Ecological momentary assessment (EMA) is a popular assessment method in psychology that aims to capture events, emotions, and cognitions in real time, usually repeatedly throughout the day. Because EMA typically involves more intensive monitoring than traditional assessment methods, missing data are commonly an issue and this missingness may bias results. EMA can involve two types of missing data: known missingness, arising from nonresponse to scheduled prompts, and hidden missingness, arising from nonreporting of focal events (e.g., an urge to smoke or a meal). Prior research on missing data in EMA has focused almost exclusively on nonresponse to scheduled prompts. In this study, we introduce a scaled inverse probability weighting approach to assess the risk of bias due to nonreporting of events due to fatigue on estimates of exposure or correlates of exposure. In our proposed approach, the inverse probability is the estimated probability of compliance with random prompts from a model that uses participant and contextual factors to predict this compliance and a fatigue factor that adjusts for attrition in event reporting over time. We demonstrate the use and utility of our bias assessment method with the Tracking and Recording Alcohol Communications Study, an EMA study of adolescent exposure to alcohol advertising.