Factor structure and validation of the Motor Imagery Vividness Scale (MIV-S)

Motor imagery, the mental rehearsal of movements without physical execution, plays a crucial role in skill acquisition, performance optimization, and rehabilitation. While existing measures, such as the Vividness of Movement Imagery Questionnaire-2 (VMIQ-2), assess visual and kinesthetic imagery, they do not capture additional dimensions essential to motor imagery vividness, including emotional connection, temporal accuracy, and motivational impact. To address these limitations, this study developed and validated the Motor Imagery Vividness Scale (MIVS), a comprehensive 10-item multidimensional measure designed to assess motor imagery vividness across cognitive, emotional, and sensory domains. A total of 300 participants (mean age = 30.1 years, SD = 6.8) completed the MIVS, VMIQ-2, and Perceived Stress Scale (PSS) to evaluate its reliability, validity, and factor structure. Exploratory factor analysis (EFA) identified a three-factor structure-Spatial and Temporal Imagery, Emotional and Motivational Imagery, and Sensory Clarity and Flow-explaining 38.6% of the total variance. Confirmatory factor analysis (CFA) supported this structure, yielding satisfactory model fit indices (CFI = 0.92, RMSEA = 0.06, SRMR = 0.05). Internal consistency for the total MIVS score was excellent (Cronbach's α = 0.92), and test-retest reliability demonstrated high stability (ICC = 0.85, 95% CI: 0.80-0.87). Construct validity analyses revealed strong positive correlations between MIVS and VMIQ-2 subscales, supporting convergent validity, and weak correlations with PSS scores, confirming discriminant validity. The inclusion of novel dimensions, such as temporal reproducibility and emotional connection, distinguishes the MIVS as a more comprehensive tool for motor imagery assessment. These findings establish the MIVS as a psychometrically robust, multidimensional measure with practical applications in sports psychology, rehabilitation, and cognitive neuroscience. Future research should explore its predictive validity and applicability across diverse populations to further enhance its contribution to motor imagery research and practice.