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. 2021 Jun 14;6:17. doi: 10.1038/s41539-021-00095-7

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Figure a shows the change in % STEM Courses associated with a 1 SD (standard deviation) increase in Math Anxiety and Math Ability. Zero-order relations between each predictor and % STEM Courses are plotted alongside unique relations between each predictor and % STEM Courses predicted by a multiple regression model including the following measures as predictors: Math Anxiety, Math Ability, STEM Grades, Trait Anxiety, Verbal Working Memory, Gender, non-STEM Grades, and Semesters Absent. Figure b is the same as Fig. a but displays the change in STEM Grades (in points out of 100) associated with a 1 SD increase in Math Anxiety and Math Ability as the dependent variable. The multiple regression model that generated the unique predictions included the same measures as predictors as that of Fig. a, but substituted % STEM Courses for STEM Grades. In both figures, the left y-axis shows the DV in standardized units, which corresponds to standardized β coefficients. The right y-axis shows the DV in the native units of each measure. Error bars reflect standard errors.