Homoscedasticity suggests equal levels of variability between quantitative dependent variables across a range of independent variables that are either continuous or categorical. This entry focuses on defining and evaluating homoscedasticity in both univariate and multivariate analyses. The entry concludes with a discussion of approaches used to remediate violations of homoscedasticity.

Homoscedasticity as a Statistical Assumption

Homoscedasticity is one of three major assumptions underlying parametric statistical analyses. In univariate analyses, such as the analysis of variance (ANOVA), with one quantitative dependent variable (Y) and one or more categorical independent variables (X), the homoscedasticity assumption is known as homogeneity of variance. In this context, it is assumed that equal variances of the dependent variable exist across levels of the independent variables.

In multivariate analyses, homoscedasticity means all pairwise combinations of ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles