Multimodality and skewness in emotion time series
Jonas Haslbeck1, Oisín Ryan2, Fabian Dablander3
1Department of Clinical Psychological Science, Maastricht University.
Emotion (Washington, D.C.)
|May 11, 2023
Summary
Emotional states measured by mobile devices often show multiple peaks (multimodality) and uneven distributions (skewness). These patterns are common in daily life emotion research and vary by scale type and valence.
Area of Science:
- Psychology
- Computational Social Science
- Digital Health
Background:
- Mobile devices enable real-time emotion measurement, fueling research on emotional dynamics.
- Significant potential in analyzing temporal emotion data remains untapped.
Purpose of the Study:
- To systematically investigate the prevalence and characteristics of emotion measurement modality and skewness.
- To analyze heterogeneity in within-person emotion data across different studies and individuals.
Main Methods:
- Reanalyzed data from seven open-access experience sampling methodology studies (N=835).
- Investigated unimodal, bimodal, and multimodal distributions and skewness of emotion measurements.
- Quantified heterogeneity across items, individuals, and measurement designs.
Main Results:
- Multimodality and skewness are prevalent in within-person emotion measurements.
- Analog slider scales yield more multimodal data than Likert scales.
- Negative emotions show higher skewness; longer time series increase modality for positive and skewness for negative emotions.
Conclusions:
- Emotion data exhibit complex distributional properties (multimodality, skewness) that are often overlooked.
- Findings have implications for emotion theory, measurement practices, and time series analysis in psychological research.
- Understanding these patterns is crucial for accurate interpretation of digital mental health data.
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