Analysis of Variance vs. Analysis of Covariance

Decent Essays
ANOVA (Analysis of Variance) and ANCOVA (Analysis of Covariance) are both types of statistical tests that are used to determine the relationship between datasets typically obtained from experiments. ANOVA is used when the available dataset consists of interval or scale variables while ANCOVA is used in the case where the available dataset consists of categorical or continuous variables. At least two types of variables (independent and dependent) are required for ANOVA but there can only be one dependent variable. For ANCOVA, at least three types of variables (independent, dependent and covariate(s)) are required. ANOVA investigates whether or not data from different groups have the same mean. The procedure is efficient and more powerful compared to simple t-tests as it gives better results. The ANCOVA test basically is an ANOVA test that includes the controlling effect of other available variables on the relationship between a dependent and independent variables. It is a combination of an ANOVA test and reg ression. However, there can be only one dependent variable, but multiple covariates and independent variables when using ANCOVA. ANCOVA uses the covariate to control extraneous variables and thus minimizes error compared to ANOVA. As a result, ANCOVA is considered to be relatively more efficient and more powerful than ANOVA. It should be noted that no covariates are
Get Access