This study aimed to evaluate the psychometric properties (factor structure, reliability and construct validity) of the Motivation of User-generated Technical Instructional Videos (MUTIV) scale. Employing a cross-sectional research design, two rounds of self-administered surveys were conducted in mainland China (N = 271/N = 318). Phase 1 involved item generation through both deductive methods (reviewing existing scales in the literature) and inductive methods (analyzing online discourse). Phase 2 focused on the scale development and questionnaire design. Five factors sixty items were extracted by Exploratory Factor Analysis (EFA; Principal Components Analysis) and confirmed by analysis of moment structures, forming the initial five-factor-16-item MUTIV scale. Phase 3 included the scale evaluation and validation assessment. All goodness of ft indices generated by Confirmatory Factor Analysis (CFA) were found satisfactory (χ2/df = 2.620, root mean square residual = 0.033, root-mean-square error of approximation = 0.076, incremental fit index = 0.934, Tucker-Lewis index = 0.915, comparative fit index = 0.933). Cronbach’s alpha value formed at α = 0.911. The five-factor-16-item MUTIV scale was successfully verified and developed. The MUTIV scale effectively assesses the motivation to produce user-generated technical instructional videos, identifying key factors such as sharing, membership, competence, autonomy, and reward. Technical and Vocation Education and Training (TVET) stakeholders and media platforms can use this tool to promote and assess motivation in educational contexts.
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This study aimed to evaluate the psychometric properties (factor structure, reliability and construct validity) of the Motivation of User-generated Technical Instructional Videos (MUTIV) scale. Employing a cross-sectional research design, two rounds of self-administered surveys were conducted in mainland China (N = 271/N = 318). Phase 1 involved item generation through both deductive methods (reviewing existing scales in the literature) and inductive methods (analyzing online discourse). Phase 2...
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