Perception and Applications of Multivariate Statistical Methods in Modern Laboratory-Based Research: A Systematic Review

Multivariate statistical analysis is a crucial aspect of contemporary laboratory research in chemistry, health, and environmental sciences, facilitating the analysis of complex high-dimensional data, such as spectroscopic, clinical, and genomic data. Although multivariate statistical analysis has been proven to be highly effective, its adoption has been uneven across various fields of study and across different regions of the world. This review examined the perception and application of multivariate statistical analytical methods in laboratory research. The review critically examined the previous studies published between 2015 and 2025, synthesizing evidence from peer-reviewed articles on applications, adoption rates, barriers, and training effects. Results reveal widespread application in pharmaceutical quality control, epidemiological modeling, and genomic studies, but substantial gaps in adoption still exist. The major barriers include a lack of statistical training, cited by nearly 70% of respondents, computational complexity, software limitations, and data quality issues. Scientists in developing countries face much more daunting challenges than their counterparts in developed countries. Notably, formal training programs led to a 45% increase in adoption rates, with improved competency scores of 37-81% and increased research productivity. It is concluded that multivariate statistical analysis methods have demonstrated transformative potential across chemistry, health, and environmental research based on a bibliometric analysis using Scopus and VOSviewer