二分化对网络恢复的影响
Nikola Sekulovski1, Tessa F Blanken2, Jonas M B Haslbeck2
1Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, PO Box 15906, 1001, NK, Amsterdam, The Netherlands. n.sekulovski@uva.nl.
Behavior research methods
|November 11, 2025
概括
单元化心理数据导致信息丢失,网络结构恢复不那么准确. 通过直接使用顺序马尔科夫随机场模型分析顺序数据,可以确保更可靠的网络推理.
科学领域:
- 心理网络分析 心理网络分析
- 统计建模 统计建模
- 数据科学是数据科学.
背景情况:
- 图形模型对于心理网络分析至关重要.
- 传统的模型 (高斯式,伊辛式) 不适合顺序的利克特尺度数据.
- 当将标准方法应用于普通心理数据时,存在模型数据不匹配.
研究的目的:
- 为了研究二分化顺序变量的对网络结构恢复的影响.
- 为了比较二分化和原始顺序数据之间的网络恢复准确性.
- 为了评估心理数据的顺序马尔科夫随机场模型的有效性.
主要方法:
- 对顺序马尔科夫随机场模型的贝叶斯分析.
- 采用R套餐进行实施.
- 检查二分化对网络恢复准确性的影响.
主要成果:
- 排序数据的二体化会导致大量的信息丢失,损害网络恢复的准确性.
- 网络恢复的准确性受到所选择的二分化切线和类别分布的影响.
- 顺序模型有效地恢复条件独立关系,优于二分化方法.
结论:
- 使用适当的模型对顺序数据进行直接分析对于有效的心理网络推断至关重要.
- 在网络分析中对顺序变量的二元化导致不太可靠的结果.
- 顺序马尔科夫随机场模型为使用顺序数据进行心理网络研究提供了一种优越的方法.
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