Abstract

The fact that causal laws in the social sciences are most realistically expressed as both multivariate and stochastic has a number of very important implications for indirect measurement and generalizability. It becomes difficult to link theoretical definitions of general constructs in a one-to-one relationship to research operations, with the result that there is conceptual slippage in both experimental and nonexperimental research. It is argued that problems of this nature can be approached by developing specific multivariate causal models that incorporate sources of measurement bias, along with the theoretical variables of interest. Many general concepts are defined in such a way that causal assumptions are built into the definitions themselves. Additionally, in any given piece of research it is necessary to omit many variables from consideration, and this is often done without careful consideration of the assumptions required to justify such omissions. Finally, generalization to more inclusive populations or a diversity of settings ordinarily requires one to replace "constants" by variables. It is concluded that the criteria of parsimony, generalizability, and precision are incompatible, given the multivariate nature of social causation, and the author expresses his own preference for sacrificing parsimony in favor of the objectives of achieving increased precision and generalizability of social science laws.