Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…

Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single…

Selection bias is a problem for mathematics education researchers interested in using observational rather than experimental data to make causal inferences about the effects of different instructional methods in mathematics on student outcomes. Propensity score methods represent 1 approach to dealing with such selection bias. This article…

In November 2008 at Purdue University, the 2nd Workshop on Human Problem Solving was held. This workshop, which was a natural continuation of the first workshop devoted almost exclusively to optimization problems, addressed a wider range of topics that reflect the scope of the "Journal of Problem Solving." The workshop was attended by 35…

To study how the organization of information affects the way that information is interpreted, a total of 404 undergraduates in two studies (151 and 253 students, respectively) solved statistical reasoning problems based on data presented in a variety of types of graphs and tables. When assessing relative probabilities, students were equally…