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Cited by 555Decision-Making and Behavioral EconomicsNeural and Behavioral Psychology StudiesPsychometric Methodologies and Testing

The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.

An algorithm for identifying nonsystematic delay-discounting data.

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Johnson, Matthew W., Bickel, Warren K.·2008·Experimental and Clinical Psychopharmacology
DOI 10.1037/1064-1297.16.3.264PMC PMC2765051
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Abstract

Several [corrected] discounting studies have use the R2 measure to identify data [corrected] with poor fits to a mathematical discounting model as nonsystematic data to be eliminated [corrected] Data from three previous delay-discounting studies (six separate groups, with a total of 161 individuals) were used to demonstrate why using R2 to assess the fits of discounting data is problematic. A significant, positive correlation between discounting rate parameter and R2 was found in most groups, showing that R2 is more stringent as a measure of fit for low discounting rates than for high discounting rates. Furthermore, it is suggested that identifying nonsystematic data based on any measure of fit to a mathematical discounting model may be problematic because it confounds discounting rate comparison with the issue of discounting model assessment. Therefore, a model-free method to identify nonsystematic data is needed. An algorithm for identifying nonsystematic data is presented that is based on the expectation of a monotonically decreasing discounting function. This algorithm identified 13 cases out of the 161 reanalyzed data sets as nonsystematic. These nonsystematic data are presented, along with examples of data not identified as nonsystematic. This algorithm, or modifications of it, may be useful in a variety of human and nonhuman animal discounting studies (e.g., delay discounting, probability discounting) as an alternative to the R2 measure for identifying nonsystematic data. The algorithm may be used in empirical investigations to improve discounting methodology, and may be used to identify outliers to be removed from analyses.

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Metadata

Authors
Johnson, M. W., & Bickel, W. K.
Affiliations
  • Johns Hopkins University (US)
  • University of Arkansas for Medical Sciences (US)
ORCID
Venue
Experimental and Clinical Psychopharmacology
Publisher
American Psychological Association (APA)
Published
2008
Volume
16
Issue
3
Pages
264-274
ISSN
  • 1064-1297 (ISSN-L)
  • 1936-2293
DOI
10.1037/1064-1297.16.3.264
PMC
PMC2765051
OpenAlex
W2092110116
Cited by
555
References
73
Citations by year
2026: 28 · 2025: 33 · 2024: 35 · 2023: 37 · 2022: 39 · 2021: 40 · 2020: 81 · 2019: 34 · 2018: 32 · 2017: 40 · 2016: 31 · 2015: 36 · 2014: 28 · 2013: 18 · 2012: 11
FWCI
6.51
Type
journal article
Language
en
Update notices
correction (2008-08-25)
Open access
Locations
  • Experimental and Clinical Psychopharmacology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute on Drug Abuse (R01 DA 11692; T32 DA07242)
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Keywords
delay discounting, discount rate, model fit assessment, outlier detection, probability discounting
MeSH terms
Algorithms, Animals, Behavior, Humans, Motivation, Reinforcement, Psychology, Smoking, Time Factors, Models, Statistical

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