Over the past decades, time use researchers have been increasingly interested in analyzing wellbeing in tandem with the use of time (Juster and Stafford, 1985; Krueger et al, 2009). Many methodological issues have arose in this endeavor, including the concern about the quality of the time use data. Survey researchers have increasingly turned to the analysis of paradata to better understand and model data quality. In particular, it has been argued that paradata may serve as proxy of the respondents’ cognitive response process, and can be used as an additional tool to assess the impact of data generation on data quality. In this presentation, data quality in the American Time Use Survey (ATUS) will be assessed through the use of paradata and survey responses. Specifically, I will talk about a data quality index I have created, which includes measures of different types of ATUS errors (e.g. low number of reported activities, failures to report an activity), and paradata variables (e.g. response latencies, incompletes). The overall objective of this study is to contribute to data quality assessment in the collection of timeline data from national surveys by providing insights on those interviewing dynamics that most impact data quality. These insights will help to improve future instruments and training of interviewers, as well as to reduce costs.