Cargando…

Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research

Operant behavioral economic methods are increasingly used in basic research on the efficacy of reinforcers as well as in large-scale applied research (e.g., evaluation of empirical public policy). Various methods and strategies have been put forward to assist discounting researchers in conducting la...

Descripción completa

Detalles Bibliográficos
Autores principales: Gilroy, Shawn P., Strickland, Justin C., Naudé, Gideon P., Johnson, Matthew W., Amlung, Michael, Reed, Derek D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096832/
https://www.ncbi.nlm.nih.gov/pubmed/35571278
http://dx.doi.org/10.3389/fnbeh.2022.806944
_version_ 1784706063495331840
author Gilroy, Shawn P.
Strickland, Justin C.
Naudé, Gideon P.
Johnson, Matthew W.
Amlung, Michael
Reed, Derek D.
author_facet Gilroy, Shawn P.
Strickland, Justin C.
Naudé, Gideon P.
Johnson, Matthew W.
Amlung, Michael
Reed, Derek D.
author_sort Gilroy, Shawn P.
collection PubMed
description Operant behavioral economic methods are increasingly used in basic research on the efficacy of reinforcers as well as in large-scale applied research (e.g., evaluation of empirical public policy). Various methods and strategies have been put forward to assist discounting researchers in conducting large-scale research and detecting irregular response patterns. Although rule-based approaches are based on well-established behavioral patterns, these methods for screening discounting data make assumptions about decision-making patterns that may not hold in all cases and across different types of choices. Without methods well-suited to the observed data, valid data could be omitted or invalid data could be included in study analyses, which subsequently affects study power, the precision of estimates, and the generality of effects. This review and demonstration explore existing approaches for characterizing discounting and presents a novel, data-driven approach based on Latent Class Analysis. This approach (Latent Class Mixed Modeling) characterizes longitudinal patterns of choice into classes, the goal of which is to classify groups of responders that differ characteristically from the overall sample of discounters. In the absence of responders whose behavior is characteristically distinct from the greater sample, modern approaches such as mixed-effects models are robust to less-systematic data series. This approach is discussed, demonstrated with a publicly available dataset, and reviewed as a potential supplement to existing methods for inspecting and screening discounting data.
format Online
Article
Text
id pubmed-9096832
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-90968322022-05-13 Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research Gilroy, Shawn P. Strickland, Justin C. Naudé, Gideon P. Johnson, Matthew W. Amlung, Michael Reed, Derek D. Front Behav Neurosci Neuroscience Operant behavioral economic methods are increasingly used in basic research on the efficacy of reinforcers as well as in large-scale applied research (e.g., evaluation of empirical public policy). Various methods and strategies have been put forward to assist discounting researchers in conducting large-scale research and detecting irregular response patterns. Although rule-based approaches are based on well-established behavioral patterns, these methods for screening discounting data make assumptions about decision-making patterns that may not hold in all cases and across different types of choices. Without methods well-suited to the observed data, valid data could be omitted or invalid data could be included in study analyses, which subsequently affects study power, the precision of estimates, and the generality of effects. This review and demonstration explore existing approaches for characterizing discounting and presents a novel, data-driven approach based on Latent Class Analysis. This approach (Latent Class Mixed Modeling) characterizes longitudinal patterns of choice into classes, the goal of which is to classify groups of responders that differ characteristically from the overall sample of discounters. In the absence of responders whose behavior is characteristically distinct from the greater sample, modern approaches such as mixed-effects models are robust to less-systematic data series. This approach is discussed, demonstrated with a publicly available dataset, and reviewed as a potential supplement to existing methods for inspecting and screening discounting data. Frontiers Media S.A. 2022-04-28 /pmc/articles/PMC9096832/ /pubmed/35571278 http://dx.doi.org/10.3389/fnbeh.2022.806944 Text en Copyright © 2022 Gilroy, Strickland, Naudé, Johnson, Amlung and Reed. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Gilroy, Shawn P.
Strickland, Justin C.
Naudé, Gideon P.
Johnson, Matthew W.
Amlung, Michael
Reed, Derek D.
Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title_full Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title_fullStr Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title_full_unstemmed Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title_short Beyond Systematic and Unsystematic Responding: Latent Class Mixture Models to Characterize Response Patterns in Discounting Research
title_sort beyond systematic and unsystematic responding: latent class mixture models to characterize response patterns in discounting research
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096832/
https://www.ncbi.nlm.nih.gov/pubmed/35571278
http://dx.doi.org/10.3389/fnbeh.2022.806944
work_keys_str_mv AT gilroyshawnp beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch
AT stricklandjustinc beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch
AT naudegideonp beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch
AT johnsonmattheww beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch
AT amlungmichael beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch
AT reedderekd beyondsystematicandunsystematicrespondinglatentclassmixturemodelstocharacterizeresponsepatternsindiscountingresearch