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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information

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Summary
This summary is machine-generated.

This study introduces a smart method for ecological momentary assessment (EMA) surveys. It reduces survey length by predicting and skipping uninformative questions, improving data quality and user compliance.

Keywords:
Ecological momentary assessmentexperience samplingmachine learningquestion informativenesssurvey lengthuser burden

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Area of Science:

  • Psychological Science
  • Computer Science
  • Health Informatics

Background:

  • Ecological momentary assessment (EMA) collects real-time, self-reported data via mobile devices in natural environments.
  • High participant burden from lengthy EMA surveys can compromise data quality and compliance.
  • Current EMA methods often struggle to balance data richness with participant engagement.

Purpose of the Study:

  • To develop and evaluate a novel method for reducing EMA survey length while minimizing information loss.
  • To enhance participant compliance and data quality in longitudinal EMA studies.
  • To enable more comprehensive data collection or reduce participant burden in EMA.

Main Methods:

  • Proposed a question-answer prediction model to estimate the information gain of each EMA question.
  • Prioritized informative questions and skipped uninformative ones in a sequential, question-by-question approach.
  • Evaluated the method using simulated question omission on four real-world EMA datasets.

Main Results:

  • The proposed method reduced imputation errors by 15%-52% compared to random omission (50% skipped).
  • Achieved a 34%-56% reduction in mean survey length for surveys with five answer options.
  • Demonstrated real-time prediction accuracy of 72%-95% for skipped questions.

Conclusions:

  • The intelligent question-skipping method significantly enhances EMA efficiency and data integrity.
  • This approach offers a viable solution for reducing participant burden in longitudinal studies.
  • Facilitates sustainable EMA data collection by balancing comprehensiveness and user experience.