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

Abstract

PurposeTo examine (1) whether use of a recommended algorithm (Johnson and Bickel, 2008) improves upon conventional statistical model fit (R2) for identifying nonsystematic response sets in delay discounting (DD) data, (2) whether removing such data meaningfully effects research outcomes, and (3) to identify participant characteristics associated with nonsystematic response sets. MethodsDiscounting of hypothetical monetary rewards was assessed among 349 pregnant women (231 smokers and 118 recent quitters) via a computerized task comparing $1000 at seven future time points with smaller values available immediately. Nonsystematic response sets were identified using the algorithm and conventional statistical model fit (R2). The association between DD and quitting was analyzed with and without nonsystematic response sets to examine whether the inclusion or exclusion impacts this relationship. Logistic regression was used to examine whether participant sociodemographics were associated with nonsystematic response sets. ResultsThe algorithm excluded fewer cases than the R2 method (14% vs. 16%), and was not correlated with logk as is R2. The relationship between logk and the clinical outcome (spontaneous quitting) was unaffected by exclusion methods; however, other variables in the model were affected. Lower educational attainment and younger age were associated with nonsystematic response sets. ConclusionsThe algorithm eliminated data that were inconsistent with the nature of discounting and retained data that were orderly. Neither method impacted the smoking/DD relationship in this data set. Nonsystematic response sets are more likely among younger and less educated participants, who may need extra training or support in DD studies.