In plain language
Most mindfulness questionnaires are built and validated by comparing groups of people to each other. But does a group average tell you anything about the particular person sitting in front of a therapist? This chapter argues that it often does not. Drawing on the ergodic theorem — a mathematical result showing that group-level findings only apply to individuals under very strict conditions that psychology virtually never meets — the authors make the case that traditional psychometrics can hide, or even reverse, what is really happening inside individual lives.
The authors propose an alternative: idionomic assessment, which starts by modelling each person’s own patterns over time using intensive longitudinal data (for example, daily or several-times-daily reports), and only then looks for generalisations across people. Through empirical examples they show why this matters. In one reanalysis, standard multilevel models suggested everyone benefited from “doing what matters,” with estimates ranging only from 0.21 to 0.58 — yet individual-first i-ARIMAX analyses revealed the true range ran from -0.58 to 1.00, meaning that for some people the process was actually linked to worse outcomes. The chapter also uses AI language models to classify the items of 16 mindfulness questionnaires, showing the measures emphasise very different processes, with attention being the only dimension covered by all of them.
The conclusion is that mindfulness processes are highly individualised: their effects on well-being vary substantially from person to person, and averaged, one-size-fits-all assessment can mislead both researchers and clinicians. The chapter charts a path toward personalised, process-based, and contextually sensitive assessment that serves the ultimate purpose of clinical measurement — helping the particular person being treated.
Key findings
- Conventional psychometrics rests on the ergodic assumption — that group statistics apply to individuals over time — which is virtually never met in behavioural science, making non-ergodicity a nearly universal problem.
- In a reanalysis of experience-sampling data, multilevel models shrank individual estimates of the link between valued action and joyfulness to a range of 0.21 to 0.58, while raw and i-ARIMAX individual estimates ranged from -0.58 to 1.00 — meaning standard models hid everyone for whom the effect was negative.
- A meta-analysis of individual i-ARIMAX estimates showed a pooled positive effect (0.35) but extreme heterogeneity (I² = 88.71%) with a prediction interval spanning -0.30 to 1.00, so the average effect cannot be safely applied to a new individual.
- AI language-model classification of 16 adult and adolescent mindfulness questionnaires showed the measures emphasise very different psychological processes; attention was the only dimension addressed by all 16.
- Network analyses showed that mindfulness related to other psychological variables differently in subgroups defined by individual-level estimates — patterns invisible to purely group-level analysis.
- The authors conclude that idionomic assessment offers a promising route to improving the treatment utility of mindfulness measures through personalised, process-based intervention strategies rather than one-size-fits-all approaches.
How to cite
APA
Hernández, C., Sahdra, B. K., Ciarrochi, J., & Hayes, S. C. (2025). Idionomic assessment of mindfulness. In O. N. Medvedev, C. U. Krägeloh, R. J. Siegert, & N. N. Singh (Eds.), Handbook of assessment in mindfulness research (pp. 87–113). Springer. https://doi.org/10.1007/978-3-031-47219-0_136
BibTeX
@incollection{hernandez2025idionomic,
author = {Hern{\'a}ndez, Crist{\'o}bal and Sahdra, Baljinder Kaur and Ciarrochi, Joseph and Hayes, Steven C.},
title = {Idionomic Assessment of Mindfulness},
booktitle = {Handbook of Assessment in Mindfulness Research},
editor = {Medvedev, Oleg N. and Kr{\"a}geloh, Christian U. and Siegert, Richard J. and Singh, Nirbhay N.},
publisher = {Springer},
year = {2025},
pages = {87--113},
doi = {10.1007/978-3-031-47219-0_136}
}
Related work
- All publications by Joseph Ciarrochi (searchable, with free PDFs)
- Process-Based Therapy & Idionomic Analysis
- The Process-Based Assessment Tool (free download)
Author: Joseph Ciarrochi (ORCID 0000-0003-0471-8100). Free copy hosted with permission for scholarly use. Please cite the published version via the DOI above.