Background/purpose: 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 R[superscript 2], Q[superscript 2], and Composite Reliability. It compared PLS-SEM with MRA, CB-SEM, and Factor Analysis. Method B involved surveys, interviews, usability testing, and case studies to evaluate NVivo's capabilities. NVivo was compared with ATLAS.ti, MAXQDA, and Dedoose on parameters like coding flexibility, usability, visualization, and collaborative features. Results. The manuscript demonstrates how PLS-SEM can model latent concepts, such as student engagement, learning outcomes, and institutional support, while NVivo can analyze qualitative data, including interview transcripts, reflective diaries, and classroom discourse. NVivo outperformed competing QDA tools in advanced coding, data visualization, and integration features, with 72% of surveyed researchers preferring it for its effectiveness and usability. Usability testing revealed NVivo had a 30% higher task efficiency and a high user satisfaction score (8.5/10), despite a moderate learning curve. NVivo was particularly effective in thematic exploration and supported collaborative research. Conclusion. PLS-SEM proves to be a robust and adaptable statistical method for complex quantitative research, especially when data quality or sample size is constrained. NVivo stands out as a versatile and user-friendly QDA tool, enhancing the rigor and efficiency of qualitative analysis. Together, these tools offer a methodological advancement for researchers undertaking mixed-methods studies, promoting more accurate, predictive, and interpretable research outcomes across disciplines.