The use of multiple regression as a data-analytic tool is examined for the cases of balanced and unbalanced designs. The utility of this method for testing specific contrasts, both orthogonal and nonorthogonal is discussed and some interpretive cautions are examined. (Author)

A brief introduction is presented on how multiple regression and linear model techniques can handle data analysis situations that most educators and psychologists think of as appropriate for analysis of variance. (Author/BJG)

Regions of significance in aptitude-by-treatment-interaction studies are examined by the traditional statistical approach and an alternative approach which integrates: (1) testing for the parallelism of two or more regression lines; (2) testing for their identity; and (3) Scheffe's theorem. (Author/RL)

Similarities between regression analysis and analysis of variance are noted and it is argued that the former has advantages over the latter. It is also argued that canonical correlation analysis is more suitable than factor analysis in certain cases. The argument is illustrated with four recent pieces of educational research. (DG)

Differences in ratings of the same instructor teaching the same course on two different occasions are related to differences in background characteristics: (1) higher levels of workload/difficulty, (2) higher expected grades, and (3) instructor experience. (PN)