We examine the heterogeneity of text-based behavioral interventions in a series of 5 preregistered studies across one in-person and 10 online panels, with over 11000 respondents in total. We observe large heterogeneity across settings and paradigms. Model the heterogeneity we introduce a framework that measures typically omitted moderators: Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience. Variation in these factors are associated with different effect sizes and explain variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

Past choices can influence subsequent choices in employee se-lection. Previous approaches rather described similar sequen-tial effects with feedback learning or the misperception of ran-domness. However, in the selection of job candidates also theaccumulation of the moral impact of previous choices mightinfluence subsequent choices. We investigated that questionby making two major contributions to the literature. First, wedeveloped an experimental paradigm for measuring sequentialchoices in employee selection and second, we implementeda widely applicable computational model, the Dependent Se-quential Sampling Model, for explaining sequential effects inchoices. By using this methodological approach, we uncov-ered sequential effects in employee selection. Participants(N=600) were especially motivated to compensate for morallydubious choices, with some participants showing consistentchoice behavior if their previous choices had been morally vir-tuous. These results support the assumption of asymmetriccompensation of morally dubious choices, sometimes referredto as the moral cleansing hypothesis.

Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast real-world behaviors. These tasks typically ask a series of similarly structured questions. The authors posit that every time a respondent answers an additional elicitation question, two things happen: (1) they provide information about some parameter(s) of interest, such as their time preference or the partworth for a product attribute, and (2) the respondent increasingly “adapts” to the task—that is, using task-specific decision processes specialized for this task that may or may not apply to other tasks. Importantly, adaptation comes at the cost of potential mismatch between the task-specific decision process and real-world processes that generate the target behaviors, such that asking more questions can reduce external validity. The authors used mouse and eye tracking to trace decision processes in time preference measurement and conjoint choice tasks. Respondents increasingly relied on task-specific decision processes as more questions were asked, leading to reduced external validity for both related tasks and real-world behaviors. Importantly, the external validity of measured preferences peaked after as few as seven questions in both types of tasks. When measuring preferences, less can be more.