Multivariate Modeling

TL;DR

• Uses several predictor variables together to explain or predict a single outcome.
• Commonly applied in fields like psychology, sociology, and marketing.
• Helps identify which variables have the strongest impact on the outcome.

Definition

Multivariate modeling is a statistical technique that involves using multiple variables to predict or explain a particular outcome.

Explanation

Multivariate modeling analyzes relationships among several variables simultaneously to understand their combined effect on a specific outcome. By incorporating multiple predictors (for example, age, gender, and education level), researchers can determine which variables most strongly influence the outcome and produce more nuanced predictions or explanations than single-variable approaches.

Examples

Psychology

Researchers may use multiple variables such as age, gender, and education level to predict an individual’s likelihood of developing a mental health disorder. In this case, the researchers may use a multivariate regression model to analyze the relationships between the variables and the outcome, and to identify which variables have the strongest impact on the likelihood of developing the disorder.

Marketing

Researchers may use multiple variables such as income, education level, and purchasing behavior to predict an individual’s likelihood of purchasing a particular product or service. In this case, the researchers may use a multivariate logistic regression model to analyze the relationships between the variables and the outcome, and to identify which variables have the strongest impact on the likelihood of making a purchase.

Use cases

• Psychology
• Sociology
• Marketing

Related terms

• Multivariate regression (mentioned as “multivariate regression model”)
• Multivariate logistic regression (mentioned as “multivariate logistic regression model”)
• Predictor variables / outcome