MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lan Luo, Kathleen M Gates, Kenneth A Bollen
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引用次数: 0

Abstract

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.

MIIVefa:一个使用模型隐含工具变量的新型探索性因子分析的R包。
我们提出了R包MIIVefa,旨在实现MIIV-EFA算法。该算法在一组变量中探索并识别潜在的因素结构。所得到的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,它允许用户包括假设的相关误差,例如纵向数据可能发生的误差。因此,它类似于验证性因素分析(CFA)模型。但是,与CFA不同的是,MIIV-EFA算法直接从数据中确定因素的数量和加载在这些因素上的项目。我们提供了模拟和经验例子来说明MIIVefa的应用,并讨论了它的优点和局限性。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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