From design of experiments to analysis of variance of multivariate data: a tutorial review on ANOVA simultaneous component analysis

2026-04-21

AI Summary

This study addresses the challenges of interpreting and analyzing high-dimensional experimental data within the traditional design of experiments (DoE) framework. By integrating analysis of variance (ANOVA) with simultaneous component analysis (SCA), the authors develop ASCA—a multivariate extension of ANOVA—and systematically unify a century of ANOVA and DoE theory to establish a rigorous application protocol tailored for high-dimensional data. Through a comprehensive literature review and illustrative case studies, the work defines a standardized analytical workflow and best practices that substantially enhance the interpretability and reliability of results from high-dimensional DoE studies. This contribution fills a critical methodological gap in the analysis of multivariate experimental data.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

ANOVA Simultaneous Component Analysis (ASCA) is the current state-of-theart chemometric tool for analyzing and interpreting high-dimensional experimental data from a Design of Experiment (DoE). Being a multivariate extension of the ANOVA, ASCA makes a perfect tandem with DoE. This tutorial review recommends best practices for using ASCA, building upon the long-established combination of ANOVA and DoE theory developed over the last century. These recommendations are grounded in a comprehensive literature review and illustrated through a guiding example.

Problem

• ANOVA Simultaneous Component Analysis
• Design of Experiments
• multivariate data
• high-dimensional data
• chemometrics

Innovation

• ASCA
• Design of Experiments
• multivariate data analysis
• ANOVA
• chemometrics