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Reassessing the metrics of integration: Toward eliminating the blur between theory and statistics to clarify effect sizes, measurement, and causality in acculturation psychology
The debate around the integration hypothesis in acculturation research frequently centers on the interpretation of effect sizes. While critics argue that these effects are too small and inconsistent to be meaningful, supporters maintain that they reflect statistically robust and theoretically coherent patterns. This controversy reveals a broader epistemological challenge in psychology: persistent ambiguity regarding what constitutes a ‘sufficient’ effect size, rooted in limited attention to the philosophical foundations of measurement and causality. In particular, this includes neglect of the ergodic fallacy—the mistaken assumption that group-level patterns apply directly to individuals—and confusion between statistical regularities and causal explanations. This paper addresses these concerns through three interrelated discussions. First, it re-evaluates the empirical status of the integration hypothesis in light of recent meta-analyses and the epistemic weight of small effects in complex systems. Second, it analyzes how effect sizes should be interpreted across different levels of analysis—individual, inter-individual, and group—and emphasizes the need to align interpretation with the appropriate unit of explanation. Third, it explores the philosophical foundations of psychological measurement, distinguishing between data patterns, theoretical constructs, and causal inferences. Rather than viewing effect sizes as direct indicators of psychological properties or causal strength, we conceptualize them as structured regularities shaped by research design, measurement models, and ontological assumptions. By clarifying these issues, this paper offers a framework for more coherent, theoretically informed interpretations of empirical findings in acculturation psychology and calls for a shift from simplistic magnitude judgments to context-sensitive evaluation of what effect sizes represent.