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Psychological theories often invoke interactions but remain vague regarding the details. As a consequence, researchers may not know how to properly test them and may potentially run analyses that reliably return the wrong answer to their research question. We discuss three major issues regarding the prediction and interpretation of interactions. First, interactions can be removable in the sense that they appear or disappear depending on scaling decisions, with consequences for a variety of situations (e.g., binary or categorical outcomes, bounded scales with floor and ceiling effects). Second, interactions may be conceptualized as changes in slope or changes in correlations, and because these two phenomena do not necessarily coincide, researchers might draw wrong conclusions. Third, interactions may or may not be causally identified, and this determines which interpretations are valid. Researchers who remain unaware of these distinctions might accidentally analyze their data in a manner that returns the technically correct answer to the wrong question. We illustrate all issues with examples from psychology and issue recommendations for how to best address them in a productive manner.
Rohrer et al. (Thu,) studied this question.