Methods of Analysis of Linear Models with Unbalanced Data

Abstract The objective of this article is to review existing methods for analyzing experimental design models with unbalanced data and to relate them to existing computer programs. The methods are distinguished by the hypotheses associated with the sums of squares which are generated. The choice of a method should be based on the appropriateness of the hypothesis rather than on computational convenience or the orthogonality of the quadratic forms. The sums of squares are described using the R ( ) notation as applied to the over-parameterized linear model, but the hypotheses are stated in terms of the full-rank cell means model. The zero-cell frequency situation is treated briefly.