6
The Effect of Goal Difficulty and Goal Orientation on Running Performance in Young Female Athletes Gershon Tenenbaum, Ron Spence, and Steven Christensen University of Southern Queensland Locke and Latham (1985)hypothesised that goals are more efficiently attained when they are perceived to be difficult but realistic to achieve. Furthermore, goal orientation is believed to be a strong determinant of effort exertion and adherence in performing tasks. To verify these two concepts, in real-life situations, 28 young female runners (13-16 years) were blocked (randomly assigned) to one of three goal-difficulty conditions over a 4-week period: easy, difficult/realistic, and improbable/unattainable. Short-term goals for each condition were set (1.25%. 2.51,and 3.75% improvement per week, respectively), as were long-term goals (5%, lo%, and 15% overall improvement, respectively).Participants completed the Task and Ego Orientation in Sport Questionnaire prior to the goal intervention. L o d e and Latham’s (1985)goal attainability hypothesis was not supported by the results of the study, as athletes enhanced their running performance equally regardless of their specific goals. Both ego and task orientations were found to be moderately but significantly correlated with running improvement rate. However, task and ego orientation were found to be significantly correlated to each other, indicating that, in this sample of athletes, these two orientations were not independent of each other. Together, they accounted for 30% of the improvement rate variance across 4 weeks. The additive effect of goal orientation and goal setting on athletic performance should be investigated for a longer period of time and with experienced athletes of a high calibre.
G
oal setting and goal orientation are two psychological constructs that affect the motivation of humans to achieve and to do well in a variety of tasks in which they are involved. Surprisingly, these two constructs have rarely been jointly investigated in the sport domain. This study is aimed at investigating the interactive effect of goal setting and goal orientation on running performance of young female athletes. Goal setting is generally viewed as a strategy that focuses attention, promotes increased intensity and effort, and encourages persistence in the pursuit of an individual’s aims. It assumes that performance is the result of purposeful action directed by conscious goals and intentions, which regulate human behaviour (Karoly, 1993; Locke & Latham, 1990). As a technique for enhancing task Performance it has been found to be highly effective in industrial and organisational settings (Locke & Latham, 1990; Locke, Shaw, Saari, & Latham, 1981). With the efficacy of goal setting well supported in the industrial and organisational literature, specific suggestions offered by Locke and Latham (1985) influenced sport psychologists to study the effectiveness of goal setting in both field and laboratory settings. This research has concentrated mainly on the areas of goal specificity, goal proximity, and goal difficulty (Weinberg, 1992). A review of these studies by Weinberg concluded that results in sport settings have been less than consistent; only some studies support Locke and Latham’s hypothesis that specific hard goals produce higher levels of performance than no goals or do-your-best goals. A recent meta-analysis of goal setting in sport and exercise by Kyllo and Landers (1995) indicated that moderately difficult goals resulted in a significantly higher effect size than difficult, improbable, and easy goals (0.53 vs. 0.09, -0.01, and 0.07, respectively). With respect to goal specificity, absolute goals resulted in greater effect size than relative and do-yourbest goals (0.93 vs. 0.27 and 0.38, respectively). For goal proximity, the mean effect sizes for combined short- and longterm goals and for short-term goals were significantly larger than the effect size for long-term goals alone (0.48 and 0.38,
respectively vs. 0.19) (see Kyllo & Landers, 1995, Table 2, p. 122). Males and females were found to be equally affected by goal-setting conditions, and adolescents responded with greater effect to goal setting than their younger and older counterparts. In addition, familiarity with the task reduced the goal setting effect compared to novel tasks, and competitive tasks produced higher effects than mastery and cooperative tasks. Meta-analytic reviews of the goal-setting literature (Mento, Steel, & Karren, 1987; Tubbs, 1991) found strong support for the effectiveness of setting specific and challenging goals. Specific difficult goals were found to produce stronger mean effect sizes for performance enhancement and productivity than did easy goals, no goals, or do-your-best goals. Thus, it may be concluded that, to be most effective, goal setting practices should (a) consist of long- and short-term goals, (b) be moderate to difficult, (c) involve competition, and (d) be absolute (see Kyllo & Landers, 1995. Table 3, p. 124). However, these conclusions are limited in their generalisability to the sport setting, as most of the participants included in the Kyllo and Landers meta-analyses were not competitive athletes. Athletes are more familiar with the task and are more knowledgeable about their abilities and limitations than nonathletes in sport-related tasks. Locke and Latham (1985) have recommended that set goals should be difficult, yet realistic and attainable. It was believed that unrealistic g o d s should be avoided as they lead to continuing failure, decreased motivation, and deterioration in subsequent performance. The assumption of goal attainability has influenced researchers to recommend to physical educators and coaches that performance goals be realistic (Gould, 1986). Despite the zero effect-size reported by Kyllo and Landers (1995) f o r improbable goals, limited research has been conducted on goal difficulty/attainability in sport settings. For example, some studies (Weinberg, Bruya, & Jackson, 1985; Weinberg, Bruya, Jackson, & Garland, 1987; Weinberg. Fowler, Jackson, Bagnoll, & Bruya, 1991) have failed to find a decline in performance when unreachable goals are set. A
Address for correspondence: Gershon Tenenbaum. Department of Psychology, Univenit). of Southern Queensland, Toowoomba QLD 4350, Australia E-mail:
[email protected] Australian Journal of Psychology Vol. 51, No. I, 1999 pp. 6-1 I
The Effect of Goal Difficulty and Goal Oriencacion on Running Performance in Young Female Athletes
recent study by Bar-Eli, Tenenbaum, Pie, Btesh, and Almog (1997) confirmed that specific very difficultlunattainable goals do not result in deterioration in performance of sit-ups. In contrast, performance improves, but not to the extent of difficult/realistic goals. Goal-setting theory and research have also focused on the effect of goal proximity on performance. However, it should be noted that both proximity and difficulty are somewhat problematic terms. The time to attain goals in measurable sports such as track and field may last a whole training season, and improvement by 0.5% may be perceived as very difficult for top athletes. However, at non-elite levels of sport and with young competitive athletes, the relationship of goal difficulty to performance found in non-athletes and employees in business organisations still apply. In this study, therefore, young female competitive athletes were given improvement scores in absolute terms though difficulty level has been determined in percentage terms. This approach has been chosen because the young runners were in their earlier stages of training and could substantially improve their running times. As suggested by Burton (1989) and Hall (1990), goal setting may interact with goal-orientation perspectives to elicit a motivational state that affects performance. The goal perspective approach to understanding achievement motivation behaviours was first introduced in educational settings (Ames, 1984a. 1984b, 1992; Dweck, 1986; Dweck & Elliot, 1983; Nicholls, 1984a. 1984b. 1989, 1992) and later applied extensively to the sport domain (see Duda, 1993, for a review). This approach assumes that there are two predominant goal perspectives through which people perceive success and failure, and accordingly judge their level of competence. These two perspectives are task and ego. A task-oriented goal perspective is associated with behaviours such as skill improvement suited for individual capabilities, task mastery, working hard, and persistence. As Duda (1993) surnmarised, “it is assumed that a task involved goal perspective establishes the basis for maximal motivation and adaptive behaviours” (p. 422). An ego-type goal orientation consists of demonstrating ability in comparison to others. Thus, competence is achieved when superiority is exhibited. In situations where ability cannot be demonstrated, effort will be reduced to minimal levels and a drop of confidence will be evident (Jagacinski & Nicholls, 1990). In sport, it was argued that athletes may be both ego- and task-oriented (Duda, 1989; Duda & Nicholls. 1992; Nicholls, 1989). In other words, athletes are driven by several motives at the same time (i.e., they invest effort to accomplish their selfstandards, strive to win, and avoid failure at the same time), and perceive success and failure in relation to both their own and their opponent’s skills, without developing negative affect and attitudes that have negative consequences in the future (i.e., drop-out and lack of consistency). The joint effect of goal-orientation and the nature of goal setting in the realm of competitive sports should be further investigated. In this respect, age and experience should be considered as important mediators. Young athletes can improve substantially during a relatively short period of time, while experienced athletes would require a great deal of effort to improve their results by a millisecond. This has significant implications on the nature of the goal setting as well as on the goal-Orientation style. In the present study, the additive effect of setting goals and goalorientation is examined in young female runners. The two goal orientations, task and ego, though somewhat related, were consistently found to be independent of each other (Dweck & Leggett, 1988; Nicholls, 1989). This independence has been reported in educational, academic, and sport settings (Duda & Nicholls, 1992). Based on several studies,
7
Duda (1993) concluded that those individuals with either high ego or high task orientation can be competitive, but they may have different perceptions of the competitive experience. While “winning” is important to both, the ego-oriented runners are concerned more with the outcome and the task-oriented runners are concerned more with the process. However, type of goal orientation, and its relationship to goals set, has not been examined with regard to athletes. The purpose of this study was to examine whether goal orientations adds to the effect of goal setting on running performance (i.e., achievement gain) in conditions that mimic the real world of competitive crosscountry running. METHOD Participants
The participants in this study were 28 female secondary-school middle-distance runners training for school and interschool crosscountry competition. All were students at the same boarding school and were primarily from a middlehpper-class background. Their ages ranged from 13 to 16 years with a mean of 14.6 (SD= 1.2). The runners were selected to represent their school in competitions and were considered by their coach as having a high potential for improvement. They were described by their coach as very disciplined students and dedicated athletes. Manipulation/lntervention
The literature on goal setting suggests that long- plus shortterm goals result in the most substantial improvement in endurance performance (Kyllo & Landers, 1995). Therefore, in this study of female distance runners, the goal-setting conditions were manipulated to produce three groups of runners. each pursuing short-term goals and a long-term goal. All participants were asked to compete together on a 2.3-km course during a training session. In this pre-test, all athletes were instructed to produce their best performance. The resulting baseline times for the pre-test were ordered by rank and participants were assigned to one of three goal conditions by block randomisation. Estimates of goal difficulty were derived from the records of an athletics coach who had conducted timed trials on the 2.3-km course for many years with female athletes aged from 13 to 17 years. It was calculated that a 10% improvement in performance during the 4-week period could be viewed as an attainable and realistic, yet challenging, goal. Similarly, a 5% improvement was assumed to be an easy goal, and a 15% improvement over a 4-week period was viewed as an improbable goal. These three goal conditions conform to Locke and Latham’s (1985) definitions of easy, challenging. and unrealistic goals. Long-term Plus Short-term Goals
All runners were assigned long-term and short-term goals. One week after the baseline data were collected, the runners were given a time in seconds that represented improvement to be accomplished by the end of 4 weeks. In addition, they were given a time (in seconds) that specified their expected improvement at the end of each week. The specific goal conditions were as follows:
Easy goals. An improvement time in seconds that was 1.25% faster per week than the participant’s baseline. This time represents a 5% improvement to be achieved by the end of the 4week testing period. Difficult and realistic goals. A goal of 2.5% faster per week as described above, representing a 10% improvement from baseline.
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Genhon Tenenbaum, Ron Spence. and Steven Chrisrensen
Zmprobable/unattainable goals. A goal of 3.75% faster per week, representing a 15% improvement. No control group or placebo was used in this study. The main concern of the study was not whether goal setting was effective, but rather how it affected the young competitive athletes. Therefore, effects of each goal condition were compared to baseline values. Assessment
Participants were asked to complete the Task and Ego Orientation in Sport Questionnaire (TEOSQ;Duda & Nicholls, 1992). The TEOSQ consists of two subscales that measure individual differences in the tendency to be task- and ego-oriented. The TEOSQ has been used in this study to examine how ego and/or task orientation, as psychological dispositions, are correlated with the rate of running improvement, and how much they add to the improvement variance when taking the goal setting into account as a manipulated motivational technique. Participants were asked to think when they felt most successful in sport and then respond to 13 items that indicate a task orientation or ego orientation. For example, the item “I feel most successful in sport when I work really hard’ is indicative of task orientation, while the item ‘‘I feel most successful in spon when I’m the best” is indicative of ego orientation. Responses are evaluated based on a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Mean scale scores (i.e., sum of item responses / number of items) for task orientation and ego orientation were calculated for each participant with a range from 1 (low) to 5 (high). Duda (1989) reported that the internal consistency coefficients (Cronbach’s alpha) of the questionnaire were 0.82 and 0.89 for task and ego scales, respectively, and were generated from a large sample of basketball players. For university athietes in other sports, the alpha values were 0.62 and 0.85 respectively. Finally, a manipulation check of the perceived difficulty of assigned long- plus short-term goals was conducted by administering a three-item questionnaire at the end of the study, as described by Weinberg et al. (1991). The first question asked for a rating of the perceived difficulty of the participant’s goal. Responses were scored on a scale ranging from 1 (extremely easy) to 4 (extremely dificulr). In addition, participants were also asked the following questions: “Did you reach the goal set for you?” and “Did you set any other goals beside the ones given to you?”. Answers to these questions were yes or no. These questions were used to ensure that the specific goals set for the participants were also perceived and accepted according to the various difficulty levels, in line with the basic propositions of Locke (1968) and Locke et al. (1981). Procedure
Informed consent was obtained from the runners and their coaches, and the runners were told that the purpose of the experiment was to measure improvement over a 4-week period. No inducements were offered to participate, and participants were told that they were free to withdraw from the study at any stage. Times for running a 2.3-km road course were the primary dependent measure. Shortly after the pre-test, the runners were taken aside individually and given their goals, which were stated verbally by the experimenter. All participants were aware that running times were individually assigned based on a percentage improvement of their first time for the course. The runners expressed verbally that they accepted the goals and committed to achieve them. Before each run, and prior to a 5-minute warm-up. the runners were reminded of their goal times. The runs were set-up in a competitive fashion similar to official crosscountry runs. As each runner completed the 2.3-km course, her time was called
out and recorded. The results were again available to participants as soon as the last runner completed the course. Following each run, participants were informed whether they achieved their short-term goal and were reminded of the coming week’s target for improvement. To prevent social comparison effects, the athletes were asked to keep their assigned goals a n d not s h a r e information with their colleagues. To t h e best of our knowledge, the runners maintained this request for confidentiality. The TEOSQ was administered to the athletes 1 week before the running baseline was determined. RESULTS
Manipulation Check
(x’)
Non-parametric analyses were performed to gauge the participants’ perception of the difficulty of the goal conditions to which they were assigned. Participants tended to perceive the difficulty of their assigned goal conditions according to the three levels of difficulty, and in line with the basic propositions postulated by Locke (1968) and Locke et al. (1981). However, the relationship between goal-setting conditions and perceived degree of difficulty was not statistically significant, X’(n = 28) = 10.75, p < .lo.Nevertheless, the majority of participants in the easy goal condition (5% + 1.25%) reported that their goal was very easy (11%) or easy (45%). with 22% finding it difficult and 22% claiming it was very difficult. Of those participants given a difficuWrealistic (10% + 2.50%)goal, 26% found it easy, 44% found it difficult, and 30% found it was very difficult. In the difficultlunattainable (15% + 3.75%) condition, the majority (56%) found their goals very difficult, and 44% found them difficult. Only one athlete reported setting a new goal after attaining the set goal. It should be noted that, although these resuIts do not fit sufficiently to the originally assigned goal-difficulties, runners’ perceptions were given at the end of the experiment and were based on their real experiences. These results, however, do not reduce or eliminate the fact that the athletes accepted the specific goals given to them and were committed to achieving them. With respect to perceived goal attainability, 76.5% of the athletes in the difficulthnattainable condition reported that they had not attained the goal set to them, compared to 48.2% in the difficult condition and only 7.4% in the easy condition. These differences were statistically significant, X’(n = 28) = 35.4, p < .001. Effect of Goal Difficulty on Running Times
To determine if there were initial differences between the groups assigned to the three goal-setting conditions on a block random procedure, a one-way analysis of variance (ANOVA) was conducted on the baseline performance times. Results indicated no significant between-group differences, F (2.25) = 0.25, p < .78. Performance means and standard deviations for running times (in seconds), representing three goal conditions for each of the 4 weeks of training, are presented in Table 1. Runners assigned difficult/realistic goals (10% + 2.5%) improved by an average of 9.82%,with the easy (5% + 1.50%) group improving by 6.88%,and the difficulthmprobable (15% + 3.75%) group improving by 6.52% when the criterion was their personal best time during the study. However, these differences were not found to be statistically significant. Table 1 indicates that the runners assigned easy goals improved on the average by 2.76% after 1 week of practice and consequently by 6.29%. 4.81%. and 4.04%after Weeks 2. 3, and 4 respectively, compared to baseline measures. The runners assigned difficult/realistic goals improved by 4.048, 6.36%. 6.78%. and 7.24% from Week 1 through Week 4. Similarly,
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The Effect of Goal Difficulty and Goal Orientation on Running Performance in Young Female Athletes
Table I Means and Standard Deviations for 2.3-km Running Times by Goal Setting Condition (in Seconds) at Each W e e k .-
~___
Wk 4
Baseline
Wk I
Wk 2
Wk 3
M 674.1 I
63 I .67
64 I .67
SD 76.92
655.44 87.28
ao.74
83.45
646.67 98.22
M 707.70 SD 110.89
679. I0 100.87
662.70 78.22
659.70 106.34
656.40 aI3 3
15% + 3.75%
M 69 I .8 I
SD 114.15
656.78 I 12.21
66 I .56
(n = 9)
6ao.89 115.32
663.67 98. I 4
672.07
650.7 1
654.00
98.62
88.84
95.10
Goal Condition
___
Easy 5% + 1.25% ( n = 9)
Difficuldrealistic 10% + 2.5%
(n = 10)
Improbable
Total (n
= 28)
M 691.82 99.55
SD
102.77
655.60 89.25
the runners assigned to improbable goals improved by 1.57%, 5.06%, 4.37%. and 4.06% compared to baseline measures. These results may indicate more consistent and substantial continuous improvement by the runners who were assigned difficultlrealistic goals, but the goal-by-trial (week) interaction was not statistically significant. A 3 x 4 (Group x Trials) repeated measures analysis of covariance (ANCOVA) was applied to the 4 weeks of running results. Goal condition was treated as a between-subjects factor and weeks of practice as a repeated within-subjects factor. The baseline times were used as a covariate (despite the insignificant initial differences amongst the three groups). This analysis was conducted to examine the possible interaction of goal difficulty and time period on running performance. No significant differences were found for goal difficulty or for the time period by goal difficulty interaction. However, the whole sample of runners improved on the average by 5.23% after 4 weeks. Using personal best times achieved during the 4 weeks, the improvement averaged 7.82%. This personal best gain was statistically significant, F (3, 75) = 3 . 6 1 , ~c .02. Additive Effect of Goal Orientation and Goal Condition on Running Performance
Prior to examining the additive effect of goal-orientation and goal condition on running performance, the three groups were compared on both task orientation and ego orientation, using one-way ANOVA. As expected, no significant differences were found between the three groups of runners. Pearson product-moment correlations were calculated for the task orientation, ego orientation, weekly running times, and final rate of improvement scores across the sample. The correlations between both ego and task orientations and performance in each of the 4 weeks were low and nonsignificant @, > .05). However, significant correlations were found between
the ego and task orientations and improvement rate scores. The improvement in running times for the 2.3-km course (best time subtract baseline time) correlated significantly ( p < .05), -0.39 and -0.38 with ego and task orientations, respectively. The results also showed that task and ego orientations were significantly (r = 0.53. p < .05) correlated to each other for this group of athletes. Finally, ego and task orientation were entered in a linear hierarchical regression analysis to estimate the additive effect of the three goal conditions and two goal orientations on the rate of running improvement. The analysis (shown in Table 2) revealed that all three goal-conditions had an equivalent effect on the improvement in running times. This, however, was indicated by the ANOVA results. The ego and task orientations added 16% and 14% to the explained variance of the improvement rate of the runners respectively, indicating that both are important determinants of running improvement under conditions of goal setting. DISCUSSION
The purpose of the present study was to examine the effect of goal difficulty and the additive effect that goal orientation has on running performance. Based on a review of published research, it was hypothesised that difficult/realistic goals would result in greater performance gains than easy goals or difficultlunattainable goals (Locke & Latham, 1985). It was also expected that task orientation would contribute more to the improvement rate of the athletes’ performance than an ego orientation. The findings of the present study fail to support the goal attainability hypothesis of Locke and Latham. which was supported by Kyllo and Landers’ (1995) meta-analysis findings. The goal-attainability hypothesis proposes that improbable goals and easy goals do not enhance performance. This hypothesis has been supported by Burton ( 1992). The present results, however, are in line with those reported by Weinberg et al. (1985), Weinberg et a]. (1987), and Weinberg et al. (1991). who found that setting very difficult and unattainable goals does not result in performance deterioration in physical tasks. In our study with young female competitive runners, all participants improved their initial baseline running times for a 2.3-km course independently of the assigned difficulty of long short- and long-term goals. The 5% + 1.25% group improved their baseline times by a mean of 0.59 of a standard deviation, the 10% + 2.5% improved by 0.66 of a SD,and the 15% + 3.75% group improved by mean of 0.41 of a SD.These effect sizes were not significantly different from each other due to the large variation in each of the three groups of the study. However, they were significantly and substantially higher than baseline measures (see Table 1). The overall standardised improvement of the three groups was 0.56 of a standard deviation. For young athletes, this improvement can be considered as a substantial one, taking into consideration the relatively short time of practicing goal-setting conditions. The goalattainability hypothesis may be shown to have some practical importance with young athletes when there is longer exposure
Table 2
Hierarchical Regression Analysis with Goal Condition and Goal Orientation (Ego and Task) as Predictors and Time Improvement as a Dependent Variable Mult R
Mult R2
RZ Change
F change
P
I
Goal condition
.oo
.oo
-
0.00 I
.97
2
Ego orientation
.40
.I6
.I6
4.75
.04
3
Task orientation
.54
.30
.I4
4.88
.04
Step
Variable ~
9
~~~
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Gershon Tenenbaum. Ron Spence. and Steven Christensen
to goal conditions. During longer periods of practice with predetermined goals, runners evaluate how realistic are the goals that were set for them. This internal evaluation process may affect their practice motivation to meet the goals, and subsequently their performance. It should be taken into consideration that the meta-analysis conducted by Kyllo and Landers (1995) consisted of effect sizes which were calculated against do or do-your-best control participants who were mostly not athletes. The results in the present study, pertaining to young female athletes, show that easy, difficultlrealistic, and difficultlunattainable goals are all equally beneficial to the athletes, though a tendency toward such conclusions is unavoidable in this study. It seems, therefore, that the notion that goals, when set too high, can be stressproducing, and consequentiy lead to drop in motivation as a result of failure to maintain high perceived ability and impair performance (Burton, 1989) was not verified in this study. Studies in sport and exercise concerning goal specificity concluded equivocal results (see reviews by Weinberg, 1992, 1994), with only some of the studies supporting Lock and Latham’s (1985) claim that “specific hard goals” produce higher levels of performance than easy or very hard, taking into consideration conditions of do or do-your-best goals. A recent study by Bar-Eli et al. (1997) used a design in which goal attainability/difficultywas nested within goal duration, testing high-school students in sit-up gains. They found that all specific groups performed better than “no” goals and do-yourbest groups, and the difficuWrealistic group gained more than the other specific groups (i.e., easy and very difficulthnrealistic). In this study, nonspecific goals were not assigned, but the results, though not significant, are similar to those reported by Bar-Eli et al. There are several limitations in the present study. First, a control group has not been employed. The aim here was to compare the three goal conditions rather than to compare them to a control group (i.e,, the effect of training). The second limitation is that some athletes failed to perceive the goals in line with the researcher’skoach’s perception. This limitation is inherent in almost every study where goals are set by external sources. Furthermore, even if athletes set goals by themselves, they may perceive them differently at the end of the practice process. The athletes in this study have reported acceptance, commitment, and no alteration of the goals given to them. These are the most important psychological behaviours required for a successful goal-setting program (Locke & Latham, 1985). Thus, despite these limitations, the results of the study are believed to be sound. Several studies claim that skill improvement and task mastery are more characteristic of people with high task orientation (see Duda, 1993; Roberts, 1993 for reviews). These claims were based mainiy on works that have been conducted in educational settings (Nicholls, 1992) and recreational-type activities (Duda & Nicholls, 1992), but less with competitive athletes (Burton, 1989). The results of this study show that the more the athlete perceived winning as important, the stronger she perceived her dedication to the task, and vice versa. This relation between the two orientation types are in line with the results reported by Duda (1989), Duda and Nicholls (1992), and Nicholls (1989). It was also confumed that higher task and ego orientations were associated with a greater improvement in the female athletes across all three assigned goal-setting conditions. This is important, considering that the goals given to the athletes were personal (i.e.. task-oriented) while the performance took place under the competitive (i.e., egooriented) conditions typical of sport settings. Thus, it seems that athletes should be high in both task and ego orientation in order to achieve more in the real world of sport. However, one
should be cautious when interpreting the correlations between task and ego orientation with the rate of running improvement. Correlations in the magnitude obtained (0.39 and 0.38) are indicative of about 16% shared variance. Overall, the correlations between ego and task orientations with performance gains can be considered as weak relationships, smaller than Lerner and Locke (1995) reported from using the Sport Orientation Questionnaire (SOQ; Gill & Deeter, 1988) in studying non-athletes in a sit-up task. In their study. the SOQ was significantly (0.41) related to sit-up performance, but its effects were fully mediated by personal goals and self-efficacy. In the present study, the athletes reported that they did not set personal goals in addition to the goals they were given, and self-efficacy was not measured. In addition, one should keep in mind that SOQ and the TEOSQ were derived from different theoretical perspectives, and therefore cannot be sufficiently compared. Though goal conditions have not affected the improvement rate of the young runners in this 4-week study, ego and task orientation jointly accounted for 30% of the variance in their running improvement times (see Table 2). This suggests that young runners with high levels of both ego and task orientation, who practice regularly and are given goals in absolute terms, improve more than runners with low ego and task orientation. This effect, though in accordance with the literature, should be further examined over a longer period, such as a whole season or even several seasons, and include athletes who have reached high standkds of performance. Moreover, the results of this study call for more attention to Burton’s (1992) competitive goal-setting model, and to claims made by Duda (1993). In short, when athletes can improve substantially, both ego and task orientations are of substantial importance. Young athletes strive to improve skills, master the task, win, feel pride and accomplishment, and avoid failure, ego-threat and negative affect at the same time. Goal orientation and goal conditions may share a unified concept when applied to competitive spon. The goal-setting techniques and the goalorientation constructs should be further studied in line with Burton’s (1992) integrative concept of the two. However, such an integrated concept may be better examined when participants vary substantially in orientation when performing a task, for example, in competitive running as opposed to recreational running. Once the integrated goal model is applied to competitive sport, more sensitive paradigms and instruments should be developed. Further, more than one goal orientation can charactense the athlete, and may result in different behavioural consequences. Such studies are very much encouraged. REFERENCES Ames, C. (1984a). Competitive. co-operative, and individualistic goal structures: A motivational analysis. In R. Ames & C. Ames (Eds.). Research on motivation in education: Student motivation (pp. 177-207). New York Academic Press. Ames, C. (1984b). Conceptions of motivation within competitive and noncompetitive goal structures. In R. Schwarzer (Ed.),Self-relared cognitions in m i e t y and morivorion (pp. 205-241). Hillsdale, NJ Erlbaum. Ames, C. (1992). Achievement goals, motivational climate, and motivational process. In G.C. Roberts (Ed.). Motivation in spon and exercise (pp. 161-176). Champaign, IL:Human Kinetics. Bar-Eli, M., Tenenbaum, G., Pie, J.S., Btesh, Y.. & Almog, A. (1997). Effects of goal difficulty, goal specificity and duration of practice time intervals on muscular endurance performance. Journal of Sport Sciences, IS, 125-135. Burton, D. (1989). Winning isn’t everything: Examining the impact of performance goals on collegiate swimmers’ cognitions and performance. The Spon Psychologist, 3, 105-132. Burton, D. (1992). The JeckylVHyde nature of goals: Reconceptualizing goal setting in sport. In T.H. Horn (Ed.), Advances in spon psychology (pp. 267-297). Champaign, IL:Human Kinetics.
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Australian journal of Psychology
-April I999
