TABLE 4.
Summary of linear regression models exploring the relationship between the type of data students analyzed and their post-CURE scientific self-efficacy and scientific identify scores, controlling for students’ pre-CURE scores and demographicsa.
| Variable | Model A: Self-Efficacy | Model B: Science Identity | ||||||
|---|---|---|---|---|---|---|---|---|
| B | SE B | β | p | B | SE B | β | p | |
| Intercept | 8.8 | 2.9 | <0.01 | 9.5 | 2.4 | <0.001 | ||
| Type of data analyzed (own) | 0.5 | 0.8 | 0.1 | 0.56 | 1.6 | 0.8 | 0.2 | <0.05 |
| Pre-CURE score | 0.8 | 0.1 | 0.7 | <0.0001 | 0.6 | 0.1 | 0.6 | <0.001 |
| Major (other) | −1.7 | 1.4 | −0.1 | 0.22 | −1.1 | 1.3 | −0.1 | 0.43 |
| Prior research (yes) | −0.6 | 0.9 | −0.1 | 0.51 | 0.2 | 0.8 | 0.3 | 0.80 |
| Gender (woman) | −1.3 | 0.9 | −0.2 | 0.14 | −0.7 | 0.8 | −0.1 | 0.38 |
| Race/ethnicity (Asian) | 0.2 | 1.0 | 0.0 | 0.83 | −0.7 | 1.0 | −0.1 | 0.50 |
| Race/ethnicity (BLPA) | 2.1 | 1.0 | 0.2 | 0.05 | −0.6 | 1.0 | −0.1 | 0.57 |
| Adjusted R2 | 0.43 | 0.39 | ||||||
a
B represents unstandardized coefficients and β represents standardized coefficients. Focus categories are provided in parentheses in column 1.