The capabilities in SmartPLS for covariance-based structural equation modeling (CB-SEM) also support confirmatory factor analysis (CFA). Thus, CB-SEM models in SmartPLS allow for the execution of CFA, which is a statistical method designed to validate the underlying factor structure of a given set of observed variables. It empowers researchers to rigorously assess the hypothesis that a meaningful relationship exists between these observed variables and the unobservable latent variables (i.e., constructs) that underpin them (Hair et al., 2018). SmartPLS supports the creation of graphical CFA models. The model estimation assumes that constructs are represented by common factors and uses the maximum likelihood (ML) approach.

SmartPLS is a clear alternative to IBM SPSS Amos.

The following screenshot shows the CFA results in SmartPLS for Kline's (2023) CFA textbook example on the context of job satisfaction:

The maximum number of iterations that the optimizer will perform. This parameter should be high to ensure that a good model solution can be found. The default value is 1,000.

Only if you check this option, the use-specified starting values from the model will be used. If this option is not selected, we will always use the default starting values.

This strategy mimics the default starting values from lavaan. It uses Fabin style estimates for loadings, 0.0 for path coefficients and covariances, 0.5*indicator variance for residual variances, 0.05 for latent variable variances.

This strategy uses much more simple starting value, with 1.0 for loadings and variances, and 0.0 for path coefficients and covariances.

The optimizer stops when one of the two stop criteria are fulfilled and convergence to the optimum is assumed. In this case, the optimizer terminates when ||g|| < stop criterion * max(1, ||x||), where ||.|| denotes the Euclidean (L2) norm. The default value is 10^-6.

The optimizer stops when one of the two stop criteria are fulfilled and convergence to the optimum is assumed. In this case, the optimizer terminates when the decrease in the objective function (maximum likelihood value) is small enough. The condition is met if (f' - f) / f < stop criterion, where f' is the objective value of the past iteration and f is the objective value of the current iteration. The default value is 10^-9.

Select this option if you want to estimate correlations between all exogenous latent variables. Usually, if no correlation arrow is drawn in the model, the correlation between exogenous latent variables is constrained to zero. With this option correlations are also estimated freely when no arrow is drawn.

Select this option if you want to estimate correlations between all causal indicators of a latent variable. Usually, if no correlation arrow is drawn in the model, the correlation between causal indicators is constrained to zero. With this option correlations are also estimated freely when no arrow is drawn.

If you choose this option, all variances of causal indicators are constrained to 1.0. This also overwrites use-specified values. This option should help to mimic the default lavaan results.

SmartPLS provides directly computable CFA examples from reputable textbooks (Byrne, 2016; Hair et al., 2018; Kline, 2023; Schumacker & Lomax, 2010) and SmartPLS yields the same results as those textbooks provide. Take a look try out these CFA examples in SmartPLS!