Observation oriented modeling: Principles and tools for person-oriented research.

Abstract

Observation Oriented Modeling (OOM) is a person-oriented approach to data conceptualization and analysis that departs significantly from traditional methodological and statistical frameworks. Rather than relying on means, variances, and covariances, OOM focuses on the patterns formed by individual observations. Instead of estimating population parameters, OOM prioritizes the development of explanatory theories based on those observed patterns, reflecting a broader philosophical shift from logical positivism toward philosophical realism. In this paper, we illustrate the principles and methods of OOM through a series of re-analyses of published studies. These examples demonstrate how OOM can reveal meaningful behavioral patterns at the individual level that are often obscured by conventional aggregate analyses. In addition to being simple and intuitive to use, OOM encourages researchers to engage more deeply with causal models and less with statistical procedures. We conclude by discussing the broader implications of OOM for person-oriented psychological science and the field as a whole.