Composite, Common Factor and Mixed Models

Abstract

PLS-SEM allows estimating proxies of latent variables that represent different model types (i.e., composite models and common factor models). One can use only latent variables of the one or the other type in a PLS path model. However, it also is possible to employ latent variables of both types in a PLS path model (i.e., mixed models).

Composite Models

PLS principally estimates composite models when using the PLS algorithm. When a reflective measurement model is considered (i.e., with relationships from the construct to the indicators), the PLS algorithm computes the composites using Mode A (i.e., the outer weights are the correlations between the construct and the indicators). In case of a formative measurement model (i.e., with relationships from the indicators to the construct), the PLS algorithm computes the composites using Mode B (i.e., the outer weights are the multiple regression coefficients with the indicators as independent variables and the latent variable as dependent variable). By double clicking on the latent variable, one can change the computation method of the composites (e.g. to sumscores).

Common Factor Models

When using PLSc, it is possible to mimic common factor model results. When a reflective measurement model is considered (i.e., with relationships from the construct to the indicators), the PLSc algorithm computes the composites using Mode A. Then, the composite’s relationships in the measurement model and to other latent variables in the structural model are corrected for attenuation and, as a results, mimic the common factor model results.

Mixed Composite and Common Factor Models

When using PLSc, it is possible to mimic common factor model results. When a reflective measurement model is considered (i.e., with relationships from the construct to the indicators), the PLSs algorithm computes the composites using Mode A . Then, the composite’s relationships in the measurement model and to other latent variables in the structural model are corrected for attenuation and, as a results, mimic the common factor model results. In case of a formative measurement model (i.e., with relationships from the indicators to the construct), the PLSc correction is not applied. The PLS algorithm computes the composites using Mode B. By double clicking on the latent variable, one can change the computation method of the composites (e.g., switch it from Mode B to Mode A).

Switching Modes in SmartPLS

Per default, SmartPLS 3 uses Mode A (correlations weights) estimations for reflective measurement models and Mode B (regression weights) for formative measurement models. When double-clicking on a construct, you can specify a specific estimation (Mode A, Mode B, sumscores, pre-defined weights) of the construct.

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