2013. Vol.4, No.6, 495-505
Published Online June 2013 in SciRes (
Copyright © 2013 SciRes. 495
Depressive Expression and Anti-Depressive Protection in
Adolescence: Stress, Positive Affect, Motivation and Self-Efficacy
Mats Lindahl1*, Trevor Archer2,3
1Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, Sweden
2Department of Psychology, University of Gothenburg, Gothenburg, Sweden
3Department of Sport Science, Linnaeus University, Kalmar, Sweden
Email: *
Received March 12th, 2013; revised April 14th, 2013; accepted May 22nd, 2013
Copyright © 2013 Mats Lindahl, Trevor Archer. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
The present study aims at identifying predisposing and protective factors for the purpose of showing their
respective contribution and interaction for adolescents’ stress disorders and depressive states, and to find
key attributes for the identification of pupils at risk in a normal population of adolescents. The study was
performed with 211 high-school pupils over a period of 18 months. The results are reported from the pu-
pils participating in 4 consecutive administrations of the instruments (N = 115). The following instru-
ments were used: “Kutcher Adolescent Depression Scale”, “Stress”, “Helplessness”, “Hopelessness”,
“Uppsala Sleep inventory”, “Barratt’s Impulsiveness Scale”, “Positive Affect and Negative Affect Scale”,
“Life Orientation Test”, “General Self-Efficacy”, “Locus of Control”, “Situational Intrinsic Motivational
Scale”. The Positive Affect and Negative Affect Scale were also used to categorize participants into four
affective profiles: “self-fulfilling”, high affective”, “low affective” and “self-destructive”. Linear regres-
sion analyses showed that situational depression (hopelessness) was predicted by depressive. Negative
affect predicted stress, which in turn predicted general and situational depressiveness. General self-effi-
cacy, positive affect and Identified regulation were found to be protective factors to both general and situ-
ational depressiveness. Depressiveness was found to be linked to the “self-destructive” affective personal-
ity type. “Negative affect” and distractiveness are suggested as markers for pupils at risk, whereas posi-
tive affect, self-efficacy and identified regulation appear to have protecting roles.
Keywords: Adolescent Depressiveness; Impulsivity; Negative Affect; Motivation; Self-Efficacy
The prevalence of depressive symptoms among adolescents
has seen a notable increase during the last two decades (Ave-
nevoli, Knight, Kessler, & Merikangas, 2008). Depression dur-
ing adolescence is predictive for future depressive episodes by
individuals (Lewinsohn, Allen, Seeley, & Gotlib, 1999; Rutter,
Kim-Cohen, & Maughan, 2006; Solomon et al., 2000). Ado-
lescents appear more vulnerable than adults, as indicated by
their comparatively more pronounced proneness to suicidal
behaviour as a result of depression (Cole, 1989; Foley, Gold-
ston, Costello, & Angold, 2006; Gould et al., 1998). Further
differences between adults and adolescents include the concur-
rence of vulnerability factors. Adults are assumed to be more
predisposed to depression by the number of different vulner-
ability factors present, as proposed by the “additive” approach
(Abela & Hankin 2008). However, the occurrence of depression
among adolescents may instead be conceptualized effectively
following the “weakest link” approach, i.e. the notion that pre-
disposition for depression depends on the magnitude of one of
the depressogenic inferential styles of self, causes, and con-
sequences (Abela & Sarin, 2002).
In addition to the serious problem with adolescent proneness
to suicide, depression in adolescence impacts also on academic
performance (Heiligenstein & Guenther, 1996) in a manner that
may be described as learned helplessness (Seligman, 1975). In
the context of educational performance (Diener & Dweck, 1978),
the notion of learned helplessness was used to describe young
pupils’ “situational depression”, a reaction to stressful life
events that brings on depressive symptoms, as a result of aca-
demic failure. Later, Abramson, Metalsky and Alloy (1989)
reformulated a more general and cognitive model of depression
as a form of learned hopelessness, in the sense that negative
expectancy of future events is acquired. Learned hopelessness
is dependent on helplessness insofar as it is expected that nega-
tive outcomes cannot be avoided since the situation is perceived
as uncontrollable and cannot be altered.
Depression and Stress in Adolescence
There is notable increase in depressive mood from preado-
lescence to adolescence (Brown & Siegel, 1988; Hankin et al.,
1998), possibly an outcome of the cognitive-emotional devel-
opment in children (e.g. Ushijima, Usami, Saito, Kodaira, &
Ikeda 2012). However, more accepted explanation is derived
from consideration of the accumulation of stressful life events
(SLE) (Abela & Hankin, 2008). According to this notion, re-
*Corresponding author.
petitive experiences of helplessness produce hopelessness and
are enhanced through depressogenic attributions, essentially the
search for explanations (Alloy et al., 1999; Lewinsohn, Joiner
Jr., & Rohde, 2001). Social and academic demands at school
are stressors to children and adolescents; school performance
offers one of the main stressors for adolescents (Byrne, Dav-
enport, & Mazanov, 2007). Research has shown that even pre-
adolescent pupils are at risk of succumbing to learned hope-
lessness as a result of academic failure (Dweck & Wortman,
1982; Burhans & Dweck, 1995). Severe stress reactions, seen
as the increased tendencies towards activation of the hypotha-
lamic-pituitary-adrenal (HPA) axis, are present already among
preadolescent pupils (Lindahl, Theorell, & Lindblad, 2005).
Stressful events contribute to the development of helplessness
and hopelessness leading to poor academic performance, which
in turn may worsen symptoms of helplessness and hopelessness
(Au, Watkins, Hattie, & Alexander, 2009).
It has been proposed that the augmented prevalence of de-
pression among adolescents, compared to preadolescents, may
be attributed to sensitization (Morris, Ciesla, & Garber, 2010).
Adolescents, due to psychobiological potentiation effects, are
more vulnerable to uncontrollable stressful situations and sub-
sequently more predisposed for negative affect (Chorpita &
Barlow, 1998). Adverse childhood events provided stress sensi-
tization in clinically depressed adolescents (LaRocque, 2011).
Increased affective responsiveness towards stressors in adoles-
cence has been suggested to be associated with adolescent brain
development (Archer, Kostrzewa, Beninger, & Palomo, 2010;
Archer, 2011). Brain development during adolescence appears
to be linked to adolescent impulsive behaviour (Crews & Boet-
tiger, 2009) as a risk factor for addiction and other related
problems (Churchwell, Lopez-Larson, & Yurgelun-Todd, 2011;
White et al., 2011), especially when combined with low levels
of positive affect (Colder & Chassin, 1997). However, it seems
that impulsivity decreases in normal populations (Steinberg et
al., 2008) and that instead sensation-seeking appears as a better
explanatory factor for adolescent risk-taking behaviour (Harden
& Tucker-Drob, 2011). Impulsivity rather appears associated
with “externalizing behaviour” whereas depression is associ-
ated with “internalizing behaviour” among children and ado-
lescents (Lahey et al. 2004). The former refers to problems that
are manifested in outward behavior reflecting a child’s negative
reactions to his/her environment whereas the latter involve
actions directed toward the self, including withdrawal, anxiety,
fearfulness, and depression (Rapport, Denney, Chung, & Hus-
tace, 2001).
Affective Personality and Stress
Negative affect among individuals is another trait associated
with both helplessness (Evans & Stecker, 2004) and depression
(Ebestuani, Okamura, Higa-McMillan, & Chorpita, 2011, Luten,
Ralph, & Mineka, 1997; Watson, Clark, & Carey, 1988a). This
is consistent with the finding that positive affect is negatively
associated with depression (Watson et al., 1988a). By combin-
ing high/low positive affect with high/low negative affect, four
different types of affective personality have been described
(Bood, Archer, & Norlander, 2004; Norlander, Bood, & Archer,
2002). Affective personality types with high negative affect
have been shown to report a higher degree of stress and a lower
degree of coping and control than those with high positive af-
fect (Karlsson & Archer, 2007; George & Brief, 1992; Spector
& O’Connell, 1994). Moreover, adolescents as well as adults
with “Self-destructive” personality, i.e. high negative affect and
low positive affect, typically report a higher degree of depress-
sion than the other personality types (Archer, Adrianson, Plan-
cak, & Karlsson, 2007). Negative affect has also been associ-
ated with pessimism (Marshall, Wortman, Kusulas, Hervig, &
Vickers Jr., 1992), an association linked to learned hopeless-
ness depression, since pessimism implies negative expectancy
on the future. Indeed, dispositional optimism has been shown to
be negatively associated with depression (Scheier, Carver, &
Bridges, 1994) and positively associated with active coping
strategies when confronted with stressful situations (Scheier,
Weintraub, & Carver, 1986, Aspinwall & Taylor, 1992).
Self-Efficacy and Motivation among Students
Self-efficacy presents a cognitive capacity suggested to
moderate stressful events and depression (Maciejewski, Priger-
son, & Mazure, 2000). Self-efficacy promotes positive expecta-
tions on handling future situations and engagement in learning
activities (Bandura, 1977, 1997), and is negatively associated
with depression among adolescents (Ehrenberg, Cox, & Koop-
man, 1991). Among college students, academic self-efficacy
and optimism are positively associated, whereas stress and
optimism are negative associated (Chemers, Hu, & Garcia,
2001). Positive outcome expectations in school are associated
with self-efficacy (Hackett, Betz, Casas, & Rocha-Singh, 1992),
and an intervention with the purpose of preventing depressive
symptoms by increasing students’ general self-efficacy was
shown to be effective (Pössel, Baldus, Horn, Groen, & Hautz-
inger, 2005). Self-efficacy has also been proposed as a moti-
vating factor (Zimmerman, 2000). Indeed, considering the pos-
sibility that self-efficacy supports an expectancy of positive
outcome, it seems likely to sustain motivation (Eccles et al.,
1983). Affect, i.e. positive affect, has also been shown to be
important for sustaining motivation in terms of positive expec-
tations and seeking involvement (Erez & Isen, 2002). The de-
scription of positive affect together with the resulting motive-
tional behaviour coincides with definitions of intrinsic motive-
tion (Deci & Ryan 1985). In Self-Determination Theory, three
types of motivation are described: Intrinsic motivation (Auto-
nomous), extrinsic motivation (Controlled) and Amotivation
(Deci & Ryan, 1985; 2008). Adolescent anxiety and distraction
in the classroom is negatively associated with intrinsic motiva-
tion but positively associated with amotivation (Ratelle, Guay,
Vallerand, Larose, & Senécal, 2007). Amotivation in the class-
room refers to a state in which individuals cannot perceive a
relationship between their behavior and that behavior’s sub-
sequent outcome (Shen, Winqert, Li, Sun, & Rukavina, 2010).
Furthermore, high-school pupils’ amotivation has been shown
to be multidimensional in the sense that there are four main
reasons for amotivation: pupils’ ability beliefs, pupils’ effort
beliefs, what value is placed on academic tasks? and the cha-
racteristics of the academic tasks (Legault, Green-Demers, &
Pelletier, 2006). High-school pupils’ amotivation has been
shown to predict impulsiveness (Palomo, Beninger, Kostrzewa,
& Archer, 2008).
Aims of the Study
An understanding of potentially predisposing factors and
their interaction with potentially protective factors may provide
Copyright © 2013 SciRes.
information regarding individuals at risk and possible interven-
tions to avoid affective disorders. The purpose of this explor-
ative study is to identify predisposing and protective factors
thereby showing their respective contribution and interaction
for adolescents’ stress disorders and depressive states. For pre-
ventive purposes, it is attempted to point out key attributes for
the identification of pupils at risk.
The participants were high-school pupils from a small town
(50.000 inhabitants) in the south of Sweden. Initially, a total of
211 pupils (154 girls and 57 boys), 17.1 years of age, partici-
pated. They were all recruited during their first year in high-
school (year 10). The pupils were randomly recruited from the
following 4 different Swedish national study programmes (or-
dered by decreasing emphasis on theoretical studies): Natural
science programme (73); Art, music and drama programme
(66); Child-care and recreation programme (48); Restaurant and
food programme (24).
Ethical Considerations
The project was undertaken in cooperation with the school
principal for the purpose of highlighting stress as a problem of
concern for both teachers and pupils. The agreement included
total anonymity and fluidity of results through discussion with
teachers as well as the participating pupils after the second and
fourth semester. The pupils were informed two months in ad-
vance about the data collection. Reassurances were made that
pupils only should participate at their free will and could de-
cline at any time. They were also informed that data were to be
coded immediately after collection and the code kept safe to
ensure their anonymity both during analysis and any presenta-
tion of results.
Kutcher Adolescent Depression Scale (KADS-6). KADS-6
(LeBlanc, Almudevar, Brooks, & Kutcher, 2002), in a Swedish
translation, was used to measure depressive mood. The 6-item
instrument consisted of statements concerning general feelings
of sadness, somatic and cognitive stress, helplessness, hope-
lessness and thoughts of self-injury. Respondents answered on
a 4-point scale: “hardly ever”, “much of the time”, “most of the
time”, “all the time”. The internal consistency was .82 (Chron-
bach’s ).
Stress. Experiences of somatic stress reactions during ex-
amination tests in school (in Sweden small tests in every single
subject occur with about 4-week intervals) were measured us-
ing an abbreviated version of a Swedish translation of SSE—
Subjective Stress Experience (Lòpez-Ibor, 2002). Items (11)
concerning somatic stress were chosen from SSE. They were
modified to focus the school test situation and presented on a
7-point scale with endpoints described as “Does not apply to
me at all” (1) or “Applies to me fully” (7). The internal consis-
tency was found to be .92 (Chronbach’s ).
Helplessness and Hopelessness. Instruments for measuring
Helplessness and Hopelessness in the school test situation were
constructed through the adaptation of existing constructs de-
rived from several articles (Evans & Stecker, 2004; Boggiano et
al., 1992; Ursin & Eriksen, 2004). Both instruments were pre-
sented on a 7-point scale with endpoints described as “Does not
apply to me at all” (1) or “Applies to me fully” (7). The help-
lessness scale included 9 items (Chronbach = .92), i.e. “I get
the feeling I want to give up when I encounter a really difficult
question on a test”. The hopelessness scale included 3 items
(Chronbach = .77), i.e. “When I fail on a test I usually think
of myself as a worthless person” (cf. Abramson, Seligman, &
Teasdale 1978).
Uppsala Sleep Inventory (USI). 17 items from the USI scale
(Hetta et al., 1985) were used to measure two factors: Difficul-
ties to sleep and Difficulties to fall asleep. Difficulties to sleep,
a 7-item construct including potential problems of getting suffi-
cient sleep, was presented on a 5-point scale ranging from
“none” to “very big” (Chronbach’s = .78). Difficulties to fall
asleep, was a 10-item construct consisting of a variety of
symptoms of stress considered to cause difficulties to fall
asleep. They were presented on a 5-point scale ranging from
“never” to “very often” (Chronbach’s = .82).
Barratts Impulsiveness Scale (BIS-11, modified). This mo-
dified, Swedish version BIS-11 instrument consisted of 25 of
the 30 BIS-11 items (Patton et al., 1995). In order to measure
impulsivity, participants were asked to estimate how the 25
statements fit him/her as an individual on a 5-point scale (from
“not at all” to “precisely”). The items were, according to Patton,
Stanford and Barratt (1995), divided into three factors: Distrac-
tiveness (originally called Attentional impulsiveness) with
Chronbach’s = .81 (8 items); Non-planning impulsiveness
with a Chronbach’s = .72 (8 items); and Motor impulsiveness,
comprising 9 items (Chronbach’s = .67).
Positive affect and negative affect scale (PANAS). The
PANAS-instrument provides a self-estimation of “affect”, both
positive and negative. It consists of 10 adjectives for the Nega-
tive Affect (NA) dimension (Negative affect: Chronbach’s
= .83) and 10 adjectives for the Positive Affect (PA) dimension
(Positive affect: Chronbach’s = .88). The adjectives describe
feelings (affect) according to Watson, Clark and Tellegen
(1988b). Respondents give their estimates on a 5-point scale
(from “not at all” to “very much”). Previous studies (Bood et al.,
2004; Norlander et al., 2002) have modified and developed the
PANAS instrument further through a subject-response based
derivation of four types of affective personality. Following their
procedure the PA-scale was divided into two parts to enable
grouping participants into “high PA” and “low PA” (cut-off
point = 53.2%). A similar procedure was applied on the NA-
scale, enabling the formation of “high NA” and “low NA”
(cut-off point = 48.9%). Participants could then be assigned to
one of four affective personality groups combined as follows:
Self-Fulfilling (high PA and low NA); High Affective (high PA
and high NA); Low Affective (low PA and low NA); and
Self-Destructive (low PA and high NA).
Life orientation Test (LOT). This instrument measures indi-
vidual’s degree of dispositional optimism. The instrument is
based on a general model, regarding self-regulated behaviour,
which indicates that optimism exerts meaningful behavioural
consequences based on the model (Norlander & Archer, 2002).
The instrument consists of 12 statements on a 5-point scale
(from “strongly disagree” to “strongly agree”). When this in-
strument was used in this study the internal consistency was
found to be .73 (Chronbach’s ).
General Self-Efficacy (GES). General Self-Efficacy (Koski-
nen-Hagman, Schwarzer, & Jerusalem, 1999) is a 10-item in-
Copyright © 2013 SciRes. 497
Copyright © 2013 SciRes.
strument measuring self-efficacy in the sense that the respon-
dent can relate their self-efficacy to all or any situation
(Chronbach’s = .88). The statements are presented on a 4-
point scale ranging from “I totally disagree” to “I totally agree”.
Locus of Control. This is a modified version according to
Millet and Sandberg (2003), using an abbreviated version of
Rotter scale (Rotter, 1966), developed for use in Swedish work
settings. The score from four of the 8 items (on a 5-point scale)
was reversed to enable recalculation to represent External locus
of control (Chronbach’s = .70).
Situational Intrinsic Motivational Scale (SIMS). The SIMS
instrument provides an estimation of the constructs (factors),
intrinsic motivation, identified regulation, external regulation
and amotivation (cf. Deci & Ryan, 1985, 1991). The version
used in this study include four items for each factor, modified
to relate to the school setting, e.g. intrinsic motivation: “Be-
cause I think this attending school is interesting”, identified
regulation: “Because doing schoolwork is for my own good”,
external regulation: “Because I am supposed to do it”, and
amotivation: “I go to school but I am not sure it is worth it”.
The statements were responded to on a 7-point scale out of
which 3 points were specified: 1) does not apply to me at all; 4)
between applicable/ non-applicable; 7) applies to me exactly.
The internal consistency (Chronbach’s ) for the four factors
were: .85 (Intrinsic motivation); .71 (Identified regulation); .77
(External regulation), and .86 (Amotivation).
Data were collected at four occasions with 6 months intervals,
starting in the middle of the students’ second (spring) term at
high school. Data collection was done with one class (24 - 30
pupils) at a time, during a weekly scheduled meeting with their
class teacher. The instruments were administrated by one of the
authors and the pupils were encouraged to ask questions in the
case any item appeared unclear. Out of the initially 211 pupils,
114 attended at all (4) occasions for data collection.
Statistical Analyses
One-way ANOVA was used with Affective personality
(Self-Destructive, High-Affective, Low-Affective and Self-
Fulfilling) as independent variable to demonstrate any signify-
cant differences between affective personality types with regard
to depressive mood, vulnerability factors and protective factors
(Table 1). Post hoc tests according to Bonferroni were per-
formed for analyses exhibiting homogeniety of variance ac-
cording to Levene. When homogeniety of variance according to
Levene was not met, post hoc tests according to Games-Howell
were performed. For these variables, Welch test of equality of
means was used to assess any significant differences between
General linear models for the four consecutive terms were
Table 1.
Personal attributes associated, or not, with the four affective personality types self-destructive, low-affective, high-affective and self-fulfilling.
Affective personality types
Variable Self-destructive (n = 31)
M (SE)
Low-affective (n = 21)
M (SE)
High-affective (n = 23)
M (SE)
Self-fulfilling (n = 40)
M (SE)
Depressive mood* (F = 25.54) 2.00abc (.46) 1.47 (.31) 1.63a (.29) 1.31 (.25)
Stress* (F = 18.55) 2.75ac (.96) 1.70 (.50) 2.23 (1.11) 1.47 (.30)
Helplessness* (F = 13.85) 3.80a (1.16) 2.93 (1.15) 2.92 (1.13) 2.27 (.61)
Hopelessness* (F = 32.35) 4.00abc (1.08) 2.60 (.89) 2.90a (.96) 1.98 (.55)
Difficult to sleep (F = 5.61) 2.82a (.59) 2.58 (.57) 2.75 (.60) 2.28 (.59)
Difficult to fall asleep* (F = 11.37) 2.24a (.53) 1.88 (.43) 2.40a (.62) 1.75 (.39)
Distractiveness (F = 7.23) 2.99a (.44) 2.80 (.45) 2.85 (.46) 2.51 (.46)
Non-planning impulsivity (F = 4.18) 2.88a (.49) 2.75 (.42) 2.69 (.50) 2.50 (.42)
Motor impulsivity (F = .59) 2.64 (.41) 2.70 (.46) 2.78 (.46) 2.65 (.43)
Optimism (F = 10.53) 2.10a (.38) 2.34 (.31) 2.37 (.38) 2.63 (.47)
Self-Efficacy (F = 9.15) 2.62a (.35) 2.83 (.31) 2.89 (.41) 3.05 (.33)
External Locus of Control (F = 1.03) 2.80 (.40) 2.70 (.26) 2.76 (.46) 2.64 (.45)
Intrinsic motivation (F = 6.27) 3.33ab (1.15) 3.76 (.90) 4.37 (1.12) 4.37 (1.19)
Identified regulation (F = 4.36) 5.30 (.70) 5.27 (1.17) 5.89 (.81) 5.85 (.78)
External regulation (F = 3.63) 5.05 (1.13) 4.88 (1.12) 4.52 (1.16) 4.17 (1.29)
Amotivation (F = 5.56) 2.31a (.95) 2.15 (.90) 1.80 (.85) 1.55 (.68)
Notes: M = mean; SE = Standard error; *Homogeniety of variance according to Levene was not met. Welch test of equality of means revealed a significant difference be-
tween groups; aSignificant difference (p < .01) from self-fulfilling; bSignificant difference (p < .01) from high-affective; Significant difference (p < .01) from low-affective.
calculated to estimate the within-subject effects for each vari-
able, in order to follow changes for the time-span of the invest-
tigation. Although seven variables showed small but significant
changes, the incongruent changes between terms gave little
intelligible information about age and possible relationships
between variables for the studied period of time. The small
number of participants limited the possibilities to in detail study
changes for selected groups of participants. Hence, data from
all four occasions for data collection was pooled.
Stepwise linear regression calculations were performed with
the pooled data and all latent variables using stress, helpless-
ness, hopelessness, depressive mood and distractiveness as
dependent variables. Although one-way ANOVA showed that
girls reported significantly higher values for difficulties to fall
asleep F(1,113) = 8.14, p < .05 and negative affect F(1,60) =
8.95, p < .05 than boys, no differences regarding the order of
predictors were found. Hence, the results reported here repre-
sents pooled data for both sexes.
The data were collected initially for a longitudinal study of
potential changes over the time-span of 1.5 high-school years.
Over test occasions, a decrease was found for the following
latent variables: negative affect F(3,111) = 4.62, p < .01;
stress F(3,111) = 3.03, p < .05; helplessness F(3,111) = 3.37,
p < .05. During the same period the following variables in-
creased: distractiveness F(3,111) = 4.14, p < .01; dispositional
optimism F(3,111) = 4.76, p < .01; general self-efficacy
F(3,111) = 8.72, p < .001; and intrinsic motivation F(3,111)
= 3.26, p < .05.
Affective Personalities
With affective personality as independent variable in a
one-way ANOVA, significant effects demonstrating differences
in vulnerability between individuals having “Self-Destructive”
affective personality was found. “Self-Destructive” individuals
expressed significantly less dispositional optimism, general
self-efficacy and intrinsic motivation compared to “Self-Ful-
filling” individuals (see Table 1). They also expressed signify-
cantly more distractiveness, non-planning impulsivity, amoti-
vation, stress, helplessness, hopelessness, depressive mood,
difficulties to sleep and difficulties to fall asleep. The “Self-
Destructive” pupils were demonstrated to present significantly
more hopelessness and depressive mood than all other pupils.
Linear Regres s i on Analyses
Linear Regression Analyses Indicated That
Stress was predicted by helplessness and negative affect, but
counter predicted by amotivation [F(3,111) = 60.53, p < .001;
adjusted R2 = .61]. Table 2 presents the regression analysis
with Stress as dependent variable and Helplessness, General
self-efficacy, External locus of control, Distractiveness, Motor
impulsivity, Non-planning impulsivity, Intrinsic motivation,
Identified regulation, External regulation, Amotivation, Dispo-
sitional optimism, Difficulties to sleep, Difficulties to fall
asleep, Hopelessness, Depressive mood, Positive affect and
Negative affect as independent variables.
Helplessness was predicted by hopelessness, distractiveness
and stress, while counter predicted by identified regulation
[F(4,110) = 82.10, p < .001; adjusted R2 = .74]. Table 3 pre-
sents the regression analysis with Helplessness as dependent
variable and Stress, General self-efficacy, External locus of
control, Distractiveness, Motor impulsivity, Non-planning im-
pulsivity, Intrinsic motivation, Identified regulation, External
regulation, Amotivation, Dispositional optimism, Difficulties to
sleep, Difficulties to fall asleep, Hopelessness, Depressive
mood, Positive affect and Negative affect as independent vari-
Hopelessness was predicted by helplessness, depressive
mood and stress, but counter predicted by dispositional opti-
mism, non-planning impulsivity and general self-efficacy
[F(6,108) = 61.68, p < .001; adjusted R2 =.76]. Table 4 pre-
sents the regression analysis with Hopelessness as dependent
variable and Helplessness, Stress, General self-efficacy, Exter-
nal locus of control, Distractiveness, Motor impulsivity, Non-
planning impulsivity, Intrinsic motivation, Identified regulation,
External regulation, Amotivation, Dispositional optimism, Dif-
ficulties to sleep, Difficulties to fall asleep, Depressive mood,
Positive affect and Negative affect as independent variables.
Depressive mood was found to be predicted by negative af-
fect and difficulties to sleep. And positive affect and motor
impulsivity were counter predictors of depressive mood
[F(4,110) = 54.74, p < .001; adjusted R2 =.69]. Table 5 pre-
sents the regression analysis with Depressive mood as depend-
ent variable and Helplessness, Stress, General self-efficacy,
External locus of control, Distractiveness, Motor impulsivity,
Table 2.
Standardized weights from linear regression analysis with stress as
dependent variable.
Standardized beta
Predictor variables
Helplessness .61***
Negative affect .36***
Amotivation .18**
Note: ***p < .001, **p < .01; Predictor variables: General self-efficacy, External
locus of control, Distractiveness, Motor impulsivity, Non-planning impulsivity,
Intrinsic motivation, Identified regulation, External regulation, Dispositional
optimism, Difficulties to sleep, Difficulties to fall asleep, Hopelessness, Depres-
sive mood and Positive affect were non-significant.
Table 3.
Standardized weights from linear regression analysis with helplessness
as dependent variable.
Standardized beta
Predictor variables
Stress .36***
Distractiveness .34***
Hopelessness .25***
Identified regulation .18**
Note: ***p < .001, **p < .01; Predictor variables: General self-efficacy, External
locus of control, Motor impulsivity, Non-planning impulsivity, Intrinsic motiva-
tion, External regulation, Amotivation, Dispositional optimism, Difficulties to
sleep, Difficulties to fall asleep, Depressive mood, Positive affect and Negative
affect were non-significant.
Copyright © 2013 SciRes. 499
Table 4.
Standardized weights from linear regression analysis with hopelessness
as dependent variable.
Standardized beta
Predictor variables
Helplessness .32***
Depressive mood .30***
Dispositional optimism .26***
Non-planning impulsivity .24**
General Self-Efficacy .20**
Stress .20**
Note: ***p < .001, **p < .01; Predictor variables: External locus of control, Dis-
tractiveness, Motor impulsivity, Intrinsic motivation, Identified regulation, Exter-
nal regulation, Amotivation, Difficulties to sleep, Difficulties to fall asleep, Posi-
tive affect and Negative affect were non-significant.
Table 5.
Standardized weights from linear regression analysis with depressive
mood as dependent variable.
Standardized beta
Predictor variables
Negative affect .50***
Positive affect .31***
Difficulties to sleep .30***
Motor impulsivity .17**
Note: ***p < .001, **p < .01; Predictor variables: Helplessness, Stress, General
self-efficacy, External locus of control, Distractiveness, Non-planning impulsivity,
Intrinsic motivation, Identified regulation, External regulation, Amotivation,
Dispositional optimism, Difficulties to fall asleep and Hopelessness were non-
Non-planning impulsivity, Intrinsic motivation, Identified
regulation, External regulation, Amotivation, Dispositional
optimism, Difficulties to sleep, Difficulties to fall asleep,
Hopelessness, Positive affect and Negative affect as independ-
ent variables.
Distractiveness was predicted by helplessness, difficulties to
sleep and motor impulsivity F(3,111) = 65.59, p < .001; ad-
justed R2 = .63. Table 6 presents the regression analysis with
Distractiveness as dependent variable and Helplessness, Stress,
General self-efficacy, External locus of control, Motor impul-
sivity, Non-planning impulsivity, Intrinsic motivation, Identi-
fied regulation, External regulation, Amotivation, Dispositional
optimism, Difficulties to sleep, Difficulties to fall asleep, Hope-
lessness, Depressive mood, Positive affect and Negative affect
as independent variables.
The primary purpose of the present study was to identify the
factors that predispose and protect individuals, respectively,
from stress disorders and depressive states, and the interaction
between them. Thus, two basic notions are discussed that
originate from this purpose: firstly, predisposing factors, and
Table 6.
Standardized weights from linear regression analysis with distractive-
ness as dependent variable.
Standardized beta
Predictor variables
Helplessness .47***
Difficulties to sleep .31***
Motor impulsivity .23***
Note: ***p < .001; Predictor variables: Stress, General self-efficacy, External locus
of control, Non-planning impulsivity, Intrinsic motivation, Identified regulation,
External regulation, Amotivation, Dispositional optimism, Difficulties to fall
asleep, Hopelessness, Depressive mood, Positive affect and Negative affect were
secondly, protective factors. Predisposing factors included
negative affect and distractiveness primarily, and stress, help-
lessness and hopelessness secondarily. Protective factors in-
cluded: Positive affect, identified regulation and self-efficacy.
Predisposing Factors
The interplay between stress and depressive states is the ful-
crum of the diathesis-model of depression. In this study of a
healthy young population, helplessness and hopelessness in
relation to examinations may be considered situational expres-
sions of behaviour whereas depressive mood offers a more
generalized expression. Hence, it may be reasoned that situ-
ational helplessness and hopelessness are more likely to impact
upon each other than upon depression. The factors stress, help-
lessness and hopelessness are closely linked as they describe
experiences in the examination situation. In this respect, they
ought to be regarded as situational conditions, affective and
consequential to unsuccessful attempts to cope with failure,
implying a progressive learned helplessness (Au et al., 2009;
Burhans & Dweck, 1995; Diener & Dweck, 1978; Dweck &
Wortman, 1982). Although it may be hypothesized that stress
may be aggravated by helplessness, it is not unreasonable to
assume that stress precedes helplessness, which in turn pre-
cedes hopelessness (Au et al., 2009; Ursin & Eriksen, 2004).
The present results seem to follow the same reasoning since 1)
stress and hopelessness are predictors of helplessness (Table 3)
and 2) stress and helplessness are predictors of hopelessness
(Table 4). Stress may be impelled by negative affect (Denollet
& De Vries, 2006), which here is suggested to be a source of
vulnerability, especially since negative affect may present a
genetic attribute (Trzaskowski, Zavos, Haworth, Plomin, &
Eley, 2012). To further understand the assessed helplessness
and hopelessness, they should be perceived as examples of
socio-cognitive-emotional conditions and one expression of
unsuccessful coping during exams (Abrahamson et al., 1989;
Dweck & Wortman, 1982; Hess & Copeland, 2001).
It ought to be noted that while depressive mood promoted the
“situational hopelessness” among the high-school pupils in the
present investigation (Table 4), situational stress and learned
helplessness or hopelessness had no impact on depressive mood.
Hence, the vulnerability to situational hopelessness appears in
the present circumstance to depend on one general predisposing
factor: depressive mood; and on two situational predisposing
factors: stress and helplessness. Although adolescent-related,
Copyright © 2013 SciRes.
the present results offer alternative notions to previous findings
that negative expectancies of schoolwork, at least among col-
lege students, induce depression (Metalsky, Abramson, Selig-
man, Semmel, & Peterson, 1982). They observed that students
with an internal or global attributional style for negative out-
comes at Time 1 experienced a depressive mood response when
confronted with a subsequent low midterm grade, whereas stu-
dents with an external or specific attributional style for negative
outcomes were invulnerable to this depressive mood response.
In the present study, negative affect was found to be the main
predisposing factor for depressive mood (Table 5). As expected,
depressive mood was not only predicted by negative affect but
also counter-predicted by positive affect (Luten et al., 1997;
Watson et al., 1988b). In accordance with the diathesis-model,
stressors, not accounted for here, that promote the depressive
state since “difficulties to sleep” predicted depressive mood
(Table 5) may be present. Sleeping difficulties may contribute
an effect of stress, rather than a “true” (causal) predictor of de-
pressive mood (Archer, Adolfsson, & Karlsson, 2008). Nega-
tive affect is not only a predisposition for depressive mood, but
also for stress during academic examinations. Indeed, negative
affect is a predisposing factor for both stress reactions and
helplessness. If one assumes that the experience of helplessness
during examinations is more cognitive-emotionally oriented
than the more biologically oriented stress reaction. Then, the
predisposition for negative affect ought to exert a direct impact
upon stress reactions but not on the experience of helplessness
during examinations. Instead, helplessness appears to be medi-
ated differently since distractiveness is a predictor for helpless-
ness. Here, distractiveness may be interpreted as an example of
avoidance and externalizing behaviour in a stressful situation
(Lahey et al., 2004), reducing school performance but elevating
risk of helplessness. Amotivation, another form of avoidance,
predicted impulsiveness in several different studies (Archer &
Bright, 2012; Archer et al., 2008; Archer, Oscar-Berman, Blum,
& Gold, 2012; Palomo et al., 2008). Distractiveness, although
possibly moderated by stress, presents a generalized condition
prevalent in adolescence (Crews & Boettiger, 2009; Lahey et
al., 2004; Steinberg, Albert, Cauffman, Banich, Graham, &
Woolard, 2008). Hence, distractiveness presents not only a
psychosomatic (Harden & Tucker-Drob, 2011), but also a ge-
netic attribute, which from the present results ought to predict
and therewith promote situational helplessness. As a genetic
attribute distractiveness has an impact on temperament, i.e.
novelty-seeking, harm avoidance and reward dependence
(Cloninger, Svrakic, & Przybeck, 1993), and is likely to be
modulated through childhood and adolescence as character is
formed (Gillespie, Cloninger, Heath, & Martina, 2003; Joyce et
al., 2003). From the results in the present investigation it is
suggested that stress accentuates distractive behaviour.
Protective Factors
General self-efficacy was a counter predictor for hopeless-
ness in school test situations. Hence, self-efficacy is interpreted
to have a protective effect against hopelessness. This is in line
with the concept of self-efficacy (Bandura, 1977) and supported
by research on college and university students (Chemers et al.,
2001; Hackett et al., 1992). General self-efficacy has also been
shown to prevent depressive symptoms (Pössel et al., 2005) and
to promote positive expectations (Eccles et al., 1983; Erez &
Isen, 2002). Positive expectations are likely to counteract the
emergence of any negative experiences following confrontation
with failure. Hopelessness was also found to be moderated by
dispositional optimism, a biological disposition that contributes
to adaptive coping in inescapable and demanding situations
(Scheier et al., 1986). Hence, both general self-efficacy and
dispositional optimism are protective factors against hopeless-
ness in school test situations.
Positive affect counteracts depressive mood in agreement
with previous research (Watson et al., 1988a). What is more
intriguing is the observation that motor impulsivity counter-
predicts depressive mood, thus providing some protective effect.
In view of the two suggested dimensions of motor impulsivity:
functional and dysfunctional; such a notion is not unwarranted
(Miller, Joseph, & Tudway, 2004). Hence, the functional di-
mension of motor impulsivity, i.e. little worries in relation to
acting in face of any risks being involved, can be interpreted to
moderate depressive mood, especially since this study is on a
normal population of high-school pupils.
Identified regulation was found to be a protective factor
against situational helplessness. This may be understood in the
sense that motivation to do school work reduces risks of en-
countering helplessness in test situations. It was surprising to
find that amotivation also appeared to be a protective factor as
it counter-predicts situational stress. Applying the notion of
Self-Determination Theory (Deci & Ryan, 1985), amotivation
may be seen here as a type coping behaviour (albeit short-term)
by reducing the value of the academic task (Legault et al.,
2006). Hence, amotivation as a coping response, although an
internalizing behaviour, offers a rather dysfunctional protection
towards stress (Ratelle et al., 2007).
Markers for Comparis on with Previous Studies
The notion that cognitive-affective domains such as depres-
sive mood and impulsivity impact upon situational helplessness
and hopelessness provides an avenue for understanding the
vulnerability of adolescent pupils as they struggle through the
educational system. Depressive mood, as demonstrated from
the present findings, does impact upon situational hopelessness,
whereas none of the situational stress reactions: stress, help-
lessness and hopelessness produced any impact on depressive
mood. Hence, among these factors, only depressive mood may
be considered to be a general predisposing factor. Following the
weakest link approach notion (Abela & Sarin, 2002), depressive
mood appears to be the best general marker for situational
hopelessness. However, helplessness may provide a situational
marker for hopelessness and ought to be considered as a more
suitable marker for interventional procedures in an educational
practice. Situational stress emerges also as a marker for situ-
ational helplessness applicable in educational practice.
Negative affect was a predictor of depressive mood among
the adolescents in this study. This is in contrast to a study on
young adults, which show a lack of relation between depression
and negative affect (Nima, 2012). This discrepancy may be
related to differences in levels of brain maturation processes
(Archer et al., 2010; Archer, 2011), possibly in concert with the
selection occurring in the transition of pupils from high-school
to university. Nevertheless, two studies applying Beck’s De-
pression Inventory and the Hospital Anxiety and Depression
test demonstrated the relationship between depressive expres-
sion and negative affect in young adults (Archer et al., 2008).
Negative affect predicts stress, and following the reasoning of
Copyright © 2013 SciRes. 501
Ursin & Ericsen (2004) whereby stress is the starting point for
helplessness and hopelessness, negative affect emerges as the
most reliable marker even for situational stress. Since negative
affect offers a genetic attribute (Melke et al., 2003; Wei et al.,
2012), it is here considered to be a key attribute for both de-
pressive mood and situational stress. This notion is supported
by the findings that the affective personality type “Self-De-
structive” (high negative affect and low positive affect) indi-
viduals, who typically report high degrees of the factors pre-
disposing for stress disorders, depressive mood states and dys-
functional coping, while showing low degrees of protective
factors (Agerström, Möller, & Archer, 2006; Andersson-Arntén,
Jansson, & Archer, 2007; Huemer et al., 2012; Karlsson &
Archer, 2007). Distractiveness is another genetic attribute
(Coldren et al., 2009; Melke et al., 2003), which is accentuated
when protective factors of cognitive nature are pealed off as a
result of exposure to stressors. This association is demonstrated
here by the prediction of situational helplessness by distrac-
tiveness in concert with stress.
Strenghts an d Limitations
The study provides a relative straightforward description of
predisposing and protective factors for depressive mood in
adolescence. Nevertheless certain limitations ought to be indi-
cated, including the high rate of attrition and the relatively
small number of participants.
Conclusion and Clinical Implications
Identification of risk through negative affects and distractive-
ness attributes involves moderating and/or mediating factors,
e.g. positive affect, self-efficacy and identified regulation. It is
suggested that the interplay between predisposing and protec-
tive factors require consideration for developing strategies to
help children and adolescents combat situational helplessness
and hopelessness.
This study was supported by Faculty of Health, Social Work
and Behavioural Sciences at Linnaeus University. We also wish
to convey our gratitude to the participating pupils for lending
their time to answer the questionnaires.
Abela, J. R. Z., & Sarin, S. (2002). Cognitive vulnerability to hope-
lessness depression: A chain is only as strong as its weakest link.
Cognitive Therapy and Research, 26, 811-829.
Abela, J. R. Z., & Hankin, B. L. (2008). Cognitive vulnerability to
depression in children and adolescents. A developmental psychopa-
thology perspective. In: J. R. Z. Abela, & B. L. Hankin (Eds.), Hand-
book of depression in children and adolescents (pp. 35-78). New
York: Guilford Press.
Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness
depression: A theory-based subtype of depression. Psychological Re-
view, 96, 358-372. doi:10.1037/0033-295X.96.2.358
Abramson, L., Seligman, M., & Teasdale, J. (1978). Learned help-
lessness in humans: Critique and reformulation. Journal of Abnormal
Psychology, 87, 49-74. doi:10.1037/0021-843X.87.1.49
Agerström, J., Möller, K., & Archer, T. (2006). Influence of affective
personality and dilemma content on moral reasoning: Effect of gen-
der. Social Behavior & Personality, 34, 1259-1276.
Alloy, L. B, Abramson, L. Y., Whitehouse, W. G., Hogan, M. E.,
Tashman, N. A., Steinberg, D. L., Rose, D. T., & Donovan. P. (1999).
Depressogenic cognitive styles: Predictive validity, information pro-
cessing and personality characteristics, and developmental origins.
Behaviour Research and Therapy, 37, 503-531.
Andersson-Arntén, A. C., Jansson, B., & Archer, T. (2007). Influence
of affective personality type and gender upon coping behaviour,
mood and stress. Individual Differences Research, 6, 139-168.
Archer, T. (2011). Effects of exogenous agents on brain development:
Stress, abuse and therapeutic compounds. CNS Neuroscience &
Therapeutics, 17, 470-489. doi:10.1111/j.1755-5949.2010.00171.x
Archer, T., & Bright, P. (2012). Functional and structural MRI studies
on impulsiveness: Attention-deficit/hyperactive disorder and border-
line personality disorder. In P. Bright (Ed.) Neuroimaging—Cogni-
tive and clinical neuroscience (pp. 205-228). Rijeka: InTech.
Archer, T., Adolfsson, B., & Karlsson, E. (2008). Affective personality
as cognitive-emotional presymptom profiles regulatory for self-re-
ported health predispositions. Neuroto x i city Research, 14, 21-44.
Archer, T., Adrianson, L., Plancak, A., & Karlsson, E. (2007). Influ-
ence of affective personality on cognition-mediated emotion proc-
essing: Need for empowerment. European Journal of Psychiatry, 21,
248-262. doi:10.4321/S0213-61632007000400002
Archer, T., Kostrzewa, R. M., Beninger, R. J., & Palomo, T. (2008).
Cognitive symptoms facilitatory for diagnoses in neuropsychiatric
disorders: Executive functions and locus of control. Neurotoxicity
Research, 14, 205-225. doi:10.1007/BF03033811
Archer, T., Oscar-Berman, M., Blum, K., & Gold, M. (2012). Neuro-
genetics and epigenetics in impulsive behaviour: Impact on reward
circuitry. J ou rn al of G e ne ti c S yn dr o mes & Gene Therapy, 3, 1-15.
Archer, T., Kostrzewa, R. M., Beninger, R. J., & Palomo, T. (2010).
Staging perspectives in neurodevelopmental aspects of neuropsy-
chiatry: Agents, phases and ages at expression. Neurotoxicity Re-
search, 18, 287-305. doi:10.1007/s12640-010-9162-6
Aspinwall, L. G., & Taylor, S. E. (1992). Modeling cognitive adaption:
A longitudinal investigation of the impact of individual differences
and coping on college adjustment and performance. Journal of Per-
sonality and Social P sychology, 63, 989-1003.
Au, R. C. P., Watkins, D., Hattie, J., & Alexander, P. (2009). Reformu-
lating the depression model of learned hopelessness for academic
outcomes. Educational Research Review, 4, 103-117.
Avenevoli, S., Knight, E., Kessler, R. C., & Merikangas, K. R. (2008).
Epidemiology of depression in children and adolescents. In: J. R. Z.
Abela, & B. L. Hankin (Eds.), Handbook of depression in children
and adolescents (pp. 6-32). New York: Guilford Press.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behav-
ior change. Psychological Review, 84, 191-215.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York:
Boggiano, A. K., Shields, A., Barrett, M., Kellam, T., Thompson, E.,
Simons, J., & Katz, P. (1992). Helpless deficits in students: The role
of motivational orientation. Motivation & Emotion, 16, 271-296.
Bood, S. A., Archer, T., & Norlander, T. (2004). Affective personality
in relation to general personality, self-reported stress, coping and op-
timism. Individual Diffe rences Research, 2, 26-37.
Brown, J. D., & Siegel, J. M. (1988). Attributions for negative life
events and depression: The role of perceived control. Journal of
Personality and Social P s y chology, 54, 316-322.
Byrne, D. G., Davenport, S. C., & Mazanov, J. (2007). Profiles of ado-
lescent stress: The development of the adolescent stress question-
naire (ASQ). Journal of Adolescence, 30, 393-416.
Copyright © 2013 SciRes.
Chemers, M. M., Hu, L., & Garcia, B. F. (2001). Academic self-effi-
cacy and first-year college student performance and adjustment.
Journal of Educational Psycholo gy, 93, 55-64.
Chorpita, B. F., & Barlow, D. H. (1998). The development of anxiety:
The role of control in early environment. Psychological Bulletin, 124,
3-21. doi:10.1037/0033-2909.124.1.3
Churchwell, J. C., Lopez-Larson, M., & Yurgelun-Todd, D. A. (2010).
Altered frontal cortical volume and decision making in adolescent
cannabis users. Frontiers in Psychology, 1, 225.
Cloninger, C. R., Svrakic, D. M., & Przybeck, T. R. (1993). A psycho-
biological model of temperament and character. Archives of General
Psychiatry, 50, 975-990.
Colder, C. R., & Chassin, L. (1997). Affectivity and impulsivity: Tem-
perament risk for adolescent alcohol involvement. Psychology of Ad-
dictive Behaviors, 11, 83-97. doi:10.1037/0893-164X.11.2.83
Coldren, C. D., Lai, Z., Shragg, P., Rossi, E., Glidewell, S. C., Zuffardi,
O., Mattina, T., Ivy, D. D., Curfs, L. M., Mattson, S. N., Riley, E. P.,
Treier, M., & Grossfeld, P. D. (2009). Chromosomal microarray
mapping suggests a role for BSX and Neurogranin in neurocognitive
and behavioural defects in the 11q terminal deletion disorder (Jaco-
bsen syndrome). Neurogenetics, 10, 89-95.
Cole, D. (1989). Psychopathology of adolescent suicide: Hopelessness,
coping beliefs, and depression. Journal of Abnormal Psychology, 98,
248-255. doi:10.1037/0021-843X.98.3.248
Crews, F. T., & Boettiger, C. A. (2009). Impulsivity, frontal lobes and
risk for addiction. Pharmacology, Biochemistry and Behavior, 93,
237-247. doi:10.1016/j.pbb.2009.04.018
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-de-
termination in human behavior. New York: Plenum.
Deci, E. L., & Ryan, R. M. (1991). A motivational approach to self:
integration in personality. In: R. Dienstbier (Ed.), Nebraska sympo-
sium on motivation: Vol. 38. Perspectives on motivation (pp 237-
288). Lincoln, NE: University of Nebraska Press.
Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A
macrotheory of human motivation, development, and health. Cana-
dian Psychology, 49, 182-185. doi:10.1037/a0012801
Denollet, J., & De Vries, J. (2006). Positive and negative affect within
the realm of depression, stress and fatigue: The two-factor distress
model of the Global Mood Scale (GMS). Journal of Affective Dis-
orders, 91, 171-180. doi:10.1016/j.jad.2005.12.044
Diener, C. I., & Dweck, C. S. (1978). An analysis of learned helpless-
ness: Continuous changes performance, strategy, and achievement
cognitions following failure. Journal of Personality and Social Psy-
chology, 36, 451-462. doi:10.1037/0022-3514.36.5.451
Dweck, C. S., & Wortman, C. B. (1982). Learned helplessness, anxiety,
and achievement motivation: Neglected parallels in cognitive, affec-
tive, and coping response. In H. W. Krohne, & L. Laux (Eds.),
Achievement, stress, and anxiety (pp. 93-125). New York: Academic
Ebestuani, C., Okamura, K., Higa-McMillan, C., & Chorpita, B. F.
(2011). A psychometric analysis of the positive and negative affect
schedule for children-parent version in a school sample. Psychologi-
cal Assessment, 23, 406-416. doi:10.1037/a0022057
Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., &
Meece, J. (1983). Expectancies, values and academic bahavoirs. In J.
T. Spence (Ed.), Achievement and achievement motives (pp. 75-146).
San Fransisco, CA: W. H. Freeman.
Ehrenberg, M. F., Cox, D. N., & Koopman, R. F. (1991). The relation-
ship between self-efficacy and depression in adolescents. Adoles-
cence, 26, 361-374.
Erez, A., & Isen, A. M. (2002). The influence of positive affect on the
components of expectancy motivation. Journal of Applied Psychol-
ogy, 87, 1055-1067. doi:10.1037/0021-9010.87.6.1055
Evans, G. W., & Stecker, R. (2004). Motivational consequences of
environmental stress. Journal of Environmental Psychology, 24, 143-
165. doi:10.1016/S0272-4944(03)00076-8
Foley, D. L., Goldston, D. B., Costello, E., & Angold, A. (2006).
Proximal psychiatric risk factors for suicidal thoughts and behaviors
in youth: The great smoky mountains study. Archives of General
Psychiatry, 63, 1017-1024. doi:10.1001/archpsyc.63.9.1017
George, J. M., & Brief, A. P. (1992). Feeling good-doing good: A con-
ceptual analysis of the mood at work-organizational spontaneity rela-
tionship. Psychologic a l Bulletin, 112, 310-329.
Gillespie, N. A., Cloninger, C. R., Heath, A. C., & Martina, N. G.
(2003). The genetic and environmental relationship between Clon-
inger’s dimensions of temperament and character. Personality and
Individual Differences, 35, 1931-1946.
Gould, M. S., King, R., Greenwald, S., Fisher, P., Schwab-Stone, M.,
Kramer, R., Flisher, A. J, Goodman, S., Canino, G., & Shaffer D.
(1998). Psychopathology assoiciated with suicidal ideation and at-
tempts among children and adolescents. Journal of the American
Academy of Child & Adolescent Psychiatry, 37, 915-923.
Hackett, G., Betz, N. E., Casas, J. M., & Rocha-Singh, I. A. (1992).
Gender, ethnicity, and social cognitive factors predicting the aca-
demic achievement of students in engineering. Journal of Counseling
Psychology, 39, 527-538. doi:10.1037/0022-0167.39.4.527
Hankin, B. L., Abramson, L. Y., Moffitt, T. E., Silva, P. A., McGee, R.,
& Angell, K. E. (1998). Development of depression from preadole-
cence to young adulthood: Emerging gender differences in a 10-year
longitudinal study. Jour nal of Abnormal Psychology, 107, 128-140.
Harden, K. P., & Tucher-Drob, E. M. (2011). Individual differences in
the development of sensation seeking and impulsivity during adoles-
cence: Further evidence for a dual systems model. Developmental
Psychology, 47, 739-746. doi:10.1037/a0023279
Heiligenstein, E., & Guenther, G. (1996). Depression and academic
impairment in college students. Journal of American College Health,
45, 59-64. doi:10.1080/07448481.1996.9936863
Hetta, J., Almqvist, M., Ågren, H., Hambert, G., Liljenberg, G. B., &
Roos, B. E. (1985). Prevalence of sleep disturbances and related
symptoms in a middle-aged Swedish population. In W. P. Koella, E.
Ruther, & H. Schultz (Eds.), Sleep, 84 (pp. 373-376). Stuttgart: Gus-
taf Fischer Förlag.
Huemer, J., Völkl-Kernstock, S., Karnik, N. G., Denny, K., Granditsch,
E., Mitterer, M., Humphreys, K., Plattner, B., Friedrich, M., Shaw, R.
J., & Steiner, H. (2012). Personality and psychopathology in African
unaccompanied refugee minors: Repression, resilience and vulner-
ability. Child Psychiatry and Human Development, 44, 39-50.
Joyce, P. R., McKenzie, J. M., Luty, S. E., Mulder, R. T., Caner, J. D.,
Sullivan, P. F., & Cloninger, C. R. (2003). Temperament, childhood
environment and psychopathology as risk factors for avoidant and
borderline personality disorders. Australian and New Zealand Jour-
nal of Psychiatry, 37, 756-764.
Karlsson, E., & Archer, T. (2007). Relationship between personality
characteristics and affect: Gender and affective personality. Individ-
ual Differences Research, 5, 44-58.
Koskinen-Hagman, M., Schwarzer, R., & Jerusalem, M. (1999). Swed-
ish version of the general self-efficacy scale.
Lahley, B. B., Applegate, B., Waldman, I. D., Loft, J. D., Hankin, B. L.,
& Rick, J. (2004). The structure of child and adolescent psychopa-
thology: Generating new hypotheses. Journal of Abnormal Psychol-
ogy, 113, 358-385. doi:10.1037/0021-843X.113.3.358
LaRocque, C. L. (2011). Childhood maltreatment and stress sensitiza-
tion in depression: Moderation by age group and depression history.
Master’s Thesis, Kingston: Queen’s University.
LeBlanc, J. C., Almudevar, A., Brooks, S. J., & Kutcher, S. (2002).
Screening for adolescent depression comparison of the Kutcher
Adolescent Depression Scale with the Beck Depression Inventory.
Psychopharmacology, 12, 113-126.
Legault, L., Green-Demers, I., & Pelletier, L. (2006). Why do high
Copyright © 2013 SciRes. 503
school students lack motivation in the classroom? Toward an under-
standing of academic amotivation and the role of social support.
Journal of Educational Psycholo gy, 98, 567-582.
Lewinsohn, P. M., Allen, N. B., Seeley, J. R., & Gotlib, I. H. (1999).
First onset versus recurrence of depression: Differential processes of
psychosocial risk. Journal of Abnormal Psychology, 108, 483-489.
Lewinsohn, P. M., Joiner Jr., T. E., & Rohde, P. (2001). Evaluation of
cognitive diathesis-stress models in predicting major depressive dis-
order in adolescents. Journal of Abnormal Psychology, 1 10, 203-215.
Lindahl, M., Theorell, T., & Lindblad, F. (2005). Test performance and
self-esteem in relation to experienced stress in Swedish sixth and
ninth graders-saliva cortisol levels and physiological reactions to
demands. Acta Paediatrica, 94, 489-495.
Lòpez-Ibor, J. J. (2002). The classification of stressrelated disorders in
ICD-10 and DSM-IV. Psychopathology, 35, 107-111.
Luten, A. G., Ralph, J. A., & Mineka, S. (1997). Pessimistic attribu-
tional style: is it specific to depression versus anxiety versus negative
affect? Behavioral Research and Therapy, 35, 703-719.
Maciejewski, P. K., Prigerson, H. G., & Mazure, C. M. (2000). Self-
efficacy as a mediator between stressful life events and depressive
symptoms. Differences based on history of prior depression. British
Journal of Psychiatry, 1 7 6 , 373-378. doi:10.1192/bjp.176.4.373
Marshall, G. N., Wortman, C., Kusulas, J. W., Hervig, L. K., & Vickers
Jr., R. R. (1992). Distinguishing optimism from pessimism: Relations
to fundamental dimensions of mood and personality. Journal of Per-
sonality and Social Psychology, 62, 1067-1074.
Maier, S. F., & Seligman, M. E. P. (1976). Learned helplessness: The-
ory and evidence. Journal of Experimental Psychology: General, 105,
3-46. doi:10.1037/0096-3445.105.1.3
Melke, J., Westberg, L., Nilsson, S., Landén, M., Soderstrom, H.,
Baghaei, F., Rosmond, R., Holm, G., Björntorp, P., Nilsson, L.-G.,
Adolfsson, R., & Eriksson, E. (2003). A polymorphism in the sero-
tonin receptor 3A (HTR3A) gene and its association with harm
avoidance in women. Archives of Genetic Psychiatry, 60, 1017-1023.
Metalsky, G. I., Abramson, L. Y., Seligman, M. E. P., Semmel, A., &
Peterson, C. R. (1982). Attributional styles and life events in the
classroom: Vulnerability and invulnerability to depressive mood re-
actions. Journal of Personality and S oc i a l Psychology, 43, 612-617.
Miller, E., Joseph, S., & Tudway, J. (2004). Assessing the component
structure of four self-report measures of impulsivity. Personality and
Individual Differences, 37, 349-358.
Millet, P. & Sandberg, K. (2003). Locus of control and its relationship
with rehabilitation outcomes of unemployed sick leavers in Sweden.
Journal of Vocational Rehabilitation, 19, 59-66.
Morris, M. C., Ciesla, J. A., & Garber, J. (2010) A prospective study of
stress autonomy versus stress sensitization in adolescents at varied
risk for depression. Jo urnal of Abnormal Psychology, 119, 341-354.
Nima, A. A. (2012). Mediational and moderating effects influencing
depressiveness experienced among students: Anxiety, stress, self-es-
teem and affective personality. Master Thesis, Gothenburg: Gothen-
burg University.
Norlander, T. & Archer, T. (2002). Prediction performance in ski and
swim championships: Effectiveness of mood, perceived exertion, and
dispositional optimism. Perceptual and Moto r Skills, 94, 153-164.
Norlander, T., Bood, S. Å., & Archer, T. (2002). Performance during
stress: Affective personality, age and regularity of physical exercise.
Social Behavior and Personality, 30, 495-508.
Palomo, T., Beninger, R. J., Kostrzewa, R. M., & Archer, T. (2008).
Focusing on symptoms rather than diagnoses in brain dysfunction:
Conscious and nonconscious expression in impulsiveness and deci-
sion-making. Neur ot ox ic ity Research, 14, 1-20.
Patton, J. D. A., Stanford, M. S., & Barratt, E. S. (1995). Factor struc-
ture of the barratt impulsive scale. Journal of Clinical Psychology,
51, 768-774.
Pössel, P., Baldus, C., Horn, A. B., Groen, G., & Hautzinger, M. (2005).
Influence of general self-efficacy on the effects of a school-based
universal primary prevention program of depressive symptoms in
adolescents: A randomized and controlled follow-up study. Journal
of Child Psychology and Psychiatry, 46, 982-994.
Ratelle, C. F., Guay, F., Vallerand, R. J., Larose, S., & Senécal, C.
(2007). Autonomous, controlled, and amotivated types of academic
motivation: A person-oriented analysis. Journal of Educational Psy-
chology, 99, 734-746. doi:10.1037/0022-0663.99.4.734
Rapport, M. D., Denney, C. B., Chung, K. M., & Hustace, K. (2001).
Internalizing behavior problems and scholastic achievement in chil-
dren: Cognitive and behavioral pathways as mediators of outcome.
Journal of Clinical Child Psychology, 30, 536-551.
Rotter, J. B. (1966). Generalized expectancies for Internal versus Ex-
ternal Control of Locus of Reinforcement. Psychological Mono-
graphs, 80, 1-28. doi:10.1037/h0092976
Rutter, M., Kim-Cohen, J., & Maughan, B. (2006). Continuities and
discontinuities in psychopathology between childhood and adult life.
Journal of Child Psychology and Psychiatry, 47, 276-295.
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing
optimism from neuroticism (and trait anxiety, self-mastery, and
self-esteem): A reevaluation of the life orientation test. Journal of
Personality and Social Psychology, 67, 1063-1078.
Scheier, M. F., Weintraub, J. K., & Carver, C. S. (1986). Coping with
stress: Divergent strategies of optimists and pessimists. Journal of
Personality and Social Psychology, 51, 1257-1264.
Schwarzer, R., Schmitz, G. S., & Tang, C. (2000). Teacher burnout in
Hong Kong and Germany: A cross-cultural validation of the Maslach
Burnout Inventory. Anxiety, Stress and Coping, 13, 309-326.
Seligman, M. E. P. (1975). Helplessness: On depression, development,
and death. San Francisco, CA: Freeman.
Shen, B., Winqert, R. K., Li, W., Sun, H., & Rukavina, B. K. (2010).
An amotivational model in physical education. Journal of Teaching
in Physics Education, 29, 72-84.
Solomon, D. A., Keller, M. B., Leon, A. C., Mueller, T. I., Lavori, P.
W., Shea, M. T., Coryell, W., Warshaw, M., Turvey, C., Maser, J. D.,
& Endicott, J. (2000). Multiple recurrences of major depressive dis-
order. American Journal of Ps ych iatry, 157, 229-233.
Spector, P. E., & O’Connell, B. J. (1994). The contribution of personal-
ity traits, negative affectivity, locus of control and Type A to the
subsequent reports of job stressors and job strains. Journal of Occu-
pational and Organizational P s ycho logy , 67, 1-12.
Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., &
Woolard, J. (2008). Age differences in sensation seeking and impul-
sivity as indexed by behavior and self-report: Evidence for a dual
systems model. Developmental Psychology, 44, 1764-1778.
Trzaskowski, M., Zavos, H. M. S., Haworth, C. M. A., Plomin, R., &
Eley, T. C. (2012). Stable genetic influence on anxiety-related be-
haviours across middle childhood. Journal of Abnormal Child Psy-
chology, 40, 85-94. doi:10.1007/s10802-011-9545-z
Ursin, H., & Eriksen, H. R. (2004). The cognitive activation theory of
stress. Psychoneuroendocrinology, 29, 567-592.
Copyright © 2013 SciRes.
Copyright © 2013 SciRes. 505
Ushijima, H., Usami, M., Saito, K., Kodaira, M., & Ikeda, M. (2012).
Time course of the development of depressive mood and opposi-
tional defiant behavior among boys with attention deficit hyperactiv-
ity disorder: Differences between subtypes. Psychiatry and Clinical
Neurosciences, 66, 285-291. doi:10.1111/j.1440-1819.2012.02340.x
Watson, D., Clark, L. A., & Carey, G. (1988a). Positive and negative
affectivity and their relation to anxiety and depressive disorders.
Journal of Abnormal Psychology, 97, 346-353.
Watson, D., Clark, L. A., & Tellegen, A. (1988b). Development and
validation of brief measures of positive and negative affect. The
PANAS scale. Journal of Personality and Social Psychology, 54,
Wei, S. M., Eisenberg, D. P., Kohn, P. D., Kippenhan, J. S., Kolachana,
B. S., Weinberger, D. R., & Berman, K. F. (2012). Brain-derived
neurotrophic factor Val66Met polymorphism affects resting regional
cerebral blood flow and functional connectivity differentially in
women versus men. Journal of Neurosci en ce , 32, 7074-7081.
White, H. R., Marmorstein, N. R., Crews, F. T., Bates, M. E., Mun, E.
Y., & Loeber, R. (2011). Associations between heavy drinking and
changes in impulsive behavior among adolescent boys. Alcoholism:
Clinical and Experimental Researc h, 35, 295-303.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn.
Contemporary Educational Psychology, 25, 82-91.