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Positive Expectations – Optimism and Hope in Models

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ICEEPSY 2016 : 7th International Conference on Education and Educational Psychology
Positive Expectations Optimism and Hope in Models
Jaroslava Dosedlová a*, Martin Jelínek a, Helena Klimusová a, Iva Burešová a
* Corresponding author: Jaroslava Dosedlová, dosedlova@mail.muni.cz
a Institute of Psychology, Masaryk University, Arne Nováka 1, 602 00 Brno, Czech Republic
Abstract
Current approaches to optimism accentuate its many different aspects: dispositional optimism (Scheier, & Carver,
2002), defensive pessimism (Norem, & Cantor, 1989), unrealistic optimism (Weinstein, 1980) and hope (Snyder,
1994). The aims of the research (supported by GACR, no. 13-19808S) were to design a model of
optimism/pessimism using the dimensions of dispositional optimism, defensive pessimism, unrealistic optimism
and hope, and to determine the degree of conceptual overlap between optimism and hope. We collected data from
1,774 respondents (men 33.3%, women 66.7 %) at the age from 15 to 79 (m 33.5, SD 15.8, med 27). Following
measures were used: the Defensive Pessimism Questionnaire (DPQ), the Life Orientation Test (LOT-R), Adult
Dispositional Hope Scale (ADHS) and Unrealistic Optimism Scale. We created three models and evaluated them
using confirmatory factor analysis. The first model was first order factor analysis model with correlated factors
represented by individual measures. The second model implemented a second order factor of general optimism.
The third model used two second order correlated factors - optimism and hope. We consider model three as the
best suited both statistically and for interpretation. Our model corresponds to Snyder’s concept (in Chang, 2002),
who views his construct of hope more actively and specifically in comparison to optimism. Optimism focuses
more broadly on the expected quality of future outcomes in general.
© 2016 Published by Future Academy www.FutureAcademy.org.uk
Keywords: Optimism; pessimism; hope; models.
1. Introduction
Optimism and pessimism can be seen as umbrella terms used in relation to many current constructs.
They do not always correspond to the commonly used meaning of the terms. Specialized literature
specifies and differentiates, inter alia, dispositional optimism (Scheier, & Carver, 1985), optimistic or
pessimistic attributional (explanatory) style (Peterson, & Seligman, 1987), optimism as a positive
illusion (Taylor, & Brown, 1988), unrealistic optimism (Weinstein, 1980), unrealistic pessimism
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(Heine, & Lehman, 1995), defensive pessimism and strategic optimism (Norem, & Cantor, 1986).
Current psychological approaches consider optimism most commonly a cognitive characteristic; the
authors focus on the goals, the expectations and the causal attributions. However, we must not forget
the emotional entourage accompanying optimism and pessimism. We may be surprised that both
optimism and pessimism may be emotionally defensive in nature, and that optimism is motivating
along the road towards a goal, but may also be an appealing goal in itself.
Scheier & Carver (2002, a, b, 2014) are proponents of the theory of dispositional optimism and
define it as a generalized expectation of positive course of events and a positive result of the event or
activity itself. To any challenge, an optimist reacts with faith in reaching desirable results and
continually taking part in the activity even if the process is long and difficult. Pessimists, on the other
hand, are full of doubt and hesitation. These differences also significantly influence, inter alia, coping
with stress. Dispositional optimism is, to a large extent, affected by genetics. Authors consider it a
relatively stable and fixed facet of personality.
The theory of optimism as an explanatory style stems from theory of attribution and theory of
learned helplessness (Seligman, 1990). An optimistic explanatory style can be characterized by the
attribution of external, unstable and specific causes to failures and negative events; a pessimistic
explanatory style is typical by attributing internal, stable and global causes to negative events. The
basic difference between the optimistic and pessimistic explanatory style is seen by Seligman (2003) in
the different assessment of the causes of success or failure, as well as the pervasiveness of good and
bad events and the ability to maintain and foster hope. According to Seligman et al., explanatory style
is malleable and can be changed from pessimistic to optimistic (Seligman, 2013).
Weinstein (1980) defined unrealistic optimism as a general tendency to expect that negative
events are more likely to happen to others and positive are more likely to happen to us. This approach
builds on the concept of so-called positive illusions (Taylor, & Brown, 1988, 1994), which can be
commonly found in satisfied, psychologically healthy and well-adapted persons (slight self-
overestimation, overestimation of the ability to control the course of events in their life and viewing the
future in the light of unrealistic optimism).
People using an optimistic strategy do not think too much about possible risks and imagine
themselves in a situation where they are coping successfully. The occasion in question does not cause
them anxiety, and they feel they have the situation under control. By contrast, people using the
cognitive strategy of defensive pessimism expect very little of the coming events, since they
experienced success in similar situations before. These defensive pessimists are more anxious, and the
reduced expectations serve as a “cushion” which dampens the effects of a possible failure. They
imagine all the bad things that can happen and try to estimate the risks in order to be able to prepare for
them. The effort they exert to reach the objectives is not reduced in any way by defensive pessimism.
Cantor & Norem (1989) thus describe defensive pessimism as a cognitive strategy characterized by low
expectations despite previous success, and rumination regarding potential risks and their management.
Similar to optimism, hope is also based on the anticipation of positive future developments.
According to Snyder (2000) hope is a multi-dimensional construct which can take on the form of a
current state (hope state) or a personality trait (hope trait). As a personality trait, hope then expresses
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the subjective extent of hope experienced over a long period of time. It results from the cognitive-
motivational setup and consists of three basic components goals, will, sometimes also called will-
power or agency, and the pathways to the goal themselves. It is thus the collection of subjectively
perceived sources and abilities to find pathways towards the set goals, and subjectively perceived
activity needed to utilize these pathways. It reflects the belief that the person will find a path to achieve
the desired goal and will be motivated to walk this path (Snyder, 1994, 2000, a, b).
The conceptual and psychological differentiation of optimism and hope based on commonly used
connotations in the language was studied by Bruininks & Malle (2005). Based on qualitative coding of
the respondent statements, the authors concluded that the intuitive understanding of optimism and hope
does not correspond to theoretical constructs, but nonetheless aids in differentiating them. Optimism
appears in situations where the likelihood of achieving a goal is higher. It is associated with joy. Hope
as an emotion is activated in situations which we have little control over and whose realization is
uncertain. The authors elaborate on the obvious contradictions in the intuitive understanding of the
concepts in question (primarily hope) and the theoretical constructs, and speak out against the willful
creation of psychological theoretical concepts which do not respect the folk concept, i.e. the generally
accepted concept of a particular phenomenon.
Snyder et al. (in Chang, 2002) perceive optimism as a more general construct, which emphasizes
active thinking directed towards a goal, and view hope as a set of goal-oriented thoughts which include
both motivation and the potential ways of reaching the goal.
Alarcon, Bowling & Khazon (2013) published a meta-analytical study examining the relationship
between optimism and hope. They arrived at the conclusion that both constructs correlate, but are not
redundant.
2. Research questions and purpose of the study
In the context of the above, we have decided to create a model which would aid us in better
understanding the relationships among the specific dimensions of the constructs of
optimism/pessimism and hope. The model incorporates dispositional optimism, defensive pessimism,
unrealistic optimism and hope. We are interested in whether the constructs of optimism/pessimism and
hope reflect the same underlying trait and how closely optimism and hope are related if they are
distinct.
The aims of the research were
- to design a model of optimism/pessimism using the dimensions of dispositional optimism,
defensive pessimism, unrealistic optimism and hope
- to determine the degree of conceptual overlap between optimism and hope
3. Research methods
3.1. Research sample
The research sample consisted of 1,774 respondents (33.3 % men, 66.7 % women) between 15 and
79 (m = 33.5, SD = 15.8, med = 27). In terms of education, 21.9 % of respondents completed
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elementary education or secondary education without completed school-leaving exams, 48.8 %
completed secondary education with exams and 29.3 % completed university education. The final
sample only included persons who had no more than 3 results missing from the 9 scales used.
3.2. Instruments
The respondents were subjected to an extensive questionnaire survey. The research presented makes
use of data from the Life Orientation Test, Defensive Pessimism Questionnaire, Adult Dispositional
Hope Scale and Unrealistic Optimism and Subjective Feeling of Control Questionnaire.
Life Orientation Test Revised, LOT-R (Scheier, Carver, & Bridges, 1994).
In 1985, Scheier & Carver developed the Life Orientation Test (LOT) method, which focuses
directly on the generalized expectations which determine activity, and on expectations of good or bad
future. Optimism and pessimism are seen as basic personality characteristics which influence people’s
orientation during events in their life.
The newer, shortened and revised version of the questionnaire, Life Orientation Test Revised
LOT-R (Scheier, Carver, & Bridges, 1994) is based on six rated items and shows good internal
consistency and stability over time. In our group, we verified the internal consistency of the scale: the
Cronbach’s alpha reached a value of 0.751 for optimism (hereinafter referred to as LOTR_O) and
0.769 for pessimism (hereinafter referred to as LOTR_P).
Defensive Pessimism Questionnaire, DPQ (Norem, 2002).
The inventory consists of 17 statements which the respondents react to by expressing the extent to
which they agree or disagree with the statement on a seven point scale. The author of the questionnaire
subjected the items to factor analysis which confirmed two basic factors: pessimism and reflection. The
inventory distinguishes well defensive pessimism from dispositional pessimism (in case of the
respondent’s experience with repeated failures in the past). From a total of 17 questions, we score only
12. The “fillers” and experimental questions are not included in the evaluation. The reliability of the
tool is satisfactory; Cronbach’s alpha for pessimism (DPQ_P) is 0.688, and for reflection (DPQ_R)
0.636.
Adult Dispositional Hope Scale, ADHS (Snyder, 1991)
The scale contains 12 items/statements, of which 4 focus on agency needed to achieve a goal and 4
on pathways thinking (generating the means to achieve said goal). The last 4 serve as distractors which
are unrelated to the concept of hope (Snyder, 1995). The extent of agreement with a statement is
expressed by the respondents on an 8 point scale (1 = does not fit me at all, 8 = fits me perfectly).
We verified the internal consistency of the scale; Cronbach’s alpha reached a value of 0.830 for
agency (ADHS_A) and 0.859 for pathways to the goal (ADHS_P).
Unrealistic Optimism and Subjective Feeling of Control Questionnaire (Weinstein, 1980;
Dosedlová, Jelínek, & Klimusová, 2012)
In line with the research method of Weinstein (1980, 1982), we created a list of 18 life events (8
positive and 10 negative) which the respondents may expect in their professional and private life. We
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specified the life events taking into account the age of the respondents, which gave rise to two sets, one
for younger respondents up to 34 years of age, and one for respondents 35 years and above. Three
positive situations and eight negative situations remained unchanged in both sets. Due to the fact that
the result evaluated is the difference in the assessment of one’s own chances for experiencing the given
situation, and the chances of their peers of the same gender, there is no need to use a unified list of
specific life situations.
Examples of items:
positive events for the respondents aged 34 or less (I will successfully complete my studies and/or
will find a satisfactory employment according to my wishes and choice. I will be in good health at least
until I am 45 years old. My salary will be above average compared to the nation-wide average. I am
going to have a lasting partner/marital relationship.)
positive events for respondents aged 35 and above (I will successfully live through and create
everything important I wish to achieve in life. I will live to at least 80. I will live my whole life in
abundance. I am going to have a lasting marital/partner relationship.)
negative events for respondents aged 34 or less (I will get divorced. I will be overweight. I will be
unemployed for an extended period of time. I will die of cancer or cardiovascular disease.)
negative events for respondents aged 35 and above (I will get divorced. I will feel lonely and
without help for an extended period of time. My intellectual abilities and my personality overall will be
disrupted by degenerative changes in the brain tissue dementia, Alzheimer disease. I will die of
cancer or cardiovascular disease.).
In the first part of the questionnaire, the respondent’s task was to estimate in percentages the chance
they will experience the given situation (0 % = no chance, 100 % = certainty). In the second part, the
respondents estimated the chance that their peers of the same gender will experience such events (0 %
= no chance, 100 % = certainty).
The third part of the questionnaire consisted of a five point scale focused on the subjective feeling
of control over life events (1 = cannot influence at all, 2 = can influence only to a small degree, 3 = can
influence to a middling degree, 4 = can influence to a large degree, 5 = can influence completely).
Furthermore, based on the first two parts of the questionnaire, we created a scale of unrealistic
optimism, which we view as the expectation by the person evaluated that they will experience positive
events in their life in a greater extent than their peers, and experience negative events in their life in a
smaller extent than their peers. We created 18 new variables which were created by deducting the
chances of the peers from the chances of the respondents in positive events or vice versa in negative
events. After adding the values together and calculating averages for all 18, a scale was created to
assess the extent of unrealistic optimism in our respondents. The reliability of the scale achieved
satisfactory values; Cronbach’s alpha for positive events (POS) was 0.813, for negative events (NEG)
0.730 and for subjective feeling of control (CTRL) 0.832.
3.3. Method of obtaining and processing data
Data collection took place over the course of several months from spring until the end of 2014. The
data was collected electronically using an online form or in pen-and-paper printed form. (Based on
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previous research, we lean towards the opinion that results are not significantly affected by the form in
which the questionnaire is administered - Jelínek, Květon, Vobořil & Klimusová, 2007). Occasional
selection of respondents was done via e-mail, advertisements using the Masaryk University Print
Centre or Radio Brno or based on personal contact. The filling of the test survey took 60 minutes on
average.
Confirmatory factor analysis was performed within the lavaan package in software R (Rosseel,
2012) using the FIML method. For each factor of the first order, we used three indicators (parcels;
Little, 2013) which were created by averaging the items. The individual parcels used items based on
their discrimination power. Due to the relatively large sample, we only interpret results at the 1 %
significance level.
4. Findings and interpretation
Table 1 lists the mutual correlations between the results of the individual scales. The correlations
show that the scores in individual methods are linked by moderately close relationships.
Table 1. Correlations between the scales used
LOTR_O
LOTR_P
DPQ_P
DPQ_R
POS
NEG
ADHS_P
LOTR_P
-0.606*
DPQ_P
-0.552*
0.521*
DPQ_R
-0.243*
0.220*
0.455*
POZ
0.306*
-0.282*
-0.261*
-0.095*
NEG
-0.217*
0.179*
0.149*
0.043
0.049*
ADHS_A
0.438*
-0.414*
-0.380*
-0.057
0.334*
-0.216*
ADHS_P
0.367*
-0.335*
-0.328*
-0.043
0.253*
-0.196*
OVL
0.280*
-0.267*
-0.190*
-0.029
0.228*
-0.208*
0.316*
* Correlation is significant at the 1 % level of significance
Using structural equation modeling methods, we tested 3 models. Model no. 1 represents the
confirmatory factor analysis of the first order with correlated factors, where the factors correspond to
individual scales described in the Instruments section. Model no. 2 is the result of factor analysis of the
second order with one common factor explaining the relationships between factors on the lower level.
Model no. 3 operates with two correlated factors of the second order.
4.1. Model no. 1
In this model, we use nine mutually correlated factors, each represented by three parcels created
from the corresponding items. Overall, we can state that the model describes the data fairly well
(χ2=1432.08; df=288; p 0.01; RMSEA=0.047; 90%CI (0.045; 0.050); Hoelter N 1%=430.54). The
details of the model with structural parameters are shown in Figure 1. The regression coefficients of the
measurement model can generally be described as sufficiently high. Figure 1 does not contain mutual
correlations between factors; those are listed in Table 2. There are relatively few non-significant
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correlations among the factors, which allows room for simplifying the model using superordinate
factors of the second order.
Table 2. Correlations between factors of the first order within Model no. 1
LOTR_O
LOTR_P
DPQ_P
DPQ_R
POS
NEG
ADHS_P
LOTR_P
-0.782*
DPQ_P
-0.740*
0.696*
DPQ_R
-0.341*
0.323*
0.687*
POZ
0.373*
-0.323*
-0.306*
-0.123*
NEG
-0.302*
0.247*
0.218*
0.074
-0.016
ADHS_A
0.561*
-0.506*
-0.481*
-0.098*
0.371*
-0.273*
ADHS_P
0.467*
-0.405*
-0.419*
-0.067
0.265*
-0.246*
OVL
0.350*
-0.319*
-0.233*
-0.039
0.249*
-0.258*
0.374*
* Correlation is significant at the 1 % level of significance
Figure 1. Model no. 1 - first order factor analysis with correlated factors. The figure shows standardized
coefficients; error elements are omitted for clarity.
4.2. Model no. 2
In this model, we use nine factors of the first order, each represented by three parcels created from
the corresponding items. The mutual relationships between the factors found in model no. 1 are
explained here through a single factor of the second order. In summary, we can state that model no. 2
describes the data relatively well (χ2=2706.35; df=315; p 0.01; RMSEA=0.065; 90%CI (0.063;
0.068); Hoelter N 1%=247.67), though naturally slightly less than model no. 1.
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The details of the model with structural parameters are shown in Figure 2. The regression coefficients of the
measurement model are sufficiently high. The common factor of the second order can be described as a construct
of Optimism. The factor has the closest relationships to the dimensions of optimism and pessimism (LOTR_O,
DPQ_P, LOTR_P), and moderately close relationships to two dimensions of the hope scale. All the relationships
found between the factors are higher than 0.3, but the significant variations suggest the existence of a possible
third construct not captured in this model.
Figure 2. Model no. 2 - factor analysis of the second order with one common factor. The figure shows standardized
coefficients; error elements are omitted for clarity
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4.3. Model no. 3
In this model, we use nine factors of the first order, each represented by three parcels created from the
corresponding items. The relationships between the factors of the first order are explained here via two
correlated factors of the second order. Overall, we can state that the model describes the data well (χ2=1893.63;
df=314; p 0.01; RMSEA=0.053; 90%CI (0.051; 0.056); Hoelter N 1%=352.52). Model no. 3 shows
significantly higher congruence with the data than model no. 2 (decrease in RMSE by 0.012) and its adequacy is
even on the level of the significantly more complex model no. 1. The details of the model with structural
parameters are shown in Figure 3. The regression coefficients of the measurement model are sufficiently high;
the common factors of the second order can be described as Optimism (or Pessimism based on the direction of
the regression coefficients) and Hope. These two factors are interrelated, but cannot be perceived as identical.
Figure 3. Model no. 3 - factor analysis of the second order with two common factors. The figure shows standardized
coefficients; error elements are omitted for clarity.
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5. Conclusions
The aim of the study was to test a model of optimism with regard to the dimensions of the
constructs of dispositional optimism (LOT_O dispositional optimism, LOT_P dispositional
pessimism), defensive pessimism (DPQ_P defensive pessimism, DPQ_R reflectivity), unrealistic
optimism (POS expectations in relation to positive events, NEG expectations in relation to negative
events, CTRL subjective feeling of control over the situation) and hope (ADHS_A agency, ADHS_P
pathways). Another goal was to examine the relationship between the constructs of optimism and the
construct of hope.
Analyses were performed in three steps and three models were created.
The first model examined factors of the first order. All the selected variables correlated
significantly, by which we confirmed their mutual interdependence and their suitability for inclusion as
factors of the first order in subsequent models.
The second model operated with a single factor of the second order, Optimism. The closest
relationships were found with the factor of dispositional optimism in the positive direction
(LOT_O=0.86), followed by the dimension of hope - pathways to the goal (ADHS_P=0.67) and
dimension of hope - agency (ADHS_A=0.76). A strong negative relationship to Optimism was found
for defensive pessimism (DPQ_P = -0.81) and dispositional pessimism (LOT_P=-0.79). Other factors
show only moderately close relationships: expectation of positive events and feeling of subjective
control in the positive direction, with anticipation of negative events and reflectivity working in the
negative direction.
The third model presents two factors of the second order, called Pessimism (PES) and Hope
(HOPE).
The second order factor Pessimism has a strong negative relation to dispositional optimism
(LOT_O=-0.91), while defensive pessimism (DPQ_P=0.88) and dispositional pessimism
(LOT_P=0.84) operate in the positive direction. The relationship of the factor of reflectivity (DPQ_R =
0.46) is positive and moderately close.
The second order factor Hope has a strong positive relationship to the factor of hope pathway
(ADHS_P=0.88) and the factor of hope agency (ADHS_A=0.98). These factors directly correspond to
the theoretical basis of the construct of hope. Other factors show only moderately close relationships,
with the feeling of subjective control (CTRL=0.41) and the expectation of positive events (POS=0.38)
working in the positive direction, while the expectation of negative events (NEG=-0.29) operates in the
negative direction.
Both second order factors, Pessimism and Hope, do correlate in the expected negative direction, but
their relationship is only moderately close (r=-0.60), and can therefore be confirmed as distinctive
constructs which are not redundant. We consider model three as the best suited both statistically and in
terms of its explanatory power.
Our model corresponds to Snyder’s concept (in Chang, 2002), who views his construct of hope
more actively and specifically in comparison to optimism; it is seen as a collection of thoughts
incorporating both the will to take action and the modelling of various pathways towards achieving the
set goal. This way of thinking, which is directly reflected in behaviour, is supported in our model also
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by the subjective feeling of control (dimension of unrealistic optimism), which mirrors the assessment
of one’s own resources, capabilities and options. It remains unclear whether this construct corresponds
to the real human experience, as discussed in relation to Bruininks & Malle (2005). In both Snyder’s
and our definition, the factor of Optimism/Pessimism is associated more with general ideas about our
future.
Similar results have also been reached based on structural equation modeling by Bryant &
Cvengros (2004). They confirm that both the concept of two independent constructs of optimism and
hope, and the reflection of a single dimension which interconnects both constructs are valid. However,
even in their research, the greater explanatory power is attributed to the two-factor model of optimism
and hope as independent constructs.
Acknowledgements
The present study is a part of the realization of a research project registered under no. 13-19808S
supported by the Czech Science Foundation (GA CR).
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