GeSCA

GeSCA is a web-based software program for generalized structured component analysis that represents a component-based approach to structural equation modeling. This program provides a graphical user interface that allows users to easily express their model as a path diagram and to view the estimates of model parameters. GeSCA is free of charge and capable of analyzing up to 1000 cases and 100 observed variables. GeSCA currently enables users to: use Microsoft® Excel (.xls or .xlsx) data files. conduct all single-group analyses of user-specified structural equation models. use the bootstrap method to estimate the standard errors of parameter estimates. specify both reflective and formative indicators. impose user-defined or equality constraints on loadings and path coefficients. conduct all multi-group analyses with the optional imposition of cross-group constraints. deal with missing observations via three different procedures. handle second-order latent variables in single-group analyses.

Keywords for this software

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Loisel, Sébastien; Takane, Yoshio: Comparisons among several methods for handling missing data in principal component analysis (PCA) (2019)
  2. Schlittgen, Rainer: Estimation of generalized structured component analysis models with alternating least squares (2018)
  3. Tenenhaus, Michel; Tenenhaus, Arthur; Groenen, Patrick J. F.: Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods (2017)
  4. Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S.: Multilevel dynamic generalized structured component analysis for brain connectivity analysis in functional neuroimaging data (2016)
  5. Takane, Yoshio: Professor Haruo Yanai and multivariate analysis (2016)
  6. Zhou, Lixing; Takane, Yoshio; Hwang, Heungsun: Dynamic GSCANO (generalized structured canonical correlation analysis) with applications to the analysis of effective connectivity in functional neuroimaging data (2016)
  7. Hwang, Heungsun; Takane, Yoshio: Generalized structured component analysis. A component-based approach to structural equation modeling (2015)


Further publications can be found at: http://www.sem-gesca.org/reference.php