Sensor data provide an objective view of reality but fail to capture the subjective motivations behind an individual's behavior. This latter information is crucial for learning about the various dimensions of the personal context, thus increasing predictability. The main limitation is the human input, which is often not of the quality that is needed. The work so far has focused on the usually high number of missing answers. The focus of this paper is on \textit{the number of mistakes} made when answering questions. Three are the main contributions of this paper. First, we show that the user's reaction time, i.e., the time before starting to respond, is the main cause of a low answer quality, where its effects are both direct and indirect, the latter relating to its impact on the completion time, i.e., the time taken to compile the response. Second, we identify the specific exogenous (e.g., the situational or temporal context) and endogenous (e.g., mood, personality traits) factors which have an influence on the reaction time, as well as on the completion time. Third, we show how reaction and completion time compose their effects on the answer quality. The paper concludes with a set of actionable recommendations.

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