Repeated-measures contrasts for "multiple-pattern" hypotheses

Contrast analysis of repeated-measures data generally focuses on hypotheses when only 1 pattern of results is of theoretical interest. This article articulates a framework for contrast analysis in repeated-measures contexts in which researchers have hypotheses relevant to 1 potential pattern or multiple potential patterns of results. For example, a researcher might ask whether participants exhibit a pattern of (a) immediate symptom reduction or (b) delayed symptom reduction. Alternatively, the researcher might ask whether 2 or more groups exhibit 2 or more patterns to differing degrees. Building on the familiar logic and computational procedures for 1-pattern hypotheses, the authors present a contrast analysis framework that integrates analysis of 1-pattern and multiple-pattern hypotheses and accommodates 1 group or multiple groups of participants.