Background: Food acquisition diary surveys are important for studying food expenditures, factors affecting food acquisition decisions, and relations between these decisions with selected measures of health (e.g., body mass index, self-reported health). However, to our knowledge, no studies have evaluated the errors associated with these diary surveys, which can bias survey estimates and research findings. The use of paradata, which has been largely ignored in previous literature on diary surveys, could be useful for studying errors in these surveys.Objective: We used paradata to assess survey errors in the National Household Food Acquisition and Purchase Survey (FoodAPS).Methods: To evaluate the patterns of nonresponse over the diary period, we fit a multinomial logistic regression model to data from this 1-wk diary survey. We also assessed factors influencing respondents' probability of reporting food acquisition events during the diary process by using logistic regression models. Finally, with the use of an ordinal regression model, we studied factors influencing respondents' perceived ease of participation in the survey.Results: As the diary period progressed, nonresponse increased, especially for those starting the survey on Friday (where the odds of a refusal increased by 12% with each fielding day). The odds of reporting food acquisition events also decreased by 6% with each additional fielding day. Similarly, the odds of reporting ≥1 food-away-from-home event (i.e., meals, snacks, and drinks obtained outside the home) decreased significantly over the fielding period. Male respondents, larger households, households that eat together less often, and households with frequent guests reported a significantly more difficult time getting household members to participate, as did non-English-speaking households and households currently experiencing difficult financial conditions.Conclusions: Nonresponse and underreporting of food acquisition events tended to increase in the FoodAPS as data collection proceeded. This analysis of paradata available in the FoodAPS revealed these errors and suggests methodologic improvements for future food acquisition surveys.