Daniel Jerez

Slow responding in International Large-Scale Assessments (ILSAs) has received far less attention than rapid guessing, despite its potential to reveal heterogeneous response processes. Unlike disengaged rapid responders, slow responders may differ in time management, off-task behavior, or specific cognitive operations. This exploratory study uses sequence analysis of log-file data from a complex problem-solving item in PISA 2012 to examine whether slow responders can be grouped into homogeneous subtypes. The item required students to explore causal relations and externalize them in a diagram. Results indicate two distinct clusters among slow responders, each marked by characteristic interaction patterns and difficulties at different stages of the solution process. One cluster exhibited long pauses interspersed with repeated, inefficient attempts at representing causal relationships; the other showed shorter pauses coupled with inefficient exploratory actions targeting those relationships. These findings demonstrate that sequence analysis can parsimoniously identify clusters of action sequences associated with slow responding, offering a finer-grained account of aberrant behavior in low-stakes, digital assessments. More broadly, the approach illustrates how process data can be leveraged to differentiate mechanisms underlying slow response behaviors, with implications for validity arguments, diagnostic feedback, and the design of mitigation strategies in ILSAs. Directions for future research to better understand the differences among slow responders are provided.

This article presents a model of consumer behavior that incorporates anchoring and price effects in describing purchase demand. The model, called F-Cap, for Finite Consumption Anchored to Price, offers an alternative to traditional microeconomic models of demand. This model is based on recent findings in psychology and behavioral economics and connects concepts from behavioral and traditional economics to the language and findings of behavior analysis. In particular, the model incorporates the idea of maximum consumption and reinforcement power developed in the exponential and exponentiated models of demand, and adds the possibility to estimate reference prices using a new, simpler estimation method. These elements are organized in a model based on the sigmoid function. A function estimation algorithm is proposed. This algorithm linearizes the function and estimates the parameters using ordinary least squares regressions. A core feature of the algorithm is that it allows the identification of reference prices, which is not possible in prior models. First, this article illustrates how the parameters of F-Cap modify the maximum level of consumption, the anchor point, and the decrease in consumption after that point, as proposed in the model. Next, using simulated data, the article shows that the algorithm estimates these parameters correctly both in standard and in mixed models. Third, the article presents evidence that F-Cap describes the behavior of human subjects in the hypothetical purchase task with less unexplained variance than alternative demand models. This function correctly estimates the parameters associated with the good’s contribution to utility, which in behavior analysis language is equivalent to reinforcing power. It also estimates the response to reference prices, which can be interpreted as behavior governed by rules in the tradition of behavior analysis. The F-Cap model overall helps connecting the findings of operant behavioral economics with the practices of mainstream economics.