Gyeongcheol Cho

Gyeongcheol Cho
McGill University | McGill · Department of Psychology

PhD Candidate

About

23
Publications
11,174
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
368
Citations
Citations since 2017
23 Research Items
368 Citations
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
Introduction
I am a Ph.D. candidate in Quantitative Psychology and Modelling at McGill University, under the supervision of Dr. Heungsun Hwang. I am expected to graduate in the spring of 2023. My main research interest is the advancement of quantitative methods to statistically test and explore the hypothetical relationships between human behavioural, psychological, and biological variables including constructs (i.e., theoretical variables).
Education
September 2017 - May 2022
McGill University
Field of study
  • Quantitative Psychology
March 2009 - August 2017
Korea University
Field of study
  • Economics (with a minor of Mathematics)

Publications

Publications (23)
Article
Full-text available
In this paper, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement...
Article
Full-text available
With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized...
Article
Full-text available
Structural equation modeling (SEM) has emerged into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical S...
Article
Generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambigu-ously specify the relationships between variables in th...
Article
Jöreskog’s covariance-based approach (JCA) has been considered a standard method for structural equation modeling. However, JCA is prone to the occurrence of improper solutions and cannot make probabilistic inferences about the true factor scores. To address the enduring issues of JCA, we propose a data matrix-based alternative, termed structured f...
Data
List of Appendices Appendix S1. A full description of the two stages in SFA Appendix S2. Theorem 1 and its proof. Appendix S3. Theorem 2 and its proof. Appendix S4. The proposed ALS algorithm for the first stage of SFA Appendix S5. Theorem 3 and its proof Appendix S6. A supplementary procedure for the ALS algorithm Appendix S7. A non-iterative est...
Article
Full-text available
Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. component-based, depending on whether a construct is modeled by either a factor or a compo-nent (i.e., weighted composite of indicators). Research in international management (IM) and inter-national business (IB), however, needs to accommodate a more ge...
Article
Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study is the first to offer a comparative evaluation of GSCA and PL...
Preprint
Full-text available
Importance Given the importance of early childhood in psychopathology, identification of social and biological mechanisms of cognitive and psychological development in preadolescence is essential for better diagnostics and therapeutics. Objective To examine causal relationships among genotypes, negative and positive environments, cognitive outcome...
Article
Full-text available
This study investigates the relationship between sensory impressions and perceived value in explaining the behavior of floating market tourists. It also explores whether the effects of sensory elements and perceived value on the behavioral responses of tourists differ among the three stages of destination development. Integrated generalized structu...
Article
Full-text available
Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) an...
Article
Full-text available
Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach remains a limited-information method that carries out two separate stages sequentially to estimate parameters (component weights, loadin...
Article
Full-text available
Owing to its potentially far-reaching impact on a large population, an educational policy may lead to unintended consequences beyond the educational area. The High School Equalization Policy (HSEP), introduced into South Korea in the mid-1970s, is representative of such a policy. HSEP prohibits high school entrance exams and randomly assigns studen...
Article
Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are component-based, or also called variance-based, structural equation modeling (SEM). They define latent variables as components or weighted composites of indicators, attempting to maximize the explained variances of indicators or endogenous component...
Article
Full-text available
Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently prov...
Article
Full-text available
Objective Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can sett...
Book
이 책의 목표와 구성: 이 책은 행동∙사회과학에서 자주 사용되는 통계 기법들을 독자들이 프로그램들을 이용해 직접 하나 하나 사용해보면서, 그 각각의 통계 기법들을 실용적으로 쓸 수 있도록 돕는 것을 목표로 한다. 이를 위해, 1) 책의 전반부에서 기초 통계 관련 개념들은 비교적 가볍게 리뷰하였고, 이후 각 통계 기법들을 실질적으로 사용하는데 필요한 지식들을 보다 자세히 설명하고자 하였다. 2) 예를 들어 어떤 연구 상황에서 어떤 통계 기법이 적절하게 쓰일 수 있는지 스스로 판단할 수 있도록, 각 통계 기법에 대응되는 연구 설계 조건이나 상황, 그리고 각 기법을 사용시 고려되어야 하는 조건 등에 대해 구체적으로 설...
Article
Full-text available
Objective: This study evaluated the psychometric properties of the Korean Anxiety Screening Assessment (K-ANX) developed for screening anxiety disorders. Methods: Data from 613 participants were analyzed. The K-ANX was evaluated for reliability using Cronbach's alpha, item-total correlation, and test information curve, and for validity using foc...
Article
The recent onset of replication crisis in the behavioral and social sciences has led researchers to attend to the generalizability of their empirical research. Cross validation is a useful way of comparing generalizability of theoretically plausible a priori models in terms of prediction and has been utilized in structural equation modeling (SEM)....
Article
Full-text available
The purpose of this study is to investigate the interrelationship among mortgage interest rate, housing consumer sentiment, and housing market. This study used a Structural VAR(SVAR) model that can analyze the ripple impacts among the variables. The results are as follows. Firstly, the change of mortgage interest rate did not have a statistically s...

Network

Cited By

Projects

Projects (2)
Project
SFA Prime (www.sfaprime.com) is free and user-friendly standalone software for structured factor analysis (GSCA) for factor-based structural equation modeling. This software provides a graphical user interface (GUI) that enables users to conduct SFA step by step and obtain the results. We will continue to update the software.
Project
GSCA Pro (www.gscapro.com) is free and user-friendly standalone software for generalized structured component analysis (GSCA) for component-based structural equation modeling. It offers various features of GSCA, including constrained analysis, higher-order components, mediation/moderation analysis, regularized analysis, multilevel analysis, and integrated GSCA. We will continue to update the software.