Pre-Doctoral Programme K02: Mastering Quantitative Analysis with PLS-SEM

The sessions focused on Structural Equation Modeling (SEM) – one of the most advanced analytical methods today, enabling researchers to simultaneously examine multiple causal relationships between observed and latent variables. This method is particularly valuable in economics, management, and social sciences, where research concepts are often abstract and multidimensional.

Building on this foundation, participants gained in-depth knowledge of PLS-SEM (Partial Least Squares Structural Equation Modeling). Based on the partial least squares approach, PLS-SEM is recognized for its strong predictive capability, flexibility in handling complex models, and adaptability to diverse empirical data conditions.

The sessions logically systematized the core components of a rigorous quantitative study:

• Measurement Model: Assessing the relationships between observed variables and latent variables.
• Structural Model: Testing hypotheses and examining the underlying relationships among latent variables.

The lecturer also clarified fundamental concepts such as exogenous and endogenous variables, along with rigorous criteria for reliability and validity. In particular, participants were guided in designing questionnaires according to academic standards, ensuring internal consistency and accurate measurement of research concepts.

From a practical perspective, the course introduced a comprehensive analytical process, from developing the research model and establishing hypotheses to evaluating the outer model and inner model. Participants also explored advanced techniques, including:

• Bootstrapping: A technique for testing the statistical significance of estimated coefficients.
• Mediation: Analyzing indirect effects and the mechanisms through which factors influence one another.
• Moderation: Examining how relationships change under different contextual conditions.

Beyond software-based techniques, the sessions helped develop a systematic quantitative research mindset – from constructing theoretical models to conducting empirical testing. Participants gained a clearer understanding of how quantitative methods can be applied to practical issues while meeting increasingly rigorous requirements for the reliability and generalizability of research findings in interdisciplinary contexts.

The course contributed significantly to strengthening participants’ methodological foundations, analytical capabilities, and academic thinking, supporting the Pre-Doctoral Programme journey toward international research standards and high-quality scientific publications at UEF.