Extending the CLASSI model for the study of individual differences in sequential processes: from crossed to nested data

Abstract

Summary. In this paper we will focus on the modeling of binary data regarding individual differences in the emotions that people experience in specific situations. Underlying such data, emotion psychologists typically assume a sequential process with two links: situations activate specific appraisals in persons (link 1); subse- quently, specific patterns of activated appraisals elicit the experience of particular emotions (link 2). It is further hypothesized that these two sorts of links may differ across persons. An important challenge then consists of retrieving the place and the nature of the key individual differences in the process under study. To meet this challenge, Ceulemans & Van Mechelen (2008) recently introduced the CLASSI model. However, the CLASSI model requires the persons and situations to be fully crossed, implying that each person has to rate the same set of situations. This is a major restriction since not all situations are equally relevant for every person. To overcome this restraint we propose an extension of the CLASSI model which permits the set of rated situations to differ across the persons, implying that the situations are nested within persons rather than crossed. Like the original CLASSI model for crossed data, the new CLASSI model for nested data (1) reduces the sit- uations, appraisals, emotions, and persons to a few types, and (2) defines linking structures between the situation types and the appraisal types on the one hand and the appraisal types and emotion types on the other hand, which represent individual differences in these two sorts of links.status: publishe