| Summary: | People often perform
poorly on stock-flow reasoning tasks, with many (but not all) participants
appearing to erroneously match the accumulation of the stock to the inflow – a
response pattern attributed to the use of a “correlation heuristic”. Efforts to
improve understanding of stock-flow systems have been limited by the lack of a
principled approach to identifying and measuring individual differences in
reasoning strategies. We present a principled inferential method known as
Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow
reasoning. HBLMMs use Bayesian inference to classify different patterns of
responding as coming from multiple latent populations. We demonstrate the
usefulness of this approach using a dataset from a stock-flow drawing task
which compared performance in a problem presented in a climate change context,
a problem in a financial context, and a problem in which the financial context
was used as an analogy to assist understanding in the climate problem. The
hierarchical Bayesian model showed that the proportion of responses consistent
with the “correlation heuristic” was lower in the financial context and
financial analogy context than in the pure climate context. We discuss the
benefits of HBLMMs and implications for the role of contexts and analogy in
improving stock-flow reasoning. % chages in abstract
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