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<p>This study evaluates the validity and reliability of a Turkish Life Satisfaction Scale developed using artificial intelligence (ChatGPT) to explore AI’s potential in creating psychometric tools. The scale was tested on three independent samples of Turkish university students: 503 for Exploratory Factor Analysis (EFA), 301 for Confirmatory Factor Analysis (CFA), and 79 for test–retest reliability. EFA identified a unidimensional structure, accounting for 67.50% of the total variance (factor loadings .75–.89). CFA confirmed adequate model-data fit (e.g., χ<sup>2</sup>/sd = 2.63, RMSEA = 0.07). The scale demonstrated high internal consistency (Cronbach’s α = .88) and temporal stability (test–retest correlation = .95). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r = .74) and General Well-Being (r = .63) scales. These findings indicate that AI can expedite scale development while yielding robust psychometric instruments. This research underscores the innovative potential of AI-supported psychometric tools in the social sciences and offers valuable insights for future scale development.</p>

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Validity and reliability analysis of the Turkish life satisfaction scale developed through artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Servet Ati̇k,
  • Nuri Erdemi̇r

摘要

This study evaluates the validity and reliability of a Turkish Life Satisfaction Scale developed using artificial intelligence (ChatGPT) to explore AI’s potential in creating psychometric tools. The scale was tested on three independent samples of Turkish university students: 503 for Exploratory Factor Analysis (EFA), 301 for Confirmatory Factor Analysis (CFA), and 79 for test–retest reliability. EFA identified a unidimensional structure, accounting for 67.50% of the total variance (factor loadings .75–.89). CFA confirmed adequate model-data fit (e.g., χ2/sd = 2.63, RMSEA = 0.07). The scale demonstrated high internal consistency (Cronbach’s α = .88) and temporal stability (test–retest correlation = .95). Criterion validity was supported by strong positive correlations with established Life Satisfaction (r = .74) and General Well-Being (r = .63) scales. These findings indicate that AI can expedite scale development while yielding robust psychometric instruments. This research underscores the innovative potential of AI-supported psychometric tools in the social sciences and offers valuable insights for future scale development.