Abstract

Social Judgement Theory (SJT) evolved from Egon Brunswik's Probabilistic Functionalist psychology coupled with multiple correlation and regression-based statistical analysis. Through its representational device, the Lens Model, SJT has become a widely used, systems-oriented perspective for analysing human judgement in specific ecological circumstances. Judgements are assumed to result from the integration of different cues or sources of perceptual information from the environment. Special advantages accrue to the SJT approach when criterion values (or correct values) for judgement are also available, as this permits the comparison of judgement processes to environmental processes and leads naturally to the generation of cognitive feedback as an aid to facilitate learning. In contrast to more prescriptive approaches to decision analysis, the SJT approach analyses judgements by decomposing the judgement process after judgements have been rendered. This a posteriori decomposition is accomplished by first using multiple regression analysis to recover prediction equations for both the judgement and ecological systems and then using the Lens Model Equation to compare those systems. SJT methods maintain close contact with ecological circumstances by employing the principle of representative design (which focuses on how the researcher obtains the stimuli for judgement) and avoiding unwarranted over-generalisations from nomothetic aggregation (e.g. averaging across judges) through the use of idiographic statistical analysis. SJT methods have proven valuable in the analysis of individual judgements as well as groupbased judgements where conflict becomes likely.