Psychometric Evaluation of the Emotion Regulation Instrument for Teacher Education Institution Students Utilizing AI in Scientific Writing
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Abstract
This study examined the validity and reliability of an emotion regulation measurement instrument for university students using artificial intelligence (AI) in scientific writing, involving a sample of 100 respondents. Data were analysed using Confirmatory Factor Analysis (CFA) within the Covariance-Based Structural Equation Modelling (CB-SEM) framework, implemented using AMOS with Maximum Likelihood estimation. The adapted instrument consists of 18 items across two dimensions: Cognitive Reappraisal (Items 1–13) and Expressive Suppression (Items 14–18), expanded contextually from the original 10-item Gross & John’s ERQ. The measurement model demonstrated good overall fit (/df = 1.31; RMSEA = 0.056; CFI = 0.955; TLI = 0.949; IFI = 0.956). All 18 indicators loaded significantly (p < 0.001) with standardized factor loadings ranging from 0.57 to 0.76, confirming convergent validity. Construct Reliability (CR) was also computed and found to meet the acceptable threshold (CR ≥ 0.70). Overall, the instrument demonstrates adequate psychometric properties and provides a sound basis for assessing emotion regulation in the context of AI-assisted scientific writing.
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