<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>PsycheMatrica Lab News</title><link>https://psychematrica.org</link><description>Research updates from PsycheMatrica Lab</description><item><title>PsycheMatrica Lab Launches Its Official Website</title><link>https://psychematrica.org/news/website-launch-2026-10-17/</link><guid>https://psychematrica.org/news/website-launch-2026-10-17/</guid><pubDate>Sat, 17 Oct 2026 12:00:00 GMT</pubDate><description>We are pleased to announce the launch of the official PsycheMatrica Lab website.

The website introduces our mission and research directions in quantitative methodology for psychological and learning sciences. It brings together our publications, public preprints, research software, people, and lab news in one place.

Our Publications section distinguishes peer-reviewed publications from preprints while keeping manuscript versions linked within a single record for each study. Through News, we will share updates on research, conference presentations, software releases, and developments in the lab.

We invite researchers, students, and prospective collaborators to explore our work and stay connected. For research and collaboration inquiries, please contact g.cho@psychematrica.org.</description></item><item><title>New publication: A Practical Guide to Conditional Mediation Analysis with GSCA: Method, Simulation, and Software</title><link>https://psychematrica.org/news/conditional-mediation-guide/</link><guid>https://psychematrica.org/news/conditional-mediation-guide/</guid><pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate><description>“A Practical Guide to Conditional Mediation Analysis with GSCA: Method, Simulation, and Software” was published online in Structural Equation Modeling: A Multidisciplinary Journal. A practical tutorial on conditional mediation analysis with generalized structured component analysis, covering method, simulation, and software.</description></item><item><title>Public preprint: Data-Matrix-Based Overall Goodness-of-Fit Testing for Confirmatory Factor Analysis: In-Sample Prediction Error for Observed Variables</title><link>https://psychematrica.org/news/data-matrix-goodness-of-fit-preprint-posted-2026-10-06/</link><guid>https://psychematrica.org/news/data-matrix-goodness-of-fit-preprint-posted-2026-10-06/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>A public preprint of “Data-Matrix-Based Overall Goodness-of-Fit Testing for Confirmatory Factor Analysis: In-Sample Prediction Error for Observed Variables” is available. A public preprint develops and evaluates data-matrix-based tests of overall and nested-model fit in confirmatory factor analysis, using Monte Carlo simulation and an empirical illustration. The public manuscript version and its DOI are retained on the paper’s permanent record.</description></item><item><title>New publication: Modeling conditional dependencies between recidivism and cognitive emotion regulation strategies among prisoners using a Bayesian network with interpretable summary indexes</title><link>https://psychematrica.org/news/recidivism-bayesian-network/</link><guid>https://psychematrica.org/news/recidivism-bayesian-network/</guid><pubDate>Thu, 06 Aug 2026 12:00:00 GMT</pubDate><description>“Modeling conditional dependencies between recidivism and cognitive emotion regulation strategies among prisoners using a Bayesian network with interpretable summary indexes” was published online in PLOS One. Bayesian networks and interpretable summary indexes are applied collaboratively to questions about recidivism and cognitive emotion regulation.</description></item><item><title>UKC 2026, Florida: Combining Convex GSCA and Discrete Bayesian Networks: A Proof-of-Concept Application to Cognitive-Therapy Prioritization in CBT-I</title><link>https://psychematrica.org/news/cbti-ukc-2026/</link><guid>https://psychematrica.org/news/cbti-ukc-2026/</guid><description>“Combining Convex GSCA and Discrete Bayesian Networks: A Proof-of-Concept Application to Cognitive-Therapy Prioritization in CBT-I,” by Gyeongcheol Cho and Younyoung Choi, was presented as a poster at UKC 2026, Florida.</description></item><item><title>IMPS 2026, Seoul: Completing Partial Least Squares Path Modeling: Generalized Structured Component Analysis</title><link>https://psychematrica.org/news/completing-pls-imps-2026/</link><guid>https://psychematrica.org/news/completing-pls-imps-2026/</guid><pubDate>Fri, 24 Jul 2026 12:00:00 GMT</pubDate><description>“Completing Partial Least Squares Path Modeling: Generalized Structured Component Analysis,” by Gyeongcheol Cho, was presented at IMPS 2026, Seoul.</description></item><item><title>IMPS 2026, Seoul: Extending Generalized Structured Component Analysis with Kernel Functions for Modeling Nonlinear Data</title><link>https://psychematrica.org/news/kernel-gsca-imps-2026/</link><guid>https://psychematrica.org/news/kernel-gsca-imps-2026/</guid><pubDate>Fri, 24 Jul 2026 12:00:00 GMT</pubDate><description>“Extending Generalized Structured Component Analysis with Kernel Functions for Modeling Nonlinear Data,” by Hye Won Suk, Gyeongcheol Cho, and Heungsun Hwang, was presented at IMPS 2026, Seoul.</description></item><item><title>IMPS 2026, Seoul: Evaluation of Classical and Machine Learning-Based Missing Data Methods in Generalized Structured Component Analysis</title><link>https://psychematrica.org/news/missing-data-imps-2026/</link><guid>https://psychematrica.org/news/missing-data-imps-2026/</guid><pubDate>Fri, 24 Jul 2026 12:00:00 GMT</pubDate><description>“Evaluation of Classical and Machine Learning-Based Missing Data Methods in Generalized Structured Component Analysis,” by Luqi He, Gyeongcheol Cho, and Heungsun Hwang, was presented at IMPS 2026, Seoul.</description></item><item><title>IMPS 2026, Seoul: Multilevel GSCA with Group-Level Variables: An Illustration Using GSCA Pro</title><link>https://psychematrica.org/news/multilevel-gsca-imps-2026/</link><guid>https://psychematrica.org/news/multilevel-gsca-imps-2026/</guid><pubDate>Fri, 24 Jul 2026 12:00:00 GMT</pubDate><description>“Multilevel GSCA with Group-Level Variables: An Illustration Using GSCA Pro,” by Sunmee Kim, Gyeongcheol Cho, Heungsun Hwang, Gabriel Cepeda Carrión, Silvia Martelo-Landroguez, and José Roldán, was presented at IMPS 2026, Seoul.</description></item><item><title>IMPS 2026, Seoul: Second-Order Structural Equation Modeling with both Factors and Components</title><link>https://psychematrica.org/news/second-order-imps-2026/</link><guid>https://psychematrica.org/news/second-order-imps-2026/</guid><pubDate>Thu, 23 Jul 2026 12:00:00 GMT</pubDate><description>“Second-Order Structural Equation Modeling with both Factors and Components,” by In-Hyun Baek, Gyeongcheol Cho, Florian Schuberth, and Heungsun Hwang, was presented at IMPS 2026, Seoul.</description></item><item><title>IMPS 2026 poster: Data-matrix-based overall goodness-of-fit testing</title><link>https://psychematrica.org/news/cfa-fit-imps-poster-2026/</link><guid>https://psychematrica.org/news/cfa-fit-imps-poster-2026/</guid><pubDate>Tue, 21 Jul 2026 12:00:00 GMT</pubDate><description>The IMPS 2026 program lists the poster “A Data-Matrix-Based Overall Goodness-of-Fit Test for Confirmatory Factor Analysis: In-Sample Prediction Error for Observed Variables,” by Young Ze Kim, Gyeongcheol Cho, and Hanjoe Kim, in Poster Session I on July 21 in Seoul.</description></item><item><title>Invited talk at Ajou University, Department of Psychology, Suwon</title><link>https://psychematrica.org/news/ajou-psychology-2026/</link><guid>https://psychematrica.org/news/ajou-psychology-2026/</guid><description>GyeongCheol Cho gave an invited presentation, “Beyond the latent–descriptive construct dichotomy: Structural construct analysis for integrating factor- and component-based structural equation modeling,” at Ajou University, Department of Psychology, Suwon.</description></item><item><title>ISDSA 2026: Data-matrix–based local diagnostics for confirmatory factor analysis: In-sample prediction error for individual observed variables</title><link>https://psychematrica.org/news/ling-zhu-isdsa-2026/</link><guid>https://psychematrica.org/news/ling-zhu-isdsa-2026/</guid><description>Ling Zhu presented “Data-matrix–based local diagnostics for confirmatory factor analysis: In-sample prediction error for individual observed variables,” coauthored with Gyeongcheol Cho, at ISDSA 2026. The work develops local diagnostics for confirmatory factor analysis using prediction error for individual observed variables.

The meeting took place in July 2026. ISDSA Academy published the presentation recording on August 12, 2026.</description></item><item><title>ISDSA 2026, Beijing: Evaluation of Classical and Machine Learning-Based Missing Data Methods in Generalized Structured Component Analysis</title><link>https://psychematrica.org/news/missing-data-isdsa-2026/</link><guid>https://psychematrica.org/news/missing-data-isdsa-2026/</guid><description>“Evaluation of Classical and Machine Learning-Based Missing Data Methods in Generalized Structured Component Analysis,” by Luqi He, Gyeongcheol Cho, and Heungsun Hwang, was presented at ISDSA 2026, Beijing.</description></item><item><title>Public preprint: Psychological resources confer greatest protection against late-life depression in genetically vulnerable adults</title><link>https://psychematrica.org/news/psychological-resources-depression-preprint-posted-2026-06-17/</link><guid>https://psychematrica.org/news/psychological-resources-depression-preprint-posted-2026-06-17/</guid><pubDate>Wed, 17 Jun 2026 12:00:00 GMT</pubDate><description>A public preprint of “Psychological resources confer greatest protection against late-life depression in genetically vulnerable adults” is available. Collaborative longitudinal research uses machine learning, regression, and generalized structured component analysis to study psychological resources, genetic vulnerability, and depressive symptoms in later life. The public manuscript version and its DOI are retained on the paper’s permanent record.</description></item><item><title>Canadian Psychological Association’s 87th Annual National Convention, Montréal: Second-order structural equation modeling with both factors and components</title><link>https://psychematrica.org/news/second-order-cpa-2026/</link><guid>https://psychematrica.org/news/second-order-cpa-2026/</guid><description>“Second-order structural equation modeling with both factors and components,” by In-Hyun Baek, Gyeongcheol Cho, Florian Schuberth, and Heungsun Hwang, was presented as a poster at Canadian Psychological Association’s 87th Annual National Convention, Montréal.</description></item><item><title>Korean Society of Psychological Measurement and Assessment, Busan: A data-matrix-based overall goodness-of-fit test for confirmatory factor analysis</title><link>https://psychematrica.org/news/cfa-fit-kspma-2026/</link><guid>https://psychematrica.org/news/cfa-fit-kspma-2026/</guid><description>“A data-matrix-based overall goodness-of-fit test for confirmatory factor analysis,” by Y. J. Lee, Gyeongcheol Cho, and H. J. Kim, was presented as a poster at Korean Society of Psychological Measurement and Assessment, Busan.</description></item><item><title>Invited talk at Seoul National University, Department of Psychology, Seoul</title><link>https://psychematrica.org/news/snu-psychology-2026/</link><guid>https://psychematrica.org/news/snu-psychology-2026/</guid><description>GyeongCheol Cho gave an invited presentation, “Beyond the latent–descriptive construct dichotomy: Structural construct analysis for integrating factor- and component-based structural equation modeling,” at Seoul National University, Department of Psychology, Seoul.</description></item><item><title>Korean Society of Psychological Measurement and Assessment, Busan: A data-matrix-based structural construct analysis for integrating factor-based and component-based structural equation modeling</title><link>https://psychematrica.org/news/structural-construct-kspma-2026/</link><guid>https://psychematrica.org/news/structural-construct-kspma-2026/</guid><description>“A data-matrix-based structural construct analysis for integrating factor-based and component-based structural equation modeling,” by Gyeongcheol Cho, was presented at Korean Society of Psychological Measurement and Assessment, Busan.</description></item><item><title>New publication: Power Analysis in Generalized Structured Component Analysis: Implementation in GSCA Pro</title><link>https://psychematrica.org/news/power-analysis-gsca-pro/</link><guid>https://psychematrica.org/news/power-analysis-gsca-pro/</guid><pubDate>Thu, 26 Feb 2026 12:00:00 GMT</pubDate><description>“Power Analysis in Generalized Structured Component Analysis: Implementation in GSCA Pro” was published online in Structural Equation Modeling: A Multidisciplinary Journal. Power analysis for generalized structured component analysis, with implementation guidance using GSCA Pro.</description></item><item><title>Invited talk at Sogang University, Department of Psychology (online)</title><link>https://psychematrica.org/news/sogang-psychology-2026/</link><guid>https://psychematrica.org/news/sogang-psychology-2026/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Sogang University, Department of Psychology (online).</description></item><item><title>Invited panel: Personalized treatment recommendations</title><link>https://psychematrica.org/news/iaap-panel-2026/</link><guid>https://psychematrica.org/news/iaap-panel-2026/</guid><description>GyeongCheol Cho gave an online invited panel presentation, “Building a personalized treatment recommender that mimics clinicians’ decision-making,” at an event listed as IAAP 2026 in his CV.</description></item><item><title>Honorable Mention for collaborative scholarly work</title><link>https://psychematrica.org/news/kspma-honorable-mention-2026/</link><guid>https://psychematrica.org/news/kspma-honorable-mention-2026/</guid><description>Collaborative scholarly work involving GyeongCheol Cho received a 2026 Honorable Mention from the Korean Society of Psychological Measurement and Assessment.</description></item><item><title>Invited talk at Georgia Institute of Technology, School of Psychology (online)</title><link>https://psychematrica.org/news/georgia-tech-2025/</link><guid>https://psychematrica.org/news/georgia-tech-2025/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Georgia Institute of Technology, School of Psychology (online).</description></item><item><title>Public preprint: Modeling conditional dependencies between recidivism and cognitive emotion regulation strategies among prisoners using a Bayesian network with interpretable summary indexes</title><link>https://psychematrica.org/news/recidivism-bayesian-networks-preprint-posted-2025-10-24/</link><guid>https://psychematrica.org/news/recidivism-bayesian-networks-preprint-posted-2025-10-24/</guid><pubDate>Fri, 24 Oct 2025 12:00:00 GMT</pubDate><description>A public preprint of “Modeling conditional dependencies between recidivism and cognitive emotion regulation strategies among prisoners using a Bayesian network with interpretable summary indexes” is available. Bayesian networks and interpretable summary indexes are applied collaboratively to questions about recidivism and cognitive emotion regulation. The public manuscript version and its DOI are retained on the paper’s permanent record.</description></item><item><title>Public preprint: Bayesian Networks with Interpretable Summary Indexes for Modeling Clinicians’ Decision-Making in Treatment Recommendation for Mental Disorders</title><link>https://psychematrica.org/news/bayesian-treatment-recommendations-preprint-posted-2025-10-23/</link><guid>https://psychematrica.org/news/bayesian-treatment-recommendations-preprint-posted-2025-10-23/</guid><pubDate>Thu, 23 Oct 2025 12:00:00 GMT</pubDate><description>A public preprint of “Bayesian Networks with Interpretable Summary Indexes for Modeling Clinicians’ Decision-Making in Treatment Recommendation for Mental Disorders” is available. A public manuscript combining Convex GSCA summary indexes with Bayesian networks to model clinicians’ treatment recommendations, illustrated with insomnia-related decisions. The public manuscript version and its DOI are retained on the paper’s permanent record.</description></item><item><title>New publication: Influence of genetic polymorphism and trauma on cortical structures and PTSD severity: imaging genetics generalized structured component analysis</title><link>https://psychematrica.org/news/imaging-genetics-trauma-ptsd-first-online-2025-09-17/</link><guid>https://psychematrica.org/news/imaging-genetics-trauma-ptsd-first-online-2025-09-17/</guid><pubDate>Wed, 17 Sep 2025 12:00:00 GMT</pubDate><description>“Influence of genetic polymorphism and trauma on cortical structures and PTSD severity: imaging genetics generalized structured component analysis” was published online in Frontiers in Psychiatry. Collaborative empirical research connecting genetic variation, trauma, cortical structures, and PTSD severity.</description></item><item><title>Ohio State Statistics seminar</title><link>https://psychematrica.org/news/osu-statistics-seminar-2025/</link><guid>https://psychematrica.org/news/osu-statistics-seminar-2025/</guid><pubDate>Thu, 11 Sep 2025 12:00:00 GMT</pubDate><description>GyeongCheol Cho presented “Constructs may or may not be latent: Studies on two domains of structural equation modeling” in the Department of Statistics seminar series at The Ohio State University.</description></item><item><title>New publication: Regularized Structural Equation Modeling with Both Factors and Components</title><link>https://psychematrica.org/news/regularized-factors-components-first-online-2025-09-10/</link><guid>https://psychematrica.org/news/regularized-factors-components-first-online-2025-09-10/</guid><pubDate>Wed, 10 Sep 2025 12:00:00 GMT</pubDate><description>“Regularized Structural Equation Modeling with Both Factors and Components” was published online in Structural Equation Modeling: A Multidisciplinary Journal. Regularization for structural equation models that contain both factors and components.</description></item><item><title>IMPS 2025, Minnesota: Generalized structured component analysis accommodating convex components: A knowledge-based multivariate method with interpretable composite indexes</title><link>https://psychematrica.org/news/convex-gsca-imps-2025/</link><guid>https://psychematrica.org/news/convex-gsca-imps-2025/</guid><description>“Generalized structured component analysis accommodating convex components: A knowledge-based multivariate method with interpretable composite indexes,” by Gyeongcheol Cho and Heungsun Hwang, was presented at IMPS 2025, Minnesota.</description></item><item><title>IMPS 2025, Minnesota: Comparison of missing data techniques in generalized structured component analysis</title><link>https://psychematrica.org/news/missing-data-imps-2025/</link><guid>https://psychematrica.org/news/missing-data-imps-2025/</guid><description>“Comparison of missing data techniques in generalized structured component analysis,” by Luqi He, Gyeongcheol Cho, and Heungsun Hwang, was presented at IMPS 2025, Minnesota.</description></item><item><title>IMPS 2025, Minnesota: Evaluating statistical power in generalized structured component analysis</title><link>https://psychematrica.org/news/power-gsca-imps-2025/</link><guid>https://psychematrica.org/news/power-gsca-imps-2025/</guid><description>“Evaluating statistical power in generalized structured component analysis,” by Z. Shen, Gyeongcheol Cho, and Heungsun Hwang, was presented at IMPS 2025, Minnesota.</description></item><item><title>Invited talk at Pusan National University, Department of Sports Science, Busan</title><link>https://psychematrica.org/news/pusan-sports-2025/</link><guid>https://psychematrica.org/news/pusan-sports-2025/</guid><description>GyeongCheol Cho gave an invited presentation, “Introduction to Generalized Structured Component Analysis,” at Pusan National University, Department of Sports Science, Busan.</description></item><item><title>New publication: Comparison of Component-Based Structural Equation Modeling Methods in Testing Component Interaction Effects</title><link>https://psychematrica.org/news/component-interaction-effects-first-online-2025-05-27/</link><guid>https://psychematrica.org/news/component-interaction-effects-first-online-2025-05-27/</guid><pubDate>Tue, 27 May 2025 12:00:00 GMT</pubDate><description>“Comparison of Component-Based Structural Equation Modeling Methods in Testing Component Interaction Effects” was published online in Structural Equation Modeling: A Multidisciplinary Journal. A methodological comparison of approaches to testing interactions between components.</description></item><item><title>Invited talk at Ajou University, Department of Psychology, Suwon</title><link>https://psychematrica.org/news/ajou-psychology-2025/</link><guid>https://psychematrica.org/news/ajou-psychology-2025/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Ajou University, Department of Psychology, Suwon.</description></item><item><title>Academy of Marketing Science Annual Conference, Montreal: Regularized Structural Equation Modeling with Both Factors and Components</title><link>https://psychematrica.org/news/regularized-sem-ams-2025/</link><guid>https://psychematrica.org/news/regularized-sem-ams-2025/</guid><description>“Regularized Structural Equation Modeling with Both Factors and Components,” by Gyeongcheol Cho, J. Choi, M. Sarstedt, and Heungsun Hwang, was presented at Academy of Marketing Science Annual Conference, Montreal.</description></item><item><title>Invited talk at Seoul National University, Department of Psychology, Seoul</title><link>https://psychematrica.org/news/snu-psychology-2025/</link><guid>https://psychematrica.org/news/snu-psychology-2025/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Seoul National University, Department of Psychology, Seoul.</description></item><item><title>Anne Anastasi Dissertation Award</title><link>https://psychematrica.org/news/anastasi-dissertation-award-2025/</link><guid>https://psychematrica.org/news/anastasi-dissertation-award-2025/</guid><description>GyeongCheol Cho received the 2025 Anne Anastasi Dissertation Award from APA Division 5, Quantitative and Qualitative Methods.</description></item><item><title>Published correction: Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes</title><link>https://psychematrica.org/news/convex-components-correction-2024-09-14/</link><guid>https://psychematrica.org/news/convex-components-correction-2024-09-14/</guid><pubDate>Sat, 14 Sep 2024 12:00:00 GMT</pubDate><description>A correction to “Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes” addresses section numbering and internal section references. It is linked to the original paper’s canonical record and is not counted as a new research article.</description></item><item><title>Invited talk at Ajou University, Department of Preventive Medicine &amp; Public Health (online)</title><link>https://psychematrica.org/news/ajou-public-health-2024/</link><guid>https://psychematrica.org/news/ajou-public-health-2024/</guid><description>GyeongCheol Cho gave an invited presentation, “Introduction to convex generalized structured component analysis with interpretable composite indexes,” at Ajou University, Department of Preventive Medicine &amp; Public Health (online).</description></item><item><title>Version of Record update: Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children</title><link>https://psychematrica.org/news/gene-environment-cognition-version-of-record-update-2024-08-12/</link><guid>https://psychematrica.org/news/gene-environment-cognition-version-of-record-update-2024-08-12/</guid><pubDate>Mon, 12 Aug 2024 12:00:00 GMT</pubDate><description>eLife released an update to the Version of Record for “Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children.” This entry records a version update rather than a separate paper.</description></item><item><title>Psychometric Society Dissertation Award address</title><link>https://psychematrica.org/news/imps-dissertation-address-2024/</link><guid>https://psychematrica.org/news/imps-dissertation-address-2024/</guid><pubDate>Tue, 16 Jul 2024 12:00:00 GMT</pubDate><description>GyeongCheol Cho delivered the Dissertation Award address at IMPS 2024 in Prague: “Constructs May or May Not Be Latent: Studies on Two Domains of Structural Equation Modeling.”</description></item><item><title>ISDSA 2024, Vienna: Deep learning generalized structured component analysis: An interpretable artificial neural network model with composite indexes</title><link>https://psychematrica.org/news/deep-learning-isdsa-2024/</link><guid>https://psychematrica.org/news/deep-learning-isdsa-2024/</guid><description>“Deep learning generalized structured component analysis: An interpretable artificial neural network model with composite indexes,” by Gyeongcheol Cho and Heungsun Hwang, was presented at ISDSA 2024, Vienna.</description></item><item><title>IMPS 2024, Prague: Comparison of component-based SEM methods in testing component interaction effects</title><link>https://psychematrica.org/news/interaction-imps-poster-2024/</link><guid>https://psychematrica.org/news/interaction-imps-poster-2024/</guid><description>“Comparison of component-based SEM methods in testing component interaction effects,” by Z. Shen, Gyeongcheol Cho, and Heungsun Hwang, was presented as a poster at IMPS 2024, Prague.</description></item><item><title>Invited talk at Korea University, School of Psychology, Seoul</title><link>https://psychematrica.org/news/korea-psychology-2024/</link><guid>https://psychematrica.org/news/korea-psychology-2024/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Korea University, School of Psychology, Seoul.</description></item><item><title>Invited talk at Pusan National University, Department of Sports Science, Busan</title><link>https://psychematrica.org/news/pusan-sports-2024/</link><guid>https://psychematrica.org/news/pusan-sports-2024/</guid><description>GyeongCheol Cho gave an invited presentation, “Constructs may or may not be latent: Two domains of structural equation modeling,” at Pusan National University, Department of Sports Science, Busan.</description></item><item><title>ISDSA 2024, Vienna: Regularized integrated generalized structured component analysis</title><link>https://psychematrica.org/news/regularized-gsca-isdsa-2024/</link><guid>https://psychematrica.org/news/regularized-gsca-isdsa-2024/</guid><description>“Regularized integrated generalized structured component analysis,” by Heungsun Hwang and Gyeongcheol Cho, was presented at ISDSA 2024, Vienna.</description></item><item><title>IMPS 2024, Prague: A two-step approach for personalized treatment recommendations in mental health using convex generalized structured component analysis and Bayesian networks</title><link>https://psychematrica.org/news/treatment-imps-poster-2024/</link><guid>https://psychematrica.org/news/treatment-imps-poster-2024/</guid><description>“A two-step approach for personalized treatment recommendations in mental health using convex generalized structured component analysis and Bayesian networks,” by Gyeongcheol Cho and Younyoung Choi, was presented as a poster at IMPS 2024, Prague.</description></item><item><title>Invited talk at KAIST, School of Digital Humanities and Computational Social Sciences, Daejeon</title><link>https://psychematrica.org/news/kaist-2024/</link><guid>https://psychematrica.org/news/kaist-2024/</guid><pubDate>Tue, 18 Jun 2024 12:00:00 GMT</pubDate><description>GyeongCheol Cho gave an invited presentation, “Structured factor analysis: A data matrix-based alternative approach to structural equation modeling,” at KAIST, School of Digital Humanities and Computational Social Sciences, Daejeon.</description></item><item><title>New publication: Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling</title><link>https://psychematrica.org/news/method-choices-sem-first-online-2024-04-04/</link><guid>https://psychematrica.org/news/method-choices-sem-first-online-2024-04-04/</guid><pubDate>Thu, 04 Apr 2024 12:00:00 GMT</pubDate><description>“Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling” was published online in Journal of Product Innovation Management. How choices among SEM methods can influence the conclusions drawn from the same model and data.</description></item><item><title>Version of Record: Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children</title><link>https://psychematrica.org/news/gene-environment-cognition-version-of-record-2024-03-05/</link><guid>https://psychematrica.org/news/gene-environment-cognition-version-of-record-2024-03-05/</guid><pubDate>Tue, 05 Mar 2024 12:00:00 GMT</pubDate><description>The Version of Record for “Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children” was released by eLife. Earlier reviewed-preprint versions remain part of the paper’s publication history.</description></item><item><title>Journal issue publication: Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes</title><link>https://psychematrica.org/news/deep-learning-gsca-journal-issue-2024-03-03/</link><guid>https://psychematrica.org/news/deep-learning-gsca-journal-issue-2024-03-03/</guid><pubDate>Sun, 03 Mar 2024 12:00:00 GMT</pubDate><description>“Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes” appeared in the March 2024 journal issue of Structural Equation Modeling: A Multidisciplinary Journal. The article was first available online on August 25, 2023; this entry records the later issue publication.</description></item><item><title>Invited talk at Penn State University, Department of Psychology (online)</title><link>https://psychematrica.org/news/penn-state-2024/</link><guid>https://psychematrica.org/news/penn-state-2024/</guid><description>GyeongCheol Cho gave an invited presentation, “Deep learning generalized structured component analysis: An interpretable artificial neural network model with composite indexes,” at Penn State University, Department of Psychology (online).</description></item><item><title>New publication: Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes</title><link>https://psychematrica.org/news/convex-components-first-online-2024-02-16/</link><guid>https://psychematrica.org/news/convex-components-first-online-2024-02-16/</guid><pubDate>Fri, 16 Feb 2024 12:00:00 GMT</pubDate><description>“Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes” was published online in Psychometrika. Convex components provide interpretable composite indexes within generalized structured component analysis.</description></item><item><title>Psychometric Society Dissertation Prize</title><link>https://psychematrica.org/news/psychometric-dissertation-prize-2024/</link><guid>https://psychematrica.org/news/psychometric-dissertation-prize-2024/</guid><pubDate>Fri, 09 Feb 2024 12:00:00 GMT</pubDate><description>The Psychometric Society announced GyeongCheol Cho as the recipient of its 2024 Dissertation Prize. His award address followed at IMPS 2024 in Prague.</description></item><item><title>GyeongCheol Cho promoted to Assistant Professor</title><link>https://psychematrica.org/news/assistant-professor-promotion-2024/</link><guid>https://psychematrica.org/news/assistant-professor-promotion-2024/</guid><pubDate>Mon, 01 Jan 2024 12:00:00 GMT</pubDate><description>GyeongCheol Cho was promoted from Instructor to Assistant Professor in the Department of Psychology at The Ohio State University, effective January 1, 2024. He continues to lead PsycheMatrica Lab’s research in quantitative methodology for psychological and learning sciences.</description></item><item><title>ISDSA Best Paper Award for Deep Learning GSCA</title><link>https://psychematrica.org/news/isdsa-best-paper-2024/</link><guid>https://psychematrica.org/news/isdsa-best-paper-2024/</guid><description>GyeongCheol Cho and Heungsun Hwang received the 2024 ISDSA Best Paper Award for their work on deep learning generalized structured component analysis.</description></item><item><title>New publication: GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling</title><link>https://psychematrica.org/news/gsca-pro-software-paper-first-online-2023-12-19/</link><guid>https://psychematrica.org/news/gsca-pro-software-paper-first-online-2023-12-19/</guid><pubDate>Tue, 19 Dec 2023 12:00:00 GMT</pubDate><description>“GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling” was published online in Structural Equation Modeling: A Multidisciplinary Journal. A tutorial introducing GSCA Pro and models with factors, components, or both.</description></item><item><title>Reviewed preprint: Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children</title><link>https://psychematrica.org/news/gene-environment-cognition-reviewed-preprint-revision-2023-09-06/</link><guid>https://psychematrica.org/news/gene-environment-cognition-reviewed-preprint-revision-2023-09-06/</guid><pubDate>Wed, 06 Sep 2023 12:00:00 GMT</pubDate><description>A revised reviewed preprint of “Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children” became available through eLife on September 6, 2023. The study examines gene–environment pathways in cognitive development and psychotic-like experiences. Later manuscript versions and the journal article are linked from the publication page.</description></item><item><title>GyeongCheol Cho begins faculty appointment at Ohio State</title><link>https://psychematrica.org/news/lab-founded-2023/</link><guid>https://psychematrica.org/news/lab-founded-2023/</guid><pubDate>Tue, 15 Aug 2023 12:00:00 GMT</pubDate><description>GyeongCheol Cho began his tenure-track appointment as an Instructor in the Department of Psychology at The Ohio State University on August 15, 2023.</description></item></channel></rss>