2012. Vol.3, No.12A, 1208-1214
Published Online December 2012 in SciRes (
Copyright © 2012 SciRes.
Effects of Feedback on Self-Efficacy Expectations Based on the
Athlete’s Optimistic Profile
Ricardo de la Vega1, Roberto Ruiz1, Francisco Batista2, Francisco Ortín3,
Carlos Giesenow4
1Autonomus University of Madrid, Madrid, Spain
2Sport & Exercise Psychology (UAM-COE), Madrid, Spain
3University of Murcia, Murcia, Spain
4National University of Lomas de Zamora, Buenos Aires, Argentina
Received September 28th, 2012; revised October 22nd, 2012; accepted November 22nd, 2012
In recent years, the study of optimism and its possible influence on athletic performance has increased
considerably. The key purposes of this study can be summarized as: 1) analyze how self-efficacy expecta-
tions vary depending on the level of optimism an individual has; 2) ascertain how progressively receiving
negative feedback affects performance expectations; and 3) evaluate whether there are differences in per-
formance depending on the presence or absence of feedback. In order to achieve this, 53 Spanish football
players were assessed using the Revised Life Orientation Test and an objective test of strength using hand
dynamometry. The level of feedback provided was manipulated to study its influence on perceived self-
efficacy in the applied strength task. The results show no significant difference between the three levels of
optimism/pessimism respecting self-efficacy. Significant differences do appear when the effects of the in-
dividual feedback provided for each experimental group is analyzed. The findings are discussed and fu-
ture research is proposed based on the results obtained.
Keywords: Positive Psychology; Optimism; Self-Efficacy; Feedback; Sport
Since Martin Seligman’s address to initiate his presidency of
the American Psychological Association (Seligman, 1998),
positive psychology has evolved and developed as a branch of
psychology which, based on scientific research, strives to un-
derstand the processes that underlie the qualities, emotions and
positive attitudes of human beings as psychological processes
and resources that prevent the occurrence of different forms of
mental illness. Thus, in recent decades, investigation of psy-
chological resources that promote health and wellness from a
scientific viewpoint has experienced a significant increase
(Kamen, Rodin, & Seligman, 1987; Scheier, Carver, & Bridges,
1994; Seligman & Csikszentmihalyi, 2000; Snyder & Lopez,
2002; De la Vega, Ruiz, & Rivera, 2011).
The study of optimism as a psychological construct comes in
response to the reformulation of the Learned Helplessness The-
ory done by Abramson, Seligman and Teasdale (1978). For
these authors, optimism could be understood as a positive form
of explaining the ways in which people cope with negative or
adverse events that occur in their lives, promoting the existence
of two distinct explanatory styles, one for pessimistic people
and another for optimists. This characteristic has proven to be a
good predictor of health and wellness resources (Scheier &
Carver, 1989, 1993; Seligman et al., 1988).
Currently there are two complementary perspectives to try to
analyze the influence of these processes (Regourd-Laizeau,
Martin-Krumm, & Tarquinio, 2012; Remor, Amorós, & Car-
robles, 2006), the pessimistic-optimistic explanatory styles of
Peterson and Seligman (1984), based on the classic approaches
from Weiner, Frieze, Kukla, Reed, Rest and Rosenbaum (1971),
and the dispositional optimism approach proposed by Scheier
and Carver (1985). In the first perspective, there are two essen-
tial components in Seligman’s model, on the one hand, learned
helplessness, which is “the reaction of giving up, to not assume
any responsibility and to not fight, as a result of believing that
anything that we can do will be irrelevant” (Seligman, 2004: p.
30) and, on the other hand, the pessimistic-optimistic explana-
tory style that the subject uses to explain the causes of positive
or negative events (Buchanan & Seligman, 1995), relating,
directly, to theories focused on the analysis of attributional
processes and their effects on athletic performance. In this
sense, optimists would have an external, unstable and specific
explanatory style, whereas pessimists, by contrast, would at-
tribute a negative event in an internal, global and stable over
time manner (Davis & Zaichkowsky, 1998). There is research
to suggest that people with optimistic explanatory styles have
better overall health, reduced vulnerability to physical illness
(Kamen et al., 1987), better immune function (Peterson & De
Avila, 1995), as well as a lower mortality rate (Peterson, Selig-
man, Yurko, Martin, & Friedman, 1998), better athletic per-
formance, better jobs and even children with these styles per-
form better in school (Seligman, 2004) compared with those
who possess pessimistic explanatory styles. Parkes and Mallet
(2011) made an interesting study with a sample of seven pro-
fessional rugby players that highlights how the intervention to
increase the attributions typical of the optimist attributional
style enabled the players to perceive how they coped better with
adverse situations, and to perceive themselves as more resilient
and face competitions with greater self-confidence. More em-
pirical evidence is found in studies that conclude that optimistic
subjects, when faced with different stressors, experience less
negative mood, take on more adaptive health behaviors and
have a better immune condition (Segerstrom et al., 1998). Ac-
cording to these authors, increasing perceived control of illness
enhances the optimistic predisposition of risk perception. Ac-
cording to the study by Räikkönen et al. (1999), pessimistic and
anxious adults have altered blood pressure levels and feel in
worse shape than the low anxiety optimistic adults. The results
of the study suggest that pessimism brings physiological and
psychological consequences.
Regarding the second perspective, the dispositional theory
established by Scheier and Carver (Scheier & Carver, 2003)
propose that, under difficulties, positive expectations increase
the people’s effort to reach objectives while negative expecta-
tions reduce such effort to even evade the task. In that sense,
optimistic people have positive expectations and perceptions
about their life whereas pessimistic people tend to represent
their life in a negative mode. Moreover, such expectations are
considered as stable dispositions (features) (Garcia-Naveira &
Díaz, 2010). One of the benefits of this model is the opportu-
nity of use a questionnaire such us the “Life Orientation Test-
Revised” (LOT-R), which consist of ten items only, whose
psychometric properties are suitable to establish dispositional
features of the personality (Ferrando, Chico, & Tous, 2002;
Herzberg, Glaesmer, & Mayer, 2006). Interestingly, Ortin,
Marin y Garcés de los Fayos, (2012) made a bibliometric analy-
sis of the optimisms within physical activities and sport and
they highlighted the LOT-R and ASQ questionnaires as those
most frequently used to evaluate the optimisms and the pessi-
Other studies, as that made by Van de rest, Geleijnse, Kok,
Van Staveren, Dullemeijer, Olderikkert, Beekman, & de Groot,
(2008), reinforce the usefulness of this tool to evaluate dis-
positional optimisms.
Following the dispositional perspective, Chang (1998),
analyze the influence of optimism and appraisals on coping and
psychological and physical adjustment in college students.
These results indicate that stress-related appraisals were asso-
ciated with optimism, coping, and adjustment. In other hand,
Myers and Steed (1999), found an inverse correlation between
the dispositional optimisms and the anxiety state, establishing
that individuals with an higher optimistic profile present lower
levels of anxiety.
Besides that, Ramírez-Maestre, Esteve y López (2012) used a
sample of 98 patients with chronic pain to analyze the relation-
ship between dispositional optimisms and active strategies to
face pain, finding a positive correlation between both whereas a
pessimisms trend was correlated with the use of passive strate-
gies to deal with pain.
The relationship of optimism and pessimism with athletic
and academic performance has become a line of research of
increasing interest in psychology. In an initial experiment done
by Seligman with 21 male and 26 female swimmers (Seligman,
Noles-Hoeksema, Thornton, & Thornton, 1990), the athletes in
the “high level of optimism” group were told that they had
made a worse time than they really had. In spite of their disap-
pointment, when asked to rest and try again, their perform-
ance—that was actually very good—was even better. In turn,
the athletes in the “high level of pessimism” group did worse in
their second attempt after being mistakenly informed they had
made a bad mark the first time. In a second study, with 33
swimmers from the first study, published in the same article
(Seligman et al., 1990), the same procedure was carried out but
this time the amount of pessimistic information offered was
manipulated (e.g., if the subject swam 100 meters then 1.5 sec-
onds was sub- tracted, if they swam 200 meters, 2 seconds),
with the aim of predicting the reactions only in defeat. The
results indicated that optimistic swimmers at least maintained
the same time, while the pessimists performed worse on the
second turn.
Studies where optimisms had been evaluated either from the
dispositional perspective as from the explicative styles got
similar results. Gordon (2008), in a study with football (soccer)
players along eight games quantified. After reviewing 8 games
and tallying certain variables such as goals, fouls, passes com-
pleted and attempted passes, optimistic players showed better
performance during a defeat than pessimists, while no signifi-
cant differences in performance where found between the two
groups after a victory. From this perspective, the papers that
study psychological characteristics of high-level athletes (Api-
tzsch, 1994; García & Díaz Morales, 2010; Gould, Dieffenbach,
& Moffatt, 2002), as well as those that relate personality factors
with performance measures (Gordon, 2008; Norlander, Bood,
& Archer, 2002; Wilson, Raglin, & Pritchard, 2002) indicate
that optimism and positive thinking may be beneficial for in-
creasing athletic performance (Garcia et al., 2010). These au-
thors advocate that, in the face of difficulties, positive beliefs
and expectations for the future facilitate self-regulation of indi-
vidual behavior, increasing efforts to achieve goals.
In another study by Seligman (2004), after analyzing the
statements in the press of players and coaches of all the base-
ball teams of the American national league and of two NBA
teams after every game they won and lost, a positive or nega-
tive explanatory pattern was set for each team, establishing a
predictive judgment about the teams that would perform better
in the next season. The results showed that optimists did better
in the following season while pessimists’ performance deterio-
rated and that teams with optimistic explanatory styles per-
formed significantly better under pressure than pessimists. In
another study by Hale (1993) to confirm the effect of optimistic
and pessimistic explanatory styles on performance, based on
Seligman’s studies, no statistically significant relationship was
found; this highlights the existing scientific controversy.
Explanatory style has been studied in relation to different
psychological variables. So, Vera-Villarroel and Buela-Casal
(2000), related the explanatory styles with anxiety. San Juan
and Magallares (2007) established a relationship between an
optimistic or pessimistic profile and coping styles. In a study by
Martin-Krumm, Sarrazin, Peterson, and Famose (2003), a sam-
ple of 62 basketball players (mean age 14 years) performed a
basketball dribbling trial and were given false feedback indi-
cating that they had failed. Consistent with prediction, in the
second trial similar to the first, the optimistic participants were
less anxious, exhibited more confidence and performed better
than pessimistic participants.
The relationship between optimism and pessimism, mental
toughness and coping style, has also been studied, finding, in a
sample of 677 athletes, that mental toughness correlated sig-
nificantly with 8 of the 10 subscales of coping and with opti-
mism (Nicholls, Polman, Levy, & Backhouse, 2008).
Another line of research associated to sport and optimism is
the possible relation with resilience and burnout. In a study by
Chen, Kee and Tsai (2008), with 139 volleyball players, the
Copyright © 2012 SciRes. 1209
relationship between optimism and burnout was evaluated,
finding that optimism scores of athletes were negatively related
with burnout scores. Subsequently, Gustafsson and Skoog
(2012) corroborated these findings in a sample of 217 athletes,
finding an inverse relationship between perceived stress, burn-
out and optimism. Finally, the work of Fernandez and Ber-
mudez (2001), aimed at helping to understand the cognitive
strategies (expectations) of pessimists and optimists, should be
noted. The results showed that the control of the situation is an
important factor in the differential activation of strategies and
that different types of expectations have different functions
inside and outside of a same group.
Concerning the central theme of the present research, which
revolves around the relationship between optimistic and pessi-
mistic profiles of athletes, and the influence this can have on
their performance upon receiving certain types of information
about how they are doing (outcome feedback), three works
should be mentioned as the most relevant, the first two from the
explicative styles theory, and the third from the dispositional
theory. The first is the classic study by Seligman at the Univer-
sity of Berkeley in 1990 with swimmers, in which a controlled
negative feedback was provided based on the results obtained
in a trial that was freely chosen by the research participants.
Martin-Krumm, Sarrazin, Peterson and Famose (2003) con-
ducted a similar study in basketball, measuring perceived per-
formance in dribbling and the level of anxiety experienced by a
sample of 42 players. After providing negative feedback and
giving them the possibility of a second attempt, on second
chance the more optimistic participants were less anxious and
performed better. Ortin, Garcés of Fayos, Gosalves, Ortega and
Olmedilla (2011), in turn, replicated Seligman’s study in an
investigation with 66 youth swimmers, where information
about their mark was manipulated in a trial they were later to
repeat, giving them negative feedback (longer times than the
one actually obtained). The results revealed that swimmers with
a pessimistic profile, evaluated through the LOT-R question-
naire, significantly changed in a negative sense their times be-
tween the first and second attempts. From here, interesting
research perspectives emerge, like the need to broaden the age
range of the participants to study the effects on adults, as well
as the importance of knowing the possible mediating effect of
feedback when, as it occurs in many sports, it is obtained in
succession but at various times, and how it can differentially
influence athletes of both profiles.
Having presented the main conceptual issues that support the
relevance of the study of optimism in the sport context, this
paper attempts to analyze a specific aspect that has not been
sufficiently addressed in the context of basic and applied re-
search, which is how progressively receiving controlled nega-
tive feedback can affect, depending on the athlete’s pessimistic
or optimistic profile, a task that allows objective and accurate
quantification of performance, as is strength assessed with ma-
nual dynamometry, under the assumption that athletes with a
more optimistic profile will be less affected by the negative
progressive feedback provided than the other groups. This will
allow us to know, precisely, how each group evolves along this
process, as well as to analyze the important implications these
results can have on sports training and coach-athlete communi-
cation. Specifically, the key purposes of this study can be
summarized as: 1) analyze how self-efficacy expectations vary
depending on the level of tendency toward optimism an indi-
vidual has; 2) ascertain how receiving negative progressive
feedback affects performance expectations; 3) evaluate whether
there are differences in performance depending on the presence
or absence of feedback.
The research sample consisted of 53 male soccer players, in-
tegrated into three youth category competition teams belonging
to the same club in the Community of Madrid (Spain). The
sample selection was performed by incidental sampling and
voluntary participation, after authorization to participate in the
study was obtained from each participant’s tutors throughout a
signed informed consent form. Participants compete in succes-
sive categories: youth “A” competes in Honor Division (n = 14,
M = 18.14 years, S.D. = 0.66 years), youth “B” in the National
League (n = 20, M = 17.45 years, S.D. = 0.75 years), and youth
“C” in the Autonomic League (n = 19, M = 16.36 years, S.D. =
0.59 years). The three teams train four days a week plus one
day of competition, totaling 12 hours a week of athletic in-
volvement. Total descriptive values are: n = 53, M = 17.24 and
S.D. = 0.97.
Assessment of tendency toward optimism. Tendency toward
optimism is a continuous variable categorized into three levels.
To measure tendency toward optimism the Revised Life Orien-
tation Test was employed (LOT-R, Scheier, Carver, & Bridges,
1994) in the Spanish version developed by Otero-Lopez, Lu-
engo, Romero, Triñanes, Gomez and Castro (1998). The
LOT-R is a reduced version of the original Life Orientation
Test (LOT, Scheier & Carver, 1985), with a correlation of 0.95
among both versions (Scheier, Carver, & Bridges, 1994). This
test consists of 10 items, four of which are filling, not for scor-
ing, in which subjects are asked to indicate their level of agree-
ment or disagreement with various statements presented on a
5-point Likert scale (0-4), where 0 is “strongly disagree” and 4
“strongly agree”. Of the six items, three are worded in a posi-
tive direction (tendency toward optimism) and 3 in negative
(tendency toward pessimism). As to the interpretation of the
results, it is possible to keep each disposition separate (trait
optimism vs trait pessimism) summing the items in each sub-
scale within a range from +12 to 12, although it is also possi-
ble to obtain a total score. The first criterion has been selected
by the research team to place participants in the different ex-
perimental groups according to the levels obtained on tendency
toward optimism (low, medium and high). Regarding the psy-
chometric properties of the test, confirmatory factor analysis
has shown a better fit of the two-factor model against a one-
factor model for both the LOT and the LOT-R (Chico & Fer-
rando, 2008; Ferrando, Chico, & Tous, 2002). The scale has a
range from 12 as minimum score to +12 as maximum score.
Finding the percentiles of the study sample, subjects who
scored between 12 and 2 were classified within the group of
“low level of optimism” (LLO) (N = 13), those who scored 3, 4
or 5, in the group of “medium level of optimism” (MLO) (N =
23), and those who had a score of 6 or higher in the group of
“high level of optimism” (HLO) (N = 17).
Measurement of manual strength. To provide feedback in
accordance to the manual force applied, a TKK-5401 digital
hand dynamometer was used, allowing the analysis of the
Copyright © 2012 SciRes.
maximum static bending strength in both arms, simplifying
significantly the accurate measurement of these indices. The
measurement arch of the dynamometer goes from 0.5 to 100
kgf; the minimum unit of measure is 0.1 kgf, and the level of
accuracy shown is ±2.0 kgf. An ad hoc spreadsheet in the pro-
gram Excel was designed to record the force exerted by each
athlete and to calculate the percentage of feedback that was
offered, according to the group in which each participant be-
longed. This test was chosen following the criteria established
by Ribes (2007), so that it was not influenced by the subject’s
skill level in performing the task, and by De la Vega, Ruiz,
Rivera and Ortín (in press), who used it as an objective behav-
ioral assessment to evaluate the influence of others in young
soccer players.
A two-phase protocol was established to ensure that the ap-
plication conditions were identical for all participants, whilst
respecting the ethical standards established in research with
minors. As noted above, the participants and their tutors were
informed that their participation was voluntary and that the
results would be confidential and for scientific purposes only.
The first phase involved the assignment of participants to one
of two experimental groups or to the control group, while the
second phase was the actual experimental phase.
The use of two experimental groups is justified by the need
of know if the order in which the feedback with the modified
results are provided (10% 20% vs 20% 10%) influence in the
expectations of individuals.
In the first phase, to complete the LOT-R and enable the dis-
tribution of the two experimental groups and the control group,
all participants were summoned to arrive one hour before the
start of training in one of the rooms of the club. From the re-
sults, the following values of level of optimism were obtained:
13 LLO, 23 MLO and 17 HLO (see Table 1). The athletes were
then randomly assigned to the two experimental groups (EG1 =
18; EG2 = 18) and the control group (CG = 17).
For the second phase of the investigation, each athlete was
instructed in the way they should perform the test to obtain
maximum strength in their two attempts, after each of which a
type of feedback would be offered in terms of the assigned
experimental condition. Then came the actual experimental
phase where the value of the force that was obtained with the
dynamometer was manipulated at 10% or 20% less than the
actual value. In the experimental groups, participants made two
attempts, with one-minute intervals to prevent the effect of
fatigue on the test, as a baseline measure (experimental phase 1
-EP1-). After completion, the average value obtained was noted
and the participant was informed the true mean achieved. The
participants were asked to perform the test again and to now
determine a value judgment about their future expectation of
Table 1.
Distribution of participants to groups by levels.
Group/Level LLO MLO HLO n
EG1 4 8 6 18
EG2 4 8 6 18
CG 5 7 5 17
Total 13 23 17 Total
efficacy (Expectation 1 -E1-), so as to establish how efficacious
they perceived themselves without manipulation of the feed-
back given. Two minutes later, two more attempts were made
(experimental phase 2 -EP2-), with a one minute rest between
the two, and the true mean was calculated. In this case, the
feedback provided to the athlete was 10% less than the actual
average value for the EG1, and 20% below the actual value for
EG2. The participants had two more minutes of resting time,
and once more they were told that they had two new opportuni-
ties, again the expectation of efficacy of each athlete was regis-
tered (Expectation 2 -E2-). Subsequently, two other attempts
were performed (experimental phase 3 -EP3-) with a one min-
ute interval between them, the true mean was registered, and
inverse feedback to the previous phase was provided, i.e., sub-
jects in EG1 were informed of a 20% decrease of the true value,
while the EG2, had a 10% decrease in value. During the two
minute rest, it was informed that the participant had to make a
last attempt (experimental phase 4 -EP4-) and self-efficacy
expectation was measured again (Expectation 3 -E3-), although
in this case no information on the result obtained in the test was
offered. For the control group, the process was similar but
without offering any kind of feedback at the different stages.
For data analysis, from the descriptive statistics, an analysis
of fit to the normal distribution curve using Kolmogorov-
Smirnov nonparametric test for a single sample was performed
on all the variables involved in the study, including expecta-
tions of self-efficacy. A nonparametric test for k independent
samples (Kruskal-Wallis test) was conducted to compare
whether significant differences existed within each experimen-
tal group at the three levels between baseline and the remaining
expectations separately. To verify whether significant differ-
ences existed within the experimental groups between baseline
and expectations, analysis was performed using the nonpara-
metric Friedman’s test with the value of W for the Wilcoxon
test on pair comparison in the case of significant differences.
To compare whether there were differences between the three
levels (LLO, MLO and HLO) in the control group in all four
phases, Kruskal-Wallis was used; while the nonparametric
Mann-Whitney U test was employed to compare two inde-
pendent samples (both experimental groups) and to analyze
whether there were differences among both groups between the
means obtained at baseline and the self-efficacy expectations
held. Finally, to test whether there were significant differences
among the three groups (two experimental and control) in the
mean score of the four attempts, the nonparametric Kruskal-
Wallis test was used. The statistical program SPSS 19.0 was
used to perform all data analysis.
The normality analysis of the variables, using the Kolmo-
gorov-Smirnov test for a single sample, was performed for all
scores and subscales of the test, and for the average of the base-
line and the three self-efficacy expectations. In the LOT-R, for
level of optimism (p = 0.103) was obtained; (p = 0.440) for
level of pessimism, and (p = 0.611) for the total score, so all
follow normal distribution. Also, the mean of the baseline and
the three efficacy expectations follow a normal distribution:
Mean Baseline (p = .654), E1 (p = 0.432), E2 (p = 0.429) and
E3 (p = 0.430). Despite the values obtained, it was decided to
perform nonparametric analyzes due to the sample size in the
different experimental groups and, therefore, at the different
Copyright © 2012 SciRes. 1211
levels in each group.
To test whether significant differences appeared between the
baseline and the three expectations in the different levels of
optimism (low, medium and high) of EG1, the nonparametric
Kruskal-Wallis test for k independent samples was performed.
No significant differences between the four measures and the
different levels of tendency toward optimism (p > .05) were
found. This lack of significant differences points towards the
non influence of the LLO, MLO and HLO typology on per-
ceived self-efficacy at different times of the test when pessimis-
tic progressive feedback was offered (being that the value of-
fered to the athlete on their perceived efficacy for the tasks fell
by 10% and then 20%). On the other hand, to determine if there
were differences between different stages of the test, a Fried-
man analysis of variance was done, in which statistically sig-
nificant differences appeared with a significance of p < .001
between the baseline and the expectations of athletes in EG1 (p
= .000). Since Friedman’s test was significant, Wilcoxon’s W
was applied in order to determine between what specific mo-
ments in EG1 (between the baseline and the E1, E2 and E1, E2
and E3, E3 and mean of E4) significant differences existed, this
enables to unravel the specific effect of the manipulation when
the feedback provided is progressive and pessimistic (EG1).
The results obtained are shown in Table 2.
It can be concluded that significant differences exist between
the baseline and E1, between E3-E2, and between E3 and the
mean of E4.
To test whether significant differences appeared between the
baseline and the three expectations in the different levels of
optimism (low, medium and high) of EG2, the nonparametric
Kruskal-Wallis test for k independent samples was performed,
finding no significant differences between the four measures
and the different levels of tendency toward optimism (p > .05).
This lack of significant differences points towards the non in-
fluence of the LLO, MLO and HLO typology on perceived
self-efficacy at different times of the test when pessimistic
feedback was offered progressively (being that the value of-
fered to the athlete on their perceived efficacy for the task was
20% below the actual value and later 10% less).
With respect to the analysis of the potential influence be-
tween the phases of the research, in the case of EG2 the proce-
dure followed the same path as noted above, applying Fried-
man’s analysis of variance and finding that, with a significance
level (p < .001), significant differences were found between the
baseline and the expectations of the EG2 participants (p =
0.000). Since Friedman’s test was significant, Wilcoxon’s W
was applied in order to determine between what specific mo-
Table 2.
Differences between variables in pairs Experimental Group 1 (EG1)
and Experimental Group 2 (EG2).
E1 -
Baseline mean E2 - E1 E3 - E2 E4 Mean - E3
Z-score (EG1) –3.549(a) –1.738(b) –3.296(b) –3.593(a)
Asymp. Sig.
.000 .082 .001 .000
Z-score (EG2) –3.724(a) –3.630(b) –1.975(a) –3.506(a)
Asymp. Sig.
.000 .000 .048 .000
ments in EG2 significant differences existed, this clarifies the
specific effect of the manipulation when the feedback provided
increases the pessimism of the actual value obtained (EG2).
The results obtained are exposed in Table 2. As shown, there
are significant differences in all possible relationships between
expectations. Again it can be said that there is an effect of per-
ceived self-efficacy that was measured by means of the judg-
ment of strength expectation made, regarding the manipulation
of the feedback provided in each of the experimental phases.
With respect to CG analysis, as is the case in the two experi-
mental groups, no significant differences were found between
the three levels of tendency toward optimism (p > .05) in the
four averages obtained throughout the performance of the test.
As was done with the experimental groups, Friedman’s
ANOVA test was done to determine whether there were sig-
nificant differences relating the stages of the test and no statis-
tically significant differences were found (p = .984). When
performing the Wilcoxon test for paired samples, no statistic-
cally significant differences appeared between the different
moments of the test (p > .05).
Moreover, to verify if there were significant differences be-
tween the baseline and the three expectations comparing both
experimental groups, the nonparametric Mann-Whitney U test
was employed for two independent samples (see Table 3).
The results show the existence of significant differences in
all variables, with a significance level (p < .05), both at baseline
and in the E1 and E3, reaching significance of (p < .001) in E2.
Therefore, experimental manipulation influences expectations
and causes significant differences between the two experimen-
tal groups.
The purpose of this study was to analyze changes in expecta-
tions of self-efficacy in young athletes with different levels of
tendency toward optimism, under the hypothesis that the ath-
letes in the high level of optimism group would make an evalu-
ation of their own self-efficacy in the dynamometry task that
would be more resistant to the manipulated feedback that was
offered. In this sense, one of the contributions of this work in
comparison to previous studies conducted and presented in the
introduction, is precisely that the comparison variable is not the
actual value obtained on the dynamometer, i.e., the perform-
ance achieved, but the perceived self-efficacy the athlete holds
and that has shown to have a large effect on performance
(Bandura, 1977; Chase & Feltz, 1999; Moleiro & Villamarín,
2004). Also, it could be expected that the decrease in the per-
ceived self-efficacy would be lower in the experimental group
which was provided a more gradual pessimistic feedback, than
in the rest. In addition, differences would be found in the effi-
Table 3.
Diferences between Experimental Groups 1 and 2.
Expectati on
Mann-Whitney U87.000 100.000 55.500 96.500
Wilcoxon’s W 258.000 271.000 226.500 267.500
Z-score –2.373 –1.969 –3.380 –2.082
Asymp. Sig.
(2-sided) .018 .049 .001 .037
Exact Sig. (1-sided).017 .050 .000 .037
Copyright © 2012 SciRes.
cacy evaluations in comparison to the control group, in which
the information offered had not been manipulated.
From a detailed examination of the results presented, at least
two relevant ideas should highlighted: first, that the classifica-
tion system used, which established three different levels of
optimism from the LOT-R results was not useful, since in no
case statistically significant differences appeared between them,
and, secondly, that the most significant effect found appears
when the self-efficacy judgments in the development of the test
are analyzed, which underscores the relevance of this research.
In this regard, it should be emphasized that simply being
aware of the force exerted by activating the dynamometer and
having to make a subsequent judgment on one’s capacity,
without it being necessary to give a false or distorted feedback,
leads the athlete to mobilize his own resources and statistically
significant differences to appear, only in the case of both ex-
perimental groups (comparison Baseline-E1).
When the feedback offered about the result is distorted in a
negative direction, but is offered gradually, it seems that there
is resistance, evidenced by the EG1 between E1 and E2, where
the self-efficacy judgment issued is sustained. This assessment
is modified when the distortion of the information supplied is
greater and the distorted feedback offered is 20% more negative.
This same effect can be seen from the start in the EG2, where
accentuated feedback provided first causes statistically signifi-
cant differences in the value judgment of the athlete from the
beginning. This emphasizes the relevance of studies on coach-
athlete communication styles and the importance of caring for
the proportion of the information offered, not so much based on
the pessimistic or optimistic personality of the athlete (accept-
ing for its assessment the test used in this study), but rather on
the criterion of success offered and the outcome assessment
provided. In fact, several studies have emphasized the impor-
tance of taking care of various aspects such as the emotional
charge of the situation, the information processing, attention,
etc. (Beniscelli & Torregrosa, 2010; Moreno et al., 2005; Ortín,
Ortega, Lopez, & Olmedilla, 2012), variables that can be stud-
ied with experimental study methodologies similar to that used
in our study.
As future research, the importance of also studying the pos-
sible effect of providing incremental positive feedback to ath-
letes with different optimistic and pessimistic profiles should be
emphasized, as well as being able, as Ortín et al. (2011) point
out, to link several relevant and mediating variables, such as
anxiety or frustration tolerance, as moderators in the possible
relationships that occur between these factors, the personality
profiles and sport performance.
Finally, it is important to clarify the theoretical impact that
the conceptualization of optimistic-pessimistic profiles as per-
sonality traits has, or rather as variables that can be developed
based on the learning history of the subjects in general and of
athletes in particular. Following the ideas published by Kam
and Meyer (2012), based on the dispositional model of opti-
misms and pessimisms of Marshal, Wortman, Kusulas, Herving
and Vickers (1992), it could be convenient review the tool in-
cluding the evaluation of the value given by each individual to
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