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Taxonomic "personality" models are widely used in research and applied fields. This article applies the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) to scrutinise the three methodological steps that are required for developing comprehensive "personality" taxonomies: 1) the approaches used to select the phenomena and events to be studied, 2) the methods used to generate data about the selected phenomena and events and 3) the reduction principles used to extract the "most important" individual-specific variations for constructing "personality" taxonomies. Analyses of some currently popular taxonomies reveal frequent mismatches between the researchers' explicit and implicit metatheories about "personality" and the abilities of previous methodologies to capture the particular kinds of phenomena toward which they are targeted. Serious deficiencies that preclude scientific quantifications are identified in standardised questionnaires, psychology's established standard method of investigation. These mismatches and deficiencies derive from the lack of an explicit formulation and critical reflection on the philosophical and metatheoretical assumptions being made by scientists and from the established practice of radically matching the methodological tools to researchers' preconceived ideas and to pre-existing statistical theories rather than to the particular phenomena and individuals under study. These findings raise serious doubts about the ability of previous taxonomies to appropriately and comprehensively reflect the phenomena towards which they are targeted and the structures of individual-specificity occurring in them. The article elaborates and illustrates with empirical examples methodological principles that allow researchers to appropriately meet the metatheoretical requirements and that are suitable for comprehensively exploring individuals' "personality".

integrative psychological and behavioral science

Developing “Personality” Taxonomies: Metatheoretical and Methodological Rationales Underlying Selection Approaches, Methods of Data Generation and Reduction Principles