Precompetition anxiety levels are assumed to moderate athletic performance. Unfortunately, cross-sectional and nomothetic research designs have often shown non-significant findings; intra-individual variability may be a contributing factor. The extent of variability in precompetition anxiety and self-confidence responses as related to golf performance and trait measures were therefore examined using an idiographic approach. Individual patterns of variability were found for Cognitive and Somatic Anxiety and Self-Confidence scores yielded prior to the games played. Variability in Somatic Anxiety was significantly related to variability in golf performance. Players low in anxiety variability scored significantly higher on Private Self-Consciousness. The findings suggest the influence of anxiety and self-confidence on performance may be better understood when trait characteristics of the individual are also considered.
Sport psychology researchers have assumed that an individual's anxiety level experienced immediately before a competition (i.e., state anxiety) has a moderating effect on subsequent athletic performance (cf. Martens, 1971). Early research suggested the relation between anxiety and sport performance is best described by an inverted-U function (Landers & Boutcher, 2001; Sonstroem & Bernardo, 1982). Reviews of both general and sport anxiety related literature have, however, failed to find support for the inverted-U hypothesis (Gould & Krane, 1992; Gould & Udry, 1994; Hardy, 1990; Jones, 1995; Naatanen, 1973; Neiss, 1988).
One of the primary criticisms of the inverted-U hypothesis and other traditional models is they do not account for individual differences in anxiety responses often observed in athletes (Fazey & Hardy, 1988; Jones, 1995; Raglin, 1992). This lack of efficacy has led to the development of sport specific explanations of anxiety and athletic performance. Theoretical approaches such as Hardy's Catastrophe Model (1990, 1996), Hanin's Individualized Zones of Optimal Functioning Model (IZOF, 1978, 1997), and Kerr's Reversal theory (1990, 1997) all explicitly incorporate the concept of individual differences. Although these models are somewhat different conceptually, each indicates that the optimal level of anxiety for performance can vary considerably across athletes.
While mounting evidence indicates athletes in many sports vary significantly in the level of anxiety that benefits performance (Raglin & Hanin, 2000; Turner & Raglin, 1996), the reasons for this variability remain poorly understood. Indeed, a primary criticism of the IZOF model is that it provides no explanation for why comparably skilled athletes competing in the same sport would vary in their precompetition anxiety responses (Gould & Tuffey, 1996). A related issue that has received little attention is intra-individual variability of precompetition anxiety responses (Gould, Greenleaf, & Krane, 2002). Not only do athletes differ from one another in the level of anxiety experienced before a given competition, they also are likely to exhibit differences in the range of variability of precompetition anxiety across competitions. Some athletes may be consistent in precompetition anxiety values (e.g., either all low, medium, or high) while others may differ considerably from competition to competition (Raglin & Hanin, 2000). In cases where the variation in anxiety intensity from competition to competition is narrow, it is likely that the net impact of anxiety on performance would be minimized, whereas anxiety is likely to be a more influential factor in athletes displaying wide variability in anxiety. Unfortunately, research to date has been largely limited to one or two assessments of precompetition anxiety and there has been an absence of work in which precompetition states are assessed repeatedly throughout an entire competitive season, despite the call for such research (e.g., Gould et al., 2002).
The traditional perspective in sport psychology (e.g., Landers & Boutcher, 2001) is that athletes with higher trait anxiety generally will have a relatively lower optimal precompetition state anxiety intensity level than would athletes scoring lower in trait anxiety. Research incorporating groups of individuals has, however, shown that trait anxiety exhibits moderate positive correlations with both precompetition (state) anxiety and optimal anxiety (Eysenck, 1992; Raglin & Turner, 1993; Turner & Raglin, 1996). But the relation between trait and state anxiety scores were more similar for some athletes than for others, thereby suggesting some mediating factor or factors of a psychological nature.
Psychological factors of a more stable nature (i.e., traits) have indeed been associated with precompetition state responses. In a study with elite golfers, measures of self-consciousness and trait anxiety interacted with precompetition mood states, as well as subsequent golf performance (Hassmen, Koivula, & Hansson, 1998).
The present study was conducted to examine the extent of variability in precompetition anxiety, as related to actual golf performance, among elite golfers in their most important competitions across a full season. Golf was chosen because it is traditionally presumed that success in golfing is associated with low levels of anxiety, in particular somatic anxiety due to the fine motor skills involved (cf. Landers & Arent, 2001), and by extension low variability in anxiety responses. It was hypothesized that high variability in anxiety and self-confidence would influence golf performance more negatively than consistent high, medium or low anxiety intensity. In addition, and based on Hassmen et al. (1998), self-consciousness, social anxiety, and trait anxiety were assessed in an effort to explore to what extent these trait characteristics were associated with precompetitive anxiety scores.
Eight of the ten male members of the Swedish National Amateur Golf Team (mean age 21.0 years, range: 18 to 23 years) volunteered to take part in this study. The procedures and instruments were described to the golfers, who acknowledged this by signing a written informed consent. All procedures were consistent with National standards for non-invasive research for humans. The golfers were followed during an entire competitive season running from March to October. During this time, they participated in an average of 27 competitions (SD = 6.6). However, the present report focuses on the 10 games that each player judged to be the most important.
Instruments and Procedures
Between two and four weeks before the competitive season, each player individually completed two trait inventories. Descriptions of these instruments follow.
Sport Competition Anxiety Test (SCAT, Martens, Vealey, & Burton, 1990). This 15-item measure of trait anxiety is designed to measure the predisposition to respond with anxiety in competitive sport situations. The SCAT has been regarded to be a sensitive and valid indicator of sport related anxiety (Martens et al., 1990).
Self-Consciousness Scale (SCS, Fenigstein, Scheier, & Buss, 1975). This instrument, with 23-items, consists of three subscales. One of the subscales measures Social Anxiety, which relates to persons who commonly react with shyness, embarrassment, and anxiety in the presence of others. The other two subscales measure Private and Public Self-Consciousness respectively. The former relates to an individual's tendency to attend to covert aspects of the self such as thoughts and feelings. The latter relates to the disposition to attend to aspects of the self that are publicly observable (e.g., appearance and behavior).
Competitive State Anxiety Inventory-2 (CSAI-2, Martens, Vealey, & Burton, 1990). Before each competition played during the season, the 27-item CSAI-2, assessing Cognitive and Somatic Anxiety together with Self-Confidence, was completed. The golfer was instructed to complete the inventory "immediately" before each game played. In reality, this meant that the inventory was completed about 45 minutes (range 30-60) before the individual played his first ball of the game. After each game, the players also added their golf score on the form.
Table 1 displays the trait scores for each individual; also shown are means and standard deviations for the whole group. The players' Trait Anxiety, represented by their SCAT scores, ranged between 15 and 27. The group mean (20.3) is comparable to that reported for athletes in individual sports and approximately two units higher than the value reported for athletes in team sports (Martens et al., 1990). The mean scores on the three subscales of the SC Scale (Social Anxiety, Private, and Public Self-Consciousness) compare closely to norms presented by Fenigstein et al. (1975), and replicated for Swedish college students (Nystedt & Smari, 1989).
State anxiety and self-confidence: Intensity, intra-individual variability, and performance
Table 2 displays Pearson correlation coefficients between means and the means of the standard deviations for Cognitive and Somatic State Anxiety, Self-Confidence and Golf Scores.
The correlation analysis showed that mean scores of Cognitive State Anxiety were positively related to mean scores of Somatic State Anxiety ([r.sub.xy] = .79), and negatively related to Self-Confidence ([r.sub.xy] = .77). The only variable significantly related to mean Golf Score was mean scores of Self-Confidence ([r.sub.xy] = .73), indicating that a higher Self-Confidence was related to a lower Golf Score. A significant correlation (p<.05) was also obtained between intra-individual variability in Somatic Anxiety and intra-individual variability in Golf Score ([r.sub.xy] = .82).
To further explore the latter finding, players were grouped according to the degree of variability they displayed in Anxiety and Self-Confidence scores. Those with a standard deviation [greater than or equal to] 2.5 were classified as High variability individuals, those with standard deviations [less than or equal to] 1.5 as Low, and those in between (SD > 1.5-SD < 2.5) as Medium variability individuals, see Table 3.
Independent sample t-tests confirmed that the High and Low variability groups in all three CSAI-2 subscales differed significantly from each other (Cognitive Anxiety: t(5) = 3.9, p < 0.01; Somatic Anxiety: t(6) = 8.4, p < 0.001; Self-Confidence: t(4) = 4.7, p < 0.01). Two players, #2 and #6, were consistently low, and two consistently high (#4 and #8) in Anxiety and Self-Confidence variability. The remaining four players showed a somewhat more irregular pattern of variability, for example #1 who was labeled as High on Cognitive Anxiety and Self-Confidence, but as Low on Somatic Anxiety. When typical anxiety intensity levels were considered among players with similar intra-individual variability, inter-individual differences were also evident. For example, #2 had a consistent pattern of relatively low anxiety intensity scores and high self-confidence, whereas #6 showed an opposite pattern with higher Cognitive and Somatic Anxiety scores as compared to the Self-Confidence score.
Intra-individual variability of state anxiety and trait scores
The final analysis used the information displayed above to divide the individuals into high and low variability groups on account of the predominant variability pattern. That is, individuals that in Table 3 were labeled predominantly as high variability individuals (#3, 4, 5, and 8) formed one group, and the predominantly low variability individuals (#2, 6, and 7) formed the other group. Player #1, who displayed an irregular pattern (High variability on two subscales and Low on one), was omitted from the analyses. Independent sample t-tests were then performed to investigate if the High and Low groups displayed differences in trait scores. The results indicated that the players in the High group scored significantly lower on Private Self-Consciousness than the players in the Low group.
The present study examines variability in state anxiety and self-confidence scores in relation to golf performance and trait measures of anxiety and self-consciousness using an idiographic approach. The results indicate that both intra-individual and inter-individual differences of intensity and variability in precompetition anxiety are considerable among the players. Some individuals displayed consistently high scores on precompetition state anxiety, others consistently had low scores, and the third group exhibited a considerable intra-individual variability with anxiety scores in a more moderate range. This degree of variation is intriguing given the homogeneity of the sample; all participants were members of the Swedish National Amateur team competing in the same events.
Consistent responses were observed for some variables. A significant correlation was found between variability in Somatic Anxiety scores and Golf performance, a relationship that may have reflected the involvement of fine motor skills involved in golf and benefit of possessing low variability in somatic anxiety responses (cf. Landers & Arent, 2001; Martens et al., 1990). These results indicate that the intensity of precompetition anxiety, examined as a mean score for an intact sample, and intra-individual variability in precompetition anxiety, provide unique information when viewed in relation to golf performance. The findings are in accordance with previous results proposing that considerable variability in precompetition anxiety as well as the level of anxiety associated with optimal performances should be observed in similarly skilled athletes competing in the same sport (Hanin, 1997; Raglin, 1992; Turner & Raglin, 1996).
This perspective is largely based on Hanin's (1986, 1997) Individual Zones of Optimal Functioning model, which has been criticized for not providing an explanation for this variability (e.g., Gould & Tuffey, 1996). The present findings do indicate that trait factors may in part be responsible for this variability, but in a more complex manner than has been proposed by some researchers in sport psychology (e.g., Martens et al., 1990). When examined with an idiographic approach, our results clearly indicate an inconsistent pattern between Trait Anxiety and State Anxiety scores, where some players show fairly similar Trait Anxiety characteristics but still display very dissimilar State Anxiety responses. Our findings thereby support the contention that trait anxiety seldom can be used as an accurate predictor of state anxiety (cf. Man, Stuchlikova, & Kindlmann, 1995), and that other aspects of the construct of anxiety must be assessed to yield a more comprehensive understanding of both the intra-individual variability in precompetition anxiety and its influence on performance. This is supported by the observation that the Social Anxiety scores of the individuals vary substantially as well. By combining the SCAT and Social Anxiety scores (Table 1), player #6 possessed the highest aggregate anxiety score (a total of 41), followed by player #4 (with 35) and player #7 (27). Whether this can explain the fact that the same order also occurs for mean state anxiety scores is at this point merely an interesting observation warranting future research. It may, however, suggest that trait and state anxiety are related, but not necessarily in the general fashion often proposed (e.g., Cooley, 1987; Martens et al., 1990; Scanlan & Lewthwaite, 1984).
The conceptualization of anxiety as a multidimensional and sport specific construct has largely been explored through the use of the Competitive State Anxiety Inventory-2 (Martens et al., 1990), the Cognitive Somatic Anxiety Questionnaire (Schwartz, Davidson, & Goleman, 1978), and the Sport Anxiety Scale (Smith, Smoll, & Schutz, 1990). Few sport psychology researchers have yet to move beyond the cognitive-somatic conceptualization and include other specific aspects of anxiety that may be of importance in performance situations. Based on the present results, future research should address the question of identifying the most relevant aspects of anxiety in relation to athletic performance, as also suggested by Gould et al. (2002). In this respect, social anxiety may provide a relevant dimension worth exploring particularly for events in which athletes and spectators are in close proximity--as in golf. Such information would also be of importance for identifying the antecedents of various anxiety symptoms. In other sports, where there are few or no spectators, social anxiety may become less relevant, whereas other anxiety provoking stressors are more salient. This suggestion is in line with findings of Mandler and Sarason (1952) who showed that situation-specific trait anxiety measures were better for predicting state anxiety in academic situations than more general trait anxiety measures. Gill (2000) brings the above into the sport-arena by stating: "One person may become overly anxious in competitive sport, but remain calm in academic exams. Another might never become anxious in competition, but panic in social settings" (p. 159). Recently, Dunn and colleagues have argued for a widening of the anxiety construct using examples from high contact sports (Dunn, 1999; Dunn & Syrotuik, 2003). The theoretical relationship between social anxiety and aspects of self-consciousness is also worth exploring (cf. Bruch, Hamer, & Heimberg, 1995).
The finding that individuals displaying a lower variability in State Anxiety and Self-Confidence scores higher on Private Self-Consciousness than high variability individuals suggests that awareness of ones own thoughts and feelings affects state responses, which is in line with the theoretical explanations offered by Duval and Wicklund (1972; see also Fenigstein et al., 1975). Being more aware of oneself may enhance the ability to obtain a similar level of precompetition anxiety not by minimizing anxiety per se, but making it possible for the individual to more finely regulate the anxiety perceived, which in turn may benefit performance. Vealey and Greenleaf (2001) have to this effect provided a detailed description of how self-awareness imagery training may help the athlete to learn to more finely adjust her/his arousal level in order to enhance performance, much in line with the above. Although the purpose of this study was not to test the theoretical foundations of the IZOF model, results nevertheless seem to provide one possible explanation as to why individuals differ in their zones of optimal functioning. Results also support the notion by Hanin (2000) and Gould et al. (2002) that idiographic methods need to be utilized to a higher degree than previously has been the case.
In the present study, trait anxiety, as measured by the SCAT, a sport specific scale, was inconsistently associated with precompetition anxiety responses of the sample, and poorly associated with Social Anxiety measures. Clear adaptive advantages of possessing any particular level of anxiety before performance can therefore not be established based on our results. While it has been acknowledged that the assessment of specific aspects of state anxiety can potentially lead to a greater understanding of the anxiety-sport relationship (Martens et al., 1990); efforts toward this end have been largely confined to assessments of sport specific cognitive and somatic anxiety. The present results indicate that, at least in the case of golfers, other aspects of state anxiety should be considered, and these measures should not be limited to sport specific scales. The interpretation of anxiety as being facilitative or debilitative should also be examined and viewed in relation to more stable traits such as locus of control (e.g., Ntoumanis & Jones, 1998) including the concept of perceived control over the situation (Hammermeister & Burton, 2001) and optimism-pessimism (Wilson, Raglin, & Pritchard, 2002) which have all been linked to anxiety. Another relevant aspect is whether some dimensions of anxiety are more general than others. Whereas worries about injuries may be more relevant for athletes in high-risk sports (Dunn & Syrotuik, 2003), worries about failure may affect all athletes during competitions. Consequently, future research need to successfully address these questions, preferably by using longitudinal designs and incorporating athletes from different sports, in order to better understand intra-individual anxiety responses and their relation to athletic performances.
Table 1. Individual scores, means, and standard deviations on the trait measures. Self-Consciousness Player Trait Social Anxiety Anxiety Private Public 1 15 12 20 13 2 21 14 26 18 3 18 16 22 17 4 27 8 24 25 5 22 12 21 22 6 23 18 25 18 7 21 6 31 22 8 15 14 18 16 Means 20.3 12.5 23.4 18.9 SDs 4.1 4.0 4.1 3.9 Table 2. Pearson correlation coefficients between the state variables and golf score. Cognitive anxiety Somatic anxiety SD Mean SD Cognitive anxiety (Mean) .24 .79 * .17 Cognitive anxiety (SD) -- -.01 .71 * Somatic anxiety (Mean) -- -- .24 Somatic anxiety (SD) -- -- -- Self-confidence (Mean) -- -- -- Self-confidence (SD) -- -- -- Self-confidence Golf score Mean SD Mean SD Cognitive anxiety (Mean) -.77 * .23 .48 .26 Cognitive anxiety (SD) .20 .78 * -.35 .60 Somatic anxiety (Mean) -.92 * -.46 .44 .30 Somatic anxiety (SD) -.12 .53 -.24 .82 * Self-confidence (Mean) -- .47 -.73 * -.18 Self-confidence (SD) -- -- -.26 .45 * p < .05 Table 3. Means, standard deviations, and the resulting group for each player. Cognitive anxiety Player Mean SD Group # 1 12.8 3.1 High # 2 10.3 1.0 Low # 3 13.9 2.6 High # 4 17.2 4.9 High # 5 11.3 2.4 Medium # 6 21.8 1.1 Low # 7 9.4 0.5 Low # 8 11.0 2.8 High Somatic anxiety Player Mean SD Group # 1 10.7 1.3 Low # 2 14.9 1.1 Low # 3 14.6 3.1 High # 4 15.2 3.4 High # 5 13.8 3.4 High # 6 19.4 1.5 Low # 7 10.1 0.9 Low # 8 13.6 2.6 High Self-confidence Player Mean SD Group # 1 31.8 3.5 High # 2 27.8 1.3 Low # 3 27.0 1.8 Medium # 4 28.2 3.9 high # 5 25.4 3.6 High # 6 18.0 0.5 Low # 7 32.2 1.8 Medium # 8 30.1 2.5 High Table 4. Independent sample t-tests for the trait variables between high- and low variability groups. Groups High Low variability variability Trait variables Mean SD Mean SD t p Trait Anxiety 20.5 5.2 21.7 1.2 -0.37 .72 SC-Private 21.3 2.5 27.3 3.2 -2.80 <.04 SC-Public 20.0 4.2 19.3 2.3 .24 .82 SC-Social Anxiety 12.5 3.4 12.7 6.1 -0.05 .96
This study was made possible by research grants from The Swedish National Center for Research in Sports, and support from The Swedish Central Association for the Promotion of Sport and The Swedish Institute.
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Queensland University of Technology, Australia
John S. Raglin
Indiana University, U.S.A.
Stockholm University, Sweden
Address Correspondence To: Peter Hassmen, Ph.D., School of Movement Studies, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia. Email: firstname.lastname@example.org. Fax: +61 73980.…