A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs.
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| Title: | A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs. |
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| Authors: | Zitzmann, Steffen (AUTHOR), Lindner, Christoph (AUTHOR), Lohmann, Julian F. (AUTHOR), Hecht, Martin (AUTHOR) |
| Source: | British Journal of Mathematical & Statistical Psychology. May2026, Vol. 79 Issue 2, p437-452. 16p. |
| Subjects: | Time series analysis, Repeated measures design, Detection algorithms, Data analysis, Statistical models, Psychology |
| Abstract: | 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 (N≫1), 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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 (N≫1), 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00071102 |
| DOI: | 10.1111/bmsp.12394 |
