Ecological momentary assessment (EMA) is an increasingly popular assessment method in the behavioral sciences 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 is commonly an issue and this missingness may bias results. EMA can involve two types of missing data: known missingness, arising from non-response to scheduled prompts, and hidden missingness, arising from non-reporting of focal events (e.g. an urge to smoke or a meal). Prior research on missing data in EMA has focused almost exclusively on non-response to scheduled prompts. In this talk, I introduce a scaled inverse probability weighting approach to adjust for event non-reporting, which can bias estimates of event frequency and characteristics or response to events. In the 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 scaling factor that adjusts for factors specific to event- reporting (in this case, the fatigue of reporting over time). I demonstrate the utility of the proposed method with the Tracking and Recording Alcohol Communications Study, an EMA study of adolescent exposure to alcohol advertising, and discuss its broader applicability to the measurement of other habitual events, such as addictive behaviors or treatment adherence.