A multimethod SEM framework for analyzing models with latent variables

Structural equation modeling (SEM) is widely used to estimate relationships among latent variables and their indicator variables. While different approaches exist, researchers often rely on a single estimation tradition–factor-based or composite-based–despite their distinct assumptions, strengths, and limitations. This practice restricts the rigorous evaluation of structural models, particularly for theories that require both explanatory and predictive assessment. This article introduces a multimethod SEM framework that applies factor-based and composite-based estimators to the same model to assess the robustness of structural paths under alternative conceptual and statistical assumptions. We outline a workflow for implementing multimethod estimation and evaluating convergence and divergence in results. This multimethod SEM framework shifts attention from method allegiance to the empirical performance of the model, thereby improving theoretical inference, predictive assessment, and the overall credibility of SEM-based conclusions.

Subjects

Composite-based SEM

explanatory power

factor-based SEM

multimethod SEM

predictive power

structural equation modeling (SEM)

DDC Class

519: Applied Mathematics, Probabilities

001.4: Research

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

A Multimethod SEM Framework for Analyzing Models with Latent Variables.pdf

Type

Main Article

Size

2.14 MB

Format

Adobe PDF

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A multimethod SEM framework for analyzing models with latent variables