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In this article, 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 errors incorporated (GSCAM) in a unified manner and can estimate both factor- and component-model parameters, including component and factor loadings, component and factor path coefficients, and path coefficients connecting factors and components. We conduct 2 simulation studies to investigate the performance of IGSCA under models with both factors and components. The first simulation study assesses how existing approaches for structural equation modeling and IGSCA recover parameters. This study shows that only consistent partial least squares (PLSc) and IGSCA yield unbiased estimates of all parameters, whereas the other approaches always provided biased estimates of several parameters. As such, we conduct a second, extensive simulation study to evaluate the relative performance of the 2 competitors (PLSc and IGSCA), considering a variety of experimental factors (model specification, sample size, the number of indicators per factor/component, and exogenous factor/component correlation). IGSCA exhibits better performance than PLSc under most conditions. We also present a real data application of IGSCA to the study of genes and their influence on depression. Finally, we discuss the implications and limitations of this approach, and recommendations for future research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Heungsun Hwang; GyeongCheol Cho; Kwanghee Jung; Carl F. Falk; Jessica Kay Flake; Min Jin Jin; Seung Hwan Lee. An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods 2021, 26, 273 -294.
AMA StyleHeungsun Hwang, GyeongCheol Cho, Kwanghee Jung, Carl F. Falk, Jessica Kay Flake, Min Jin Jin, Seung Hwan Lee. An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods. 2021; 26 (3):273-294.
Chicago/Turabian StyleHeungsun Hwang; GyeongCheol Cho; Kwanghee Jung; Carl F. Falk; Jessica Kay Flake; Min Jin Jin; Seung Hwan Lee. 2021. "An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis." Psychological Methods 26, no. 3: 273-294.
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 structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions’ variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.
Heungsun Hwang; GyeongCheol Cho; Min Jin Jin; Ji Hoon Ryoo; Younyoung Choi; Seung Hwan Lee. A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis. PLOS ONE 2021, 16, e0247592 .
AMA StyleHeungsun Hwang, GyeongCheol Cho, Min Jin Jin, Ji Hoon Ryoo, Younyoung Choi, Seung Hwan Lee. A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis. PLOS ONE. 2021; 16 (3):e0247592.
Chicago/Turabian StyleHeungsun Hwang; GyeongCheol Cho; Min Jin Jin; Ji Hoon Ryoo; Younyoung Choi; Seung Hwan Lee. 2021. "A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis." PLOS ONE 16, no. 3: e0247592.
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, loadings, and path coefficients), indicating that it has no single optimization criterion for estimating the parameters at once. In general, limited-information methods are known to provide less efficient parameter estimates than full-information ones. To address this enduring issue, we propose a full-information method for partial least squares path modeling, termed global least squares path modeling, where a single least squares criterion is consistently minimized via a simple iterative algorithm to estimate all the parameters simultaneously. We evaluate the relative performance of the proposed method through the analyses of simulated and real data. We also show that from algorithmic perspectives, the proposed method can be seen as a block-wise special case of another full-information method for component-based structural equation modeling—generalized structured component analysis.
Heungsun Hwang; GyeongCheol Cho. Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling. Psychometrika 2020, 85, 947 -972.
AMA StyleHeungsun Hwang, GyeongCheol Cho. Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling. Psychometrika. 2020; 85 (4):947-972.
Chicago/Turabian StyleHeungsun Hwang; GyeongCheol Cho. 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling." Psychometrika 85, no. 4: 947-972.
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 students to a high school near their residence. Despite its aim of ensuring equal opportunities in education for all students regardless of socio-economic status, a frequent criticism was that HSEP could prompt students’ families to move to a region near traditional elite high schools, which, in turn, would widen the gap in house prices between different regions. Thus, we conducted an empirical study to examine the secondary influence of the HSEP on the housing market via a difference-in-differences (DD) analysis. We used house price data from the Gangwon province, as the partial introduction of HSEP into the province allowed for a quasi-experimental study on the effect of HSEP. The result revealed that, contrary to expectations, the HSEP in Gangwon had the opposite spillover effect of reducing the gap of the average house prices by 5%~9% across regions.
GyeongCheol Cho; Younyoung Choi; Ji-Hyun Kim. Investigating the Unintended Consequences of the High School Equalization Policy on the Housing Market. Sustainability 2020, 12, 8496 .
AMA StyleGyeongCheol Cho, Younyoung Choi, Ji-Hyun Kim. Investigating the Unintended Consequences of the High School Equalization Policy on the Housing Market. Sustainability. 2020; 12 (20):8496.
Chicago/Turabian StyleGyeongCheol Cho; Younyoung Choi; Ji-Hyun Kim. 2020. "Investigating the Unintended Consequences of the High School Equalization Policy on the Housing Market." Sustainability 12, no. 20: 8496.
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) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.
GyeongCheol Cho; Heungsun Hwang; Marko Sarstedt; Christian M. Ringle. Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics 2020, 8, 189 -202.
AMA StyleGyeongCheol Cho, Heungsun Hwang, Marko Sarstedt, Christian M. Ringle. Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics. 2020; 8 (4):189-202.
Chicago/Turabian StyleGyeongCheol Cho; Heungsun Hwang; Marko Sarstedt; Christian M. Ringle. 2020. "Cutoff criteria for overall model fit indexes in generalized structured component analysis." Journal of Marketing Analytics 8, no. 4: 189-202.
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 components or both. Despite this common conceptualization of latent variables, GSCA and PLSPM involve distinct model specifications and estimation procedures. This paper focuses on comparing four modeling approaches—GSCA with reflective indicators, GSCA with formative indicators, PLSPM with mode A, and PLSPM with mode B—regarding their capability of parameter recovery and statistical power via Monte Carlo simulation. For comparison, we propose a new data generating process for variance-based SEM, appropriate to handle all possible modeling approaches for both GSCA and PLSPM. It was found that although every approach produced consistent estimators, GSCA with reflective indicators yielded the most efficient estimators under variance-based structural equation models.
GyeongCheol Cho; Ji Yeh Choi. An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models. Behaviormetrika 2019, 47, 243 -272.
AMA StyleGyeongCheol Cho, Ji Yeh Choi. An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models. Behaviormetrika. 2019; 47 (1):243-272.
Chicago/Turabian StyleGyeongCheol Cho; Ji Yeh Choi. 2019. "An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models." Behaviormetrika 47, no. 1: 243-272.
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 provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.
Kwanghee Jung; Jaehoon Lee; Vibhuti Gupta; GyeongCheol Cho. Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation. Frontiers in Psychology 2019, 10, 2215 .
AMA StyleKwanghee Jung, Jaehoon Lee, Vibhuti Gupta, GyeongCheol Cho. Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation. Frontiers in Psychology. 2019; 10 ():2215.
Chicago/Turabian StyleKwanghee Jung; Jaehoon Lee; Vibhuti Gupta; GyeongCheol Cho. 2019. "Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation." Frontiers in Psychology 10, no. : 2215.
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 settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
GyeongCheol Cho; Jinyeong Yim; Younyoung Choi; Jungmin Ko; Seoung-Hwan Lee. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investigation 2019, 16, 262 -269.
AMA StyleGyeongCheol Cho, Jinyeong Yim, Younyoung Choi, Jungmin Ko, Seoung-Hwan Lee. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investigation. 2019; 16 (4):262-269.
Chicago/Turabian StyleGyeongCheol Cho; Jinyeong Yim; Younyoung Choi; Jungmin Ko; Seoung-Hwan Lee. 2019. "Review of Machine Learning Algorithms for Diagnosing Mental Illness." Psychiatry Investigation 16, no. 4: 262-269.
Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.
GyeongCheol Cho; Kwanghee Jung; Heungsun Hwang. Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis. Multivariate Behavioral Research 2019, 54, 505 -513.
AMA StyleGyeongCheol Cho, Kwanghee Jung, Heungsun Hwang. Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis. Multivariate Behavioral Research. 2019; 54 (4):505-513.
Chicago/Turabian StyleGyeongCheol Cho; Kwanghee Jung; Heungsun Hwang. 2019. "Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis." Multivariate Behavioral Research 54, no. 4: 505-513.
This study evaluated the psychometric properties of the Korean Anxiety Screening Assessment (K-ANX) developed for screening anxiety disorders. 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 focus group interviews, factor analysis, correlational analysis, and item characteristics based on item response theory (IRT). The diagnostic sensitivity and specificity of the K-ANX were compared with those of the Beck Anxiety Inventory (BAI) and Generalized Anxiety Disorder 7-item scale (GAD-7). The K-ANX showed excellent internal consistency (α=0.97) and item-total coefficients (0.92–0.97), and a one-factor structure was suggested. All items were highly correlated with the total scores of the BAI, GAD-7, and Penn State Worry Questionnaire. IRT analysis indicated the K-ANX was most informative as a screening tool for anxiety disorders at the range between 0.8 and 1.6 (i.e., top 21.2 to 5.5 percentiles). Higher sensitivity (0.795) and specificity (0.937) for identifying anxiety disorders were observed in the K-ANX compared to the BAI and GAD-7. The K-ANX is a reliable and valid measure to screen anxiety disorders in a Korean sample, with greater sensitivity and specificity than current measures of anxiety symptoms.
Yeseul Kim; Yeonsoo Park; GyeongCheol Cho; Kiho Park; Shin-Hyang Kim; Seung Yeon Baik; Cho Long Kim; Sooyun Jung; Won-Hye Lee; Younyoung Choi; Seung-Hwan Lee; Kee-Hong Choi. Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment. Psychiatry Investigation 2018, 15, 1053 -1063.
AMA StyleYeseul Kim, Yeonsoo Park, GyeongCheol Cho, Kiho Park, Shin-Hyang Kim, Seung Yeon Baik, Cho Long Kim, Sooyun Jung, Won-Hye Lee, Younyoung Choi, Seung-Hwan Lee, Kee-Hong Choi. Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment. Psychiatry Investigation. 2018; 15 (11):1053-1063.
Chicago/Turabian StyleYeseul Kim; Yeonsoo Park; GyeongCheol Cho; Kiho Park; Shin-Hyang Kim; Seung Yeon Baik; Cho Long Kim; Sooyun Jung; Won-Hye Lee; Younyoung Choi; Seung-Hwan Lee; Kee-Hong Choi. 2018. "Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment." Psychiatry Investigation 15, no. 11: 1053-1063.
최윤영; GyeongCheol Cho; 김지현. A Study on the Interrelationship among Interest Rate, Housing Consumer Sentiment and Housing Market Using SVAR Model. The Korea Spatial Planning Review 2017, 95, 3 -20.
AMA Style최윤영, GyeongCheol Cho, 김지현. A Study on the Interrelationship among Interest Rate, Housing Consumer Sentiment and Housing Market Using SVAR Model. The Korea Spatial Planning Review. 2017; 95 (null):3-20.
Chicago/Turabian Style최윤영; GyeongCheol Cho; 김지현. 2017. "A Study on the Interrelationship among Interest Rate, Housing Consumer Sentiment and Housing Market Using SVAR Model." The Korea Spatial Planning Review 95, no. null: 3-20.