比较原始分数差异,多级建模和结构方程建模方法,以估计二的差异
Amber McEnturff1, Qi Chen2, Robin K Henson2
1Alexandria City Public Schools, Alexandria, VA, United States.
Frontiers in psychology
|July 4, 2025
概括
推使用原始分数差异 (RSD) 和结构方程建模 (SEM) 方法来估计二次差异分数. 多级建模 (MLM) 的可靠性很差,因此不适合在心理学研究中的实际应用.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 量化研究方法 量化研究方法
背景情况:
- 双向数据分析在心理学研究中至关重要,用于检查对 (例如,父母-孩子,配偶) 之间的关系.
- 准确估计二次差异分数对于可靠的结果预测至关重要.
- 计算这些分数的方法有几种,但它们的比较准确性尚未得到充分证实.
研究的目的:
- 为了比较不同二次差异差异得分估计方法的准确性.
- 确定哪种方法提供最可靠的差异估计和最佳结果预测.
- 评估各种设计因素对估计准确性的影响.
主要方法:
- 使用蒙特卡洛模拟来比较三个差异得分估计方法:原始得分差 (RSD),多级建模 (MLM) 和结构方程建模 (SEM).
- 关键的模拟因素包括类内相关性 (ICC),集群数,可靠性,差异效果大小和效果大小差异.
主要成果:
- 多级建模 (MLM) 产生了差异估计的可靠性较差,特别是在高ICC,高效果大小差异和低集群数量的条件下.
- 原始分数差 (RSD) 和结构方程建模 (SEM) 方法在模拟条件下显示了可比且稳定的性能.
- 研究的设计因素并没有显著影响RSD或SEM估计的准确性.
结论:
- 原始分数差 (RSD) 和结构方程建模 (SEM) 是由于其可靠性和稳定性而被推用于实际应用,用于估计二次差异分数.
- 多级建模 (MLM) 不建议用于差异得分估计,因为它的可靠性相对较差.
- 未来的研究应该考虑这些方法在多样化的二级数据结构下的性能.
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