Asher Michael W, Hecht Cameron A, Harackiewicz Judith M, Curtin John J, Parrisius Cora, Nagengast Benjamin
Human Computer Interaction Institute, Carnegie Mellon University.
Department of Psychology, University of Rochester.
Motiv Sci. 2025 Apr 10. doi: 10.1037/mot0000394.
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.
根据动机的期望 - 价值理论,个体选择去追求那些他们预期能够成功且认为具有个人价值的任务。从历史上看,研究人员常常认为这两个因素相互作用以激发行为。然而,期望×价值的相互作用在实证研究中很少被观察到,而且即便被检测到,其效应大小通常也很小。这是否意味着在动机模型中可以安全地忽略它们呢?在本文中,我们用模拟数据进行了两项功效分析,以论证期望×价值的相互作用可能远比直接根据效应大小所表明的更为重要,并且轻视它们可能会导致理论过度简化以及干预建议过于简单。具体而言,研究1表明,测量误差、偏态和相关性这三个限制因素的现实组合会使期望×价值相互作用的估计产生超过50%的负偏差。研究2表明,这些相互作用能够在动机干预中产生有意义的变异性,并且可能有助于更好地理解治疗的异质性。