Two studies were conducted to examine antecedents of, relationships between, and temporal changes in the cognitive anxiety, somatic anxiety, and the self-confidence components of the Martens, Burton, Vealey, Bump, and Smith (1983) newly developed Competitive State Anxiety Inventory-2 (CSAI-2). In addition, the prediction that cognitive and somatic anxiety should differentially influence performance was examined. In Study 1, 37 elite intercollegiate wrestlers were administered the CSAI-2 immediately before two different competitions, whereas in Study 2, 63 female high school volleyball players completed the CSAI-2 on five different occasions (1 week, 48 hrs, 24 hrs, 2 hrs, and 20 min) prior to a major tournament. The results were analyzed using multiple regression, multivariate multiple regression, univariate and multivariate analyses of variance, and general linear model trend analysis techniques. The findings supported the scale development work of Martens and his colleagues by verifying that the CSAI-2 assesses three separate components of state anxiety. A number of other important findings also emerged. First, the prediction was confirmed that somatic anxiety increases during the time leading to competition, while cognitive anxiety and confidence remain constant. Second, CSAI-2 subscales were found to have different antecedents, although the precise predictions of Martens and his colleagues were not supported. Third, the prediction that cognitive anxiety would be a more powerful predictor of performance than somatic anxiety was only partially supported. Fourth, the prediction that precompetitive anxiety differences between experienced and inexperienced athletes initially found by Fenz (1975) result from somatic anxiety changes was not supported. It was concluded that the CSAI-2 shows much promise as a multidimensional sport-specific state anxiety inventory, although more research is needed to determine how and why specific antecedent factors influence various CSAI-2 components and to examine the predicted relationships between CSAI-2 components and performance.
Variable selection in multivariate multiple regression
