The study help researchers choose and utilize methodologies that align with their data and goals.

What is it about?

It revisits Partial Least Squares Structural Equation Modeling (PLS-SEM) as a robust tool for analyzing non-normal data and small samples, offering predictive modeling advantages. This study also compares the merits, practical applications, and added value of both tools in tackling complicated research issues, notably in education and social sciences, rather than reviewing their techniques. Simultaneously, it evaluates NVivo as a leading qualitative data analysis (QDA) tool, focusing on its effectiveness in organizing, coding, querying, and visualizing diverse qualitative datasets. Materials/Methods: The study places both tools in real-world educational research settings to help researchers choose and utilize methodologies that align with their data and goals. This mixed-methods research employed two approaches. A utilized empirical data to assess PLS-SEM's performance using statistical metrics such as R2, Q2, and Composite Reliability.

Featured Image

Why is it important?

A utilized empirical data to assess PLS-SEM's performance using statistical metrics such as R2, Q2, and Composite Reliability.

Perspectives