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Related Experiment Video

Updated: Feb 13, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Consistent Partial Least Squares Path Modeling via Regularization.

Sunho Jung¹, JaeHong Park¹

¹Kyung Hee University, Seoul, South Korea.

Frontiers in Psychology

|March 9, 2018

Summary

This summary is machine-generated.

Consistent partial least squares (PLS) path modeling is enhanced with regularization to address multicollinearity. Regularized PLSc improves statistical power and accuracy in social and psychological research when dealing with complex data structures.

Keywords:

Monte Carlo simulation consistent partial least squares multicollinearity ridge-type regularization structural equation modeling

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Last Updated: Feb 13, 2026

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Area of Science:

Social Sciences
Psychological Research
Statistical Modeling

Background:

Partial Least Squares (PLS) path modeling is a flexible structural equation modeling technique.
Consistent PLS (PLSc) offers improved path coefficient estimation for common factors.
PLSc struggles with multicollinearity, impacting statistical power and accuracy.

Purpose of the Study:

To introduce a regularized PLSc technique to mitigate multicollinearity.
To evaluate the performance of regularized PLSc against standard PLSc.
To enhance the reliability of path coefficient estimation in structural equation modeling.

Main Methods:

Incorporation of ridge-type regularization into PLSc.
Development of a novel technique termed regularized PLSc.
Conducting a comprehensive simulation study to compare methods.

Main Results:

Regularized PLSc demonstrates superior performance in the presence of multicollinearity.
The new method shows improved statistical power and parameter estimation accuracy.
Simulation results validate the effectiveness of the regularization approach.

Conclusions:

Regularized PLSc is recommended for social and psychological research with significant multicollinearity.
The technique enhances the robustness of path modeling in complex datasets.
This advancement offers a practical solution for a common methodological challenge.