Abstract

Evaluating the Sixth Tool: An Analysis of Baseball Makeup

Presenters:
Seth Haselhuhn, Washington State University, USA

Theme: Motivation and self-perceptions

Program ID: LEC-05B

Presentation: October 2, 2013 3:30 pm - 4:45 pm

Room: Napoleon

Abstract:

The primary purpose of this study was to analyze personality characteristics of professional baseball prospects to identify a specific profile of what Major League Baseball scouts refer to as “makeup” (Miller, 2012) for assessment and intervention purposes. Despite the significant investments made to secure talented prospects, no established, systematic assessment of psychological characteristics is used in the talent evaluation process (Lewis 2004, Marcos, 2012, Miller, 2012). Interviews, research, and anecdotal experiences produced a theoretical framework including: moral reasoning (i.e. decision making) assessed using the Hahm-Beller Values Choice Inventory – 16 (HBVCI-16; Beller & Stoll, 2004); motivational styles (i.e., trait characteristics of motivation) assessed using the Competitive Motivational Styles Questionnaire (CMSQ; Gut, Gillham, & Burton, 2012); beliefs regarding athletic ability (i.e., coachability) assessed using the Conceptions of the Nature of Athletic Ability – 2 (CNAAQ-2; Biddle, Wang, Chatzisarantis, & Spray, 2003); and resiliency (i.e., response to repeated failure) were assessed using the Connor-Davidson Resilience Scale (CD-RISC; Campbell-Sills, & Stein 2007). Descriptive statistics, cluster analysis, and canonical correlations were used to analyze a profile of professional baseball prospects (N = 233 collegiate baseball players). Confirmatory Factor Analysis (CFA) showed an acceptable fit (TLI and CFI >0.08; RMSEA and SRMR <0.08) for all but one instrument (i.e., HBVCI-16). Results from a one way MANOVA indicated significant overall effect between three clusters using the CMSQ as the criterion variable, Wilks’s ? = .558, F(8,448) = 18.9, p < .0005, partial ?2 = .25. The Scheffé method of post hoc analysis revealed significant differences between clusters for each dependent variable. Canonical Correlation Analysis also supported the three cluster model producing three significant canonical correlations, Wilks’s ? =.386; .752; .915. Complete analysis provides significant support for development of sport specific assessments and reinforces need for population specific mental skill interventions as part of professional prospect development.

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