Affect dynamics and well-being: explanatory power of the model of intraindividual variability in affect (MIVA)

ABSTRACT

Affective experience is inherently dynamic and short-term changes in affect are supposed to offer important insights into well-being. Past years have shown a tremendous rise in investigations into the relation between affect dynamics and well-being. The indicators that have been introduced to capture unique dynamical aspects of affect, however, have been criticised for being purely statistical measures without theoretical foundation and were shown to have little added value for explaining well-being over and above mean levels of affect. To address these concerns, we applied our newly developed theory-based MIVA model to data on daily affective experience. The MIVA model allows estimating parameters for anchoring, reactivity, and regulation based on affective states in combination with daily events. Everyday affective experience was measured with a high temporal resolution, multiple indicators of well-being (e.g. life satisfaction, depression) were assessed, and the incremental value of the MIVA model parameters in predicting well-being was determined. The MIVA model parameters reflect essential processes that accounted for observed fluctuations in affective experience. Incremental validity for predicting well-being over and above mean levels of affect, however, was low. Together, our results suggest that research on affect dynamics needs to identify how affect dynamics can be assessed more validly.

KEYWORDS:

• Affect
• well-being
• emotion regulation
• depression
• emotional reactivity

Acknowledgement

We thank Anna El-Serazh, Victoria Gerhart, Katharina Hörmann, Jonas Marquardt, Lars Niebusch, and Moritz Redlich for their assistance in conducting the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in the open science framework at https://osf.io/sn4gf/.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 A simulation study showed a good recovery of parameters in terms of (a) correlation between true and recovered values (M = 0.82, SD = 0.13), (b) bias (M = −0.008, SD = 0.04), and (c) coverage rate for CI (M = 0.90, SD = 0.04). Simulation script and detailed results can be found in the online supplemental material.

2 Expecting a medium effect size for the association between indicators of affect dynamics and well-being (f² = .15; Houben et al., 2015), a sample of 90 would yield 93% power to detect this effect (g*power for linear multiple regression; Faul et al., 2009).

3 Following the ESM phase of the study, participants answered a short questionnaire concerning their experience with the ESM study phase. On a separate appointment, participants took part in an emotion regulation task in the laboratory or completed the task online. Participants were presented with four emotion eliciting movie clips and rated their affective state before, during, and after stimulus presentation. The emotion regulation task is part of a data collection for a bachelor thesis.

4 Questionnaire was implemented using https://eu.jotform.com/, messages were sent via https://www.twilio.com/, and data was stored on the department server.

5 If event timing is indicated to be before the timing of the reply to the previous questionnaire (e.g. subject indicates that event occurred between 30-40 min ago but the last questionnaire was answered 15 min ago), for this assessment, event occurrence was set to “no”, except when the participant missed the previous questionnaire.

6 For each dependent variable, we checked if more than 50% of the data belongs to the highest or lowest category. We checked if the sum of mean ± 1 SD of a dependent variable exceeds/falls below the outer upper/lower limit of the scale. If these checks indicated ceiling or floor effects, we checked for outliers in terms of either individuals or items and deleted the according data.

Additional information

Funding

This work was supported by an early career grant and IMPULSE 2019 grant (DRM/2019-13) from Friedrich Schiller University Jena awarded to MW.