Many control loops in process plants perform poorly because of valve stiction as one of the most common equipment problems. Valve stiction may cause oscillation in control loops, which increases variability in product quality, accelerates equipment wear, or leads to control system instability and other issues that potentially disrupt the operation. In this work, data-driven valve stiction models are first reviewed and a simplified model is presented. Next, a stiction detection method is proposed based on curve fitting of the output signal of the first integrating component after the valve, i.e., the controller output for self-regulating processes or the process output for integrating processes. A metric that is called the stiction index (SI) is introduced, based on the proposed method to facilitate the automatic detection of valve stiction. The effectiveness of the proposed method is demonstrated using both simulated data sets based on the proposed valve stiction model and real industrial data sets.