The Effects of Indoor Climbing Route Rating Manipulation on Participant Climbing Self-Efficacy
Phillip James Sandlin
Abstract
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Phillip James Sandlin
Abstract
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This quantitative study utilized a classical experiment design to explore the effects of manipulation of indoor climbing route ratings on the climbing self-efficacy of 90 indoor rock climbers. Controversy surrounding the accuracy of the assigned ratings of indoor climbing routes is commonplace at indoor climbing facilities. For indoor rock climbers to accurately assess their abilities, set appropriate goals, and monitor their progress, they must be able to choose climbs that accurately represent their desired level of challenge. Statistical analysis of the data did not support a relationship between manipulation of climbing route ratings and any changes in participant climbing self-efficacy. Additionally, participant perceptions of the accuracy of the stated climbing route ratings for the under and over-rated experimental groups were not found to be significantly different (p < .05) than that of the control group.
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This quantitative study utilized a classical experiment design to explore the effects of manipulation of indoor climbing route ratings on the climbing self-efficacy of 90 indoor rock climbers. Controversy surrounding the accuracy of the assigned ratings of indoor climbing routes is commonplace at indoor climbing facilities. For indoor rock climbers to accurately assess their abilities, set appropriate goals, and monitor their progress, they must be able to choose climbs that accurately represent their desired level of challenge. Statistical analysis of the data did not support a relationship between manipulation of climbing route ratings and any changes in participant climbing self-efficacy. Additionally, participant perceptions of the accuracy of the stated climbing route ratings for the under and over-rated experimental groups were not found to be significantly different (p < .05) than that of the control group.
Key concepts: Climbing, Statistical analysis, Perception, Set (abstract data type), Computer science, Simulation, Psychology, Engineering