Active authentication is the process of continuously verifying a user based on his/her ongoing interactions with a computer. Forensic stylometry 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.