ABSTRACT
A familiar but never totally resolved problem with any experimental findings is the extent to which they may be generalized to the nonlabora-tory situation. At least three viewpoints about the problem may be discerned. First, and perhaps most familiar, is instant generalization from the
specific and often limited conditions of an experiment to any and all settings that are even remotely related. This tendency is most frequently seen at cocktail parties after the third martini and on television talk shows featuring those who popularize psychology. Second, and almost as familiar, is the notion that the laboratory is a necessary evil. It is seen as an adequate substitute for the real world, only to the extent that it reproduces the world. For example, Aronson and Carlsmith (1968) ask, “Why, then, do we bother with these pallid and contrived imitations of human interaction when there exist rather sophisticated techniques for studying the real thing [p. 4]?” They enumerate the advantages of experiments over field study, but emphasize that good experiments must be realistic in order to involve the subject and have an “impact” on him. Concern with experimental realism often is expressed in the context of positing qualitative differences between the laboratory and the outside world; it is assumed that in moving from simplicity to complexity, new and different principles are emergent. Third, and least familiar in personality and social psychology, is a view that is quite common in other fields. Laboratory research is seen not as a necessary evil, but as an essential procedure that enables us to attain isolation and control of variables and thus makes possible the formulation of basic principles in a setting of reduced complexity. If experiments realistically reproduce the nonlaboratory complexities, they provide little advantage over the field study. Continuity is assumed between the laboratory and the outside world, and complexity is seen as quantitative and not qualitative. To move from a simple situation to a complex one requires detailed knowledge about the relevant variables and their interaction. Application and the attainment of a technology depend upon such an approach.
