User Guide

34
Chapter 3
Data. The dependent variable is quantitative. Factors are categorical. They can have
numeric values or string values of up to eight characters. At least one of the factors
must be random. That is, the levels of the factor must be a random sample of possible
levels. Covariates are quantitative variables that are related to the dependent variable.
Assumptions. All methods assume that model parameters of a random effect have zero
means and finite constant variances and are mutually uncorrelated. Model parameters
from different random effects are also uncorrelated.
The residual term also has a zero mean and finite constant variance. It is
uncorrelated with model parameters of any random effect. Residual terms from
different observations are assumed to be uncorrelated.
Based on these assumptions, observations from the same level of a random factor
are correlated. This fact distinguishes a variance component model from a general
linear model.
ANOVA and MINQUE do not require normality assumptions. They are both robust
to moderate departures from the normality assumption.
ML and REML require the model parameter and the residual term to be normally
distributed.
Related procedures. Use the Explore procedure to examine the data before doing
variance components analysis. For hypothesis testing, use GLM Univariate, GLM
Multivariate, and GLM Repeated Measures.
To Obtain a Variance Components Analysis
 From the menus choose:
Analyze
General Linear Model
Variance Components...