在交叉分类的多层模型中分解特定层次的影响
Yingchi Guo1, Jeneesha Dhaliwal2, Jason D Rights2
1Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T1Z4, Canada. yingchi.guo@ubc.ca.
Behavior research methods
|November 22, 2023
概括
研究人员现在可以在交叉分类的多层模型中分解特定水平的影响. 这样可以避免将多个预测效应混为一谈,提高心理学和其他研究结果的准确性.
科学领域:
- 多层次建模的多层次建模
- 交叉分类的数据结构.
- 心理学研究方法 心理学研究方法
背景情况:
- 心理学中的数据经常表现出交叉分类结构,观察结果嵌入多个非等级集群.
- 现有的多层模型可能会在交叉分类的环境中混低层预测者的效应,这是一个经常被忽视的问题.
- 这种混可能导致对研究中的预测效应的模两可的解释.
研究的目的:
- 为了澄清交叉分类的多层模型中结合效应的问题.
- 在这些模型中引入分解特定水平效应的方法.
- 为研究人员提供关于模型规范和解释的指导.
主要方法:
- 开发了新的模型规范,包括完全以集群为中心,部分以集群为中心和上下文效应模型.
- 澄清了方法,以避免固定和随机效应的混.
- 利用模拟研究和教学示例来说明分解技术.
主要成果:
- 演示了常见的建模实践如何错误地混合多个预测效应.
- 展示了新的模型规范如何允许对特定水平效应进行独特的解释.
- 模拟结果突出了交叉分类模型中混的负面影响.
结论:
- 分解特定层次的影响对于在交叉分类的多层次模型中准确解释至关重要.
- 提出的方法和模型规范为研究人员提供了对复杂数据结构的更清晰的洞察力.
- 新的软件可用于帮助研究人员实施这些先进的建模技术.
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