Why Elusive Expectancy × Value Interactions May Be Critical for Theory and Intervention: A Simulated Power Analysis.

According to expectancy-value theories of motivation, individuals choose to pursue tasks that they expect to succeed at and find personally valuable. Historically, researchers have often suggested that these two factors interact to motivate behavior. However, expectancy × value interactions are rarely observed in empirical research and, when detected, they are often small in magnitude. Does this mean they can safely be ignored in models of motivation? In this paper we conduct two power analyses with simulated data to argue that expectancy × value interactions are likely far more important than a straightforward interpretation of effect sizes would suggest, and that downplaying them risks oversimplifying theory and recommendations for intervention. Specifically, Study 1 demonstrates that a realistic combination of three constraints (measurement error, skew, and correlation) can negatively bias expectancy × value interaction estimates by more than 50%. Study 2 shows that these interactions can create meaningful variability in motivation interventions and may contribute to a better understanding of treatment heterogeneity.