Active authentication is the process of continuously verifying a user based on his/her ongoing interactions with a computer. Forensic sty-lometry is the study of linguistic style applied to author (user) identification. This paper evaluates the Active Linguistic Authentication Dataset, collected from users working individually in an office environment over a period of one week. It considers a battery of stylometric modalities as a representative collection of high-level behavioral biometrics. While a previous study conducted a partial evaluation of the dataset with data from fourteen users, this paper considers the complete dataset comprising data from 67 users. Another significant difference is in the type of evaluation: instead of using day-based or data-based (number-of-characters) windows for classification, the evaluation employs time-based, overlapping sliding windows. This tests the ability to produce authentication decisions every 10 to 60 seconds, which is highly applicable to real-world active security systems. Sensor evaluation is conducted via cross-validation, measuring the false acceptance and false rejection rates (FAR/FRR). The results demonstrate that, under realistic settings, stylometric sensors perform with considerable effectiveness down to 0/0.5 FAR/FRR for decisions produced every 60 seconds and available 95% of the time.