Examination of a recommended algorithm for eliminating nonsystematic delay discounting response sets

Area of Science:

• Behavioral Economics
• Psychology
• Addiction Science

Background:

• Delay discounting (DD) research assesses preference for immediate rewards.
• Identifying nonsystematic response sets is crucial for data validity.
• Conventional methods like R(2) may not optimally identify these response sets.

Purpose of the Study:

• Compare a recommended algorithm to R(2) for identifying nonsystematic response sets in DD data.
• Assess the impact of excluding nonsystematic data on research outcomes.
• Identify participant characteristics associated with nonsystematic response sets.

Main Methods:

• Assessed discounting of hypothetical monetary rewards in 349 pregnant women (smokers and quitters).
• Utilized a recommended algorithm and R(2) to identify nonsystematic response sets.
• Analyzed the relationship between DD and quitting with and without excluded data using logistic regression.

Main Results:

• The algorithm excluded 14% of cases, while R(2) excluded 16%.
• Exclusion methods did not affect the core relationship between discounting and spontaneous quitting.
• Lower education and younger age were associated with nonsystematic response sets.

Conclusions:

• The algorithm effectively identified and retained orderly DD data.
• Neither exclusion method altered the smoking/DD relationship in this study.
• Younger, less educated participants may require additional support in DD studies.