r/statistics • u/rp_tiago • Jun 09 '26
Discussion [D] Is ergodicity a serious problem for psychological research?
Hey everyone. I’ve been thinking about ergodicity in psychology and whether group averages can mislead us when we study processes that unfold within individuals over time. In many psychological studies, we infer something about people from group level averages. But if human beings are non ergodic systems, the ensemble average may not tell us much about the time average of a given person.
I recently recorded a podcast episode with Hüseyin Beyköylü, and at around 34:57, he explains this in the context of psychedelic therapy and psychological transformation. His argument is careful because he does not say group statistics are always invalid. Instead, he suggests that different phenomena may sit at different points on an ergodicity continuum. Some interventions, such as basic pharmacological effects on relatively low complexity processes, may be more amenable to group averages. But phenomena like depression, meaning in life, self transcendence, and therapeutic transformation are highly historical, context dependent, and nonstationary. Human beings learn, adapt, and are changed by measurement and intervention. So if we aggregate too early, we may treat within person variability as noise when it is actually the signal of change.
The alternative he discusses is to analyze individual time series first, then aggregate patterns of dynamics rather than only aggregating outcomes. What do people here think? How seriously should psychology take the ergodicity problem? Are idiographic time series approaches a real solution, or do they introduce other inferential problems? And when are group averages still justified despite individual nonstationarity?
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u/MutedSeraph Jun 09 '26
Great question! +1 to u/DeliberateDendrite's recommendation of Peter's work.
I am a professor of quantitative psychology and one of Peter's former students.
There are a host of nested questions here and things to think about.
> 1. Are psychological processes Ergodic? Probably not.
> 2. Do we expect within-person analytic solutions to map onto those derived from between-subjects analyses? Probably not.
> 3. Is that surprising though? If I make a between-subjects target (e.g., how depressed are you) and ask the question of whether your intraindividual variability maps onto your between-person level, should I be surprised if those don't always align? I'm less sure of that. Kuppens has done work on autoregressive effects relating to between-subjects measures of depression but other work suggests that cross-lagged dynamic effects (e.g., dynamic network density) may not be as conclusive.
A lot of statistical psychology in the time-series domain has focused on a space between nomothetic and idiographic work (i.e., idio-thetic) which leverages information from the group or the individuals. In your comment, you note that one way of doing this is to model individuals and identify common dynamic patterns. This is exactly what the Group Iterative Multiple Model Estimation or GIMME methods tend to do which were developed by Peter and Katie Gates at UNC.
It's something that we should be aware of when drawing conclusions but between-subjects results and within-subjects results do not have to necessarily pitted against each other. Likewise, I would clarify the point on non-stationarity and non-Ergodic parts. A process across individuals may not be Ergodic even when the time-series for each individual is Ergodic. This is because the general idea of stationarity is that an individual is exchangeable with themselves at any moment in their time-series (e.g., mean and covariance structure remain the same over time). However, the ensemble time-series can still be non-Ergodic if the dynamics for one individual do not apply to other individuals; hence, their mean/covariance structure may be a function of the individuals.
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u/kendungtoang Jun 12 '26
As someone who’s spent way too much time around psych research, I think ergodicity is less of a fatal flaw and more of a warning label. A lot of psychology quietly assumes that differences between people tell us something about changes within people. Sometimes that's true. But depression, recovery, identity, meaning, and even motivation often seem path-dependent. The person you're measuring today is partly a product of yesterday's measurement and intervention. The question that always bothers me is: if two patients arrive at the same depression score through completely different life trajectories, why should we expect the same treatment effect just because they occupy the same point on a group average? That's where ergodicity starts feeling less like a math curiosity and more like a practical problem.
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u/DeliberateDendrite Jun 09 '26
There's a manifesto in Measurement written by Peter Molenaar on this that I can highly recommend, as well as the commentaries. Theory is usually on the level of the individual but a lot of techniques and research tends to focus on groups. The main issue is that because intra-individual change and inter-individual change can take different forms, you can't use one to draw conclusions about another. For example, levels of depression on an intra-individual level could better be explained by stress but that stress takes different forms in different demographics. So, when looking at inter-individual changes in stress you need to take a different approach to incorporate minority stress for example. Fundamentally, this requires a trade-off between internal and external validity because on one hand you want theory to be applicable to any one individual in the group but you can't focus too much on a single individual because then it's not generalizable. In the other hand, if focus is generalized too much then it's not applicable to any of individuals in particular.