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Table 4 Summary of statistical tests used

From: Observing populations and testing predictions about genetic drift in a computer simulation improves college students’ conceptual understanding

Question motivating the model

Model

Predictor

Estimate ± SE

p value

Result

Do the students in the two treatments perform differently before instruction?

pretest performance ~ treatment + course + OLRE

Intercept

0.13 ± 0.17

0.427

No. The students in the two treatments did not differ in pretest performance

Treatment

0.32 ± 0.19

0.094

Does treatment predict posttest performance (controlling for pretest raw score)?

posttest performance ~ pretest raw score + treatment + course + OLRE

Intercept

−1.19 ± 0.23

<0.001

Yes. The students in the module courses outperformed students in the control courses

Pretest

0.11 ± 0.01

<0.001

Treatment

0.83 ± 0.24

<0.001

Does treatment predict performance on items about key concepts (controlling for pretest raw score)?

posttestKC performance ~ pretestKC raw score + treatment + course + OLRE

Intercept

−0.69 ± 0.33

0.036

Yes. Students in the module courses outperformed students in the control courses

PretestKC

0.09 ± 0.03

<0.001

Treatment

1.92 ± 0.35

<0.001

Does treatment predict performance on items about misconceptions (controlling for pretest raw score)?

posttestM performance ~ pretestM raw score + treatment + course + OLRE

Intercept

−1.08 ± 0.26

<0.001

Yes. Students in the module courses outperformed students in the control courses

PretestM

0.17 ± 0.01

<0.001

Treatment

0.56 ± 0.24

0.018

  1. In all models, Course is a random factor that accounts for differences across classes that were not controlled during the study. Observation-level random effect (OLRE) is a random factor that accounts for overdispersion in the model. Subscript KC indicates the subset of items in the GeDI about key concepts; subscript M indicates the subset of items on the GeDI about misconceptions. Significant results after Holm-Bonferroni corrections are in italics