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Last update: Sep 21, 2023

Gyeongcheol Cho, Ph.D., McGill University, 2023
Assistant Professor
Emailcho.1240@osu.edu
Phone: (614) 569 6477
Office: 228 Lazenby Hall
Research Interests: Advancing quantitative methods in exploring and confirming complex relationships between human behavioral, psychological, and biological variables—factor/component analyses, structural equation modeling, and interpretable machine learning—with a strong emphasis on the measurement of theoretical constructs.


A: Welcome to my webpage. I am Gyeongcheol Cho, an expert in Quantitative Psychology. My work centers on the development and application of statistical methods to quantify psychological states and explore their connection with behavioral patterns and pertinent societal phenomena.

I firmly believe that numerous pressing societal issues, encompassing warfare, discrimination, and inequality, are closely tied to individuals’ unhealthy or imbalanced psychological states. These states can engender a proclivity to monopolize resources or to demean others, perpetuating a cycle of negativity and conflict. I am convinced that by nurturing elevated and nobler mindsets, we can guide ourselves and other individuals toward lives that are both enriched and self-fulfilling, grounded in enlightened perspectives.

To empirically pursue this line of inquiry, we must create tools capable of precisely measuring individuals’ psychological states and scrutinizing their interrelations with other relevant variables. Drawing upon my background in quantitative psychology, I aspire to pave the way for this exploratory journey through the advancement of essential statistical methodologies. Moreover, I am open to and eager for collaboration with fellow psychologists and researchers from a wide spectrum of disciplines.

At present, I am leading three methodological research programs:

  1. A deep investigation into the measurement of theoretical constructs.
  2. Advancements in the two pivotal realms of structural equation modeling: factor-based and component-based approaches.
  3. The innovative fusion of structural equation modeling with deep learning artificial neural networks.

I look forward to the profound insights and discoveries that this exploration is poised to uncover.


I welcome students who share these ideals and are willing to embark on this journey as passionate explorers. However, it’s essential to note that this journey is not easy and requires participants to be adequately prepared. If you are interested and wish to understand what preparations are necessary, please check this page.