Features
Everything ResearchPLS does today
Organized by category -- only capabilities actually implemented in the application, nothing planned or aspirational.
SEM & Modeling
Build and estimate structural equation models with more than one estimation approach.
PLS-SEM (partial least squares structural equation modeling), Mode A & Mode B
CB-SEM (covariance-based SEM, including confirmatory factor analysis)
GSCA (Generalized Structured Component Analysis)
Weighted PLS (sampling-weight-adjusted estimation)
Consistent PLS (PLSc) for factor-based composite correction
Statistical Analysis
The applied statistics researchers reach for alongside a structural model.
Multiple linear regression, with bootstrapping
Logistic regression for binary outcomes
Mediation analysis (bootstrap-based indirect effects)
Moderation analysis
Moderated mediation (conditional process analysis)
Prediction & Advanced Analysis
Assessment beyond a single model fit -- prediction, comparison, and heterogeneity.
Bootstrapping for significance testing across every analysis
Model comparison and prediction-oriented model selection
Multigroup Analysis (MGA)
Measurement Invariance (MICOM)
IPMA (Importance-Performance Map Analysis)
NCA (Necessary Condition Analysis)
FIMIX (finite mixture segmentation for unobserved heterogeneity)
PLS prediction-oriented segmentation (PLS-POS)
Gaussian Copula approach to endogeneity
Confirmatory Tetrad Analysis (CTA)
Sample Size & Power Analysis (a priori and post-hoc sensitivity)
Results & Reporting
Every analysis produces real, exportable output -- not just an on-screen number.
Full statistical result tables (loadings, weights, path coefficients, R², f², SRMR, and more)
Publication-quality charts for every result
Guided statistical interpretation alongside each result
Excel workbook export (one sheet per analysis section)
Self-contained HTML report export
Model diagram export as PNG or SVG
Want the full catalog of individual statistical procedures?