Using R Package gesca for generalized structured component analysis

被引：0
作者：
Kim S. [1 ]
Cardwell R. [1 ]
Hwang H. [1 ]
机构：
[1] Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, H3A 1B1, QC
来源：
Behaviormetrika | 2017年 / 44卷 / 1期
关键词：
Generalized structured component analysis; R package; Structural equation modeling;
D O I：
10.1007/s41237-016-0002-8
中图分类号：
学科分类号：
摘要：
The R package gesca was recently released to implement generalized structured component analysis (GSCA). GSCA represents a component-based approach to structural equation modeling (SEM) that defines a latent variable as a component or weighted composite of indicators. gesca enables users to obtain overall and local measures of model fit, parameter estimates with bootstrapped standard errors and confidence intervals, and the total and indirect effects of latent variables and indicators. It can also implement several basic extensions of GSCA, including constrained single- and multiple-group analysis, and second-order latent variable modeling. Furthermore, users easily specify their hypothesized relationships among latent variables and/or indicators based on an intuitive text-based syntax that comprises indicator names and simple numerical operators. Owing to its analytic versatility and ease of use, the package can be attractive to those wishing to apply GSCA to their research. This article provides step-by-step guidance on using the package with real examples. © 2016, The Behaviormetric Society.
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页码：3 / 23
页数：20
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