A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs

Researchers working with intensive longitudinal designs often encounter the challenge of determining whether to relax the assumption of stationarity in their models. Given that these designs typically involve data from a large number of subjects ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>N</mml:mi> <mml:mo>≫</mml:mo> <mml:mn>1</mml:mn></mml:mrow> <mml:annotation>$$ N\gg 1 $$</mml:annotation></mml:semantics> </mml:math> ), visual screening all time series can quickly become tedious. Even when conducted by experts, such screenings can lack accuracy. In this article, we propose a nonvisual procedure that enables fast and accurate screening. This procedure has potential to become a widely adopted approach for detecting nonstationarity and guiding model building in psychology and related fields, where intensive longitudinal designs are used and time series data are collected.