<p>The Somatic Marker Hypothesis, an influential neurobiological account of decision-making, states that emotional somatic markers (e.g., skin conductance responses) influence decision-making processes. Despite its prominence, the hypothesis remains controversial partly because of inconsistent results stemming from inappropriate statistical methods. Tasks designed to assess decision-making often use repeated measures designs, such as the Iowa Gambling Task (IGT), which requires participants to maximize profits by selecting 100 cards among four decks offering varying win–loss contingencies. Researchers often aggregate repeated measures into a single averaged value to simplify analyses, potentially committing an ecological fallacy by erroneously generalizing results obtained from aggregated data (i.e., interindividual effects) to individual repeated measurements (i.e., intraindividual effects). This paper addresses this issue by demonstrating how to analyze concurrent repeated measures of both independent and dependent variables using multilevel logistic models. First, the principles of logistic multilevel models are explained. Then, simulated and empirical IGT data are analyzed to compare the performance of traditional statistical approaches (i.e., general linear models) with multilevel logistic models. Our proposed multilevel logistic analyses address critical methodological gaps in decision-making research, ensuring more accurate interpretations of repeated measures data. This approach not only advances the study of the Somatic Marker Hypothesis but also provides a robust framework for similar research protocols, ultimately enhancing the reliability and validity of findings.</p>