A fundamental principle of scientific reasoning is to argue based on experimental evidence. Valid experiments isolate the causal effects of variables by contrasting
conditions that differ only with the respect to the variable for which the causal status is under investigation. The critical skills required for designing and interpreting controlled experiments
are brought together under the term control-of-variables strategy (CVS). Chen and Klahr (1999) outline the four key components of CVS. Procedurally, CVS includes the ability to create
experiments in which conditions differ with respect to only a single contrasting variable, as well as the ability to recognize confounded and unconfounded experiments. It is also important to
understand the logic of CVS, which involves the ability to make appropriate inferences from the results of unconfounded experiments (e.g., that only inferences about the causal status of the
variable being tested are warranted). Finally, understanding CVS means that one is also aware of “the inherent indeterminacy of confounded experiments” (Chen & Klahr, 1999, p.
A lot of our research focuses on CVS due to its crucial role in scientific reasoning. Search this page for research results, test instruments and teaching materials regarding CVS.
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Control of Variables Strategy Inventory now aviable in German, English an Chinese