On October 16, 2024, the Department of Agricultural Socio-Economics, Faculty of Agriculture UGM, held a training session titled “Data Analysis Using SEM-PLS” at the Venture Lab, AGLC Building, 6th floor. The event was attended by PhD students and several Master’s students from the Faculty of Agriculture who share a strong interest in quantitative data analysis methods. The training featured Ms. Tri Hanifawati, S.Si., M.Sc., a lecturer from Universitas Muhammadiyah Bandung, as the speaker, and Mr. Daffa Sandi Lasitya, S.P., M.P., a lecturer at the Faculty of Agriculture UGM, as the moderator. This training aimed to enhance students’ capabilities in understanding and implementing the SEM-PLS data analysis method.
The training covered essential aspects of SEM-PLS methodology, including an introduction to SEM and its importance, different types of SEM analysis, and the advantages of SEM-PLS in structural data analysis. Participants were also guided through various components of SEM models (outer model, inner model, and measurement indicators), multi-group analysis (MGA), sample size determination, model fit testing, hypothesis testing, and data interpretation techniques. In addition to theoretical explanations, participants engaged in hands-on practice using their laptops, allowing them to apply the concepts learned directly within the context of real-world data analysis.
This training aligns with UGM’s efforts to support the achievement of the Sustainable Development Goals (SDGs). Specifically, it resonates with SDG 4: Quality Education, by providing students with valuable technical skills essential for research. Furthermore, the training contributes to SDG 9: Industry, Innovation, and Infrastructure, by building human resource capacity that can serve the agricultural sector and related industries. Additionally, this initiative supports SDG 17: Partnerships for the Goals, as it invited an expert from outside UGM, fostering academic collaboration between universities and creating an interactive learning environment.
Through this training, it is hoped that students will be able to use SEM-PLS to improve the quality of their research data analysis, enhance research outcomes, and expand their understanding of complex data processing techniques.
Author: Adhika Hafizh Prasada, S.P.
Admin of the Website for the Department of Agricultural Socio-Economics, Faculty of Agriculture, UGM