Constant variances and constant covariances of the dependent measure across the levels of a factor in analysis of variance (i.e., the variances and covariances are equal between pairs of variables, but not necessarily between variances and covariances). This property of the variance-covariance matrix is present when the main diagonal elements of a set of multivariate data are equal to one another, which must also be the case for the off-diagonal elements. Compound symmetry is a stricter requirement than sphericity, and consequently if compound symmetry is met so is sphericity. As with homogeneity of variance, compound symmetry is rarely met exactly. Thus, a rule of thumb is that if observed covariances and variances are roughly equal, then one can assume that compound symmetry has not been violated. It is a condition that is important in the analysis of longitudinal data.<\/p>\n
See Analysis of variance (ANOVA)<\/a>, Compound symmetry<\/a>, Longitudinal studies<\/a>, Mauchly test<\/a>, Patterns of variances and covariances, Repeated measures analysis of variance<\/a>, Sphericity<\/a>, Sphericity assumption<\/a>, Variance-covariance matrix<\/p>\n <\/body><\/html><\/p>\n","protected":false},"excerpt":{"rendered":" Constant variances and constant covariances of the dependent measure across the levels of a factor in analysis of variance (i.e., the variances and covariances are equal between pairs of variables, but not necessarily between variances and covariances). This property of the variance-covariance matrix is present when the main diagonal elements of a set of multivariate … <\/p>\n