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Poster #26 - Children’s interpretation of covariation evidence: Where scientific thinking and mathematics come together

Fri, March 22, 9:45 to 11:00am, Baltimore Convention Center, Floor: Level 1, Exhibit Hall B

Integrative Statement

Data-interpretation skills are an important ability in modern knowledge societies. This aspect of scientific thinking, however, is difficult: Both children and adults struggle with the interpretation of covariation evidence, as for instance with data that is presented in contingency tables (Saffran, Barchfeld, Sodian, & Alibali, 2017). Contingency tables allow to draw conclusions about the causal relation between two variables by comparing the conditional probability that the effect will occur given the presence of the candidate cause to the probability that the effect will occur given the absence of the candidate cause. This strategy (compare conditional probabilities) is difficult, and many children use less sophisticated strategies: Some simply compare the frequencies with which the effect occurs given the presence and absence of the candidate cause (compare two), while others search for a simple ratio between two of the four cells (e.g., 2:1), to which they compare the ratio of the other two remaining cells (anchor and compare) (Osterhaus, Magee, Saffran, & Alibali, 2018).

The present study investigates factors that contribute to children’s use of these inferior strategies. We hypothesize that users of the compare-two and anchor-and-compare strategies lack the mathematics skills that are necessary to compute and compare the conditional probabilities, and that users of the compare-two strategy additionally lack conceptual understanding of the underlying data structure (which often depicts the results of an experimental or quasi-experimental comparison).

Our 233 participants (50 sixth and seventh graders, 183 undergraduates) interpreted 13 contingency tables. Mathematics skills were assessed with a speeded test of arithmetic skills; participants’ conceptual understanding of the (experimental) data structure was assessed with four tasks on experimentation skills (Osterhaus, Koerber, & Sodian, 2017). Inhibition, working memory, and language skills were assessed as control variables.

The middle-schoolers interpreted on average 9 of the 13 contingency tables correctly (SD = 3), the adults 10 (SD = 3). A latent class analysis confirmed earlier findings and revealed three distinct strategies: compare two (middle-schoolers: 24%, adults: 13%); anchor and compare (middle-schoolers: 46%, adults: 39%); and compare conditional probabilities (middle-schoolers: 30%, adults: 48%). A significant partial correlation (independent of the influences of the cognitive control variables) emerged between the number of correct contingency interpretations and participants’ experimentation skills. For the subsample of middle-schoolers, a multinomial logistic regression indicated that users of the compare-two strategy had significantly weaker experimentation and mathematics skills than did users of the conditional-probabilities strategy. Users of anchor and compare, in turn, had only weaker mathematics, but equally good experimentation skills.

Our results confirm earlier findings on strategy use in the interpretation of contingency tables, and they suggest that children’s ability to interpret such data depends on their mathematics and experimentation skills. Our findings suggest that successful interventions that teach students how to interpret contingency tables should consider children’s current strategy use. Users of anchor and compare will most likely benefit from a simple instruction on the correct mathematical procedures, whereas users of compare two may require a more extensive instruction that fosters their conceptual understanding of the underlying data structure.

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