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Modelling non-linear personality change surrounding transitions: A review of statistical approaches.

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Title: Modelling non-linear personality change surrounding transitions: A review of statistical approaches.
Authors: Levelt, Lisa (AUTHOR), Mulder, Joris (AUTHOR), Lee, Nikki C. (AUTHOR), Luhmann, Maike (AUTHOR), Denissen, Jaap J. A. (AUTHOR)
Source: European Journal of Personality. May/Jun2026, Vol. 40 Issue 3, p725-751. 27p.
Subjects: Personality change, Statistical models, Widowhood, Prediction models, Life change events, Multilevel models, Life satisfaction, Continuous time models
Abstract: Personality changes surrounding transitions in life circumstances are often non-linear, presenting challenges for statistical analysis. This paper therefore reviews approaches to modelling non-linear personality change surrounding transitions, aiming to guide readers in selecting and applying an approach that fits their objectives. Seven approaches were reviewed, including traditional mixed-effects methods, continuous-time dynamic models, and relatively novel data-driven techniques. Each approach is explained, outlining its strengths and limitations. The approaches' practical utility is assessed through a case study examining changes in life satisfaction surrounding widowhood, using LISS panel data. Interpretability and model fit are compared, and annotated R code is provided as a tutorial for implementation. Results highlighted the varied suitability of the mixed-effects approaches for studying different aspects of change. The data-driven techniques excelled in capturing average and person-specific trajectories, generalised effectively, and allowed interpretation of different change aspects than the mixed-effects approaches allowed for. Importantly, the approaches yielded distinct findings regarding life satisfaction changes surrounding widowhood, with theoretical implications. The paper concludes with practical recommendations for selecting and applying these approaches. By expanding the reader's statistical toolkit and providing an accessible overview, this resource supports the effective analysis of non-linear changes surrounding transitions, enabling a fuller understanding of personality change. Plain language summary: Personality often changes surrounding major life transitions. For example, many people become more emotionally stable when starting their first job. Understanding these changes is important, because personality changes can influence important life outcomes, such as health and income. However, it is difficult to study these changes, because they are often not straightforward. Instead of steadily increasing or decreasing, personality trait levels may go up and down over time, with periods of faster or slower change. Common statistical methods are suited for studying straight-line changes (like steady increases), but if we use these methods to study non-straight-line personality change, we risk underestimating how much personality actually changes. Statistical methods to study non-straight-line change are less well-known, and it is unclear how to choose among alternatives and apply them. This paper reviews statistical methods to study non-straight-line change. We explain each method, discussing its strengths and weaknesses. We demonstrate how to apply the methods in a case study that examines how life satisfaction changes when people become widowed. The accompanying website illustrates how to conduct the methods in the free software environment R. We find that certain methods work well for quantifying specific aspects of change (such as how much and how quickly), while other methods are better for finding the overall pattern of change. Importantly, the methods led to different findings regarding life satisfaction changes surrounding widowhood, highlighting how the choice of method can impact research conclusions. The paper ends with practical recommendations for selecting and using the methods. Altogether, this paper equips readers with statistical tools to study non-straight-line change in order to gain further understanding of how personality changes surrounding transitions. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Personality is the property of Sage Publications Inc. 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: Modelling non-linear personality change surrounding transitions: A review of statistical approaches.
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Personality%22">European Journal of Personality</searchLink>. May/Jun2026, Vol. 40 Issue 3, p725-751. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Personality+change%22">Personality change</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Widowhood%22">Widowhood</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Life+change+events%22">Life change events</searchLink><br /><searchLink fieldCode="DE" term="%22Multilevel+models%22">Multilevel models</searchLink><br /><searchLink fieldCode="DE" term="%22Life+satisfaction%22">Life satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+time+models%22">Continuous time models</searchLink>
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  Data: Personality changes surrounding transitions in life circumstances are often non-linear, presenting challenges for statistical analysis. This paper therefore reviews approaches to modelling non-linear personality change surrounding transitions, aiming to guide readers in selecting and applying an approach that fits their objectives. Seven approaches were reviewed, including traditional mixed-effects methods, continuous-time dynamic models, and relatively novel data-driven techniques. Each approach is explained, outlining its strengths and limitations. The approaches' practical utility is assessed through a case study examining changes in life satisfaction surrounding widowhood, using LISS panel data. Interpretability and model fit are compared, and annotated R code is provided as a tutorial for implementation. Results highlighted the varied suitability of the mixed-effects approaches for studying different aspects of change. The data-driven techniques excelled in capturing average and person-specific trajectories, generalised effectively, and allowed interpretation of different change aspects than the mixed-effects approaches allowed for. Importantly, the approaches yielded distinct findings regarding life satisfaction changes surrounding widowhood, with theoretical implications. The paper concludes with practical recommendations for selecting and applying these approaches. By expanding the reader's statistical toolkit and providing an accessible overview, this resource supports the effective analysis of non-linear changes surrounding transitions, enabling a fuller understanding of personality change. Plain language summary: Personality often changes surrounding major life transitions. For example, many people become more emotionally stable when starting their first job. Understanding these changes is important, because personality changes can influence important life outcomes, such as health and income. However, it is difficult to study these changes, because they are often not straightforward. Instead of steadily increasing or decreasing, personality trait levels may go up and down over time, with periods of faster or slower change. Common statistical methods are suited for studying straight-line changes (like steady increases), but if we use these methods to study non-straight-line personality change, we risk underestimating how much personality actually changes. Statistical methods to study non-straight-line change are less well-known, and it is unclear how to choose among alternatives and apply them. This paper reviews statistical methods to study non-straight-line change. We explain each method, discussing its strengths and weaknesses. We demonstrate how to apply the methods in a case study that examines how life satisfaction changes when people become widowed. The accompanying website illustrates how to conduct the methods in the free software environment R. We find that certain methods work well for quantifying specific aspects of change (such as how much and how quickly), while other methods are better for finding the overall pattern of change. Importantly, the methods led to different findings regarding life satisfaction changes surrounding widowhood, highlighting how the choice of method can impact research conclusions. The paper ends with practical recommendations for selecting and using the methods. Altogether, this paper equips readers with statistical tools to study non-straight-line change in order to gain further understanding of how personality changes surrounding transitions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Personality is the property of Sage Publications Inc. 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: 10.1177/08902070251376407
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      – Code: eng
        Text: English
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        PageCount: 27
        StartPage: 725
    Subjects:
      – SubjectFull: Personality change
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Widowhood
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Life change events
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      – SubjectFull: Multilevel models
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      – SubjectFull: Life satisfaction
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      – SubjectFull: Continuous time models
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              Text: May/Jun2026
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              Y: 2026
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