Intensive, Repeated Self-Report Measures: Should We Be Concerned About Changes in Data Quality Over Time?

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.