ABSTRACT
The central feature of MRC is its utility in examining the relationships involved when multiple independent variables (IVs or X,) are related to a single dependent variable (F)- The multiplicity of MRC can be exploited in three discriminably different ways, namely, by representing multiple research factors, by represent ing a functional group of research factors, and by representing multiple aspects of a single research factor:
1. The representation of several research factors is the familiar traditional use of the multiplicity of MRC, illustrated in Chapter 3 and in standard textbook treatments, for example: {a) Freshman grade point average (y) versus verbal score (Xj), quantitative score (X2), and high school class rank (X3); or (b) length of psychiatric hospitalization (F) versus sex (Xj), marital status (X2), length of prior hospitalization (X3), number of prior hospitalizations (X4), and admission ratings on three symptom rating scales (X5, X6, X7); or (c) for census tracts in a large city, infant mortality rate (y) versus median annual income (Xj), percent age non white (X2), and median education of adults (X3). In such analyses the multiplicity of IVs makes possible the study of their combined relationships to Y, each IV’s overall and unique relationship to Y, and redundancy among the IVs. Note that in these examples each research factor is represented by a single IV, and the multiplicity of IVs occurs solely because of the multiplicity of factors, all treated as if they were on the same footing, or in a hierarchical manner.
