Confirmatory factor analysis of the musician's self-regulation imagery scale
Bibliographic record
Abstract
Many musicians experience debilitating music performance anxiety (MPA) (Papageorgi, Creech, & Welch, 2013) and mental imagery has been used to help performers manage anxiety. MPA treatment literature has been dominated by relaxation-based performance imagery (Finch & Moscovitch, 2016). However, sport researchers additionally advocate for imagery that integrates heightened arousal (e.g., Cumming, Olphin & Law, 2007). Indeed, the Yerkes-Dodson Law (Yerkes & Dodson, 1908) suggests that relaxation imagery might not benefit all musicians. We previously investigated musicians' use of imagery containing different levels of arousal (e.g., relaxation, psyching-up) by developing the Musician's Self-Regulation Imagery Scale (MSRIS), which is similar to the motivational-general subscales of the Sport Imagery Questionnaire (Hall, Mack, Paivio, & Hausenblas, 1998). Exploratory analyses suggested that the MSRIS captures mastery and high arousal performance imagery (Finch & Oakman, in prep.). The current study sought to replicate and extend previous findings by confirming the factor structure of the MSRIS, and investigating its relation to MPA, imagery vividness, and expertise. Participants (N = 363) completed an online study including standardized questionnaires and the MSRIS. Confirmatory factor analysis model fit indices supported a two-factor solution with mastery and high arousal subscales, 2 = 13.86, df = 8, p = .086, RMSEA = .052 and CFI = .995. Additionally, our measure subscales were associated with MPA, imagery vividness, and expertise. We will discuss the clinical implications of our findings, which will also help inform future MPA imagery research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".