Research Topics:

Data Analysis | Quantitative Research

Industry/Market Focus:

Consumers

Content Type:

Glossary

Analysis Of Covariance (ANCOVA) Definition

A statistical procedure used with one dependent variable and multiple independent variables of both categorical (ordinal, dichotomous or nominal) and continuous types; it is a variation of analysis of variance that adjusts for confounding by continuous variables.

Analysis of covariance, or ANCOVA, is a statistical technique that compares the means of dependent variables across different groups while controlling the impact of one or more covariates. It combines elements of analysis of variance, or ANOVA, and linear regression to determine the influence of confounding variables. ANCOVA provides a way to remove the influence of confounding variables that could otherwise lead to incorrect conclusions. In other words, it lets researchers isolate the effects of the independent variable of interest. ANCOVA makes statistical analysis more accurate. It is most useful when researchers want to examine group differences while considering the impact of specific covariates on the outcome.

Who relies on analysis of covariance?

Among the fields in which ANCOVA is commonly utilized are marketing research, social sciences, psychology, education, health care and biological research. For instance, ANCOVA is relied upon by researchers, statisticians and analysts to study the impacts of interventions, treatments or independent variables while accounting for the influence of covariates to alter or even hit the relationships between the groups and the dependent variable.

Why should I care about analysis of covariance?

Anyone involved in conducting or interpreting research should become familiar with ANCOVA because it improves the accuracy and validity of statistical conclusions. By using ANCOVA appropriately, researchers can draw more accurate and defined inferences, thus strengthening their research and findings.