Ordinal Pattern Analysis
Our task in writing this chapter is to outline a strategy for analyzing data that we believe to be better suited to most psychological research than the most widely used statistical techniques (e.g., t, F, and chi square tests, product moment correlation, regression, covariance, discriminant, and factor analyses). We call the strategy Ordinal Pattern Analysis (OPA), and derive it from a small set of first principles that deviate somewhat from those on which most classical statistical models are based. First, we assume that the goal of statistical practice is to aid in detecting and analyzing patterns in data rather than to aid in making decisions about populations given samples of data. Second, we assume that a statistic must be useful in analyzing data generated by individual subjects as well as data based on aggregations of subjects. Third, we assume that most predictions and observations in psychological research possess no more than ordinal scale properties, and that statistics employed to assess the fit between predictions and observations must be derived on the basis of this constraint.
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