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The purpose is to discuss and exemplify how a typological approach could be designed for studying phenomena believed to be best understood within a person-oriented theoretical framework. The focus is mainly restricted to the case of studying the typological structure of a sample at a single point in time, and restricted to analyzing variable profiles where each variable has a “negative” and “positive” endpoint. An artificial data set and an empirical data set were analyzed using two different methodological approaches, one more explorative (using LICUR, a cluster analysis-based procedure) and one more model-based (using the MCLUST procedure). For the artificial data set, the LICUR procedure was successful in finding the true classification structure but the MCLUST procedure performed surprisingly badly. For the empirical data set, both procedures produced rather similar solutions and they showed moderate validity. However, the LICUR solution appeared to be slightly superior. It was argued that applying a sound classification methodology and carefully validating the resulting classifications are extremely important, even more so in a developmental context. It was also argued that, in a number of situations, a more explorative approach could be more useful than a standard model-based one.
Keywords: person-oriented approach, classification, types, typology, cluster analysis, LICUR, model-based analysis