集群稳健估计是否提供研究内部效应? 个人参与者数据方法在MASEM中的比较
Lennert J Groot1, Kees Jan Kan1, Suzanne Jak1
1University of Amsterdam.
在个人参与者数据元分析 (IPD MASEM) 中的集群强度估计可以通过误解研究内部效应和标准错误来扭曲研究结果. 仔细选择IPD MASEM方法对于准确的结果至关重要.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 进行元分析分析.
背景情况:
- 个人参与者数据元分析 (IPD MASEM) 提供先进的建模功能.
- 对于IPD MASEM存在几种方法,包括集群强度估计,两级SEM和单阶段MASEM (OSMASEM).
- 集群强度估计很受欢迎,但与其他技术相比,可能产生不同的结果.
研究的目的:
- 为了比较不同IPDMASEM方法的性能.
- 评估与集群强大估计相关的准确性和偏差,与其他方法相比.
- 为选择合适的IPD MASEM方法提供指导.
主要方法:
- 该研究使用模拟数据进行超分析结构方程建模 (MASEM).
- 模拟改变了关键因素:类内相关性,参数平等,研究数量和缺失的数据.
- 通过比较研究内部估计,标准错误和模型跨方法的合适性来评估性能.
主要成果:
- 集群强度估计经常误解研究内部估计.
- 偏差标准错误通常在集群-强大的估计中被观察到.
- 集群强度估计倾向于比其他方法更频繁地错误地拒绝模型匹配.
结论:
- 由于潜在的偏差,集群强度估计可能不适合所有IPD MASEM应用.
- 这些发现强调了IPD MASEM中方法选择的重要性.
- 研究人员应该仔细考虑替代方法,以确保准确的元分析结构方程建模.
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