ArticleMethodsX2026
PCA-PI-PLS-ABS: A new approach for uncovering directional heterogeneity in PLS-SEM.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard Partial Least Squares Structural Equation Modeling (PLS-SEM) methods often fail to detect significant structural relationships when Simpson's Paradox is present in the data (e.g., when one subgroup has a positive effect while another subgroup has a negative effect). In such cases the subgroup effects may be cancelled in the aggregate (global) model, so that the pooled coefficient describes neither subgroup. This article introduces PCA-PI-PLS-ABS, a new approach designed to efficiently reveal such directional heterogeneity. Monte Carlo simulation validation shows that this method successfully separates data groups with opposite directions (β
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Registered trials
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