Multivariate Analysis
- Analyzes datasets that involve more than one variable simultaneously.
- Reveals relationships among multiple variables and the unique contribution of each.
- Can identify underlying structures in observed variables.
Definition
Section titled “Definition”Multivariate analysis is a statistical method that is used to analyze data that involves more than one variable. This is in contrast to univariate analysis, which only involves a single variable. Multivariate analysis allows researchers to investigate the relationships between multiple variables and determine how these variables are related to each other.
Explanation
Section titled “Explanation”Multivariate analysis examines multiple variables at the same time to understand interdependencies and combined effects. By modeling several variables jointly, researchers can quantify each variable’s unique contribution to an outcome and detect latent structures that explain patterns across observed measures. The approach contrasts with univariate analysis, which treats one variable in isolation.
Examples
Section titled “Examples”Multiple regression analysis
Section titled “Multiple regression analysis”Multiple regression analysis is a statistical method that is used to investigate the relationship between a dependent variable and two or more independent variables. For instance, a researcher might use multiple regression analysis to investigate the relationship between a person’s income, education level, and age on their likelihood of owning a home. The multiple regression model would allow the researcher to determine the unique contribution of each variable to the dependent variable and determine how they are related to each other.
Factor analysis
Section titled “Factor analysis”Factor analysis is a statistical method that is used to identify the underlying structure of a set of observed variables. For instance, a researcher might use factor analysis to investigate the relationship between a person’s health habits, such as exercise frequency and diet, on their overall health. The factor analysis would allow the researcher to identify the underlying factors that are related to health, such as exercise and diet, and determine how these factors are related to each other.
Use cases
Section titled “Use cases”- Investigating relationships between multiple variables in observational or experimental data.
- Identifying underlying structure or latent factors among observed variables.
- Gaining a deeper understanding of multivariable datasets.
Related terms
Section titled “Related terms”- Univariate analysis
- Multiple regression analysis
- Factor analysis