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The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
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Intensive, Repeated Self-Report Measures: Should We Be Concerned About Changes in Data Quality Over Time?

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Repeated self-report measures, vital for behavioral and medical research, can degrade in quality over time due to factors like assessment length and participant reactivity. Researchers must monitor these changes to ensure data accuracy.

Keywords:
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Area of Science:

  • Behavioral Science
  • Medical Research
  • Mobile Health

Background:

  • Intensive, repeated self-report measures are crucial for understanding dynamic variables at a granular level.
  • Mobile health applications depend on accurate, immediate state and environmental assessments for intervention.
  • Repeated momentary assessments minimize recall bias and capture real-world data, offering insights into person-environment interactions.

Purpose of the Study:

  • To investigate how repeated completion of momentary assessments may alter data quality over time.
  • To identify phenomena that can induce non-invariance in repeated measures.
  • To alert researchers to potential changes affecting data quality.

Main Methods:

  • Examined features of repeated momentary assessments that could change data characteristics.
  • Discussed lines of inquiry questioning the assumption of invariance in assessment completion.
  • Presented four phenomena potentially inducing non-invariance: time to complete, missing data rates, careless responding, and reactivity.

Main Results:

  • Evidence suggests changes can occur over time in how individuals complete repeated assessments.
  • Four key phenomena (time, missing data, careless responding, reactivity) were identified as potential sources of non-invariance.
  • These changes can impact the overall quality and reliability of collected data.

Conclusions:

  • The assumption of invariance in repeated measures completion may not hold true.
  • Researchers must be aware that data quality can change over time due to various factors.
  • Monitoring for and addressing these changes is essential for maintaining data integrity in behavioral and medical research.