【Article by MPCG】
On the 20th, the MA Program of Counseling and Guidance invited Professor Shou Chun Chiang from the Department of Human Development and Family Sciences at Texas Tech University, USA, to lead a workshop on "Advanced Statistical Analysis and Applications of R Programming." The workshop covered foundational theories, models, and practical applications of quantitative statistics. Participants included master’s and doctoral students from diverse fields such as counseling, education, information management, Asia-Pacific studies, and land economics, showcasing an enthusiasm for interdisciplinary academic exchange.

Capturing Dynamic Data: Innovating Counseling Practices
During his lecture, Professor Chiang introduced the latest advancements in quantitative statistical methods, emphasizing dynamic measurement techniques. These techniques overcome the limitations of static data and allow for the assessment of intra-individual and dyadic relationship changes. Using a study on the dynamic nature of parent-adolescent closeness as an example, Professor Chiang demonstrated how such measurements can predict emotional regulation difficulties in adolescents. He further illustrated how dyadic measures, such as emotional co-regulation between parents and adolescents, correlate with psychological distress outcomes like suicidal ideation. These findings underscore the importance of capturing dynamic relationship processes in counseling research and practice.

Applications and Insights of Dynamic Data in Counseling
Professor Chiang highlighted the profound implications of dynamic measurement for counseling practices. For instance, assessing fluctuations in clients’ daily emotions and interpersonal relationships can help develop more personalized therapeutic strategies and enable real-time intervention adjustments. He also provided step-by-step guidance on using R programming to conduct multilevel latent class analysis (MLCA) and time-varying effect models (TVEM). From a counseling perspective, the application of dynamic data not only identifies novel mental health risk and protective factors but also informs prevention and treatment strategies. For example, measuring interactions between parental support and adolescent emotions offers critical data for mental health professionals to design more effective counseling and intervention plans. This data-driven approach undoubtedly opens new avenues for counseling research.

Enhancing Professional Expertise Among Graduate Students
Professor Chiang emphasized that the integration of statistical techniques with psychology not only deepens theoretical research but also generates empirical impacts, driving groundbreaking progress in mental health. His lecture provided attendees with a comprehensive understanding of the significance of dynamic measurement techniques, enhancing their expertise in statistical analysis and counseling practices. The session offered valuable theoretical and practical contributions, fostering academic exchange and professional development.