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Peer-reviewed article

A Practical Guide to Conditional Mediation Analysis with GSCA: Method, Simulation, and Software

In-Hyun Baek · Gyeongcheol Cho · Heungsun Hwang

Methodology
Structural Equation ModelingSoftwareTutorialSimulation Study

2026 · Structural Equation Modeling: A Multidisciplinary Journal

Abstract overview

A summary of the source abstract.

This tutorial explains how to estimate conditional mediation models in GSCA, examining indirect effects that depend on a moderator. It presents the statistical foundations, compares GSCA with partial least squares path modeling through simulation, and demonstrates an empirical analysis step by step in GSCA Pro.

Version of record · Original abstract

Figures 5 images

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Figure 1

Two diagrams for conditional mediation Model A: W moderates the X-to-M path; the statistical model includes the X-by-W interaction along with paths from X, W, and the interaction to M, followed by M to Y.
Caption & source

Conceptual and statistical specifications of CoMe Model A.

Version of record (author-provided PDF; author-uploaded ResearchGate copy) · Figure source · Author-authorized reproduction; no open license asserted

Baek, Cho, and Hwang (2026), Figure 1. Copyright 2026 Taylor & Francis Group, LLC. Reproduced from the author-provided version of record at the author's request.

Figure 2

Two diagrams for conditional mediation Model B: W moderates the M-to-Y path; the statistical model includes the M-by-W interaction, the direct X-to-Y path, and paths from M, W, and the interaction to Y.
Caption & source

Conceptual and statistical specifications of CoMe Model B.

Version of record (author-provided PDF; author-uploaded ResearchGate copy) · Figure source · Author-authorized reproduction; no open license asserted

Baek, Cho, and Hwang (2026), Figure 2. Copyright 2026 Taylor & Francis Group, LLC. Reproduced from the author-provided version of record at the author's request.

Figure 3

Two diagrams for conditional mediation Model C: W moderates both the X-to-M and M-to-Y paths; the statistical model includes X-by-W and M-by-W interaction terms.
Caption & source

Conceptual and statistical specifications of CoMe Model C.

Version of record (author-provided PDF; author-uploaded ResearchGate copy) · Figure source · Author-authorized reproduction; no open license asserted

Baek, Cho, and Hwang (2026), Figure 3. Copyright 2026 Taylor & Francis Group, LLC. Reproduced from the author-provided version of record at the author's request.

Figure 4

Path diagrams for the three simulation models: Model A has an X-by-W term predicting M, Model B has an M-by-W term predicting Y, and Model C includes both interactions.
Caption & source

Three CoMe models specified for the simulation study.

Version of record (author-provided PDF; author-uploaded ResearchGate copy) · Figure source · Author-authorized reproduction; no open license asserted

Baek, Cho, and Hwang (2026), Figure 4. Copyright 2026 Taylor & Francis Group, LLC. Reproduced from the author-provided version of record at the author's request.

Figure 5

GSCA Pro screenshot of the empirical conditional mediation model with PAS, PUC, DPND, PTC, IU, EPS, and the EPS-by-PTC interaction, plus the indicator assignment panel.
Caption & source

Specification of the example CoMe model in GSCA Pro.

Version of record (author-provided PDF; author-uploaded ResearchGate copy) · Figure source · Author-authorized reproduction; no open license asserted

Baek, Cho, and Hwang (2026), Figure 5. Copyright 2026 Taylor & Francis Group, LLC. Reproduced from the author-provided version of record at the author's request.

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Cite this work

In-Hyun Baek, Gyeongcheol Cho, Heungsun Hwang (2026). A Practical Guide to Conditional Mediation Analysis with GSCA: Method, Simulation, and Software. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2026.2674637

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