The partial least squares path modeling (PLS-PM, PLS-SEM) method to structural equation modeling allows estimating complex cause-effect relationship models with latent variables. It is a component-based estimation approach that differs from the covariance-based structural equation modeling. Unlike the covariance-based approach to structural equation modeling, PLS path modeling does not reproduce a sample covariance matrix. It is more oriented towards maximizing the amount of variance explained (prediction) rather than statistical accuracy of the estimates.
The PLS structural equation model is composed of two sub-models: the measurement model and structural model. The measurement model represents the relationships between the observed data and the latent variables. The structural model represents the relationships between the latent variables.
An iterative algorithm solves the structural equation model by estimating the latent variables by using the measurement and structural model in alternating steps, hence the procedure's name, partial. The measurement model estimates the latent variables as a weighted sum of its manifest variables. The structural model estimates the latent variables by means of simple or multiple linear regression between the latent variables estimated by the measurement model. This algorithm repeats itself until convergence is achieved.
With the availability of software applications (such as PLS-Graph, SmartPLS, ADANCO and WarpPLS), PLS-SEM became particularly popular in social sciences disciplines such as accounting, family business, marketing, management information systems, operations management, and strategic management. Recently, areas such as engineering, environmental sciences, medicine, and political sciences more broadly use PLS-SEM to estimate complex cause-effect relationship models with latent variables. Thereby, they analyse, explore and test their established and underlying their conceptual models and theory.
PLS is being viewed critically by methodological researchers such as Rönkkö et al. (2016). However, PLS-SEM is still considered preferable (over CB-SEM) when it is unknown whether the data's nature is common factor- or composite-based.