Research Topics:

Data Analysis | Quantitative Research

Content Type:

Glossary

Collinearity Definition

The correlation of independent variables with each other. Can bias estimates of regression coefficients.

Collinearity is the correlation of independent variables to each other. In other words, they are highly correlated, which can make distinguishing the individual effects of each variable on the dependent variable difficult. This highlights the importance of selecting independent variables that are not highly correlated with each other when building regression models. For marketing researchers, detecting and addressing collinearity helps with reaching accurate estimates of the variables' effects. This also enhances the overall quality of marketing insights. 

Who is affected by collinearity?

Marketing researchers, analysts and practitioners are on the lookout for collinearity in their research. They use statistical models to understand the relationships between various marketing variables and their impact on consumer behavior, market trends and business outcomes.

Why should I care about collinearity?

Collinearity can lead to inaccurate or unstable results in regression analysis, so it can greatly impact the validity and reliability of research findings. Incorrect interpretations of variable relationships could lead to ineffective strategies and decisions about products and services.