Krane, Williams and Feltz (1992) Path analysis examining relationships among cognitive anxiety, somatic anxiety, state confidence, performance expectations, and golf performance

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Path analysis examining relationships among cognitive anxiety, somatic anxiety, state confidence, performance expectations, and golf performance

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CONTRIBUTORS:

	Author:	Krane, V. (Bowling Green State University)
	Author:	Williams, J.
	Author:	Feltz, D. (Michigan State University)

JOURNAL:

Journal of Sport Behavior (JSB), 15(4), 279 - 295.

YEAR:

1992

PUB TYPE:

Journal Article

SUBJECT(S):

critical-path-method; woman; golf; anxiety; confidence; expectation

DISCIPLINE:

No discipline assigned

HTTP:

https://secure.sportquest.com/su.cfm?articleno=306746&title=306746

LANGUAGE:

English

PUB ID:

103-343-433 (Last edited on 2002/02/27 18:44:13 US/Mountain)

SPONSOR(S):

ABSTRACT:
Female collegiate golfers (N=100) completed the CSAI-2 immediately before two rounds of a collegiate golf tournament. This time frame allowed for using path analyses to investigate the influence of self-reported cognitive anxiety, somatic anxiety, state confidence, and performance expectations on performance as well as the influence of performance on subsequent mental states. The theoretical model tested in the present study was based on previous models examining performance expectations and the results of earlier studies using the CSAI-2. Two analyses were computed: the first measured performance expectations with CSAI-2 state confidence and the second with goal scores. Both used the same theoretical model. In the self-confidence model, competitive trait anxiety and precompetitive cognitive anxiety significantly influenced pre-first round state confidence. Reciprocal relationships were found between self-confidence and cognitive and somatic anxiety. In the performance expectation model, average score was the only significant predictor of first round performance expectations. Performance expectations had a reciprocal relationship with cognitive anxiety. In both models, the best predictor of performance was previous performance.

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