Guassi Moreira, J. F., Sahi, R. S., Calderon Leon, M. D., Saragosa-Harris, N. M., Waizman, Y. H., Sedykin, A. E., Ninova, E., Peris, T. S., Gross, J. J., & Silvers, J. A. (2024). A data-driven typology of emotion regulation profiles. Emotion, 24(5), 1125–1136. https://doi.org/10.1037/emo0001306
Key Points
- The study identified a typology of emotion regulation strategy usage with three clusters: individuals who use all strategies frequently (Hi), those who use all strategies infrequently (Lo), and those who selectively use some strategies (Mix).
- Membership in the Mix cluster was associated with better mental health outcomes, including lower anxiety, depression, general distress, and perceived stress.
- Increased use of cognitive reappraisal and situation selection, along with decreased use of suppression and distraction, was associated with better mental health outcomes.
- The typology was stable across multiple samples and over time, suggesting it represents meaningful individual differences in emotion regulation tendencies.
- While informative, the research has limitations, such as relying primarily on self-report measures and not examining contextual influences on strategy effectiveness.
- Understanding individual differences in emotion regulation strategy usage has important implications for mental health assessment and treatment approaches.
Rationale
Emotion regulation plays a crucial role in mental health and well-being.
While previous research has examined the use and effectiveness of individual emotion regulation strategies (Gross & John, 2003; Webb et al., 2012), less is known about how individuals use multiple strategies in combination.
Recent work on “poly-regulation” suggests people may use various strategies together (Ford et al., 2019), but no formal typology of emotion regulation strategy usage exists.
Identifying such a typology could advance understanding of individual differences in emotion regulation and inform clinical interventions.
Additionally, examining links between strategy usage patterns and mental health symptoms could provide insights into adaptive and maladaptive regulatory approaches.
This study aimed to address these gaps by using data-driven methods to identify a typology of emotion regulation strategy usage and investigate its associations with mental health outcomes across multiple samples.
- Hypothesis 1: There exist distinct patterns of emotion regulation strategy usage that can be categorized into a typology.
- Hypothesis 2: Cluster membership is associated with differences in mental health symptoms.
- Hypothesis 3: Individual strategies are differentially associated with mental health outcomes.
- Hypothesis 4: Emotion regulation strategy usage patterns are stable over time.
Method
The researchers used a combination of computational techniques, psychometric models, and growth curve modeling across six independent samples totaling 1,492 participants.
Procedure
Participants completed self-report measures of emotion regulation strategy usage and mental health symptoms.
The researchers then used K-Medoids clustering to identify groups based on patterns of strategy usage.
They examined differences in mental health symptoms between clusters and used ridge regression to investigate associations between individual strategies and outcomes.
Sample
Six independent samples were used, including online and laboratory participants. Samples varied in size from 101 to 406 participants, with a total of 1,492 participants across all samples.
Participants were predominantly young adults, with mean ages ranging from 18.36 to 41.56 years. Gender and racial/ethnic composition varied across samples.
Measures
- Extended Emotion Regulation Questionnaire (E-ERQ): Assessed use of five emotion regulation strategies (reappraisal, suppression, distraction, selective attention, situation selection)
- Positive and Negative Affect Schedule (PANAS): Measured trait positive and negative affect
- Berkeley Expressivity Questionnaire (BEQ): Assessed emotional expressivity
- Mini-Mood and Anxiety Symptom Questionnaire (Mini-MASQ): Measured anxious and depressive symptoms
- Perceived Stress Scale (PSS): Assessed perceived stress levels
Statistical measures
- K-Medoids clustering to identify emotion regulation strategy usage patterns
- Ridge regression to examine associations between strategies and mental health outcomes
- Growth curve modeling to assess longitudinal stability of cluster differences
- Cross-validation techniques to evaluate predictive utility of strategies
Results
Hypothesis 1: There exist distinct patterns of emotion regulation strategy usage that can be categorized into a typology.
Result: K-Medoids clustering consistently identified three clusters across samples: Hi (frequent use of all strategies), Lo (infrequent use of all strategies), and Mix (selective use of strategies).
Hypothesis 2: Cluster membership is associated with differences in mental health symptoms.
Result: The Mix cluster showed the most adaptive mental health profile, with lower anxiety, depression, general distress, and perceived stress compared to other clusters. The Lo cluster showed the least adaptive profile.
Hypothesis 3: Individual strategies are differentially associated with mental health outcomes.
Result: Increased use of reappraisal and situation selection was associated with better mental health outcomes, while increased use of suppression and distraction was associated with poorer outcomes.
Hypothesis 4: Emotion regulation strategy usage patterns are stable over time.
Result: Cluster membership showed high stability over a 2-week period (75.16% consistency) and predicted anxiety symptoms over a 60-day period.
Insight
This study provides evidence for a reliable typology of emotion regulation strategy usage, suggesting that individuals tend to fall into one of three patterns: using all strategies frequently, using all strategies infrequently, or selectively using certain strategies.
Importantly, the selective use of strategies (Mix cluster) was associated with the best mental health outcomes. This suggests that flexibly employing different strategies may be more adaptive than consistently high or low use of all strategies.
The findings extend previous research by examining multiple strategies simultaneously and identifying patterns of usage across individuals.
This approach provides a more nuanced understanding of emotion regulation than studies focused on single strategies.
The stability of the identified clusters over time and across samples suggests these patterns represent meaningful individual differences in regulatory tendencies.
Future research could investigate:
- How these regulatory patterns develop over the lifespan
- Whether interventions targeting specific patterns of strategy usage are more effective than those focused on individual strategies
- How contextual factors influence the effectiveness of different regulatory patterns
Strengths
- Large, diverse samples across multiple studies (total N = 1,492)
- Use of advanced statistical techniques (K-Medoids clustering, ridge regression)
- Examination of multiple emotion regulation strategies simultaneously
- Inclusion of both cross-sectional and longitudinal data
- Replication of findings across independent samples
- Assessment of both affective and clinical symptoms
Limitations
- Reliance primarily on self-report measures
- Limited examination of contextual influences on strategy effectiveness
- Samples predominantly young adults from Western cultures
- Cross-sectional nature of most analyses limits causal inferences
- Focus on a specific set of five emotion regulation strategies
These limitations suggest caution in generalizing findings to other age groups or cultural contexts. The self-report nature of the measures may not fully capture actual strategy usage in real-world situations. Additionally, the effectiveness of different regulatory patterns may vary depending on context, which was not extensively examined in this study.
ClinicalImplications
The identification of distinct emotion regulation strategy usage patterns has significant implications for understanding individual differences in emotional functioning and mental health.
Clinically, this typology could inform assessment and treatment approaches. For example, individuals in the Lo cluster might benefit from interventions focused on increasing overall strategy usage, while those in the Hi cluster may need help with selective strategy implementation.
The finding that the Mix cluster showed the best mental health outcomes suggests that flexibility in strategy usage may be key to emotional well-being.
This aligns with emerging perspectives on emotion regulation flexibility (Chen & Bonanno, 2021) and highlights the potential benefits of teaching a diverse repertoire of strategies in clinical interventions.
The stability of cluster membership over time indicates that these patterns may represent relatively enduring individual differences. This could have implications for identifying individuals at risk for mental health problems based on their regulatory tendencies.
Variables that may influence the results include:
- Cultural background and values related to emotional expression
- Developmental stage and cognitive capabilities
- Presence of psychopathology or other individual differences
References
Primary reference
Guassi Moreira, J. F., Sahi, R. S., Calderon Leon, M. D., Saragosa-Harris, N. M., Waizman, Y. H., Sedykin, A. E., Ninova, E., Peris, T. S., Gross, J. J., & Silvers, J. A. (2024). A data-driven typology of emotion regulation profiles. Emotion, 24(5), 1125–1136. https://doi.org/10.1037/emo0001306
Other references
Chen, S., & Bonanno, G. A. (2021). Components of emotion regulation flexibility: Linking latent profiles to depressive and anxious symptoms. Clinical Psychological Science, 9(2), 236-251.
Ford, B. Q., Gross, J. J., & Gruber, J. (2019). Broadening our field of view: The role of emotion polyregulation. Emotion Review, 11(3), 197-208.
Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348-362.
Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological Bulletin, 138(4), 775-808.
Keep Learning
Socratic questions for a college class to discuss this paper:
- How might cultural differences influence the patterns of emotion regulation strategy usage identified in this study?
- What are the potential benefits and drawbacks of having a typology of emotion regulation strategy usage in clinical practice?
- How might the effectiveness of different regulatory patterns vary across different types of situations or emotional contexts?
- What developmental processes might contribute to the formation of these emotion regulation strategy usage patterns?
- How could the findings of this study inform the design of more effective emotion regulation interventions?
- What additional strategies or factors should be considered in future research to provide a more comprehensive understanding of emotion regulation tendencies?
- How might the identified typology relate to other individual difference factors, such as personality traits or cognitive abilities?
- What ethical considerations should be taken into account when applying this typology in real-world settings, such as clinical assessment or workplace evaluations?
