. Main effects of sex [F(4,
3,431) = 86.40, P \ .001, gp
2 = .092] and education [F(8,
6,862) = 15.92, P \ .001, gp
2 = .018] emerged in the
MANOVA on different personality profiles. Univariate
tests showed sex effects for anxiety sensitivity [F(1,
3,434) = 110.79, P \ .001, gp
2 = .031], hopelessness [F(1,
3,434) = 5.50, P = .02, gp
2 = .002], and sensation seeking
[F(1, 3,434) = 212.69, P \ .001, gp
2 = .058]. Specifically,
we found that girls reported higher scores on anxiety sen-
sitivity and hopelessness than boys, and boys reported
higher levels of sensation seeking than girls. Associa-
tions were also found between education and hopelessness
Table 1 Means and standard deviations for personality profiles and age of onset
Gender Educational level Total
Female Male Lower Mixed Higher
Age of onset
Alcohol 10.40 (2.19)* 9.67 (2.50)* 10.41 (2.44)ab 9.93 (2.40)a 9.77 (2.30)b 10.01 (2.38)
Tobacco 11.26 (1.67)* 10.91 (1.99)* 11.31 (1.79)ab 10.90 (2.01)a 10.81 (1.81)b 11.07 (1.86)
Cannabis 12.45 (.74) 11.90 (1.53) 12.28 (1.08) 11.75 (1.24) 11.75 (2.18) 12.07 (1.36)
Personality profiles
Anxiety sensitivity 2.38 (.62)* 2.13 (.67)* 2.30 (.72)a 2.27 (.66) 2.23 (.62)a 2.26 (.66)
Hopelessness 1.55 (.53)* 1.50 (.56)* 1.64 (.63)ab 1.51 (.51)a 1.46 (.49)b 1.52 (.55)
Sensation seeking 2.38 (.66)* 2.72 (.66)* 2.49 (.70)ab 2.60 (.69)a 2.56 (.67)b 2.55 (.68)
Impulsivity 2.18 (.59) 2.23 (.64) 2.29 (.66)a 2.24 (.61)b 2.14 (.58)ab 2.21 (.62)
Means with the same superscripts are significantly different from each other. All at P \ .05 with Bonferroni corrections for educational level
478 J Behav Med (2010) 33:474–485
123
[F(2, 3,434) = 36.40, P\ .001, gp
2 = .021], sensation seeking
[F(2, 3,434) = 9.73, P \ .001, gp
2 = .006], and impulsiv-
ity [F(2, 3,434) = 21.88, P \ .001, gp
2 = .013]. Students
of higher education reported higher scores on impulsivity
and hopelessness compared to students of lower educa-
tion. The pattern for sensation seeking was somewhat
different. Students of mixed education reported higher
scores than students in both lower and higher educational
levels, but students of higher education scored higher than
students of lower education.
Another MANOVA was conducted to look at possible
differences for sex and educational level on substance use.
We found main effects for both sex [F(4, 3,411) = 11.04,
P \ .001, gp
2 = .013] and education [F(8, 6,822) = 23.80,
P \ .001, gp
2 = .027] on substance use. Univariate tests
showed sex effects for alcohol [F(1, 3,414) = 23.98, P \
.001, gp
2 = .007], tobacco [F(1, 3,414) = 17.72, P \
.001, gp
2 = .005], cannabis [F(1, 3,414) = 17.51, P \ .001,
gp
2 = .005], and poly substance use [F(1, 3,414) = 17.75,
P \ .001, gp
2 = .005]. Particularly, we found that more
boys already used the different substances compared to
girls and more boys were poly substance users in contrast
to girls. Univariate tests also showed education effects
on alcohol [F(2, 3,414) = 3.32, P = .04, gp
2 = .002],
tobacco [F(2, 3,414) = 88.89, P \ .001, gp
2 = .049], can-
nabis [F(2, 3,414) = 17.96, P \ .001, gp
2 = .010] and poly
substance use [F(2, 3,414) = 70.68, P \ .001, gp
2 = .040].
More students of lower education reported having used
alcohol, tobacco, or cannabis compared to students from
higher education. Also, students of lower education were
more likely to use more than one substance compared with
students from higher education.
We conducted a set of three ANOVA’s to test sex and
education differences for age of onset of alcohol use, tobacco
use, and cannabis use. Main effects of sex [F(1, 2,038) =
51.07, P \ .001, gp
2 = .024] and education [F(2, 2,038) =
14.05, P \ .001, gp
2 = .014] were found for the age of onset
of alcohol. With regard to tobacco use we found main effects
of sex [F(1, 745) = 5.65, P = .02, gp
2 = .008] and educa-
tion [F(2, 745) = 5.20, P \ .01, gp
2 = .014]. Finally, the
last ANOVA in which age of onset of cannabis use
was the dependent variable, showed no main effects
for sex and education. In sum, the results indicated
that boys and students from higher education start drinking
and smoking earlier compared to girls and students
from lower education. Overall, although the effects
of sex and educational level on substance use and person-
ality were small, they were still significant and
were therefore specified as covariates in the subsequent
analyses.
Personality profiles and lifetime prevalence
The model as depicted in Fig. 1 showed a good fit to
the data [v2 (df = 68, n = 3,783) = 725.791, P \ .001,
RMSEA = .051, CFI = .929]. As can be seen in Fig. 1,
standardized estimates for the associations between per-
sonality profiles and lifetime prevalences revealed signifi-
cant associations between anxiety sensitivity (b = -.08,
P = .024), hopelessness (b = .31, P \ .001), and sensa-
tion seeking (b = .43, P \ .001) with the lifetime preva-
lence of alcohol use. These results indicate that youngsters
with lower levels of anxiety sensitivity and higher levels of
hopelessness and sensation seeking were more likely to
have ever consumed alcohol. Further, we found signifi-
cant associations between hopelessness (b = .36,
P \ .001) and sensation seeking (b = .42, P \ .001) with
the lifetime prevalence of tobacco use. Adolescents who
were high on hopelessness and sensation seeking were
more likely to have ever smoked than adolescents who
were low on these two profiles. Finally, the analysis
showed significant linkages between hopelessness
(b = .21, P = .007), sensation seeking (b = .30, P =
.023) and lifetime prevalence of cannabis use. This means
that youngsters who had higher levels of hopelessness and
sensation seeking had a higher chance of having ever used
cannabis at this age than youngsters who had lower scores
on these profiles. The models showed medium to large
effect sizes for the relationships between the four per-
sonality profiles and substance use; they explained 19.1%
of the variance in lifetime prevalence of alcohol use,
31.3% of the variance in tobacco use, and 28.8% of the
variance in cannabis use.
Personality profiles and age of onset
The model that specified the relationship between person-
ality profiles and the age of onset of alcohol use showed
an adequate fit to the data [v2 (df = 62, n = 2,103) =
416.739, P \ .001, RMSEA = .052, CFI = .943]. Con-
trolling for participants’ sex and education, we found sig-
nificant associations between hopelessness and age of onset
of alcohol use (Table 2). This result showed that students
start to drink at a younger age when they have higher levels
of hopelessness. The model that assessed the relationship
between personality profiles and age of onset of tobacco
use also showed an adequate fit to the data [v2 (df = 58,
n = 768) = 228.326, P \ .001, RMSEA = .062, CFI =
.928]. Table 2 shows the standardized estimates of this
model; we did not find any significant associations between
the personality profiles and the age of onset of tobacco use.
J Behav Med (2010) 33:474–485 479
123
We could not adequately test the relationship between
personality profiles and age of onset of cannabis use con-
sidering the small sample size of cannabis users (n = 75).
As an alternative (to reduce the number of parameters to
be estimated) we applied regression analysis in Mplus
with sex and education as control variables and the four
manifest personality profiles as predictors of age of onset
of cannabis use. We found a significant relationship
between hopelessness and age of onset of cannabis
use (b = -.37, P = .001) indicating that an increase of
hopelessness is associated with a decrease of age of onset
of cannabis use. The models showed small effect sizes for
the association between the four personality profiles—
controlling for sex and educational level—and the age of
onset of alcohol (R2 = 5%) and tobacco (R2 = 3.3%) use,
and a medium effect size for the relationship between
personality profiles, sex and educational level on the one
hand and the age of onset of cannabis use on the other
(R2 = 17.7%).
Personality profiles and poly substance use
The mono substance use model showed a good fit to
the data [v2 (df = 62, n = 3,783) = 656.514, P \ .001,
RMSEA = .050, CFI = .937]. Significant associations
were found between hopelessness and sensation seeking
with mono substance use (Table 2). Thus, students that
experienced more feelings of hopelessness or students who
were higher on sensation seeking were also more likely
to use one specific substance (i.e., either alcohol, tobacco,
or cannabis). The model examining poly substance use
showed a good fit to the data [v2 (df = 62, n = 3,783) =
693.229, P \ .001, RMSEA = .052, CFI = .933]. The
results in Table 2 display significant associations between
hopelessness, sensation seeking, and poly substance use.
Thus, more feelings of hopelessness and being a sensation
seeker were related to the use of more than one substance.
The model on mono-substance use showed a medium
effect size (R2 = 11.4%) for the four personality profiles
and the model on poly substance use a large effect size
(R2 = 31.8%).
Discussion
The results clearly demonstrated that, overall, three out of
the four SURPS’ personality profiles are associated with
early adolescents’ substance use behavior. Notably, the
different models revealed that—in this sample of early
adolescents, of whom many are in the starting phase of
experimentation with substance use—especially hope-
lessness and sensation seeking are strongly associated
with a higher chance of ever-used alcohol, tobacco, and
cannabis at an early age and with poly substance use.
Individuals with higher levels of hopelessness have also a
higher chance of starting to use alcohol or cannabis at an
earlier age. Highly anxiety sensitive individuals on the
other hand are less likely to start using alcohol use at a
younger age.
Personality profiles and lifetime prevalence
Previous studies investigating the role of the SURPS
personality profiles on alcohol use mainly focused on
more advanced levels of drinking (e.g., Cooper et al.
1995). Our present results extend this knowledge by
demonstrating that the revised SURPS personality profiles
are not only indicative of already established maladaptive
drinking patterns in adolescents and adults (e.g., Sher
et al. 2000), but are also associated with alcohol use in
young adolescents. Specifically, the SURPS personality
profiles are associated with early adolescents’ alcohol use
to a moderately strong degree. For this particular age
group, we found that especially hopelessness and sensa-
tion seeking are indicative of having ever used alcohol in
early adolescence. The results with regard to sensation
seeking are not unexpected given the novelty seeking
nature of sensation seekers and that experimenting with
Table 2 Standardized estimates and standard errors for tested models
Age of onset Substance use
Alcohol Tobacco Cannabis Mono Poly
b SE b SE b SE b SE b SE
Anxiety .01 .03 .03 .06 .01 .11 -.04 .04 -.07 .04
Hopelessness -.10** .04 -.04 .05 -.37** .17 .22*** .04 .37*** .04
Sensation -.06 .05 .11 .09 -.22 .10 .34*** .06 .43*** .06
Impulsivity -.05 .05 -.09 .08 -.02 .10 -.05 .07 .01 .07
** P \ .01, *** P \ .001
480 J Behav Med (2010) 33:474–485
123
different substances can be seen as such novel experi-
ences. Although it was not clear what the role of hope-
lessness would be in our age group, we did find it
surprising that hopelessness seems this important in our
age group, since this trait was primarily found to be
predictive of a progression into substance misuse before
(e.g., Jackson and Sher 2003). One possible explanation is
that hopelessness leads adolescents to initiate substance
use as a means to cope with negative thoughts. Therefore,
we examined if higher scores on different coping strate-
gies (e.g., drinking alcohol makes me relaxed) were re-
lated to higher levels of hopelessness. However, we could
not substantiate this explanation based on these additional
analyses of our data. More information on these analyses
can be obtained from the first author.
Another explanation is that early childhood problems
(e.g., family violence, unorganized family environments,
antisocial behavior) can lead to both negative affect (e.g.,
Reinherz et al. 2003) and an early onset of substance use
(e.g., Dishion et al. 1999). The existing relationship be-
tween hopelessness and the lifetime prevalences might
then be based on a third variable explanation, indicating
that early childhood adversity can affect the development
of personality profiles, and subsequent engagement in
problem behaviors (Akse et al. 2004; Hale et al. 2008).
Since hopelessness is associated with self-harm and sui-
cide behavior (O’Connor et al. 2008), there might also be
a link between hopelessness and more ‘nihilistic’ behav-
iors. Further research is necessary to disentangle the po-
tential pathways in which hopelessness is related to early
substance use behaviors. Contradictive to our expectations
we found a negative association between anxiety sensi-
tivity and alcohol use. This can be explained by the
preventive effect that the fear for physical arousal might
have. When highly anxiety sensitive individuals have no
prior experience with alcohol they also do not know if
drinking alcohol leads to unusual body sensations, which
might keep them from drinking. Also, it could be that
highly anxiety sensitive individuals are more anxious in
general, and are for instance afraid of loosing control
when drinking.
Our findings also indicate a clear linkage between two
personality profiles (i.e., hopelessness and sensation seek-
ing) and ever-used tobacco in early adolescence. The few
studies that investigated the role of personality (i.e., Big
Five) on lifetime smoking in adolescence (Harakeh et al.
2006; Otten et al. 2008) found extraversion and openness to
be risk factors for lifetime smoking and conscientiousness,
agreeableness, and emotional stability to be protective
factors. Our results are in line with these latter findings,
considering that extraversion and openness are more
strongly related to sensation seeking and hopelessness is at
the opposite end of emotional stability. Finally, our results
show that sensation seeking is associated with an early
onset of cannabis use. This is in line with previous results
showing that sensation seeking predicts reckless behavior,
like cannabis use (Arnett 1994). It is thought that sensation
seekers use substances for the euphoric/intoxicating effects
(Comeau et al. 2001), so it might be that especially sen-
sation seekers attribute such characteristics to different
substances (e.g., cannabis) and are therefore more likely to
initiate use of a certain substance. We also found an
association between hopelessness and having ever used
cannabis in early adolescence. It is again not quite clear yet
how to interpret this finding in our age group. Previous
results in older adolescents suggest that hopelessness also
predicts reckless behavior, but particularly with regard to
the use of cocaine and other illegal drugs, not cannabis
(Woicik et al. 2009). Also, for this finding it might be that
early childhood problems directly affected both hopeless-
ness and the use of cannabis. Overall, the fact that the
SURPS personality profiles are related to early adolescents’
tobacco and cannabis use to a moderately strong degree
indicate that these profiles are important in explaining
individual differences in early adolescent substance use
behaviors.
Personality profiles and age of onset
We only found support for the role of hopelessness on the
age of onset of alcohol and cannabis use. We believe that
these findings might also be explained by the third vari-
able (i.e., early childhood problems) explanation. Besides
the findings considering hopelessness we hardly found
any support for the relationship between the personality
profiles and the age of onset of the different substances. It
could be that this outcome is due to the retrospective
character of these questions or to the restriction of range.
Adolescents were asked the age when they had their first
experience with a specific substance. In The Netherlands,
most adolescents start experimenting first with alcohol,
followed by tobacco and cannabis (Monshouwer et al.
2008). So, especially with respect to alcohol and tobacco
use, the recollection time between the first experience and
the moment of questioning is longer, and might thus be
less adequate (Bailey et al. 1992; Engels et al. 1997).
Simultaneously, this trend causes differences in the
diversity of answers. Since youngsters start using canna-
bis at a later age, less variation is visible in the ages of
onset compared to the start of using alcohol or tobacco.
These effects could explain the lack of findings on age of
onset of cannabis use and might explain the small effects
found for the associations between the SURPS personality
profiles and age of onset.
J Behav Med (2010) 33:474–485 481
123
Personality profiles and poly substance use
In the present study, we found that the SURPS personality
profiles are strongly related to early adolescents’ poly
substance use. Specifically, we found that hopelessness
and sensation seeking are indicative of poly substance use
and these results are mostly in line with earlier findings.
Previous studies suggested that poly substance users have
particularly high levels of impulsivity and sensation
seeking (e.g., Galizio and Stein 1983; Lacey and Evans
1986). Also, there is evidence suggesting that poly sub-
stance users are low on agreeableness and conscien-
tiousness and high on neuroticism (McCormick et al.
1998). Many of these studies examined the relationship
between personality and poly substance use in a clinical
(i.e., substance dependent) sample and as far as we know
little is known about the early onset of poly substance use
in young adolescents. In contrast to these findings,
although we found a strong link for hopelessness and
sensation seeking with poly substance use, we did not find
a relationship between impulsivity and poly substance
use. In the present study we defined poly substance use by
the use of more than one substance, comprising alcohol,
tobacco, and/or cannabis use. Other studies among older
or clinical samples usually operationalized poly substance
use by the use of multiple (hard) drugs, like cocaine, xtc,
and opiates (e.g., Smit et al. 2002). So, it might be that
impulsivity only has sufficient dicriminant power in poly
substance use, when the use of certain substances is
deviant enough.
Strengths, limitations, and implications for future
research
A major strength of our study is the large representative
non-clinical sample of our study. In addition, instead of
exclusively examining adolescents’ alcohol use we also
focused on tobacco and cannabis use. The large sample
allowed us to perform sophisticated SEM analyses in
which we controlled for the multilevel structure of the
data. Finally, a strength of the study is that our mea-
surements were well-validated and had all good psycho-
metric properties.
Some limitations were present in the current study as
well. First of all, a cross-sectional design was used—thus,
no causal explanations can be based on these associations.
Roberts et al. (2006) found in their meta-analysis that the
mean level of personality traits changed across the life
course, especially during adolescence. In general, it is
found that one’s personality type is only moderately stable
in childhood (e.g., Hart et al. 2003) and adolescence (e.g.,
Akse et al. 2007). So, do personality profiles precede
substance use behaviors or do experiences with substance
use modify personality profiles? We investigated the role
of personality in substance use in a group of early ado-
lescents that is in their initiation phase of alcohol and to-
bacco use and has hardly any experience with cannabis.
One might thus question if the potential changes in per-
sonality due to substance (ab)use are already noticeable in
these early adolescents. It seems more likely that these
changes will become apparent in a later stage, when ado-
lescents have more experience with substance use or when
more time has gone by after the actual initiation of sub-
stance use. It seems plausible to assume that, in a group of
early adolescents who are in their starting phase of sub-
stance use, personality precedes substance use behaviors.
However, this assumption should be interpreted carefully,
since longitudinal research is required to shed more light
on this topic.
Secondly, the fit of the models expressed in RMSEA
varied between .050 and .062, the CFI varied between .928
and .943. This means that the fit of the models were
acceptable but not excellent. There is ample literature
about fit indices and cut-off scores. In our view, an
important reason for the absence of excellent fit is related
to the measurement part of the models (the factor model).
In the factor model a simple structure is required with
cross-loadings constrained to be zero. In exploratory factor
models cross loadings are admitted resulting in better fit-
ting models. We applied a newly developed exploratory
structural equation model (ESEM) on the models in this
article. In these models the measurement part of the
structural model is estimated by the exploratory factor
model (Asparouhov and Muthén 2009). In fact, the con-
firmatory factor model in the structural model was replaced
by an exploratory factor model. The fit of all models were
improved with CFI-values [ .95 and RMSEA-val-
ues \ .05. Because the structural parameters did not
change substantially we preferred to use the classical SEM
model with the confirmatory factor model as measurement
model.
Thirdly, our use of self-reports might have lead to
measurement errors. Two perspectives can explain possible
measurement errors in self-reports on substance use,
namely a situational and a cognitive perspective (Brener
et al. 2003). The situational perspective concerns the
influence of the social environment, which might lead
adolescents to give socially desirable answers. To avoid
social desirability and optimize measurement validity we
guaranteed full confidentiality (anonymity) to our partici-
pants (e.g., Dolcini et al. 1996). The cognitive perspective
concerns the cognitive or internal processes that might
influence the self-reports. They might over or underesti-
mate their substance use behaviors in that they can not
482 J Behav Med (2010) 33:474–485
123
exactly recall what they have been using in a certain period
(e.g., Engels et al. 1997). In our study we asked participants
if they ever tried a specific substance, which is arguably
different from asking them how much they have used in a
certain period. One might expect participants to reliably
recall ever using alcohol, tobacco, or cannabis before. With
respect to the questions on age of onset the cognitive aspect
seems more relevant, thus one might argue that more
measurement errors occurred in these self-reports. How-
ever, the time between the age of first drink and assessment
seems to matter. The longer the time interval the more
severe recall bias one might expect (e.g., Engels et al.
1997; Parra et al. 2003). In our study, we investigated the
age of onset in a group of early adolescents with an average
age of 13 and assessing the reported age of onset close to
the actual age will optimize the reliability of the self-re-
ports (Kuntsche et al. 2009).
Fourthly, we only focused on the relationship between
the SURPS personality profiles and substance use behav-
iors. It would be interesting to investigate if the SURPS
personality profiles are also indicative of other risk type
behaviors. Finally, in our design we used a variable-cen-
tered approach utilizing the SURPS’ personality profiles to
examine individual differences on substance use for each
of the four profiles. However, it is also possible to inves-
tigate how constellations of traits within individuals are
organized, using a person-oriented approach (Bergman and
Magnusson 1997). The use of this approach might shed
more light on how these constellations are associated with
substance use in adolescents.
In sum, the present results suggest that in a large
sample of early Dutch adolescents especially sensation
seeking and hopelessness are strongly linked to the life-
time prevalence and age of onset of alcohol, tobacco, and
cannabis use in early adolescents. Also, hopelessness and
sensation seeking are found to be indicative of poly
substance use. Building on these new insights, it will be
crucial to conduct prospective analyses in the future to get
more insight into how personality profiles can predict the
development of substance use behaviors in adolescence
and, vice versa, to determine whether substance use may
affect adolescents’ personality development. Further, re-
cent studies investigated the effects of tailor-made inter-
ventions for the at-risk personality populations (Conrod
et al. 2006, 2008, 2010). These studies show much
promise for prevention efforts on excessive substance use,
thus it seems that knowing who is at risk and what this
risk is all about (i.e., only a risk for excessive use or also
for early initiation) in combination with such effective
prevention efforts might lead to an effective approach in
diminishing (the negative effects of) substance use among
(early) adolescents.
Acknowledgments This research was supported by a grant from
The Dutch Ministry of Health, Welfare, and Sport.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
Appendix
See Table 3.
Table 3 Pearson correlations of personality profiles, substance use, age of onset, and poly substance use
1 2 3 4 5 6 7 8 9 10
1. Anxiety sensitivity –
2. Hopelessness -.01 –
3. Sensation seeking .02 -.15** –
4. Impulsivity .24** .08** .37** –
5. Lifetime alcohol .05** -.13** -.20** -.14** –
6. Lifetime tobacco .01 -.19** -.18** -.18** .29** –
7. Lifetime cannabis -.01 -.06** -.10** -.10** .09** .23** –
8. Age of onset alcohol .03 -.06** -.08** -.06* – .04 .02 –
9. Age of onset tobacco .03 -.08* .03 .01 .05 – .01 .38** –
10. Age of onset cannabis .05 -.25* -.19 -.01 .17 .05 – .23 .31* –
11. Poly substance use -.04* .19** .24** .20** -.84** -.77** -.23** -.04 -.04 -.12
* P \ .05, ** P \ .01
J Behav Med (2010) 33:474–485 483
123
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Instructions: Students will research an article based on a specific population. The article must come from a professional journal, and or text. Students are to identify the research methods used (example: quantitative or qualitative, etc.) and discuss the research findings. Please explain how the research evidence can improve the practice setting specific to state and local policy and service delivery. Include the author’s intent for the study and how it can inform scientific research.
Please utilize the following rubric to guide you through this assignment. (5
0
points)
Criteria
Non-Performance
8.77
Partial
9.88
Proficient
11.11
Exceptional
Required Length and typed (2.5 pages at 12.5 font)
Less than 2 pages and not typed
2 pages and typed
2.25 pages and typed
2.5 pages and typed
Student utilizes subheadings for each area to be addressed
Student failed to utilize subheadings for each area to be addressed
Student partially utilized subheadings for each area to be addressed
Student was proficient in utilizing subheadings for each area to be addressed
Student was exceptional in utilizing subheadings for each area to be addressed
Student provide a reference source for the article
Student failed to provide a reference source for the article
Student partially provided a reference source for the article
Student was proficient in providing a reference source for the article
The student was Exceptional in providing a reference source for the article
Student discuss the relevance of the article to the selected population
Student failed to discuss the relevance of the article to the selected population
Student partially discuss the relevance of the article to the selected population
Student was proficient in discussing the relevance of the article to the selected population
Student was proficient in discussing the relevance of the article to the selected population
Student discuss findings and position of the author(s) that includes the author’s intent for the study and how it can inform scientific research
Competency: 4: a
Student failed to discuss findings and position of the author(s) that includes the author’s intent for the study and how it can inform scientific research
Student partially discussed the findings and position of the author(s) that includes the author’s intent for the study and how it can inform scientific research
The student was proficient in discussing the findings and position of the author(s) that includes the author’s intent for the study and how it can inform scientific research
Student was exceptional in discussing the findings and position of the author(s) that includes the author’s intent for the study and how it can inform scientific research
Student discuss the relevance of article to the course
Student failed to discuss the relevance of the article to the course
Student partially discuss the relevance of the article to the course
Student was proficient in discussing the relevance of the article to the course
Student was exceptional in discussing the relevance of the article to the course
Student discuss the implications of article for social work practice specific to how the research evidence can improve the practice setting for state and local policy and service delivery
Competency: 4: c
Student failed to discuss the implications of the article for social work practice specific to how the research evidence can improve the practice setting for state and local policy and service delivery
Student partially discussed the implications of the article for social work practice specific to how the research evidence can improve the practice setting for state and local policy and service delivery
Student was proficient in discussing the implications of the article for social work practice specific to how the research evidence can improve the practice setting for state and local policy and service delivery
Student was exceptional in discussing the implications of the article for social work practice specific to how the research evidence can improve the practice setting for state and local policy and service delivery
Student discuss their critical assessment and opinion of article
(1 full page)
Student failed to discuss their critical assessment and opinion of article
(less than ½ page)
Student partially discussed their critical assessment and opinion of article (less than ¾ page)
Student was proficient in discussing their critical assessment and opinion of article ( ¾ page)
Student was exceptional in discussing their critical assessment and opinion of article (1 full page)
Student provide summary that discuss the research methods used (example: quantitative or qualitative, etc.) and the research findings of the author
Competency: 4: b
Student failed to provide a summary that discussed the research methods used (example: quantitative or qualitative, etc.) and the research findings of the author
Student partially provided a summary that discussed the research methods used (example: quantitative or qualitative, etc.) and the research findings of the author
Student was proficient in providing a summary that discussed the research methods used (example: quantitative or qualitative, etc.) and the research findings of the author
Student was exceptional in providing a summary that discussed the research methods used (example: quantitative or qualitative, etc.) and the research findings of the author
SUBTOTAL
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