为什么双因素模型的表现优于高阶因素模型? 从网络的角度来看
Kees-Jan Kan1, Anastasios Psychogyiopoulos1, Lennert J Groot1
1Research Institute of Child Development and Education, University of Amsterdam, 1018 WS Amsterdam, The Netherlands.
Journal of Intelligence
|February 23, 2024
概括
双因素模型在统计学上表现优于更高阶的一般智能 (g) 因素模型. 网络结构是一个合理的解释,建议研究人员考虑网络模型的智能,特别是当拒绝更高阶的g因子模型时.
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:
- 智能的双因素模型往往显示出优越的统计适应性,与更高阶的一般智能 (g) 因素模型相比.
- 这种差异的现有解释包括反映真实数据生成机制的双因素模型,合适指数的偏差或底层网络结构.
- 调查这些相互竞争的解释对于准确的情报建模至关重要.
研究的目的:
- 调查三种相互竞争的解释对双因素模型比更高阶g因子模型的统计优势的有效性和合理性.
- 为了确定合适指数是否偏向于更高阶模型,或者网络结构是否更好地解释情报数据.
- 为未来的情报模型选择程序提供指导.
主要方法:
- 采用蒙特卡洛模拟,生成3000个基于双因素,高阶因素和网络模型的数据集.
- 参数值是从维克斯勒智力尺度IV的确认分析中得出的.
- 对每个模拟数据集重新安装了三个模型,获得了适应统计数据,并执行了模型选择程序.
主要成果:
- 没有发现适应度本身存在偏差的证据;然而,当不适当地使用近似或增量适应指数时,可能会出现偏差的推断.
- 对情报结构的网络解释得到了验证,并被认为是合理的,与先前的经验研究结果一致.
- 经验发现与双因素模型代表智能的真正底层结构的假设相矛盾.
结论:
- 网络智能模型是一个合理的替代方案,应在未来的模型选择中加以考虑,特别是当高阶g因子模型被拒绝而偏向双因子模型时.
- 该研究强调了解释适应性指数的潜在陷,强调需要仔细应用.
- 这些发现挑战了假设双因素模型必然代表智力的真实结构的假设.
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