APPLIED STATISTICS IN HEALTH RESEARCH: A PRACTITIONERS GUIDE TO GLM TECHNIQUES USING SPSS AND SAS

The general linear model (GLM) serves as a foundational statistical framework for clinical and health research, enabling rigorous analysis of treatment effects and intervention outcomes. The review critically examines the application, assumptions, and implementation of three core GLM techniques Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), and Analysis of Covariance (ANCOVA) within health research contexts. It highlights their respective purpose, from comparing group means and analyzing multivariate outcomes to adjusting for confounding covariates, while also providing practical guidance for conducting these analyses using widely adopted software packages such as SPSS and SAS. Key considerations include effect size interpretation, common violations of assumptions, and remedial strategies to maintain statistical validity. The review concludes by acknowledging the inherent limitations of GLM methods and emphasizing the importance of careful model selection, robust assumption testing, and transparent reporting to ensure findings are both methodologically sound and clinically meaningful.