PRIISM co-director Dr. Jennifer Hill and core PRIISM faculty George Perrett and Dr. Yoav Bergner published a paper with colleagues in the Statistics Education Research Journal that uses a randomized experiment to explore how students interpret language used to describe research findings. They find that the language used to describe the relationships between variables impacts the level of causal attribution but that, generally speaking, the context of the hypothetical example plays a more crucial role. Another surprising finding is the extent to which many students interpret even intentionally descriptive language causally. This study highlights how difficult it is to carefully describe research findings in a way that will be correctly understood by the reader.
Abstract
Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.