There are several techniques commonly used in multivariate analysis. Here are some of the most common ones:
Multivariate regression: This technique is used to explore the relationship between multiple independent variables and a single dependent variable. It is often used in predicting outcomes based on several variables.
Principal Component Analysis (PCA): This technique is used to reduce the dimensionality of a data set by identifying patterns in the relationships between variables. It can be useful for simplifying complex data sets.
Factor Analysis: This technique is used to identify underlying factors that may be driving relationships between variables. It can be useful for understanding the structure of a data set and identifying underlying causes.
Cluster Analysis: This technique is used to group data points based on similarities or differences between them. It can be useful for identifying patterns and relationships within a data set.
Discriminant Analysis: This technique is used to classify data into two or more groups based on several independent variables. It can be useful for predicting group membership based on several variables.
There are many other techniques available as well, and the choice of technique depends on the research question and the type of data being analyzed.