摘要：Observation Oriented Modeling is a novel approach toward conceptualizing and analyzing data. Compared with traditional parametric statistics, Observation Oriented Modeling is more intuitive, relatively free of assumptions, and encourages researchers to stay close to their data. Rather than estimating abstract population parameters, the overarching goal of the analysis is to identify and explain distinct patterns within the observations. Selected data from a recent study by Craig et al. were analyzed using Observation Oriented Modeling; this analysis was contrasted with a traditional repeated measures ANOVA assessment. Various pitfalls in traditional parametric analyses were avoided when using Observation Oriented Modeling, including the presence of outliers and missing data. The differences between Observation Oriented Modeling and various parametric and nonparametric statistical methods were finally discussed.
