贝叶斯对通用日志-线性Poisson项目响应模型的贝叶斯估计,用于使用brms和Stan的流利性得分
Nils Myszkowski1, Martin Storme2
1Department of Psychology, Pace University, New York, NY 10004, USA.
Journal of Intelligence
|March 26, 2025
概括
这项研究展示了如何估计创造力测量模型,特别是两参数的波桑计数模型 (2PPCM),使用R的贝叶斯多级回归. 这为分析分歧思维流性得分提供了灵活的替代方案.
科学领域:
- 心理测量 心理测量 心理测量
- 认知心理学 认知心理学
- 统计建模 统计建模
背景情况:
- 不同思维测试被广泛用于评估创造力,通常侧重于流利性 (想法数).
- 两个参数的波桑计数模型 (2PPCM) 和拉什波桑计数模型 (RPCM) 适用于分析流数据.
- 之前对这些模型的估计依赖于商业通用结构方程建模 (GSEM) 软件.
研究的目的:
- 在贝叶斯多级回归框架内证明2PPCM和RPCM的估计.
- 为使用R包brms分析创造力任务流性得分提供实用指导.
- 提供一种可复制和可访问的方法,用于创意数据的心理测量建模.
主要方法:
- 使用R包brms的贝叶斯多级回归建模,与Stan编程语言接口.
- 估计和解释两个参数的波桑计数模型 (2PPCM) 和拉什波桑计数模型 (RPCM).
- 插图用一个例子数据集来说明202名参与者在三个任务中的流度得分.
主要成果:
- 在使用brms的贝叶斯框架中成功估计了2PPCM和RPCM模型.
- 模型规范,趋同评估和模型合适性评估的演示.
- 提供关于绘制项目响应函数,比较模型和计算可靠性的实际指导.
结论:
- 贝叶斯的多级回归与brms提供了一个灵活和可访问的替代方案,用于估计2PPCM和RPCM模型的创造力研究.
- 这种方法促进了对不同思维流性数据的详细心理测量分析.
- 这项研究为希望利用现代统计技术分析创造力指标的研究人员提供了宝贵的资源.
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