Optimism Vs. Pessimism

Optimism versus pessimism and academic achievement evaluation

Yifat Harpaz-Itayy
Shlomo Kaniel
Bar-Ilan University, Israel

This article integrates three central theories of optimism–pessimism (OP). The combination
of the shared components of these theories – outcome expectancies, emotions, and
behavioral intention – may produce an integrative academic achievement evaluation. Little
has been written regarding the differentiation between general and domain-specific OP, a
factor that is essential in describing the evaluation process of academic achievement. In this
paper, we will examine this differentiation and discuss how an integrative model of the
three theories is strengthened by two domains: (a) heuristic versus systematic processing
styles and (b) the level of confidence in the accuracy of evaluating events. The gap between
required confidence in high-personal-interest domains and actual confidence in moderate
general OP influences systematic processing style and planned behavior. Based on
these principles, an intervention program is suggested.

Optimism/pessimism, heuristic versus systematic processing styles, self-confidence

Theoretical background
Studies dealing with the effects of optimism and pessimism have found that whereas optimism
usually has a positive correlation with academic achievements, pessimism has a negative
correlation with achievements in different domains, including academic performance
(e.g. Nonis and Wright, 2003; Ruthig et al., 2009; Smith and Hoy, 2007; Yates, 2002). Various
optimism–pessimism(OP) theories have tried to explain these correlations by viewing
optimism and pessimism as general dispositions that encourage and prevent goal achievement,
respectively (e.g. Carver and Scheier, 2000; Reivich and Gillham, 2003). However,
very few OP theories refer to optimism as content dependent (domain specific) rather than
general (domain general) (e.g. Chang et al., 2009; Dember et al., 1989). Furthermore, none
of the theories illustrates the process necessary for an individual to be able to evaluate a specific domain or goal such as school (academic) achievement.
This paper briefly outlines the main theories in the field of OP, and then presents a
model that both integrates the OP theories and adds to it different styles of thought
processing and levels of confidence in order to understand the way students evaluate
their achievements.

Theoretical approaches to optimism and pessimism
There are three central OP theories currently being discussed in psychological literature:
dispositional optimism, OP explanatory style, and hope theory. Their essences are
summarized below:
Dispositional optimism. The dispositional optimism theory defines optimism as a
positive future expectation and pessimism as a negative future expectation (Carver and
Scheier, 2000). The theory of dispositional optimism links an individual’s disposition
and expressive behavior, and describes dispositional optimism or pessimism as a stable
variable that is not significantly influenced by specific events (Schou et al., 2005).
According to this theory, positive outcome expectancy is a combination of the value of
a goal and the individual’s confidence that he or she can achieve it. This combination creates
the motivation necessary to achieve goals, whereas negative expectancy prevents this
achievement (Carver and Scheier, 2000). Several studies support this theory (e.g. Carver
and Scheier, 2002; Nes and Segerstrom, 2006), showing positive correlations between
school achievements and dispositional optimism (Carver and Scheier, 2002).
OP explanatory style theory. OP explanatory style theory defines an optimist as one who
views positive events as having a global, permanent, and internal significance, and
negative events as having a specific, temporary, and external consequence. Pessimism
contradicts optimism; a pessimist perceives negative events as having a global, permanent,
and internal significance, while viewing positive events as specific, temporary, and
external (Reivich and Gillham, 2003). Every person has his or her own unique OP explanatory
style, creating different positive or negative expectations of future events (Wise
and Rosqvist, 2006). Correlations between school achievement and OP explanatory style
point to the fact that higher school achievements usually correlate with an optimistic
explanatory style (Boyer, 2006).
Hope theory. The hope theory defines hope as a mental set directed towards goal
attainment (Snyder et al., 2000). Hope is composed of two inter-related components:
(a) agency – the perceived capacity to use one’s pathways to reach desired goals or a
mental motivation to initiate and sustain movement toward a goal; and (b) pathways
thinking – the perceived ability to ‘develop routes’ in order to achieve goals. Raising
future expectations involves the cognitive evaluation of both agency and pathways thinking,
and the level of hope is determined by an integrated evaluation of both components.
Where both components are positive, an expectation of positive results accompanied
by goal attainment behavior occurs, whereas one negative component (or both) leads to a
negative expectation, accompanied by goal abandonment (Edwards et al., 2007). In
analyzing the correlation between high hopes and high school achievement, Snyder
(2002) describes eight parameters that provide advantages to people with high hopes.
However, he offers no explanation of how one can move from being someone with low
hopes to someone with high hopes (Snyder et al., 1991).

Integrating the theories into a comprehensive model
While general comparisons between the theories have been made (e.g. Edwards et al
2007; Scheier and Carver, 1987; Snyder, 2002), no research has been conducted into
whether these psychological approaches can be combined into one. Our argument is that
these psychological approaches can be integrated in the same way as three different
cameras can photograph the same object from three different angles: cognition, emotion,
and behavior. Hope theory accentuates the cognitive outcome expectancy and its causes.
This entails assessing the likelihood of a future result, taking into consideration additional
factors such as personal history, characteristic traits, and judgment processes.
OP explanatory style describes the characteristics of the event that create the expectations
and the emotions accompanying the outcome expectancy. Dispositional optimism
theory focuses on behavior, that is the behavioral intention is facilitated or inhibited by
the interaction between the outcome expectancy and its concomitant emotions.

Lack of distinction between general and specific OP
The theories of dispositional optimism, OP explanatory style, and hope theory provide
three basic components for creating an integrated theory: cognitive outcome expectancy,
emotions, and behavioral intention. In addition, they provide a general description of the
interaction of the three components to demonstrate how integrating outcome expectancy
with emotions facilitates (or inhibits) behavioral intentions. However, one of the fundamental
problems of integrating the three theories is that it fails to distinguish between
general and specific OP.
The distinction between general and specific OP is discussed within the three central
OP theories. For instance, all three focus on general outcome expectancy or on a permanent
OP characteristic. However, dispositional optimism theory claims that dispositional
optimism is not always constant, and it will change in different situations (Carver and
Scheier, 2003; Shepperd et al., 2006). Hope theory also describes the possibility of a
specific expectation and provides measurements for both general and domain-specific
hopes (Lopez, Snyder and Teramoto-Pedrotti, 2003).
Similarly, OP explanatory style is defined as consisting of an extremely flexible general
disposition, which is therefore measured jointly by a number of integrated specific
events. Additional studies have documented variations between the different general and
specific OP measurements. Thus, for example, research on weight loss revealed a
stronger connection between specific optimism measurements and achievements as
compared with the general dispositional optimism and weak correlations between the
general domain and specific measurements of OP (Benyamini and Raz, 2007; Pekrun
et al., 2009; Garber, 2000).
Moreover, people can be both optimistic and pessimistic (Burke et al., 2000). Adults,
for instance, reported a higher pain threshold when dealing with their illness in situations
where optimism and pessimism existed simultaneously (Benyamini, 2005). Being
simultaneously optimistic and pessimistic can be explained by the domain-general and
domain-specific dispositions. For example, people with an optimistic disposition (general
domain) will expect to pass a certain test. In this case, general optimism influences
specific optimism with regard to succeeding in the specific test.
However, other people with similar general optimistic dispositions will expect to fail
the same test because of specific pessimism resulting from diverse reasons including a
history of failure in that subject or unfamiliarity with the designated material. In other
words, a person can be generally optimistic yet still exhibit a pessimistic outlook in a
specific area, and vice versa.
Although the above approaches do explain the differences between the general OP
disposition and events specifically evaluated as OP, they fall short of describing the
process inherent in each situation (evaluation according to the general disposition in
assessing a specific domain). In addition, they also do not present a way for integrating
all situations (Szalma et al., 2006). One attempt was conducted by a few researchers
(Chen and Chaiken, 1999; Fazio, 1990), which describes the process from the general
OP to the evaluation of a specific domain (such as school subjects) as OP combined with
a number of processing styles presented below. Their incorporation into an integrative
OP theory clarifies whether the general OP has more influence on domains such as
school achievement, or whether it is itself influenced by the specific evaluation of a
domain as OP.

Heuristic versus systematic processing styles
People can evaluate or make decisions by using one of two extreme poles of processing
styles. One is heuristic information processing with the resultant spontaneous behavioral
intention. At the other end lies systematic information processing, which encourages
planned behavioral intention.
Heuristic information processing style. Heuristic information processing is described by
Kahneman (2003) as decision shortcuts and simple generalizations that a person believes
are the correct decision protocol. Thus, for example, if ‘most of the public thinks something
is true’, others accept that it must be so. These shortcuts develop as a result of a
previous experience and are transformed into procedural information applied to evaluate
situations (Klaus et al., 1997).
In heuristic processing, an individual reaches conclusions by using simple principles
to focus on easily retrieved information details. The person generally observes the issue
presented and formulates an expectation based only on general knowledge and past
experience, without investing any further effort. Heuristic processing usually derives
from a general grasp of the situation. This process involves only a minimal absorption of
new information (Kruglanski et al., 1999).
Spontaneous behavioral intention. Heuristic information processing is likely to lead to a
spontaneous, effortless processing of behavioral intention derived from general disposition
(Fazio, 1990). This process occurs after completion of the initial evaluation of a
specific domain, an evaluation that determines the expected behavior. This evaluation
is stored in the person’s memory, and thereafter provides ‘ready aid’; in additional
encounters with similar domains, people will act directly and automatically as they will
not require the control and supervision of the initial evaluation.
Consequently, there will be no need to invest any effort in the demanding process of
behavioral clarification in any additional similar events (Fazio et al., 2000). Therefore, a
behavioral intention can be predicted as a person evaluates the event or the domain while
automatically activating the initial evaluation from memory. As such, using heuristic
processing to evaluate events or domains as OP facilitates a prediction of a behavioral
intention based on a person’s general OP.
Systematic information processing style. This style entails systematic and comprehensive
information processing, including a detailed analysis of the quality of the argumentation.
Systematic processing demands both a great deal of attention and cognitive effort
(Chaiken et al., 1989; Forehand et al., 2004). The contrasting aspects of heuristic and
systematic processing have also been described as experimental processing (based on
historical experience) versus rational processing, respectively, or as impulsive versus
reflective processes, respectively (Spence and Townsend, 2005).
When people are looking systematically for information in the field of OP, their evaluation
of events is based primarily on the content of the domain that encompasses those
events. Moreover, people using the systematic style are simultaneously and meticulously
searching for relevant information to be included in a calculated manner. In this case, the
influence of the general OP will be weakened, whereas the content-based evaluation of
the domain will be strengthened.

Planned behavioral intention
Planned behavioral intention is a premeditated and conscious behavioral process which
usually derives from a systematic processing style that demands mental effort (Fazio,
1990). During this process, details are carefully tested for additional behavioral possibilities
and their implications, until a conscious decision is made about the desired behavioral
intention. Theories describing planned behavior (Zint, 2002) demonstrate
conscious processes entailing the investment of effort in behavioral intention. As with
planned behavioral intention, when behavior is retrieved from memory, here the person
also undergoes a conscious process, including testing the different consequences of
possible behavior. Therefore, in this process, the general disposition will not usually
be a significant factor when explaining behavioral intention.

The gap between the required confidence and the actual confidence influences
the processing style
The use of heuristic information processing and spontaneous behavioral intentions is a
default option because of its economy of effort. It occurs whenever people who receive new
information balance an investment of minimal effort to process this information with the
required confidence necessary to make a decision or evaluation (Chen and Chaiken, 1999).
When the required confidence in the accuracy of events is low, or when personal interest
or involvement is low, no actual confidence is needed for a correct evaluation. In this
case, there will be no gap between required confidence and actual confidence. As a result,
heuristic processing and spontaneous behavioral intention based on the general OP will
occur. According to Crano (1995), personal interest or involvement is determined by the
level of personal interest vested in the accuracy of evaluating a domain or an event. High personal-interest domains reflect evaluations in which the accuracy has personal consequences. This occurs when a mistake in an evaluation might cause significant personal
damage, whereas accuracy can prevent damage or provide great personal benefit.
A person will invest greater effort in information processing and in planned behavior
when there is a large gap between the required confidence for an accurate evaluation and
the actual confidence in ability to evaluate correctly the events (Chen and Chaiken, 1999;
Fazio and Towles-Schwen, 1999). This situation may occur in domains of high personal
interest, such as school achievement, which is a domain with significant consequences
for the student’s future. In this case, the required confidence for the accurate evaluation
of the student’s expectancies of his or her school achievements is high, and it is important
to examine whether the actual confidence is also high or not.

Extreme or moderate general OP in high-personal-interest domains
It is important to make a distinction between events in which the general OP is extreme
(either very optimistic or very pessimistic) and those events in which the general OP is
moderate. In the case of extreme general OP, the student might have a high level of confidence
in the correct evaluation of different events. In extreme dispositions the level of
confidence is high; the more extreme the general disposition, the more consistency there
is between the cognitive and emotional components of the evaluation (Chaiken and
Yates, 1985). This, in turn, encourages behavior supporting the developed evaluation
(van Doorn et al., 2007). This is because of either a tendency of extremely confident individuals
to dismiss conflicting information by doubting, overshadowing, or refuting its
content (Wood et al., 1995) or the greater availability of information corresponding with
the general disposition (Krosnick and Petty, 1995; Thompson et al., 1995). An extreme
disposition is stronger and more stable over a period of time, and also opposes change
(Bohner and Wanke, 2002). For instance, in a study on alcohol drinking (Boninger et
al., 1995), it was found that in contrast to participants with moderate views on alcohol
consumption, those with extreme views radicalized these attitudes in response to contradictory
messages. The level of extremity is related to the level of actual confidence; a
sufficiently high level of confidence in correctly evaluating a specific event or domain
exists in an extreme general disposition (Bohner and Wanke, 2002). Accordingly, heuristic
processes and spontaneous behavioral intention will be based on this disposition.
Extreme OP is manifested in consistency among its main components (outcome
expectancy, emotions, and resulting behavioral intentions). In addition, an extreme
general OP is highly correlated with actual confidence in accurate evaluation of events
in the domain (e.g. Pekrun et al., 2009). Thus, the level of extremity of the general OP
represents the level of actual confidence in the accuracy of the evaluation of the domains.
Empirical evidence suggests that pessimism is negatively correlated with school
achievement (Smith and Hoy, 2007; Snyder, 2002; Yates, 2002). This means that students
with negative general outcome expectancies will, similarly, evaluate negatively
their predicted failures in school. A combination of this negative outcome expectancy
and the accompanying negative emotions leads to a lack of motivation, with the result
that the negative outcome expectancy is often fulfilled with low school achievements.
Extreme optimism holds a similar danger. Recent studies (Brown and Marshall, 2000;
Chang et al., 2009; Haynes et al., 2006) indicate the relative success of moderate
optimism as opposed to extreme optimism. Moderate optimists consistently attained
similar or higher achievements in school subjects than extreme optimists did. These findings
back up our contention that extreme optimists have high actual confidence in their
ability to accurately evaluate their general expectancies, including how well they will
succeed at school. There is no gap between their high required confidence (resulting
from high personal interest) and their high actual confidence (resulting from their
extreme optimism). Therefore, these individuals adapt heuristic information processing
based on their general optimism and spontaneous behavioral intention, and consequently
do not invest great effort as they believe that they are destined to succeed in any event.

Moderate OP in high-personal-interest domains
In contrast to the phenomenon described above, in the case of high-personal-interest
domains when high confidence is required, but the general OP is moderate, actual confidence
in the accurate evaluation of the domain is lower. As a result, the gap between required
and actual confidence in the accurate evaluation of the expectation increases. Therefore,
more systematic information processing and planned behavioral intention is to be expected.
Thus, systematic processing will occur when the actual level of confidence in making
a correct evaluation is low (as expressed by a moderate general OP) and the personal
interest is high. Such arguments actually can be supported by Tobin and Weary
(2008), who asked participants to evaluate different events. After that the researchers
presented opposing views to the opinions of the participants. It turned out that the participants
performed a systematic search processing for more information and explanations
of events only if both principles happened: (a) participants showed a lack of confidence
in assessing the events received because they feared that they did not understand the
events correctly; and (b) the events were of importance to the participants (e.g. an
overwhelming chance of success in an academic course).
This made the information processing much more systematic (Boninger et al., 1995;
Klaus et al., 1997). Accordingly, behavioral intention will also be more planned (Fazio
and Towles-Schwen, 1999).
In the case of school achievements, where there is moderate general optimism,
actual confidence will be lower than the required confidence. The gap between the
actual confidence and the required confidence encourages systematic processing style
and allows their behavioral intention to be planned according to the evaluated expectation
in the specific subject. As a result, the general OP will be applied to the specific
school subject. Students will try to evaluate their success in an upcoming task based on
previous achievements in the specific subject. Their behavioral intention will be more
planned by applying learning strategies that were most effective in achieving higher
grades in the past.
To reiterate, both types of processing styles represent continuous polarized ends,
which are not mutually exclusive. When systematic information processing takes place,
the influence of heuristic processes will certainly decrease, but will not necessarily disappear
altogether (Maheswaran and Chaiken, 1991). Therefore, a positive correlation
between optimism and school achievements is possible in the case of heuristic processing.
In moderate OP, systematic processing and planned behavioral intention will be
strengthened and will be based mainly on a specific domain (contrary to the general
OP). However, when the student has extreme general OP, only lowering the high level
of extremity will encourage systematic style and planned behavioral intention. This is
because extreme general OP leads to heuristic information processing with the resultant
spontaneous behavioral intention.
Figure 1 summarizes the basic processes used to evaluate school achievement as OP.
It is clear that the first step is assessing whether the school subject has a high level of
personal interest for the students. If the level of personal interest is low then a low level
of required confidence is needed. If the student does raise high personal interest, then a
high level of confidence is required. Both answers converge on the question of whether
the general OP is extreme or moderate. If the general OP is extreme (the right side of the
figure) then a high level of actual confidence is needed, resulting in a small gap between
required and actual confidence. These factors will lead to a heuristic processing style and
the general OP level will be applied to the evaluation of specific school subjects. Heuristic
processing will lead to spontaneous behavioral intentions; the student, therefore,
will not have the motivation to study because of his or her predicted evaluation failure
or success in a specific subject. Therefore, no studying efforts can be predicted.
The left side of the figure demonstrates what might occur when the general OP is
moderate. In this case, the student may have a low level of actual confidence and there
will be a large gap between required and actual confidence. This large gap leads to a systematic
processing style that entails a thorough and careful evaluation of a specific
school subject (in accordance with, or in contradiction to, the general OP).
This systematic style will also lead to planned behavioral intentions. The outcome of
such behavior cannot be predicted because a student might decide either to withdraw
from the task (no chance of succeeding) or to continue to invest more effort.

Applications to education
Below are various proposed principles for an intervention program to facilitate control of
the desired kind of processing style. This intervention program consists of three main
elements: (a) a self-evaluation of OP; (b) guided metacognitive intervention; and
(c) guided accompaniment in school.

figure 1

Figure 1. Combining the OP integrative theory with information processing principles in order to
evaluate school achievements as either optimistic or pessimistic.

Self-evaluation. A self-evaluation entails three stages: general OP self-evaluation,
self-evaluation of the general OP extremity, and self-evaluation of personal interest in
the domain of school achievement. Initially, students evaluate their general OP by completing
general OP measurements. Participants receive the results of their self-evaluation
questionnaire and are informed that optimism has a higher positive correlation with
school achievements than pessimism. After self-evaluation, the level of the general
OP extremity is evaluated (second stage). This might comprise about 3% of the most
optimistic or pessimistic of the participants (Haynes et al., 2006). The level of the
extreme general OP as compared with that of the other participants will be reported to
each student by the program counsellor. Over several meetings, the participants will
be introduced to the concepts of OP and heuristic and systematic information processing
styles, and to the nature of the behavioral intention derived from choosing either process.
In the third stage, students are asked to evaluate the importance they attribute to
specific school subjects, that is those that are most likely to affect their present or future.
The important school subjects that were selected by the students will be the focus of the
subsequent elements of the program.

Guided metacognitive intervention
The second part of the program utilizes metacognitive intervention whereby students are
taught to analyze the information available to them accurately and logically and to
consciously plan their behavioral intention.
The trainers will stress the importance of maintaining general optimism, while
explaining that students who have general pessimism can regulate the kind of information
processing and apply systematic information processing to provide an accurate
evaluation of OP expectations for a high-interest specific domain.
Once the students delivered this information, the trainers will assist students with an
extreme general OP to consciously moderate it in important school subjects by teaching
the two processing styles (heuristic and systematic). The trainer helps the students by
pointing out the gap between required and actual confidence in the accurate evaluation
of high-personal-interest domains, and thus enabling the students to recognize their own
gaps. The counsellor then stresses the necessity to invest greater effort in systematic
information processing and planned behavioral intention.
By directing the students to adopt systematic information processing, the counsellor
encourages a realistic evaluation of specific domains through the use of available information.
This encompasses the following elements: the relative level of difficulty of the school
subject to the students and their peers; their past experiences of this school subject; and the
different learning strategies they had previously used in order to cope. The counsellor will
identify and point out to the student which strategies were more helpful than others.
Toward the end of this stage, the counsellor will ask the students to accurately
evaluate their OP expectations of this specific school subject. This type of metacognitive
intervention will help students to evaluate, more accurately and more objectively, the
outcome expectancy in the specific high-interest school subjects. In addition, the
metacognitive element supports the conscious planning of behavioral intention, and
helps students understand the value of investing effort in order to achieve goals in
domains that are of high personal interest to them.

Guided accompaniment in school
The third section of the general program entails monitoring the students’ accurate
evaluation of specific high-interest school subjects and following their success in light
of these expectations. After they have taken one or more tests in the same subject, it
is advisable to request students to revise their initial evaluation of a success or failure
in that specific subject. Where meaningful gaps remain between the evaluation of the
subject as OP and actual achievements, students should redo the program. Even when
students do moderate their OP evaluation toward specific high-personal-interest
domains, the counsellor should continue to monitor their progress and direct them to
apply what they have learned to other high-personal-interest domains not necessarily
connected to school subjects.
It is important to note that this program reflects the OP evaluations of specific highpersonal-
interest domains in accordance with the theoretical background of this article,
i.e. the integration of the processing style of OP to the evaluation of specific domains
such as school subjects. This program is not a substitute for other important programs
including those of control perceptions, self-awareness, and positive emotions.
To summarize, information processing styles are recognized paradigms in the world
of psychology. The article presents an integration of recognized information processing
styles with the OP disposition. As far as we know, it is the first trial that refers to the
processes that occur when evaluating specific domains such as school achievements.
It is our belief that this article will allow for the examination of different situations
including checking the level of validity of the general OP in evaluating specific domains
as OP, differences in the evaluation of a specific domain as OP among people with
extreme versus moderate general OP, and the level of consistency between expectation,
emotion, and behavioral intention in evaluating a domain that contradicts the general OP.

This research received no specific grant from any funding agency in the public, commercial, or
not-for-profit sectors.

Declaration of Conflicting Interests
The authors declare that they do not have any conflicts of interest.

Benyamini Y (2005) Can high optimism and high pessimism co-exist? Findings from arthritis
patients coping with pain. Personality and Individual Differences 38: 1463–1473.
Benyamini Y and Raz O (2007) ‘I can tell you if I’ll really lose all that weight’: Dispositional and
situated optimism as predictors of weight loss following a group intervention. Journal of
Applied Social Psychology 37: 844–861.
Bohner G and Wanke M (2002) Attitudes and Attitude Changes. UK: Psychology Press.
Boninger DS, Krosnick JA, Berent MK and Fabrigar LR (1995) The causes and consequences of
attitude importance. In: Petty RE and Krosnick JA (eds) Attitude Strength: Antecedents and
Consequences. Mahwah, NJ: Lawrence Erlbaum Associates Inc., pp. 159–190.
Boyer W (2006) Accentuate the positive: The relationship between positive explanatory style
and academic achievement of prospective elementary teachers. Journal of Research in
Childhood Education 21: 53.
Brown JD and Marshall MA (2000) Great expectations: Optimism and pessimism in achievement
settings. In: Chang EC (ed.) Optimism and Pessimism: Implications of Theory, Research, and
Practice. Washington, DC: American Psychological Association, pp. 239–256.
Burke KL, Joyner AB, Czech DR and Wilson MJ (2000) An investigation of concurrent validity
between two optimism/pessimism questionnaires: the life orientation test-revised and the
optimism/pessimism scale. Current Psychology: Developmental, Learning, Personality and
Social  19: 129–136.
Carver CS and Scheier MF (2000) Optimism, pessimism, and self-regulation. In: Chang EC (ed.)
Optimism and Pessimism: Implications of Theory, Research, and Practice .Washington, DC:
American Psychological Association, pp. 31–51.
Carver CS and Scheier MF (2002) Optimism. In: Snyder CR and Lopez SJ (eds) Handbook of
Positive Psychology. Oxford: University Press, pp. 231–243.
Carver CS and Scheier MF (2003) Optimism. In: Lopez SJ and Snyder CR (eds) Positive
Psychological Assessment: Handbook of Models and Measures. Washington DC: American
Psychological Society, pp. 75–89.
Chaiken S and Yates S (1985) Affective–cognitive consistency and thought-induced attitude
polarization. Journal of Personality and Social Psychology 49: 1470–1481.
Chaiken S, Liberman A and Eagly AH (1989) Heuristic and systematic information processing
within and beyond the persuasion context. In: Uleman JS and Bargh JA (eds) Unintended
Thoughts. New York: Guilford, pp. 212–252.
Chang EC, Chang R and Sanna LJ (2009) Optimism, pessimism, and motivation: Relations to
adjustment. Social and Personality Psychology Compass 3/4: 494–506.
Chen S and Chaiken S (1999) The heuristic–systematic model in its broader context. In: Chiken S
and Trope Y (eds) Dual-process Theories in Social Psychology. New York: Guilford, pp. 73–96.
Crano WD (1995) Attitude strength and vested interest. In: Petty RE and Krosnick JA (eds) Attitude Strength: Antecedents and Consequences. Mahwah, NJ: Lawrence Erlbaum Associates
Inc., pp. 131–158.
Dember WN, Martin SH, Hummer MK, et al. (1989) The measurement of optimism and pessimism. Current Psychology: Research & Reviews 8: 102–119.
Edwards LM, Rand KL, Lopez SJ and Snyder CR (2007) Understanding hope: A review of
measurement and construct validity research. In: van Dulmen MHM and Ong A (eds) Oxford
Handbook of Methods in Positive Psychology. NY: Oxford University Press, pp. 83–95.
Fazio RH (1990) Multiple processes by which attitudes guide behavior: The MODE model as an
integrative framework. Advances in Experimental Social Psychology 23: 75–109.
Fazio RH and Towles-Schwen T (1999) The MODE model of attitude–behavior relations. In:
Chaiken S and Trope Y (eds) Dual-processing Theories in Social Psychology. New York:
Guilford, pp. 97–116.
Fazio RH, Ledbetter JE and Towles-Schwen T (2000) On the costs of accessible attitudes: Detecting that the attitude object has changed. Journal of Personality and Social Psychology
Forehand M, Gastil J and Smith MA (2004) Endorsements as voting cues: Heuristic and systematic processing in initiative elections. Journal of Applied Social Psychology 34: 2215–2233.
Garber J (2000) Optimism: Definitions and origins. In: Gillham JE (ed.) The Science of Optimism
and Hope. Philadelphia: Templeton Foundation Press, pp. 299–314.
Haynes TL, Ruthig LC, Perry RP, et al. (2006) Reducing the academic risks of over-optimism: The
longitudinal effects of attributional retraining on cognition and achievement. Research in
Higher Education 47: 755–779.
Kahneman D (2003) A perspective on judgment and choice: Mapping bounded rationality.
American Psychologist 58: 697–720.
Klaus J, Diehl M and Bromer P (1997) Effects of attitudinal ambivalence on information processing and attitude–intention consistency. Journal of Experimental Social Psychology 33: 190–210.
Krosnick JA and Petty RE (1995) Attitude strength: An overview. In: Petty RE and Krosnick JA
(eds) Attitude Strength: Antecedents and Consequences. Mahwah, NJ: Lawrence Erlbaum,
pp. 1–24.
Kruglanski AW, Thompson EP and Spiegel S (1999) Separate or equal? Bimodal notions of
persuasion and a single-process ‘Unimodel’. In: Cahiken S and Trope Y (eds) Dual-process
Theories in Social Psychology. New York: Guilford, pp. 293–313.
Lopez SJ, Snyder CR and Teramoto-Pedrotti J (2003) Hope: Many definitions, many measures. In:
Lopez SJ and Snyder CR (eds) Positive Psychological Assessment: Handbook of Models and
Measures. Washington DC: American Psychological Society, pp. 91–107.
Maheswaran D and Chaiken S (1991) Promoting systematic processing in low motivation settings: The effect of incongruent information on processing and judgment. Journal of Personality and Social Psychology 61: 13–25.
Nes LS and Segerstrom SC (2006) Dispositional optimism and coping: A meta-analytic review.
Personality and Social Psychology Review 10: 235–251.
Nonis SA and Wright D (2003) Moderating effects of achievement striving and situational optimism on the relationship between ability and performance outcomes of college students.
Research in Higher Education 44: 327–346.
Pekrun R, Elliot AJ and Maier MA (2009) Achievement goals and achievement emotions: Testing
a model of their joint relations with academic performance. Journal of Educational Psychology
101: 115–135.
Reivich K and Gillham J (2003) Learned optimism: The measurement of explanatory style. In:
Lopez SJ and Snyder CR (eds) Positive Psychological Assessment: Handbook of Models and
Measures. Washington DC: American Psychological Society, pp. 57–74.
Ruthig JC, Hanson BL and Marino JM (2009) A three-phase examination of academic
comparative optimism and perceived academic control. Learning and Individual Differences
19: 435–439.
Scheier MF and Carver CS (1987) Dispositional optimism and physical well-being: The influence
of generalized outcome expectancies on health. Journal of Personality 55: 169–210.
Schou I, Ekeberg O, Sandvik L and RulandCM(2005) Stability in optimism–pessimism in relation to bad news: A study of women with breast cancer. Journal of Personality Assessment
84: 148–154.
Shepperd JA, Sweeny K and Carroll PJ (2006) Abandoning optimism in predictions about the
future. In: Chang EC and Sanna LS (eds) Judgments Over Time: The Interplay of Thoughts,
Feelings, and Behaviors. New York: Oxford University Press, pp. 13–33.
Smith PA and Hoy WK (2007) Academic optimism and student achievement in urban elementary
schools. Journal of Educational Administration 45: 556–568.
Snyder CR (2002) Hope theory: Rainbows in the mind. Psychological Inquiry 13: 249–275.
Snyder CR, Irving LM and Anderson JR (1991) Hope and health. In: Snyder CR and Forsyth DR
(eds) Handbook of Social and Clinical Psychology: The Health Perspective. Elmsford, New
York: Pergamon, pp. 285–305.
Snyder CR, Sympson SC, Michael ST and Cheavens J (2000) Optimism and hope constructs: Variants on a positive expectancy theme. In: Chang EC (ed.) Optimism and Pessimism:
Implications for Theory, Research, and Practice. Washington, DC: American Psychological
Association, pp. 101–125.
Spence A and Townsend E (2005) Spontaneous evaluations: Similarities and differences between
the affect heuristic and implicit attitudes. Cognition & Emotion 22: 83–89.
Szalma SL, Hancock PA, Dember WN and Warm JS (2006) Training for vigilance: The effect of
knowledge of results format and dispositional optimism and pessimism on performance and
stress. British Journal of Psychology 97: 115–135.
Thompson MM, Zanna MP and Griffin DW (1995) Let’s not be indifferent about (attitudinal)
ambivalence. In: Petty RE and Krosnick JA (eds) Attitude Strength: Antecedents and Consequences. Mahwah, NJ: Lawrence Erlbaum, pp. 361–386.
Tobin SJ and Weary G (2008) The effects of causal uncertainty, causal importance, and initial attitude on attention to causal persuasive arguments. Social Cognition 26: 44–65.
Van Doorn J, Verhoef PC and Bijmolt THA (2007) The importance of non-linear relationships
between attitude and behavior in policy research. Journal of Consumer Policy 30: 75–90.
Wise D and Rosqvist J (2006) Explanatory style and well-being. In: Hersen M, Thomas JC and
Segal DL (eds) Comprehensive Handbook of Personality and Psychopathology, Volume 1.
Hoboken, NJ: John Wiley & Sons, pp. 285–305.
Wood W, Rhodes N and Biek M (1995) Working knowledge and attitude strength: An
information-processing analysis. In: Petty RE and Krosnick JA (eds) Attitude Strength:
Antecedents and Consequences. Mahwah, NJ: Lawrence Erlbaum, pp. 283–314.
Yates SM (2002) The influence of optimism and pessimism on student achievement in mathematics. Mathematics Education Research Journal 14: 4–15.
Zint M (2002) Comparing three attitude–behavior theories for predicting science teachers’ intentions. Journal of Research in Science Teaching 39: 819–844.

Author biography
Professor Shlomo Kaniel is a full time associated professor in the School of Education at
Bar-Ilan University. He serves now has the head of the PHd. program and has a chair on
institute of research methods on Judaism. Professor Kaniel’s major areas of research are:
attention, memory, working memory, reasoning, meta-cognition, implementing thinking
skills in the curriculum, learning strategies, emotional and motivational factors in learning
and Jewish identity. In the area of teaching, Professor Kaniel has been engaged in teaching
developmental psychology, cognitive processes, decision making, dynamic assessment
and treatment of adolescents, interpersonal communication, literacy, and writing and evaluating cognitive curriculum. Professor Kaniel has been a visiting professor at several Universities in the USA and South Africa.

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