Key Insights

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  Experience sampling methods (ESM) use participant-generated real-time data to capture lived experience longitudinally.

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  ESM combines quantitative frequent data collection with qualitative unstructured interviews for richer understanding.

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  Key ESM design tradeoffs involve balancing sampling frequency, survey length, and participant fatigue.

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  There are four trigger types in ESM: random, fixed, contextual, and self-initiated event-based.

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  Reactivity is an inherent feature of ESM, as self-monitoring often changes participant behavior.

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  Pre-study pilot tests and clear communication with participants improve data quality and reduce attrition.

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  Unstructured interviews after ESM can explore participant interpretations and meanings behind their data.

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  Visual dashboards presenting individual ESM data enhance participant engagement and interview depth.

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  ESM suits research on sensitive or complex experiences, such as health treatment or learning processes.

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  Paying participants creatively, like incremental incentives and drawings, helps maintain engagement over time.

Notable Quotes