Multivariate Analysis

Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time. Typically, MVA is used to address the situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. A modern, overlapping categorization of MVA includes:

• Normal and general multivariate models and distribution theory
• The study and measurement of relationships
• Probability computations of multidimensional regions
• The exploration of data structures and patterns

Useful literature

1. Denis, Daniel J.. Applied Univariate, Bivariate and Multivariate Statistics, John Wiley & Sons, Incorporated, 2015 (access via HSE Library).
2. Statistics and Causality: Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016 (access via HSE Library).
3. Raykov, Tenko, and George A. Marcoulides. A First Course in Structural Equation Modeling, Taylor & Francis Group, 2006 (access via HSE Library).
4. Brown, Timothy A.. Confirmatory Factor Analysis for Applied Research, Second Edition, Guilford Publications, 2014 (access via HSE Library).
5. Bollen, Kenneth A.. Structural Equations with Latent Variables, John Wiley & Sons, Incorporated, 1989 (access via HSE Library).