The statistical technique used to analyze data that arises from more than one variable. This essentially models reality where each situation, product, or decision involves more than a single variable. The information age has resulted in masses of data in every field. Despite the quantum of data available, the ability to obtain a clear picture of what is going on and make intelligent decisions is a challenge. When available information is stored in database tables containing rows and columns, multivariate analysis can be used to process the information in a meaningful fashion.
The main techniques of multivariate data analysis:
- Principal Component Analysis (PCA);
- Confirmatory Factor Analysis (CFA);
- Factor Analysis;
- Cluster Analysis;
- Discriminant Function Analysis;
- Correspondence Analysis;
- Conjoint Analysis;
- Multi-Dimensional Scaling (MDS);
- Regression Analysis;
- Logistic Regression Analysis;
- Path Analysis;
- Structural Equation Modeling (SEM);
- Partial Least Square (PLS);
- Analysis of Variance (ANOVA);
- Analysis of Covariance (ANCOVA);
- Multivariate Analysis of Variance (MANOVA);
- Multivariate Analysis of Covariance (MANCOVA);
- Canonical Correlation Analysis;
- etc.
