A Review of Analysis of Variance Designs and their Applications in Infection, Epidemiology, and Microbiology Research

Analysis of Variance (ANOVA) models, including one-way, two-way, repeated measures, and multivariate ANOVA (MANOVA), are widely used in health sciences to compare mean values across multiple groups and evaluate complex relationships among variables. This review aimed to summarize the principles, assumptions, applications, and interpretation of different ANOVA designs in infection, epidemiology, and microbiology research. The review is based on the premise that ANOVA-based models provide an essential framework for detecting group differences, interaction effects, and temporal changes that are critical for accurate biological inference and public health decision-making.

A descriptive review approach was employed. The fundamental concepts, model structures, assumptions, and hypothesis-testing procedures of major ANOVA designs were examined. Illustrative examples from medical and public health research were included to demonstrate the practical application and interpretation of these statistical methods.

Each ANOVA design addresses specific research questions and study designs. One-way ANOVA assesses the effect of a single factor, while two-way ANOVA examines two factors simultaneously and allows for the assessment of interaction effects. Repeated-measures ANOVA is appropriate for longitudinal studies involving multiple measurements on the same subjects, while MANOVA is appropriate when multiple correlated dependent variables are being analyzed together. The key features, assumptions, and applications of these methods are summarized.

The findings emphasize the importance of choosing an ANOVA model that is appropriate for the study design and data structure. Appropriate use of ANOVA increases the validity of statistical inference and facilitates a more accurate understanding of complex biological and epidemiological phenomena. Failure to consider model assumptions or interaction effects may lead to misleading conclusions.

The choice of ANOVA model should be guided by the nature of the data and the research objectives. Understanding the distinctions among one-way, factorial, repeated measures, and multivariate ANOVA is essential for conducting robust analyses in infection, epidemiology, and microbiology research.