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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.
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]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs.
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  Data: <searchLink fieldCode="AR" term="%22Zitzmann%2C+Steffen%22">Zitzmann, Steffen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lindner%2C+Christoph%22">Lindner, Christoph</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lohmann%2C+Julian+F%2E%22">Lohmann, Julian F.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hecht%2C+Martin%22">Hecht, Martin</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. May2026, Vol. 79 Issue 2, p437-452. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Repeated+measures+design%22">Repeated measures design</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology%22">Psychology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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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  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/bmsp.12394
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      – Code: eng
        Text: English
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      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Repeated measures design
        Type: general
      – SubjectFull: Detection algorithms
        Type: general
      – SubjectFull: Data analysis
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      – SubjectFull: Statistical models
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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