Appendix 1
See Table 4
Table 4 Results of recent literature examining gender bias in SET scores .
Appendix 2
See Table 5
Table 5 Definitions of predictor variables .
Appendix 3
See Table 6
Table 6 Multivariate regression results, cross-divisional analysis .
Appendix 4
Regression Models for Calculus Instructors’ SET Scores
We test for gender effects using the following ordinary least squares (OLS) multivariate models:
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \varepsilon_{ict} ,$$
(1)
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \beta_{1} \cdot {\text{rank}}_{it} + {\upbeta }_{2} \cdot {\text{age}}_{it} + \beta_{3} \cdot {\text{age}}_{it}^{2} + \varepsilon_{ict} ,$$
(2)
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \beta_{1} \cdot {\text{rank}}_{it} + \beta_{2} \cdot {\text{age}}_{it} + \beta_{3} \cdot {\text{age}}_{it}^{2} + \beta_{4} \cdot {\text{female}}_{it} \cdot {\text{rank}}_{it} + \beta_{5} \cdot {\text{year - term + }}\beta_{6} \cdot {\text{female}}_{it} \cdot {\text{year-term }} + \varepsilon_{ict} ,$$
(3)
where index i enumerates instructors, c is the course offering,
$${\text{c}} \in \left\{ {{\text{Intro to Calculus}};{\text{ Calculus}},{\text{ Series and Differential Equations}};{\text{ and Multivariable Calculus}}} \right\};$$
and time is indexed by
$$t \in \left\{ {{\text{Fall }}\;2006,{\text{ Spring }}\;2007, \ldots { },{\text{ Spring }}\;2017} \right\}.$$
Here, SETict is the average score of instructor i for course c received in semester t, and female is the dummy variable that takes value 1 if the instructor is a female and 0 otherwise. The control variable rank represents a set of four dummy variables for the instructor being a Lecturer/Preceptor, Professor, Senior non-ladder instructor, or Teaching Assistant/Teaching Fellow. For example, if an instructor is a preceptor in a given semester, then the rank variable for this instructor will be (1, 0, 0, 0), while a teaching post-doc will be represented by (0, 0, 0, 0). We notice that faculty rank may change over time and thus, generally, rank is time-dependent. Finally, ageit is the instructor age in years at the beginning of a given semester. One can see that model (1) is equivalent to a t-test for a difference between mean SET scores in the two gender groups; in model (2) we control for instructor rank and age; and finally, interactions between faculty rank and gender and also between year-term and gender are added in (3). Here, year-term denotes 1, 2,…, 22 which correspond to Fall 2006, Spring 2007,…, Spring 2017, respectively. Alternatively to the OLS models (1, 2 and 3), we consider their variations as follows:
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \alpha_{ct} + \varepsilon_{ict} ,$$
(4)
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \alpha_{ct} + \beta_{1} \cdot {\text{rank}}_{it} + {\upbeta }_{2} \cdot {\text{age}}_{it} + \beta_{3} \cdot {\text{age}}_{it}^{2} + \varepsilon_{ict} ,$$
(5)
$$SET_{ict} = \theta \cdot {\text{female}}_{i} + \beta_{0} + \alpha_{ct} + \beta_{1} \cdot {\text{rank}}_{it} + \beta_{2} \cdot {\text{age}}_{it} + \beta_{3} \cdot {\text{age}}_{it}^{2} + \beta_{4} \cdot {\text{female}}_{it} \cdot {\text{rank}}_{it} + { }\beta_{6} \cdot {\text{female}}_{it} \cdot {\text{year-term}} + \varepsilon_{ict}$$
(6)
where αct are the instructor invariant fixed effects that satisfy the following constraints:
$$\mathop \sum \limits_{c} \mathop \sum \limits_{t} \alpha_{ct} = 0$$
in each of (4, 5 and 6) cases.
Appendix 5
See Table 7
Table 7 Regression results for SET scores of calculus instructors using multivariate regression models (1, 2 and 3) and (4, 5 and 6) .