Self-efficacy, or the beliefs in one's capabilities to organize and execute the courses of action required to produce given attainments, can be a powerful force in sport because individuals' levels of motivation are more directly tied to what they believe to be true, rather than to what is objectively true (1). Specifically, individuals' self-efficacy is theorized to be formulated from 4 sources: mastery experiences or whether an individual was successful at the skill or task in the past; vicarious experiences or viewing another individual succeeding or failing; social persuasion or being told by a significant other that one has the ability to be successful; and physiological states or interpreting the body's physiological reactions as either positive or negative (13). Because these efficacy sources and perceptions of the environment one engages in can change from moment to moment, self-efficacy is a state variable that fluctuates in accordance with changing perceptions. Thus, in strength and conditioning, an athlete's confidence in his or her ability to successfully execute a task could change based on recently returning from an injury, seeing a teammate succeed, being yelled at by a coach, or feeling one's heart begin to beat faster. However, even though perceptions may change, the skill set of an athlete is not in a constant state of flux during a skill; therefore, self-efficacy is not concerned with the number of skills one possesses but with what an athlete believes he or she can accomplish with those skills (1). Based on this, it is possible that self-efficacy could be a better predictor of the amount of effort put forth to accomplish a particular skill than the actual performance related to the skill itself.
Past research examining self-efficacy and a variety of outcomes has shown that increases in self-efficacy are a benefit to individuals who engage in sport (6,10,15-18,21,23,24). Specifically in strength training, social persuasion has been used in studies to demonstrate that subjects' performances increase when they were unaware of the amount of weight on a bar or intentionally informed by a significant other that the weight is less than actually present (14,36). In addition, physiological states have also been altered and impending performances measured in strength training environments. In these works, the relationship between confidence and performance was again linear, because individuals performed better at 1RM tests when their arousal levels were manipulated by researcher-manufactured competition or they were “psyched up” before a strength test (27,37).
Research has also shown that higher levels of self-efficacy have led to greater effort when engaging in a competitive sport and facing an obstacle outside of competition compared with those with lower levels of self-efficacy (12,15). For instance, in research on self-efficacy and baseball, self-efficacy was shown to significantly predict batting effort (15). In athletic settings, it is hard to overstate the power self-efficacy has on athletes' performances, because under challenging sport conditions, where one decision can mean the difference between success and failure, self-efficacy has been shown to be the sole determinant of success (18). Thus, the majority of work on self-efficacy in sport (and other domains of human functioning) continues to show that self-efficacy is positively related to adaptive behavior and impending performances (3,12,23).
However, despite the seemingly insurmountable evidence put forth outlining a positive relationship between self-efficacy, behavior, and performance, the uniformity of these findings is currently being challenged. In particular, new results highlight the fact that individuals too high in self-efficacy will suffer debilitating performances over time, whereas other individuals—who may unsuccessfully complete a task or not meet their performance expectations based on their current self-efficacy—will improve their subsequent performance (30-32,38). The explanation for these results is that the strong relationship between self-efficacy and performance occurs because of the influence of a performance on self-efficacy, not because of the influence of self-efficacy on a performance. In other words, the consequences one experiences will have a direct result on subsequent self-efficacy.
An example of how self-efficacy may be negatively related to performance can be witnessed with a women's soccer player who strongly believes she can record a vertical jump of at least 21″ in upcoming testing. When this individual is tested, and if she successfully jumps at least 21″, she might not put forth as much as effort toward this skill in the coming weeks/months because of her confidence level. Therefore, when her ability is measured again, she is likely to again rate her self-efficacy for jumping 21″, 22″, or even 23″ as very strong, yet may suffer a decline in performance. Conversely, if this same women's soccer player was initially tested in the vertical jump and only scored a maximum vertical of 17 in., she will likely devote more practice time and greater effort to this skill in the future. Consequently, when tested again, her self-efficacy for successfully jumping 21" will be lower than at the first testing time point, because of her poor past performance; however, her new performance will most likely improve. This phenomenon has been found in National Basketball Association (NBA) playoff games, for example, the teams that lost the first game in a playoff series were more likely to win the second (22). Although self-efficacy was not directly measured in this study, it could be one possible explanation as to why the losing team tends to win the second game. Research has shown that when a team wins a game, the players' self-efficacy is significantly increased, and after a loss, it significantly decreases (10). Winning the first game will instill confidence, and possibly overconfidence in players, whereas the losing team has the opposite effect, lowering self-efficacy, and therefore, athletes may put forth a greater amount of effort toward the next game. As a result, it may be advisable for practitioners to instill mildly negative expectations, to curtail overconfidence (29).
Although some researchers claim that the notion of performance directly affecting the next performance removes one's cognitive processes from the equation, and thus is too barren to be classified as a theory (3), both “camps” of researchers do agree that self-efficacy will positively affect behavior or performance when an individual is compared with another subject (38). Most of the research in self-efficacy is conducted in regard to performance rather than to effort. Although it is often hard to measure effort, there is a considerable lack of research in this area. It is important to understand the self-efficacy–effort relationship because effort has an impact on performance and could possibly mediate this connection between self-efficacy and performance. Additionally, future directions have been spelled out by many researchers to understand the effects of self-efficacy more completely. For example, researchers should examine subjects in physical exertion skills, which have significance for the subjects (3,31). In addition, future studies should allow a significant amount of time to pass between measurements of self-efficacy and impending behavior (which has not always been done in past work), because measuring these constructs repeatedly does not allow ample time for self-efficacy to operate in a setting (3,31,32,38). Finally, although studies have examined self-efficacy and resulting behavior or performance changes for a skill or competition, more work is needed that measures subjects over multiple time points and then analyzes the data for variations within the same person over time and also between subjects simultaneously (25,35).
Therefore, the purpose of this study was to examine the relationship between self-efficacy and effort in strength training for Division I athletes during the entire off-season training program. Although maybe not as complete as the present work, the vast amount of research in sport and exercise settings has shown a positive relationship between self-efficacy and adaptive behaviors, and it is expected that this research will show similar results for athletes over time, when compared against their previous effort performance (i.e., repeated measures) and when all data are aggregated and individuals are evaluated against other athletes (i.e., between subjects).
Experimental Approach to the Problem
Although self-efficacy and behaviors have been studied longitudinally in sports such as baseball, hockey, and basketball (10,15,20), these works did not take advantage of new multilevel modeling analyses; thus, results may differ when explored in a setting devoted to training for sport (3). As previously highlighted, a multilevel approach to understanding how self-efficacy affects effort is best suited for this study because the research question is concerned with both individual changes in self-efficacy (i.e., within-person data) and how athletes deviate among each other based on self-efficacy levels (i.e., between-person data) (25,35). Therefore, by using strength training, which is a common arena for most athletes preparing for their sport, a more complete picture of how self-efficacy impacts effort can be obtained.
Approval for this study was granted by an Institutional Review Board (IRB) at a Midwestern university. Complete data were collected from 99 subjects (Mage = 20.0 years, SD = 1.2 years), who were current Division I athletes in football (n = 62), volleyball (n = 9), men's soccer (n = 19), and men's basketball (n = 9). Subjects' data showed adequate diversity in demographic variables collected. For example, self-reported ethnicity revealed that 61.6% of athletes were Caucasian, 22.2% African American, 7.1% Hispanic, 8.1% multiple ethnicities, and 1% other. In addition, the majority of subjects in this study were in their freshman year of eligibility (41.4%), followed by juniors, sophomores, and seniors (32.3, 19.2, and 7.1%), respectively. Finally, 2 dichotomous variables (scholarship status and recruitment by current head sport coach) were also collected to be used in data analysis. In this study, 70.7% of the athletes were on either a full or partial athletic scholarship, and 48.5% of subjects were recruited by their current head sport coach. (It should be noted that no demographic variables were found to be significant and thus were not included in final analyses).
The subjects in this study completed 4 distinct questionnaires. The first questionnaire was an 11-item self-efficacy effort questionnaire (SEEQ). The SEEQ was constructed by following appropriate guidelines put forth by past researchers (2,13). In particular, the subjects responded to the stem of, “Over the next week, how confident are you that you can earn an effort score of at least … between your self-rating and your head strength coach's rating?” which was then followed by 11 effort ratings of 0–100%, stratified every 10%. The athlete then responded to each of these 10% stratifications on an 11-point Likert scale, with 0 = not at all confident and 10 = absolutely confident. Therefore, the athletes essentially rated their effort perceptions for intervals of at least 10%, at least 20%, at least 30%, etc. Subjects' scores were computed by averaging responses from the SEEQ.
The second questionnaire used in this study was a 3-item value of strength training questionnaire (VQ). The 11-point Likert scale previously used for the SEEQ was also implemented with this questionnaire; however, athletes' responses were collected through a series of questions related to the construct being studied. For instance, an example item from the VQ was, “How much do you currently value giving high effort in strength training sessions for attaining your personal goals in sport?” Therefore, the responses to each item in the VQ could range from 0 = no value and 10 = high value. Again, subjects' scores were calculated by averaging VQ responses.
The third type of questionnaire used in this study was a 6-item current stress inventory measure (CSI). Again, a similar 11-point Likert scale was used for this questionnaire that ranged from 0 = no stress, pleasant to 10 = high stress, unpleasant. Example CSI items were, “Thinking about today only, what is your current stress level toward your academic work?” and “Thinking about today only, what is your current stress level toward your sport coach?” Other questions on the CSI measured stress levels associated with friend relationships, significant other relationship(s), family, and strength and conditioning coaches. Responses to the CSI were then averaged so that comparisons could be made across all psychological measures.
The final questionnaire consisted of an effort report questionnaire (ERQ), where subjects privately recorded how much effort they perceived they gave in strength training at the conclusion of a data collection week. This questionnaire simply asked the subjects to report their perceived effort, ranging from 0 to 100% in increments of 10%. In addition, the strength and conditioning coach, who worked with each sport, also completed an ERQ questionnaire for each athlete. This way, a more valid measure of effort could be calculated using both athlete and coach perceptions.
Upon IRB approval, appropriate athletic staff were approached about participation in this study and informed about their rights and responsibilities, should they choose to participate. After all appropriate athletic staff agreed to participate in this study, 4 time points for data collection were set with each sport during off-season strength and conditioning training sessions (i.e., January–May). Each time point was positioned approximately 1 month apart so that if any changes in effort or psychological constructs presented themselves, they could be observed (1).
To facilitate ease of data collection, the lead researcher attended all strength and conditioning training sessions when data were scheduled to be collected. All the athletes in this study had completed at least 1 semester of training with their current strength and conditioning coach (i.e., all were at least freshman athletes), and the majority of the sample had much more experience; thus, the subjects were considered to be previously trained. At time point #1, athletes and coaches were informed of the purpose of the study, they were informed that their answers to all questionnaires would be confidential, and they then completed the necessary IRB consent form and a demographic questionnaire. Specifically, athletes and coaches were read a script approved by the IRB, which informed them about the types of psychological constructs that were being measured, what questions this study was attempting to answer, that strength and conditioning coaches would also be evaluating athletic effort displayed, and that participation was completely voluntary. Subsequently, the athletes privately completed the SEEQ, VQ, CSI, and then returned them directly to the lead researcher, who administered all forms and questionnaires. Exactly 1 week after the completion of questionnaire packets by the subjects, the lead researcher returned, and all the athletes participating in the study were instructed to rate the perceived effort they put in strength and conditioning training sessions (over the past week) by completing the ERQ. In addition, the strength and conditioning coach in charge of each sport also gave the lead researcher an ERQ for each athlete within 48 hours of the completion of the week's training. This exact process was then replicated for time points 2–4, with the only alterations being the removal of the IRB consent form and demographic questionnaire.
After data were collected, they were entered in a traditional software analysis program (SPSS) for preliminary analysis. To provide the most comprehensive picture of an athlete's effort displayed in strength training during the weeks of inquiry, both the athlete's and the strength and conditioning coach's perceived ratings on the ERQ were used in computing the dependent variable of effort. Specifically, each individual was given a 50% weight in computing the total effort for each subject. Thus, if an athlete rated his effort in strength training for the week as 90% and the strength and conditioning coach, who directed that same sport, rated the athlete's effort as 80%, the value of 85% was used for that time point.
Once hard copies of data were properly collected, entered, and stored, initial descriptive analysis revealed that variables collected via questionnaires were normally distributed. The results showed that the most extreme skewness and kurtosis scores were found in the SEEQ at time point 3; however, these values, of −1.4 and 2.3 were within the acceptable range; thus, no data transformations were required. Additionally, internal consistencies all loaded above the uniformly accepted α = 0.7 for each questionnaire at each time point. Finally, there were no univariate or multivariate outliers among the sample of athletes, and the relationship between self-efficacy and effort was linear across all time points, further confirming the normality of the data.
Data were then exported and analyzed using Hierarchical Linear and Nonlinear Modeling (HLM) v6.04 (26). The HLM was the appropriate statistic to use for 2 reasons: first, this study had various time points that were “nested” within individuals. For example, the central question of this study was concerned with how self-efficacy affected effort, for both a subject over time and comparing that subject's aggregated effort to those of other athletes in the study (25). Without using HLM, a researcher is left with 2 choices: to disaggregate all variables to the lowest level of measurement (i.e., within-person measures over each period of time) or to aggregate all the within-person measures to the higher level. Both options have significant drawbacks; the first violates the interdependence assumption required for statistical analysis, because different measures of the same person are obviously related. The second disregards any within-person information, which may make up a significant portion of the total explained variance and have the ability to answer the research question(s) of interest (38). Second, this study also incorporated a linear growth model using HLM because it had (a) 3 or more waves of data; (b) an outcome whose values change systematically over time; and (c) a sensible metric for clocking time (28).
Table 1 presents self-efficacy and effort means, SDs, and intercorrelations for all 99 subjects at level 1, or the within-person level. The results reveal that significant correlations were achieved between self-efficacy and effort in strength training at all time points, which is not surprising considering identical findings in past research (10,15,18,32,38). At the aggregated level (i.e., level 2), the correlation between self-efficacy and effort was also quite strong (r = 0.57), which was significant at the p < 0.01 level. Finally, although effort did decline during the course of this study, this change was not significant and could have occurred because the subjects tried harder at the outset of this study when being evaluated by the strength and conditioning coach, which was a new phenomenon. Thus, when time point 4 transpired, the notion of being evaluated by a strength and conditioning coach—related to effort—was less of a concern.
When analyzing the unconditional means model in HLM v6.04 (26), it was found that the intraclass correlation coefficient (ICC, ρ) was 0.69. This simply meant that 69% of the variance in subjects' effort was located at the between-person level, and 31% of the variability was found at the within-person level. Additionally, the random effect for the effort intercept indicated that effort did vary across individuals, χ2(98, N = 99) = 757.83, p < 0.001. Thus, the previous information revealed that HLM was an appropriate statistic to use because significant variance was located both for an individual over time and between individuals when data were aggregated.
To examine the hypotheses concerning self-efficacy and effort in this study, the following level 1 and level 2 models were built in HLM v6.04 (26):
At level 1, Yij is the effort at Time i for athlete j, π0j is the level 1 intercept, π1j is the effect of TIME (i.e., 4 data collection points) for each athlete, π2j is the effect of self-efficacy for each athlete, π3j is the effect of residualized past effort for each athlete, and eij is the error associated at each time point for each athlete measured. At the between-person level (level 2), all level 1 variables become outcome variables; thus, β00 is the average intercept for all athletes, β01 is the effect of mean self-efficacy of all athletes, β02 is the effect of mean residualized past effort of all athletes, r0j is the level 2 error associated with the intercept, β10 is the effect of linear practice or TIME, β20 is the effect of self-efficacy, and β30 is the effect of residualized past effort. Finally, because the advantage of HLM is that level 1 and level 2 models are analyzed simultaneously, the complete model tested was
Findings from this model are presented in Table 2. The results show that when data for each athlete were aggregated and compared with those of other subjects, self-efficacy predicted greater effort in strength training, t(96) = 16.05, p < 0.001. At level 1 (or comparing each athlete to their previous psychological scores and effort), the effect of time had no significant impact on a subject's effort in strength training, simply demonstrating that effort did not meaningfully change over the course of this study, absent of other variables. In contrast, self-efficacy and effort again showed a positive and significant relationship, t(291) = 1.94, p = 0.05 at the within-person level. Thus, the more each athlete became confident in his or her ability to display high effort, the more effort that athlete subsequent displayed, in both the self-report ERQ and the ERQ completed by the strength and conditioning coach. It is important to note that this relationship held true when past performance was residualized, a procedure that is recommended by researchers (3,4,9). Finally, no other psychological or demographic variables collected were significant in the prediction of effort at level 1 or level 2.
Another advantage of HLM v6.04 (26) is the ability to account for how much of the variance in subjects' effort can be explained by the measured constructs. As shown in Table 3, 57.9% of the variance in effort could be explained with all significant predictors (e.g., TIME, self-efficacy, and residualized past performance). Level 1 variables accounted for only 2.3% of within-person variance or 0.7% of the total difference in effort (after multiplying by 1 − ICC). The explained variance in effort at the between-person level was 82.9% (or 57.2% of the total variance) with all variables entered. In summary, the relationship between self-efficacy and effort was positive and significant at both levels of analysis, supporting well-documented findings that if self-efficacy levels improve for a subject, his or her impending effort in strength training will also show a corresponding and positive change.
Although not the main hypotheses of the study, 2 subsequent HLM v6.04 models were run to fully examine how self-efficacy was manifested over time (26). In the first, a subject's level 1, raw past performance (i.e., the exact effort he or she earned at the previous time point and not the residual version of that effort) was analyzed to see if it predicted one's future self-efficacy. The results revealed that this relationship was nonsignificant t(293) = −0.244, p > 0.05, thus rebuking previous work (31,32) and further validating the contention that past behavior will not be the direct cause of future self-efficacy or current behavior (1). Second, self-efficacy was again examined as the dependent variable, and scores from subjects' VQ and CSI were imputed into a new model to determine if these psychological variables affected self-efficacy, which then affect effort. The findings interestingly showed that at level 2 (i.e., aggregated data), only a subject's value of strength training predicted greater self-efficacy scores when compared with other athletes t(96) = 5.581, p < 0.01. At level 1 (or within-person data analysis), the changes in value had no effect on a subject's self-efficacy; however, stress did. As one might expect, as stress levels declined for the same athlete over time, their self-efficacy increased, t(291) = −1.94, p = 0.05. Besides an actual reduction of stress associated with subjects' lives, this decline in stress levels for some athletes during the course of this study may also be attributed to increased familiarity with the measures completed; however, it should be noted that when individuals have no incentive to manipulate responses, self-report measures are an accurate way to assess psychological constructs (1). Thus, the decrease in stress levels for some athletes would most likely be related to perceived stress levels and not manipulated levels of stress caused by this study.
The purpose of this study was to determine how self-efficacy affected athletes' effort in strength training over time, because of the recent debate regarding this matter (3,9,13,30-32,38). The results confirmed our hypothesis, in that self-efficacy was positively related to effort in strength training sessions, at both within- and between-person levels of analysis. As stated in the Introduction of this article, this result is not surprising, given the vast amount of research in sport settings confirming the role of self-efficacy in producing adaptive behaviors (23). Although previous studies have examined this relationship in a strength training or a weightlifting environment (14,27,36), this was the first study to explore the relationship between self-efficacy and highly skilled athletes over the course of a training cycle. Thus, because of these methodological advancements, this study can now support research findings in other sports (e.g., crew, hockey, tennis, etc.), which showed that positive changes in self-efficacy will result in increased performance or more adaptive behaviors (8-10,15-18,21,23).
This study also examined the self-efficacy from dual perspectives of the athletes' and the strength and conditioning coaches' perceptions of effort. Although this methodology of including coaches' evaluations of athletes' effort could be considered adding a confounding variable to this study, in fact, the inclusion of coaches' ratings in this study prevented the athletes from rating themselves as high in strength training effort efficacy and then simply reporting that they displayed maximum effort to corroborate their previous efficacy beliefs. Therefore, the importance of self-efficacy was not just isolated to athletes' perceptions, because the positive relationship between self-efficacy and effort perceptions held true when using strength and conditioning coaches' effort perceptions for athletes to comprise 50% of a subject's complete effort, a novel step in research for this domain.
In addition to discovering a positive relationship between self-efficacy and effort for collegiate athletes in a strength and conditioning setting, this study also helped clarify the role of past performance (or in this case, past effort) on future self-efficacy levels. In particular, previous researchers have found that a subject's past performance will be the direct cause of his or her future self-efficacy levels (31,32). However, in these studies, researchers had either examined in self-efficacy via computer simulations, thereby only measuring self-efficacy at purely cognitive levels (3,31,32), or had drawn conclusions regarding physical performances over time that were devoid of self-efficacy measurements altogether (22). By incorporating a real-world task (i.e., not manipulating the environment for subjects), results contradicted these previous assertions and showed that past effort was nonsignificant and thus did not directly affect an athlete's impending self-efficacy for strength and conditioning effort. This finding supports the notion that past behavior will not be the direct cause of current behavior, as instead, behavior is cognitively “filtered” through current efficacy states to produce beliefs about present capabilities (1,13). All of the aforementioned findings are important for practitioners because this study has shown that self-efficacy levels result in greater perceptions of effort from athletes and strength and conditioning coaches, and furthermore, subjects' past effort (either high or low) will not impact future levels of self-efficacy or effort.
One limitation of this study was its exclusive collection of psychological data. As strength and conditioning coaches will attest to, training for sport does not exist in a vacuum where only mental factors affect behavior and performance. Future endeavors in this subject could observe the possible connection between self-efficacy and physiological measures of performance. Professionals rely on quantitative data to track their athletes' physical developments throughout the training year. Most strength and conditioning professionals assess, then reasses, the physiological profile of their athletes throughout their training year. Therefore, it would be interesting to note the self-efficacy of athletes between assessment and reassessment of athletic measures in their training year. For example, the self-efficacy of athletes could be assessed before their off-season training cycle, which would coincide with an assessment of their current athletic profile. The athletes would progress through their designed training cycle where their self-efficacy could be periodically noted. Finally, athletes' perceptions of their effort would be correlated to their reassessed athletic profile such as muscular strength (via 1RM testing), muscular power (through measured jumps, medicine ball throws, and timed jumps), flexibility (through specific joint mobility testing), and even cardiovascular endurance and speed-endurance (through specific team conditioning tests) at the conclusion of their training cycle.
Additionally, researchers could also examine the training logs of athletes during data collection time points. Although psychological measures are reliable for respondents when there is no incentive to embellish answers (1), corroborating self-efficacy levels with intensity percentages during a specific portion of the training cycle would aid researchers and practitioners with additional information athletes hold regarding their perceptions of past workouts. To date and to our knowledge, this link between these psychological and physiological aspects for competitive athletes over the course of a training cycle has not been investigated.
In addition to crossdisciplinary work that incorporates the aforementioned psychological and physiological measures of athletes, future studies should explore how self-efficacy directly affects the strength training performance (not just effort) of athletes, from a multilevel modeling approach. Although it is hypothesized that self-efficacy relates more strongly to behavior—when compared with performance (1)—this proposed link would greatly aid the strength and conditioning professionals' ability to motivate athletes and solidify the importance of this psychological variable when training for sport. However, for this relationship between confidence and effort to be fully understood, future research should also place equal emphasis on the recruitment of female athletes during the design of potential studies. Besides the sheer number of female athletes and female sports at the collegiate level of competition, these sports also potential revenue sources for highly skilled teams; thus, the aspects of performance for all athletes should not be overlooked.
Because self-efficacy was shown to be more influential in effort than were stress levels, value, or demographic variables (19), coaches should work to increase the confidence levels of their athletes. First and foremost, coaches should structure the environment to allow for mastery experiences (i.e., configuring the task in a manner that lends itself to success). Researchers studying sports ranging from baseball to wrestling have found that successful past performances increased subjects' confidence, which thereby increased their future performance (15,18,23,34). To aid in mastery skill development, complex skills can be broken down into manageable parts to better facilitate mastery; then, performance aids can be gradually removed when athletes become more comfortable with the skill set needed to accomplish a task (8,13). An example of this technique can be employed with athletes struggling in 1RM power clean testing. At the appropriate times during the training cycle, coaches can have athletes gain confidence by performing clean pulls, clean high pulls, and drop cleans to “learn how the weight feels” and master the correct technique. This alteration of the environment will produce athletes who are more comfortable with the heavier weight, or technique required, and will facilitate higher confidence during future training sessions and 1RM testing (1).
A second way to build confidence is through vicarious experiences (or social models), wherein individuals observe other people, similar to themselves, succeeding. Studies have again confirmed the link between vicarious experience and increased confidence (11); however, the benefits are contingent on the modeler. Specifically, modelers who have the same ability or a slightly higher ability provide the most useful feedback in terms of judging one's own capabilities (13). Therefore, athletes who need to increase their self-efficacy levels in strength and conditioning should seek out others who have slightly higher performances or better technique as a benchmark to measure progress. For instance, if strength and conditioning coaches find specific athletes lacking in effort production, they could pair these athletes with others who give slightly more effort. By following this protocol, athletes who give subpar effort will observe the modeler become more proficient at a skill or task and receive praise, which will trigger thoughts of, “If he or she can do this, I can too.”
A third way that confidence can be improved is through verbal (or social) persuasion or when significant others attempt to verbally persuade individuals that they have the skills or ability necessary to be successful at a task (33). Although coaches are already no doubt proficient with this suggestion, caution must be exercised by practitioners for 2 reasons. First, the debilitating effects of verbal persuasion are stronger than the enhancing effects (1). Thus, a coach can more quickly create doubt in an athlete by suggesting that the athlete lacks the ability to put in effort than he or she can create enhanced beliefs through positive suggestions. Second, the athlete must trust the source of the verbal persuasion for this method to produce increased confidence (11). For example, if a teammate conveys that there is 235 lbs on the bar (during a 3RM bench press) and demands effort in competing the set, but there is actually 245 lbs and the athlete later finds out, this athlete will be less likely to respond to this type of motivation—with increased effort levels—from the teammate in the future.
Finally, somatic or emotional states (i.e., how one's body feels) can alter athletes' confidence when interpretations of these physiological signs are linked to an impending performance (13). Specifically, perceptions such as tiredness, heart pounding, sweaty palms, fatigue, and even how a recent injury feels can positively or negatively affect confidence and effort in a skill or task (1,7). Even though coaches will have a difficult time trying to convince an athlete that he or she is not nervous before a big game, if the athlete's perceptions of nervousness can be changed from fear and anxiety to being prepared and ready, positive changes in self-efficacy should result (5,13). For instance, if athletes come into a training session feeling tired and lethargic, coaches should spend less time trying to convince these athletes that they are actually feeling fine and more time devoted to changing their perceptions—by perhaps reminding them that they have been tired before and still produced high levels of effort when necessary. It should be noted however, that this last source of self-efficacy is generally regarded as the least effective, in altering confidence levels of athletes because people generally find the other sources of self-efficacy more diagnostic of their personal capabilities (1). Thus, coaches should focus on the previous 3 sources—especially mastery experiences—to improve the confidence (and resulting effort) of their athletes in strength and conditioning.
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Keywords:Copyright © 2012 by the National Strength & Conditioning Association.
confidence; hierarchical linear modeling; football; basketball; volleyball; soccer