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Structural Equation Modeling inkl. (PLS) Partial Least Squares, Latent Growth & MIMIC AMOS X X Summary Statistics X X X non- & semi-parametric Survival Analyses X X T-Tests: Independent, Paired, One-Sample (incl. z, Welch, non-parametrics & robust bayesian) ( ) Visual Modeling: Automated Plotting, (Non-)Linear, Mixed, Generalized Linear X X
Structural equation modeling (SEM) is a diverse set of methods used by scientists for both observational and experimental research. SEM is used mostly in the social and behavioral science fields, but it is also used in epidemiology, [ 2 ] business, [ 3 ] and other fields.
Structural equation modeling software is typically used for performing confirmatory factor analysis. LISREL, [23] EQS, [24] AMOS, [25] Mplus [26] and LAVAAN package in R [27] are popular software programs. There is also the Python package semopy 2. [28]
Structural Equation Modeling: Present and Future: A Festschrift in Honor of Karl Jöreskog. Scientific Software International. pp. 3– 10. ISBN 0-89498-049-1. von Eye, Alexander; Fuller, Bret E. (2003). "A comparison of the SEM software packages Amos, EQS, and LISREL".
The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) [1] [2] [3] is a method for structural equation modeling that allows estimation of complex cause-effect relationships in path models with latent variables.
Latent growth modeling is a statistical technique used in the structural equation modeling (SEM) framework to estimate growth trajectories. It is a longitudinal analysis technique to estimate growth over a period of time. It is widely used in the field of psychology, behavioral science, education and social science.
In econometrics, the seemingly unrelated regressions (SUR) [1]: 306 [2]: 279 [3]: 332 or seemingly unrelated regression equations (SURE) [4] [5]: 2 model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially ...
Nonetheless, response surface methodology has an effective track-record of helping researchers improve products and services: For example, Box's original response-surface modeling enabled chemical engineers to improve a process that had been stuck at a saddle-point for years.