Luo Lan, Gates Kathleen M, Bollen Kenneth A
Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Multivariate Behav Res. 2025 May-Jun;60(3):589-597. doi: 10.1080/00273171.2024.2436418. Epub 2024 Dec 27.
We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.
我们展示了R包MIIVefa，其设计目的是实现MIIV-EFA算法。该算法探索并识别一组变量中的潜在因子结构。所得模型不是典型的探索性因子分析（EFA）模型，因为一些载荷被固定为零，并且它允许用户纳入假设的相关误差，比如纵向数据中可能出现的误差。因此，它类似于验证性因子分析（CFA）模型。但是，与CFA不同，MIIV-EFA算法直接从数据中确定因子数量以及加载在这些因子上的项目。我们提供了模拟和实证示例来说明MIIVefa的应用，并讨论其优点和局限性。
