Nonlinear structural equation modeling: is partial least squares an alternative?

Nonlinear structural equation modeling provides many advantages over analyses based on manifest variables only. Several approaches for the analysis of latent interaction effects have been developed within the last 15 years, including the partial least squares product indicator approach (PLS-PI), the constrained product indicator approach using the LISREL software (LISREL-PI), and the distribution-analytic latent moderated structural equations approach (LMS) using the Mplus program. An assumed advantage of PLS-PI is that it is able to deal with very large numbers of indicators, while LISREL-PI and LMS have not been investigated under such conditions. In a Monte Carlo study, the performance of LISREL-PI and LMS was compared to PLS-PI results previously reported in Chin et al. (2003) and Goodhue et al. (2007) for identical conditions. The latent interaction model included six indicator variables for the measurement of each latent predictor variable and the latent criterion, and sample size was N=100. The results showed that PLS-PI’s linear and interaction parameter estimates were downward biased, while parameter estimates were unbiased for LISREL-PI and LMS. True standard errors were smallest for PLS-PI, while the power to detect the latent interaction effect was higher for LISREL-PI and LMS. Compared to the symmetric distributions of interaction parameter estimates for LISREL-PI and LMS, PLS-PI showed a distribution that was symmetric for positive values, but included outlying negative estimates. Possible explanations for these findings are discussed.