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The derivation and analysis presented in this report allow the experimenter to perform multifactor contrasts on correlation coefficients for several populations in an experimental design. Cells of the design constitute independent groups of subjects on which two measures have been taken and correlated. Measures need not be identical for each group. An example is given in which the dependent variable represents the correlation between attitude and achievement for six secondary school science classes stratified by ability (three levels) and grade level (two levels). An overall relationship was observed between science attitude and knowledge of science for junior and senior high school students of science. No difference due to achievement was found, but the grade difference was significant. (Author/BB)

The current state of usage of regression models in analysis of variance (ANOVA) designs is empirically examined, and examples of several statistical errors made in usage are presented. The assumptions of the general linear model are that all predictors are known without error of measurement and are fixed with no replication or sample variation; in the population, errors are normally distributed independently with variance, and errors are independent of all predictors. The rules for construction of the ANOVA allow the expected mean squares to hold just as if the levels of each factor had been randomly sampled. Analysis of Covariance (ANCOVA) combines the elements of regression analysis with design, albeit in a restricted manner. The homogeneity of regression coefficients is a parameter restriction from the design view point. The regression weight associated with a given covariate level is discussed. Most regression approaches to ANOVA and ANCOVA assume a fixed factor model under all design specifications. A major oversight of expected mean squares has contributed to the current lack of concern for the level of generalizability warranted from the design specification. Aptitude treatment interaction models are examined. (Author/CM)

Samples in research on individual and group differences may be selected based on whole scores which differ from the population mean. Children are diagnosed in clinical practice with a whole score. These procedures produce regression to the population mean which can affect accuracy and adequacy of part score interpretations. (Author/DWH)

A theoretical model is developed for analysis of intervention effects in multiple autocorrelated time-series which may be classified or blocked two ways. The intervention effect is estimated independently for each series. The estimates are then entered as observations in two-factor analysis of variance with one observation per cell, utilizing a pooled variance error estimate for the mean square error. The model was tested empirically through the use of Monte Carlo techniques, and results are presented in tabular form for Type I and Type II error levels. An example is presented with data taken from a Kansas City Police study utilizing the two-factor layout. (Author)