A new approach to partialling components is used. Like conventional partialling, this approach orthogonalizes variables by partitioning the scores or observations. Unlike conventional partialling, it yields a common component and two unique components. (Author/GDC)

Examination of the stochastics of moderated multiple regression (MMR) reveals that MMR is an appropriate technique when predictors are fixed variables and the distribution of errors is normal but is not appropriate when predictors are random variables and the joint distribution of criterion and predictor variables is multivariate normal. (SLD)

Methods of regression commonality analysis are generalized for use in canonical correlation analysis. An actual data set (involving educators' attitudes toward death and age, locus of control, religion, and occupational role in working with terminally ill children) is employed to illustrate the extension. The method can be applied with respect to…

Multiple regression analysis is frequently being employed in experimental and non-experimental research. However, when data include predictor variables that are correlated, some regression results can become difficult to interpret. This paper presents a study to provide a demonstration that structure coefficients may be useful in these cases.…

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Multivariate methods were used to identify between-set factors relating the criterion set of eleven Wechsler Adult Intelligence Scale Revised subtest variables to the predictor set of demographic variables: age, race, sex, education, occupation, geographic region, and urban versus rural residence. Although factor analysis is usually used to…

Canonical correlation analysis is a multivariate statistical model which facilitates the study of interrelationships among multiple dependent variables and multiple independent variables. It identifies components of one set of variables that are most highly related linearly to the components of the other set of variables. The underlying logic of…

This paper outlines procedures used to derive variables from data in the National Survey of Postsecondary Faculty; these variables were then used to create measures not expressly included as items in that survey. The derived variables were used to examine faculty satisfaction in two contexts: first, the complexity of satisfaction, and second, the…

This paper reports the results of a Monte Carlo investigation of Type I errors in the single group repeated measures design where multiple measures are collected from each observational unit at each measurement occasion. The Type I error of three multivariate tests were examined. These were the doubly multivariate F test, the multivariate mixed…

Five statistical techniques were used to analyze the data collected in a previous article (TM 511 223) on student achievement, attention, and motivation in secondary school mathematics and science: ordinary least squares regression, canonical correlation, factorial modeling, partial least squares path analysis, and linear structural relations…