Main features
Among the main features of WarpPLS is its ability to identify and model non-linearity among variables in path models, whether these variables are measured as latent variables or not, yielding parameters that take the corresponding underlying heterogeneity into consideration. Other notable features are summarized: • Guides SEM analysis flow via a step-by-step user interface guide. • Implements classic (composite-based) as well as factor-based PLS algorithms. • Identifies nonlinear relationships, and estimates path coefficients accordingly. • Also models linear relationships, using classic and factor-based PLS algorithms. • Models reflective and formative variables, as well as moderating effects. • Calculates P values, model fit and quality indices, and full collinearity coefficients. • Calculates effect sizes and Q-squared predictive validity coefficients. • Calculates indirect effects for paths with 2, 3 etc. segments; as well as total effects. • Calculates several causality assessment coefficients. • Provides zoomed 2D graphs and 3D graphs. ==See also==