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A Multimethod SEM Framework for Analyzing Models with Latent Variables

Joseph F. Hair et al.

Journal of Global Marketing2026https://doi.org/10.1080/08911762.2026.2638909article
AJG 1ABDC B
Weight
0.50

Abstract

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.

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https://doi.org/https://doi.org/10.1080/08911762.2026.2638909

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@article{joseph2026,
  title        = {{A Multimethod SEM Framework for Analyzing Models with Latent Variables}},
  author       = {Joseph F. Hair et al.},
  journal      = {Journal of Global Marketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/08911762.2026.2638909},
}

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A Multimethod SEM Framework for Analyzing Models with Latent Variables

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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