Objectives: Research has found that trait and dispositional perfectionism are related to pre-competition emotions. However, less is known about whether other aspects of perfectionism, such as perfectionistic cognitions, are related to pre-competition emotions. To address this limitation, the current study examined (i) the relationship between self-oriented and socially prescribed perfectionism and pre-competition emotions, and (ii) whether perfectionistic cognitions predict pre-competition emotions after controlling for these two dimensions of perfectionism. Design: A cross-sectional survey. Method: Two hundred and six youth footballers (M age=15.54 years, SD=1.93) completed self-report measures prior to their next competition. Results: Regression analyses revealed socially prescribed perfectionism was a positive predictor of anger, while self-oriented perfectionism was a positive predictor of excitement. After controlling for self-oriented and socially prescribed perfectionism, perfectionistic cognitions were a positive predictor of anxiety, anger, and dejection. Conclusion: The findings suggest that perfectionistic cognitions are important in regard to pre-competition emotions. Korrektur: The authors regret they have made a reporting error in the paper. As part of the preliminary analysis they evaluated each instrument using confirmatory factor analysis (CFA) and, when the instrument had multiple factors, exploratory structural equation modelling (ESEM). Unfortunately, they reported CFA results in all cases (as opposed to ESEM where necessary). In terms of the consequences of this error, all ESEM fit indices exceed the CFA fit indices currently reported. There are also no implications in terms of the primary analyses. However, this corrigendum is provided so to highlight the error, provide the correct results, and to ensure correspondence between the results reported in the published paper and future papers using the same data. Correct ESEM results are (1) PCI-Stoeber et al. (2014) version 2 = 406.81, p < .05, df = 228, 2/df = 1.78, CFI = 0.88, TLI = 0.84, RMSEA [90% CI] = 0.06 [0.05, 0.07], SRMR = 0.05, BIC = 14357.55; (2) CAPS-Flett et al. (1997) 2 = 519.76, p < .05, df = 188, 2/df = 2.76, CFI = 0.77, TLI = 0.72, RMSEA [90% CI] = 0.10 [0.09, 0.11], SRMR = 0.07, BIC = 12112.12; (3) CAPS-McCreary et al. (2014) version 2 = 166.41, p < .05, df = 52, 2/df = 3.20, CFI = 0.87, TLI = 0.78, RMSEA [90% CI] = 0.11 [0.09, 0.12], SRMR = 0.04, BIC = 7515.33; (4) CAPS-O'Connor, Dixon, & Rasmussen (2009) version 2 = 177.54, p < .05, df = 63, 2/df = 2.82, CFI = 0.89, TLI = 0.81, RMSEA [90% CI] = 0.10 [0.08, 0.11], SRMR = 0.04, BIC = 8050.89; (5) CAPS-10 2 = 48.36, p < .05, df = 26, 2/df = 1.85, CFI = 0.95, TLI = 0.92, RMSEA [90% CI] = 0.07 [0.04, 0.10], SRMR = 0.04, BIC = 5328.76; (6) SEQ-Jones et al. (2005) 2 = 231.40, p < .05, df = 131, 2/df = 1.77, CFI = 0.95, TLI = 0.91, RMSEA [90% CI] = 0.06 [0.05, 0.08], SRMR = 0.02, BIC = 10860.30. The authors would like to apologise for any inconvenience caused.
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