Statistics - Advanced Linear/Nonlinear Models - Variance Components

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Variance Components to display the Variance Components and Mixed Model ANOVA Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models menu, select Variance Components to display the Variance Components and Mixed Model ANOVA Startup Panel.

Variance components are used in the context of experimental designs with random effects, to denote the estimate of the (amount of) variance that can be attributed to those effects. For example, if you are interested in the effect that the quality of different schools has on academic proficiency, you could select a sample of schools to estimate the amount of variance in academic proficiency (component of variance) that is attributable to differences between schools.

Use the Variance Components module to analyze designs with any combination of fixed effects, random effects, and covariates. STATISTICA will analyze standard factorial (crossed) designs as well as hierarchically nested designs, and compute the standard Type I, II, and III analysis of variance sums of squares and mean squares for the effects in the model. In addition, you can compute the table of expected mean squares for the effects in the design, the variance components for the random effects in the model, the coefficients for the denominator synthesis, and the complete ANOVA table with tests based on synthesized error sums of squares and degrees of freedom (using Satterthwaite's method). Other methods for estimating variance components are also supported (e.g., MIVQUE0, Maximum Likelihood [ML], Restricted Maximum Likelihood [REML]). For maximum likelihood estimation, both the Newton-Raphson and Fisher scoring algorithms are used, and the model will not be arbitrarily changed (reduced) during estimation to handle situations where most components are at or near zero. Several options for reviewing the weighted and unweighted marginal means, and their confidence intervals, are also available. Extensive graphics options can be used to visualize the results.

The Generalized Linear/Nonlinear Models (GLM) module will also compute variance components (based on denominator synthesis) of complex ANOVA/ANCOVA designs.