Recovery of weak common factors by maximum likelihood and ordinary least squares estimation
Recovery of weak common factors by maximum likelihood and ordinary least squares estimation
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DOI：
10.1207/s15327906mbr3801_2
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发表时间：
2003-01-01
影响因子：
3.8
通讯作者：
MacCallum, RC
中科院分区：
文献类型：
--
作者：
Briggs, NE;MacCallum, RC
本文研究了因子分析中两种常用的参数估计方法，最大似然法（ML）和普通最小二乘法（OLS）的相对性能。结果表明，ML有时会无法恢复一个已知的人口因素结构时，OLS成功。进行了模拟研究，其中两种类型的误差（模型和抽样误差）分别引入和组合到相关矩阵中产生的已知人口结构与至少一个相对较弱的主要领域的因素。使用ML和OLS对模拟相关矩阵进行因子分析，并评估相对较弱因子的回收率。在具有中等错误量的情况下，ML通常无法恢复弱因子，而OLS成功。有人建议，固有的假设之间的对应关系，在每种方法中的误差和数据中的错误的实际性质可能会影响弱共同因素的恢复的成功。还提出了一个使用经验数据的例子。
This article examines the relative performance of two commonly used methods of parameter estimation in factor analysis, maximum likelihood (ML) and ordinary least squares (OLS). It is shown that ML will sometimes fail to recover a known population factor structure when OLS succeeds. A simulation study was conducted in which two types of error (model and sampling error) were introduced separately and in combination into correlation matrices generated from known population structures with at least one relatively weak major domain factor. Simulated correlation matrices were factor analyzed using both ML and OLS, and recovery of the relatively weak factor(s) was assessed. In situations with a moderate amount of error, ML often failed to recover the weak factor while OLS succeeded. It is suggested that the correspondence between the assumptions inherent in each method regarding error and the actual nature of error in the data may affect the success of recovery of weak common factors. An example using empirical data is also presented.