The purpose of this study was to examine the relationship between decision-making self-efficacy and decision-making performance in sport. Undergraduate students (N = 78) performed 10 trials of a decision-making task in baseball. Self-efficacy was measured before performing each trial. Decision-making performance was assessed by decision speed and decision accuracy. Path analyses examined the relationships between self-efficacy, residualized past performance, and current performance. The results indicated that self-efficacy was a significant and consistent predictor of decision speed (eight of nine trials), but not decision accuracy (four of nine trials). It was also found that experience does not have a meaningful effect on the relationship between self-efficacy and decision-making performance in sport.
Key words: cognitive, confidence, decision, experience
The efficacy-performance relationship has been demonstrated in a wide variety of sports, including tennis (Barling & Abel, 1983), hockey (Feltz & Lirgg, 1998; Myers, Payment & Feltz, 2004), diving (Feltz, 1982), and basketball (Wuertle, 1986). Despite the vast body of research on self-efficacy and sport performance, most has focused on physical performance (see Feltz, Short, & Sullivan, 2008; Moritz, Feltz, Fahrbach, & Mack, 2000, for a review). However, sport performance consists of both physical and cognitive components (Thomas, 1994). Moreover, Bandura (1997) stated that "cognitive factors play an influential role in athletic development and functioning" (p. 369). While previous research supported the link between self-efficacy and physical performance, little is known about the predictive validity of serf-efficacy on cognitive performance in sport.
Although the relationship between self-efficacy and cognitive performance has not been studied extensively in sport, there has been substantial research in other areas. For example, several studies have examined the role of serf-efficacy in academic performance. Collins (as cited in Bandura, 1997) conducted a study in which children attempted to solve difficult math problems. The results showed that children with high math efficacy solved more problems, persisted longer after failure, and abandoned the use of incorrect strategies more quickly than children with low math efficacy. Another study involving 230 college undergraduates found that specific self-efficacy significantly predicted academic achievement (Choi, 2005). Overall, a meta-analysis revealed that self-efficacy beliefs account for 14% of the variance in academic achievement (Multon, Brown, & Lent, 1991).
Self-efficacy has also been shown to predict cognitive performance in areas outside the classroom. One study found that older adult men with high memory self-efficacy performed better on a test of everyday memory than those with low self-efficacy (McDougall & Kang, 2003). Likewise, self-efficacy significantly predicted problem-solving performance among adults (Artistico, Cervone, & Pezzuti, 2003).
As the link between efficacy and cognitive performance has been supported in other domains, it seems reasonable to explore this relationship in sport. One of the most important cognitive components in sport is decision making (Thomas, 1994). Decision making is a process by which athletes select one preferred action from among two or more options (Tenenbaum, 2004). Decision making is a critical aspect of sport performance, as success depends on choosing the right course of action at the right time and then effectively performing that action (Grehaigne, Godbout, & Bouthier, 2001). Decision making is one type of thought process that can be influenced by self-efficacy beliefs (Feltz, Short, & Sullivan, 2008). Specifically, self-efficacy beliefs can facilitate or impair decision making (Bandura, 1997). Many sports involve a fast-paced, dynamic environment in which athletes have mere seconds, or less, to decide on a course of action. In this type of setting, where even a moment's pause can mean the difference between success and failure, it is crucial that athletes have confidence in their decision-making capabilities to act on their decisions without hesitation.
Most of the research on decision making in sport has focused on differences between experienced and less experienced performers. Research in this area has examined many different topics, such as visual search patterns (Shank & Haywood, 1987), cue utilization (Abernethy & Russell, 1987), and signal detection (Allard & Starkes, 1980). Recent research has explored differences in decision-making speed and accuracy based on experience level. For instance, Fontana, Mazzardo, Mokgothu, Furtado, and Gallagher (2009) found that experienced soccer players made faster, more accurate decisions than less experienced players. Another study demonstrated that expert decision makers in the Australian Football League were more accurate in predicting the outcome of play than nonexpert decision makers (Berry, Abernethy, & Cote, 2008). Research involving male tennis players indicated that skilled players made faster, but not more accurate decisions, than less skilled players (Williams, Ward, Knowles, & Smeeton, 2002). Tenenbaum and Lidor (2005) posited that the cognitive mechanisms that underlie decision making in sport operate faster, more efficiently, and with less effort as athletes gain more sport experience and knowledge. According to a meta-analysis on perceptual-cognitive expertise in sport, experts make faster ([[eta].sup.2] = .42), more accurate ([[eta].sup.2] = .31) decisions than nonexperts (Mann, Williams, Ward, & Janelle, 2007). For a more comprehensive review on decision making in sport, see Tenenbaum (2003, 2004), or Tenenbaum and Lidor.
While there has been a considerable amount of research on decision making in sport, few researchers have examined the relationship between self-efficacy and decision making. This fact is somewhat surprising considering that Bandura and colleagues have established a significant link between self-efficacy and decision making in organizational settings (Bandura & Wood, 1989; Wood & Bandura, 1989). In one sport-related study, Tenenbaum, Levy-Kolker, Slade, Lieberman, and Lidor (1996) used video depictions of tennis strokes to investigate anticipatory decision making among novice, intermediate, and expert tennis players. The results showed that experts, as compared to novices and intermediates, were much more confident in their decisions that were made shortly after contact (Tenenbaum et al., 1996).
More recently, Hepler and Chase (2008) conducted a study in which participants watched videotaped defensive situations in softball and had to decide where to throw the ball. According to the results, self-efficacy beliefs did not significantly predict decision-making accuracy. However, the authors cited several limitations to the study that may have influenced the results. For instance, the study did not measure decision speed, used a generic nonconcordant efficacy measure, failed to assess beliefs after each trial, and used manipulated-failure feedback.
The purpose of the current study was to examine the relationship between self-efficacy and decision-making speed and accuracy on a simulated sport task. Based on previous research supporting the link between self-efficacy and physical performance in sport, as well as that on self-efficacy and cognitive performance in other domains, we predicted that decision-making self-efficacy would significantly influence decision speed and decision accuracy after controlling for past performance. We controlled for past performance because past performance accomplishments are related to subsequent ratings of efficacy, as well as to future performances (Bandura & Locke, 2003). Similarly, as previous research has found differences in decision-making performance based on experience, we also wanted to explore the efficacy-performance relationship after controlling for participants' experience.
Undergraduate students (38 men, 40 women) enrolled in physical activity courses at a large midwestern university participated in the study. All were between the ages of 18 and 29 years (Mage = 21.25 years, SD = 1.96). Data from 13 participants were excluded from the analysis, because they were viewed as lacking requisite incentive, as described below. Thus, only data from 65 participants (35 men, 30 women) were analyzed. While participants were not required to have prior experience, they averaged 6.72 years of baseball/softball playing experience (SD = 5.87).
Demographic Questionnaire. A demographic questionnaire was used to collect background information from all participants. General information, such as age, gender, and year in school was collected. In addition, the participants were asked to provide information about their baseball/softball experience, including number of years of playing experience, dominant throwing arm, highest level played, and position(s) played.
Importance Rating. According to self-efficacy theory, capability beliefs are related to performance only when the performer has the requisite incentive to do well. As such, participants were asked to rate how important it was for them to be successful in this baseball decision-making test. Participants rated task importance on an 11-point scale ranging from 0 (not at all important) to 10 (very important). Similar to the inclusion criteria of other studies, participants who selected an importance rating below 5 were excluded from the final data analysis (Chase, 2001; Hepler & Chase, 2008). Thirteen participants were excluded from the study due to low importance ratings.
Decision-Making Video Task. The decision-making task involved 10 scenarios depicting various infield defensive situations in baseball. Each scenario consisted of the relevant game conditions (e.g., score, inning, number of outs) and a video of the play. The videos depicted vital information not available from the game conditions, such as what side the ball was hit to (i.e., glove side or backhand), how hard the ball was hit, and the speed of the baserunners. The videos were created using a popular baseball video game and depicted male baseball players engaged in various infield defensive situations. Each video clip began by showing the pitch and ended as soon as the defensive player fielded the ball. On average, each video lasted 6.2 s. All of the scenarios had the same four choices as to where to throw the ball: first base, second base, third base, or home plate. Participants viewed the video scenarios on a laptop computer and were allowed to watch each video clip up to five times. While real-life situations in baseball only allow for one chance to make a decision, various artificial aspects of the experimental task (i.e., limited video clarity, changing camera angles, screen glare) may have made it difficult to detect the appropriate cues needed to make a decision after only one repetition. However, participants who viewed the scenarios more than once were penalized, as response time was a key performance measure. (1)
Overall, 27 different video scenarios were created. All of these scenarios were independently evaluated by three current or former collegiate baseball coaches. The coaches were asked to rank the appropriateness of the four possible decisions. To demonstrate content validity, only situations which received identical ratings from all three coaches were used in the study. (2) Overall, 10 video scenarios were retained and used in the decision-making task.
Self-Efficacy Questionnaires. Participants completed a situation-specific, self-efficacy questionnaire before watching each video. The questionnaires were designed according to Bandura's (2005) detailed guidelines, and each corresponded to a specific, unique video scenario. Accordingly, all of the self-efficacy questionnaires were identical except that the defensive position and location of runners varied based on each specific situation, thereby maximizing concordance between the efficacy measure and performance situation. Concordance between the efficacy and performance measures is a key element for accurately assessing the efficacy-performance relationship (Bandura, 1997; Moritz, Feltz, Fahrbach, & Mack, 2000; Myers & Feltz, 2007). An example questionnaire instructed participants to:
Pretend that you are playing shortstop. There are runners on first and second and a ground ball is hit to you. After fielding the ball, you must make a quick, accurate decision on the best place to throw the ball.
The stem question asked participants, "How certain are you that you can make the best decision...." Participants then rated their degree of confidence (from 0% to 100%) to make the best decision at each of the 10 hierarchical performance levels (i.e., 1 of 10 times, 2 of 10 times, up to 10 of 10 times). Decision-making self-efficacy for each scenario was defined as the average confidence rating for all 10 performance levels. (3) Self-efficacy strength was used subsequently to predict decision speed and accuracy on the corresponding trial.
Decision-Making Performance. Two different measures of decision-making performance were assessed: speed and accuracy. Decision speed indicated how long, from the start of the video, it took for the participant to make a decision. Decision speed was measured by the computer and rounded to the nearest .01 s. Decision accuracy was determined by the correctness of the decision. Each correct (accurate) decision garnered 1 point, the second-best decision received 2 points, and either of the worst two decisions got 3 points. Decision accuracy was scored in this manner became, in baseball, the second-best decision often produces a desirable result. For example, the best decision in a situation might be to throw the ball to second base to get the lead runner out and try to turn a double play. However, simply throwing the ball to first base to get the batter out is also a positive outcome. Thus, the 3-point accuracy scale distinguishes those who make acceptable decisions (best and second-best) from those who make inappropriate decisions (third- or fourth-best). Accordingly, accuracy scores for each trial could range from 1 to 3.
The institutional review board approved all measures and procedures before the study was conducted, and all participants gave informed consent. The participants received an overview of the study, and they were instructed to make fast, accurate decisions, as both the speed and accuracy of their decisions would be recorded. Also, they were informed that three baseball experts had determined the best decision for each situation. After they understood the objective of the task and completed the demographic questionnaire, the participants were taken through an example scenario. However, no performance feedback was given on this trial, as the purpose of the example was to help participants become comfortable with the procedure and to familiarize them with the task demands so that they could make an accurate self-efficacy judgment.
Next, participants completed the situation-specific, serf-efficacy questionnaire for Trial 1 and then performed the decision-making task for Trial 1. For each decision-making trial, participants first reviewed the game conditions and then pressed the spacebar to start the video. As soon as they made a decision, participants pressed the spacebar again, thereby stopping the video and the decision speed timer, and immediately verbally identified their choice as the best place to throw the ball. The experimenter then provided performance feedback, simply informing participants whether they had made the best decision for the situation. On trials in which participants failed to make the best decision, the experimenter provided them with the correct answer, but did not give any reasons why their answer was incorrect. This sequential process, which involved completing the situation-specific, self-efficacy questionnaire, performing the decisio-nmaking task, and receiving performance feedback, was repeated for all of the video scenarios. (4) On completion of all 10 experimental trials, participants were debriefed about the study?
Treatment of Data
Because previous performance accomplishments are related to subsequent ratings of efficacy as well as to future performances, researchers often control for past performance when examining the relationship between self-efficacy and performance (Wood & Bandura, 1989). However, the conceptualization of past performance is crucial because it can significantly alter the results. Previous research operationalized past performance in two ways: raw and residualized. Using raw past performance is the simplest method, in which self-efficacy and past performance, as they were directly measured, are used to predict current performance. Results using this method typically find self-efficacy to be a relatively weak predictor of performance and that this relationship gets weaker over time (Feltz, 1982; Feltz & Mugno, 1983).
However, Wood and Bandura (1989) suggested that controlling for raw past performance results in a "statistical overcorrection" in favor of past performance. Instead, they argued that the prior effects of self-efficacy should be removed from raw past performance, and the new adjusted, or residualized, past performance should be entered as the control variable in the model. In other words, "residualized past performance is what performance would be, had it not been influenced by self-efficacy" (Feltz, Chow & Hepler, 2008, p. 403). Studies controlling for residualized past performance have found that self-efficacy has an independent relationship with current performance that tends to get stronger over time (Bandura & Wood, 1989; Wood & Bandura, 1989).
A study by Feltz, Chow, & Hepler (2008) explored the impact of the different conceptualizations of past performance on the efficacy-performance relationship. Comparing various raw and residualized past-performance models, the authors found that self-efficacy was a stronger predictor of performance in the residualized models than in the raw models. Based on their findings, the researchers concluded that residualized models provide the most accurate assessment of the efficacy-performance relationship across multiple trials. Therefore, in the current study we operationally define past performance as residualized past performance. Thus, all references to past performance in this paper refer to residualized past performance. Past performance scores were residualized according to established guidelines (see Wood and Bandura, 1989, for a description of the statistical procedure). Also, we only examined data from those trials for which past performance could be controlled. As a result, only data collected on Trials 2-10 were analyzed.
Means, standard deviations, and correlations of relevant study variables are provided in Table 1. On average, participants in this study had over 6 years of baseball/ softball playing experience and exhibited relatively high decision-making self-efficacy (M = 7.35, SD = 1.72). Examination of the bivariate correlations revealed that self-efficacy and past accuracy performance were both significantly related to decision accuracy (r = -.54, p < .001; r = .91, p < .001, respectively). Likewise, self-efficacy (r = - .47, p < .001) and past decision-speed performance (r = .92, p < .001) were significantly related to decision speed. In addition, years of baseball/softball experience was related to self-efficacy (r = .57, p < .001), decision accuracy (r = -.34, p < .01), and decision speed (r = -.42, p < .001).
As the study involved both men and women, we also explored gender differences for the main variables. Compared to women, men had more baseball/softball playing experience (men = 8.2 years, women = 5.0 years), higher decision-making self-efficacy (men = 7.9, women = 6.7) and faster response time (men = 6.6 s, women = 9.4 s). The decision accuracy of men (M = 1.6) and women (M = 1.7) was almost identical.
Additionally, the reliability of each situation-specific, serf-efficacy questionnaire was assessed. The questionnaires demonstrated high internal-consistency ratings with Cronbach's alpha ranging from .956 to .963. Participants' change in self-efficacy was also explored. Across all trials, participants' self-efficacy beliefs were very stable, changing by less than 1 point (M = .73, SD = .45) on a 10-point scale. (6)
Path analysis was used to evaluate the relationship between self-efficacy and decision speed, after controlling for the effects of past decision speed, across the 10 experimental trials. Path analysis is a statistical technique that examines between-persons relationships among study variables. It is especially appropriate for research involving multiple trials, as it treats each trial as independent. Moreover, path analysis allows researchers to evaluate the reciprocal and causal relationship between self-efficacy and performance over time (Feltz, 1982; Feltz, Chow, & Hepler, 2008), which is a key aspect of self-efficacy theory (Bandura, 2005). Results indicated that self-efficacy significantly predicted decision speed on every trial except Trial 8 ([beta] = -.14, p = .17), after controlling for the influence of past decision speed. The remaining eight significant beta weights ranged from -.20 (Trial 7) to -.46 (Trial 2) and accounted for 4.1% (Trial 7) to 21.3% (Trial 2) of the variance in decision speed. All self-efficacy--decision-speed relationships were negative, suggesting that participants with higher self-efficacy made faster decisions. The complete path model is presented in Figure 1.
[FIGURE 1 OMITTED]
A second path analysis explored the relationships between self-efficacy, past decision accuracy, and current decision accuracy across all trials. After controlling for the influence of past decision accuracy, self-efficacy significantly predicted decision accuracy on only four trials (Trials 4, 6, 8, and 10). These significant relationships were all negative, indicating that higher self-efficacy beliefs were associated with more accurate decisions (i.e., lower scores). Figure 1 depicts the results for the complete model. For the trials on which self-efficacy was significantly related to decision accuracy, efficacy beliefs accounted for 6.8% (Trial 8) to 19.7% (Trial 6) of additional variance in decision accuracy above the effects of past decision accuracy.
As experience has been found to influence decision-making performance in sport, it was necessary to conduct two additional path analyses that controlled for this variable as well as past performance. Controlling for baseball/ softball playing experience and past performance slightly reduced the predictive validity of self-efficacy on decision speed. Specifically, this relationship was significant on 6 out of 9 trials, compared to 8 of 9 trials without controlling for experience. However, two (Trials 5 and 7) that were previously significant were at least marginally significant in the current analysis (p <. 10). Likewise, adding baseball/ softball experience to the model did not significantly alter file interpretation of the efficacy-accuracy relationship. In this analysis, self-efficacy predicted decision accuracy on the same number of trials (4) as in the previous analysis that did not control for experience, with one additional trial approaching significance (Trial 8, p = .072). The direction of the efficacy-accuracy relationship was also similar, as 4 of the 5 significant (or marginally significant) trials were in the negative direction. The complete path model is illustrated in Figure 2.
The purpose of this study was to examine the relationships between self-efficacy, decision accuracy, and decision speed, while controlling for past performance. Results indicated that over time, self-efficacy was a fairly consistent predictor of decision speed, but not of decision accuracy. Furthermore, the findings suggest that the relationship between self-efficacy and decision performance is not significantly influenced by a person's experience.
The finding that self-efficacy was significantly and positively related to decision-making performance on a simulated sports task was not surprising, as previous research involving sports consistently supported this relationship (Moritz et al., 2000) and various cognitive tasks (Artistico et al., 2003; Bandura & Wood, 1989; McDougall & Kang, 2003). However, the current study separated decision-making performance into the two requisite components of speed and accuracy, and the results for these two facets were quite different. Specifically, self-efficacy consistently predicted decision speed, but not accuracy.
In support of the first hypothesis, it was found that self-efficacy was negatively related to decision speed. Moreover, this relationship was rather consistent throughout the study, as it was significant on 8 out of 9 trials. It is likely that confidence in one's decision-making capabilities allows a person to make decisions very quickly because there is no self-doubt or second-guessing to delay the decision process. Accordingly, high decision-making self-efficacy allows a person to make a decision without hesitation and subsequently to act immediately on that decision.
[FIGURE 2 OMITTED]
However, the hypothesized relationship between decision-making self-efficacy and decision accuracy was only partially supported (4 of 9 trials). For those four significant trials, self-efficacy influenced decision accuracy in the predicted negative direction. In other words, high efficacious participants made more accurate decisions than did low efficacious participants. Many factors could explain the rather inconsistent relationship between efficacy and accuracy. One is the nature of the decision-making test. Specifically, the test used a multiple choice format that afforded participants a 25% chance of getting the correct answer simply by guessing. Likewise, the multiple choice answers may have oversimplified decision-making accuracy in baseball. For instance, the best answer, according to the multiple choice options, may have been to throw the ball to first base. In reality, the best decision may have been much more complex, such as check the runner at second and then throw the ball to first base. Consequently, the task may not have adequately captured the complexity of decision accuracy in baseball, thereby causing a ceiling effect and preventing self-efficacy from significantly and consistently predicting performance. Similarly, the decision situations used in the study may have been too easy, allowing even participants with the lowest self-efficacy to make good decisions. Moreover, the task did not impose a time limit on decision making, such as exists in real-life situations.
Another issue related to the assessment of decision accuracy is the role of an individual's skills. Specifically, if a person believes that he or she does not possess the requisite skills (i.e., arm strength, throwing accuracy) to successfully execute an option, then he or she will not consider that option when making a decision (French et al., 1996). For instance, a person may recognize that the optimal solution is to throw the ball to third base, but may not believe that he or she has the ability to make that throw. As a result, that person does not consider throwing the ball to third, but rather picks the best option that he or she has the ability to perform. In this manner, decision accuracy should be judged on an individual basis rather than using a generic scoring system. Likewise, Bandura (1997) suggested that self-efficacy predicts performance only when participants have the requisite skills. Thus, as many of the participants in this study had very little baseball/softball experience, it is not surprising that self-efficacy did not consistently predict accuracy performance. Additionally, participants may not have accurately judged their decision-making capabilities. In baseball/ softball, the decisions and the physical execution of those decisions are closely linked. However, a majority of the feedback (e.g., visual, kinesthetic, verbal) that players receive is based on physical performance. Consequently, participants may not have been able to separate the physical and cognitive aspects of the sport. In other words, their reported efficacy ratings may have been based more on their physical abilities in baseball/softball than on their decision-making abilities. One final explanation regarding the weak, inconsistent self-efficacy--decision-accuracy relationship is simply that the two variables are not closely linked. For instance, Hepler and Chase (2008) also failed to find a significant relationship between self-efficacy and decision-making accuracy across three trials. However, the concordance between the efficacy and performance measures in that study was somewhat low (e.g., did not use situation-specific efficacy measures), whereas the current study failed to find a consistent relationship while employing concordant measures.
Moreover, this study sought to examine the relationship between self-efficacy and decision-making performance while controlling for the influence of baseball/ softball playing experience. Overall, the results indicated that self-efficacy was a significant factor in decision-making performance even after accounting for the influence of experience. The practical interpretation of the relationship between self-efficacy and decision speed did not really change with the inclusion of baseball/softball experience. Statistically speaking, the predictive nature of self-efficacy was reduced from 8 trials to 6 trials after controlling for experience. However, those two trials that failed to reach the desired statistical significance were still marginally significant (p < .10). Based on these results, it is possible to conclude that self-efficacy is a significant and consistent predictor of decision speed above and beyond the influence of past performance and years of baseball/softball experience.
Likewise, controlling for baseball/softball playing experience had no meaningful influence on the efficacy-accuracy relationship. Self-efficacy predicted decision accuracy on the same number of trials (4) in the analysis that controlled for playing experience as it did in the analysis that did not control for this variable. In addition, one trial was marginally significant (p = .072). Of the four trials on which self-efficacy significantly predicted decision accuracy, three were in the negative direction, which is congruent with the previous analysis. However, after controlling for experience, serf-efficacy was positively related to decision accuracy on one trial. For this trial, participants with high self-efficacy beliefs made worse, or less accurate, decisions than participants with low self-efficacy beliefs. This finding is in the opposite direction than was hypothesized. There are a few possible explanations for this surprising result. Perhaps participants were exhibiting the classic speed-accuracy tradeoff. In this manner, it is likely that participants focused on the most challenging aspect of the decision-making task. Accordingly, participants with high self-efficacy may have concentrated more on decision speed, thereby compromising their accuracy. Conversely, participants with low self-efficacy may have chosen to sacrifice speed in order to improve accuracy. A second explanation relates to overconfidence. According to Stone (1994), overconfidence can reduce effort and attention to strategy, which in turn have a negative effect on performance. So, perhaps participants with high decision-making self-efficacy became complacent on this trial, whereas low efficacy participants may have maintained or even increased their efforts. Overall, the findings suggest that playing experience does not significantly influence the relationship between self-efficacy and decision accuracy.
The current study extends the literature in several ways. First, it demonstrates that decision-making performance in sport is positively related to perceptions of one's capabilities. Almost all previous research on self-efficacy in sport focused on physical performance. However, this study provides evidence that self-efficacy is also an influential factor in cognitive performance in sport. This finding has practical applications for coaches, as they can use techniques such as video simulations or verbal persuasion to enhance their players' decision-making self-efficacy, and in turn, improve players' decision-making performance. Some techniques that research has found to be effective at increasing athletes' self-efficacy beliefs include positive self-talk, acting confidently, and using drilling and instruction techniques (Tonsing, Myers, & Feltz, 2004). Efficacy-enhancing techniques for decision-making might prove to be especially useful in improving the performance of pivotal positional players, such as quarterbacks or point guards, who frequently make decisions in time-pressured, dynamic situations.
Another way this study adds to the current literature is to suggest that self-efficacy is an important factor in decision-making performance even after controlling for experience. This finding has implications for novice and expert athletes alike. First of all, it suggests that the decision-making performance of any athlete, regardless of experience, can benefit from enhanced feelings of efficaciousness. Additionally, this finding might be useful in the development and mastery of physical skills in sport. For example, it is possible that increasing one's decision-making self-efficacy in sport will allow performers to allocate more cognitive resources to physically execute skills, thereby resulting in an increased rate of physical skill improvement.
There were several limitations to the study that must be addressed. First, the decision-making task involved videogame simulations of baseball situations. Based on the artificial nature of the videos, the experimental task may have involved decision-making demands that were somewhat different from the demands of an actual baseball game. Consequently, decision-making skills in baseball/ softball may not have transferred to performance on the experimental trials. In addition, participants were able to view scenarios multiple times, which is a luxury not afforded in real-life sport settings. The rating scale of self-efficacy itself may have been a limitation of the study. In the current study, we used an 11-point rating scale based on Bandura's (2005) guidelines for constructing efficacy scales. However, recent research has suggested that a condensed rating scale (e.g., 4 point) may be a more accurate, sensitive measure of efficacy beliefs (Myers, Feltz, & Wolfe, 2008; Myers, Wolfe, & Feltz, 2005).
One final limitation of the study relates to the study participants. First, both men and women participated in the study. However, the decision-making task involved only baseball situations. While baseball and softball are similar sports, there are significant differences, such as the distance between bases or ball size, that may affect decision making. Typically, men have more experience playing baseball, whereas women tend to be more involved in softball. Consequently, women may have perceived this baseball task as more novel than men, which may have influenced their self-efficacy and performance. Participants' age and experience is another limitation of the sample. Participants in this study were undergraduate students with various levels of baseball/softball playing experience. As such, the results of the current study may not generalize to youth sport participants or highly experienced elite athletes.
There are several future research directions that could provide more insight into the relationship between self-efficacy and decision-making performance in sport. First, it is important for researchers to strive to understand better and clarify the relationship between self-efficacy and decision accuracy. In particular, future research should incorporate an improved measure of decision accuracy. For instance, a more sensitive measure of decision accuracy would require participants to generate their own decisions rather than choose from among provided options. Future researchers should also strive to get a more sensitive measurement of self-efficacy by using a condensed 4- or 5-point scale, which is more psychometrically sound than expanded rating scales. Moreover, experts should rate each decision on an individual basis instead of using a generic scoring system. Using scenarios of varying difficulty and imposing a realistic time limit are other ways of obtaining a more accurate assessment of the efficacy-accuracy relationship. Additionally, future researchers should move away from artificial decision tasks and examine self-efficacy and decision-making performance in real-life sport environments. For instance, a longitudinal field study would provide a better understanding of how these factors change over time and across opponents. Finally, researchers should examine whether the relationship between self-efficacy and decision-making performance varies according to sport or position.
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(1.) Post hoc analyses indicated that viewing scenarios more than once did not provide an advantage in terms of decision accuracy.
(2.) On completion of the study, all scenarios were independently evaluated by a softball expert with 18 years of collegiate coaching experience. The softball expert agreed with the baseball experts regarding the best decision for each of the 10 scenarios.
(3.) The unidimensional structure of the self-efficacy questionnaires was assumed based on theory and the hierarchical structure. Factor analysis indicated that there were two underlying factors, which could be dubbed "easy" and "difficult." Conceptually, these two factors could be considered opposite ends of a "difficulty" factor, and all analyses were based on this assumption using a unidimensional measure of self-efficacy.
(4.) As performance feedback could influence subsequent ratings of self-efficacy, it is important to understand that self-efficacy was assessed immediately before performing each trial. Thus, any changes in self-efficacy resulting from performance feedback were captured in each of those measures.
(5.) Scenarios were presented in two different orders, with half of the participants completing scenarios in ascending order, from 1 to 10, and the other half completing the scenarios in the reverse order. As t tests revealed no significant differences between the groups on any of the study variables, all data were combined according to trial number.
(6.) Preliminary analyses indicated that several of the variables were significantly skewed. However, the substantive interpretation of the results did not differ between analyses involving transformed and raw variables. Therefore, all reported results are based on analyses conducted using the raw variables.
This research was supported in part by a Summer Research Fellowship provided by the College of Education, Michigan State University. Please address correspondence concerning this article to Teri J. Hepler, University of Wisconsin--La Crosse, 1725 State Street, Mitchell Hall, La Crosse, WI 54601.
Submitted: October 20, 2009
Accepted: January 16, 2011
Teri J. Hepler is with the Department of Exercise and Sport Science at the University of Wisconsin-La Crosse. Deborah L. Feltz is with the the Department of Kinesiology at Michigan State University.
Table 1. Descriptive statistics and correlations for self-efficacy, decision-making performance, and years of playing experience across all trials 1 2 3 4 5 6 1. Self- -- efficacy 2. Decision accuracy -.538 * -- 3. Decision speed -.471 * .144 -- 4. Decision accuracy PP -.169 .906 * -.050 -- 5. Decision speed PP -.096 -.083 .919 * -.144 -- 6. Years playing experience .574 * -.341 * -.422 * -.172 -.226 -- M 7.35 1.63 7.88 0.00 0.00 6.72 SD 1.72 0.31 4.40 0.38 0.80 5.87 Note. M = mean; SD = standard deviation; PP = past performance; lower scores for decision speed and decision accuracy indicate better (i.e., faster, more accurate) performance. * P <.01.…