检查ChatGPT作为数据分析工具的性能
1Alanya Alaaddin Keykubat University, Alanya/Antalya, Turkey.
Educational and psychological measurement
|January 6, 2025
概括
这项研究评估了ChatGPT-4o用于探索性因子分析 (EFA),发现它与R一致,用于KMO和因子负载等计算任务. 然而,由于观察到的偏差,建议对复杂的多维结构保持谨慎.
科学领域:
- 心理测量 心理测量 心理测量
- 人工智能的人工智能
- 计算统计学 计算统计学
背景情况:
- OpenAI的ChatGPT是一个被广泛采用的AI对话工具.
- 在复杂的统计分析中应用先进的AI模型,如EFA是一个新兴的研究领域.
- 评估人工智能工具对科学数据分析的可靠性和有效性至关重要.
研究的目的:
- 评估ChatGPT-4o作为探索性因素分析 (EFA) 工具的性能.
- 在各种模拟数据条件下,将ChatGPT-4o的数据分析结果与传统的统计软件 (R) 进行比较.
- 在心理测量数据分析中使用人工智能对话工具的优点和局限性.
主要方法:
- 模拟数据集产生了各种因素,如数据分布,响应类别,样本大小,测试长度,因子负载和测量模型.
- 使用ChatGPT-4o对模拟数据进行两次EFA,间隔一周,使用相同的提示.
- 关键的EFA指标包括凯泽-梅耶-奥尔金 (KMO) 值,解释的总方差,因子负载和因子数量 (使用凯泽-古特曼,赫尔和经验凯泽标准) 被计算并与R结果进行比较.
主要成果:
- 使用ChatGPT-4o进行的分析结果与使用R代码获得的结果一致.
- 聊天GPT-4o在EFA的计算方面表现出强的表现,例如计算KMO值,解释总方差和因子负载.
- 虽然估计因素的数量是一致的,但在ChatGPT-4o对多维结构的分析中发现了偏差.
结论:
- 聊天GPT-4o作为特定EFA程序的计算工具非常有希望,特别是那些不需要研究人员主观判断的程序.
- 由于潜在的偏见,研究人员在使用ChatGPT-4o用于涉及复杂,多维数据结构的EFA时应谨慎使用.
- 需要进一步的研究来完善人工智能模型,以便在心理测量学中进行细微的统计决策.
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