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This summary is machine-generated.

This study addresses digit preference in self-reported cigarette counts, a common data issue. A novel model estimates the heaping mechanism, improving analysis of heaped-only smoking data.

Keywords:
conditional distributionecological momentary assessmentrounded datasmoking cessationtimeline followback

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Area of Science:

  • Behavioral Science
  • Biostatistics
  • Psychology

Background:

  • Self-reported daily cigarette counts often show digit preference, or heaping, skewing research data.
  • Heaping complicates analysis by obscuring true smoking behavior.

Purpose of the Study:

  • To develop a method for estimating the heaping mechanism from self-reported data.
  • To enable accurate analysis of smoking cessation trial data with heaping.

Main Methods:

  • Utilized a unique doubly-coded smoking cessation trial dataset with both recalled and instantaneous cigarette counts.
  • Proposed a novel model defining self-reported count probability based on proximity to truth and 'gravity' (attractiveness).

Main Results:

  • Successfully estimated the heaping mechanism using the novel model.
  • Demonstrated the model's ability to illuminate cognitive processes influencing self-reporting.

Conclusions:

  • The developed model accurately estimates the heaping mechanism in self-reported data.
  • This methodology allows for more robust analysis of heaped-only datasets, particularly in smoking research.