What is it about?
When the same experiment is repeated, results are often not identical. Such differences are sometimes seen as flaws, but they can also arise for more natural reasons—for example, because participants differ, treatments are delivered in slightly different ways, or the studies take place in different settings. Understanding the causes of these differences is essential for developing stronger theories about how and why treatments work. To identify causes, ideally, researchers would compare studies that are identical except for one feature of interest. In practice, however, unintended differences are almost always present and can make it difficult to know what really caused the variation in results. This article introduces a new statistical approach that helps researchers identify how study characteristics causally influence treatment effects, even when studies are not perfectly matched.
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Why is it important?
By making sense of why results vary across different studies, the method supports more informative replication studies and brings us closer to building theories that capture the complexity of human behavior.
Perspectives
This is a topic very close to my heart. It emerged through collaboration with colleagues and from discussions around our work. I find it particularly fascinating because it offers a completely new perspective on how replication studies can be designed and interpreted. This perspective makes it possible to develop much more specific theories and, in turn, to gain a more precise understanding of human behavior.
Steffi Pohl
Freie Universitat Berlin
Read the Original
This page is a summary of: A causal framework for explaining effect heterogeneity in conceptual replications., Psychological Methods, May 2026, American Psychological Association (APA),
DOI: 10.1037/met0000834.
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