Validating AnalyVa Against SmartPLS 4: A Benchmark of CB-SEM Factor Analysis, Latent Growth Curve Modelling, and Gaussian Copula Analysis

The adoption of new statistical software platforms in behavioural and social science research requires rigorous cross-platform validation before any recommendation to the scholarly community. This paper reports the numerical validation of AnalyVa, an Electron-based desktop platform, against SmartPLS 4 across five benchmark studies encompassing covariance-based structural equation modelling (CB-SEM), configural multi-group analysis (MGA), latent growth curve modelling (LGCM), and Gaussian copula endogeneity correction in both OLS regression and PLS-SEM contexts. Using the classic Holzinger-Swineford dataset for CB-SEM studies, a simulated four-wave longitudinal dataset (n = 400) for LGCM, a purpose-built endogenous simulation (n = 500) for OLS copula, and the Corporate Reputation dataset (N = 344) for PLS-SEM copula, concordance was assessed via mean absolute deviation (MAD) against established thresholds. Results revealed a consistent gradient of agreement: perfect concordance in LGCM (MAD = .0000) and OLS copula (MAD = .000), excellent concordance in CFA standardized loadings (MAD = .0003), excellent MGA concordance in both subgroups (MAD = .001; maximum |Δ| = .005), and excellent PLS-SEM copula concordance across outer loadings (MAD = .003), structural paths (MAD = .007), and R² values (|Δ| ≤ .001). Minor deviations were attributable to established statistical artefacts—AGFI sensitivity at small sample sizes, RMSEA computation conventions, and copula-induced multicollinearity—rather than platform-specific algorithmic differences. These findings validate AnalyVa's CB-SEM, LGCM, and Gaussian copula implementations as numerically equivalent to SmartPLS 4, providing applied researchers with an empirically supported basis for adopting AnalyVa in confirmatory factor analysis, longitudinal growth modelling, and endogeneity-corrected regression.