Multivariate Analysis: Overview

Abstract

Introduction: Multivariate analysis (MVA) techniques allow more than two variables to be analysed at once. Two general types of MVA technique: Analysis of dependence& Analysis of interdependence. Technique is selected depending on type of data and reason for the analysis. Cluster analysis: "Techniques for identifying separate groups of similar cases". Also used to summarize data by defining segments of similar cases in the data. Discriminant analysis: Is a statistical technique for classifying individuals or objects into mutually exclusive and exhaustive groups on the basis of a set of independent variables". Factor analysis : Multiple factor analysis (mfa): "Statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors". Correspondance Analysis: "Technique that generates graphical representations of the interactions between modalities (or "categories") of two categorical variables". Regression Analysis: "Refers to any techniques for modelling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables." Multiple Linear Regression Analysis (MLR): In Multiple linear regressions, several independent variables are used to predict with a least square approach one direct variable. Multivariate analysis of variance (MANOVA): It is a generalized form of univariate analysis of variance (ANOVA). Conclusion: Because there are many potential problems and pitfalls in the use of multivariable techniques in clinical research, these procedures should be used with care.