| Field | Before | After |
|---|---|---|
| Field Abstract | Before Researchers have documented racial and gender gaps in college enrollment decisions, choice of major, degree attainment, and earnings—despite narrowing gaps in test scores and course-taking in K-12 settings. Implicit racial and gender stereotypes of faculty members may affect their interactions with students and exacerbate these gaps, even without awareness or intent to harm members of underrepresented groups. Yet, there is no causal evidence on the extent to which faculty’s implicit bias contributes to these educational disparities and which types of interventions are cost-effective in mitigating any harmful effects of implicit bias on student achievement gaps.This study aims to address implicit bias of faculty members through the collaboration between psychologists and economists. First, we plan to understand the relationship between faculty’s implicit bias and gaps in student achievement, completion, and economic mobility using a newly constructed dataset with schools’ student-level and faculty-level administrative data, and faculty’s implicit association test (IAT) results. Second, we plan to implement a randomized field experiment to evaluate the effects of faculty implicit bias trainings on students' academic performance and attitudes through a semester-long pilot in spring 2020 at Portland Community College. | After Researchers have documented racial and gender gaps in college enrollment decisions, choice of major, degree attainment, and earnings—despite narrowing gaps in test scores and course-taking in K-12 settings. Implicit racial and gender stereotypes of faculty members may affect their interactions with students and exacerbate these gaps, even without awareness or intent to harm members of underrepresented groups. Yet, there is no causal evidence on the extent to which faculty’s implicit bias contributes to these educational disparities and which types of interventions are cost-effective in mitigating any harmful effects of implicit bias on student achievement gaps.This study aims to address implicit bias of faculty members through the collaboration between psychologists and economists. First, we plan to understand the relationship between faculty’s implicit bias and gaps in student achievement, completion, and economic mobility using a newly constructed dataset with schools’ student-level and faculty-level administrative data, and faculty’s implicit association test (IAT) results. Second, we plan to implement a randomized field experiment to evaluate the effects of faculty implicit bias trainings on students' academic performance. Due to schools’ adjustments to online education in March 2020, we will pilot the study using an online format in Fall 2020 at Reynolds Community College. The pilot will take place between October 7-December 20, 2020. |
| Field Trial Start Date | Before March 16, 2020 | After October 07, 2020 |
| Field Trial End Date | Before June 30, 2020 | After December 20, 2020 |
| Field Last Published | Before March 05, 2020 08:39 AM | After October 06, 2020 04:31 PM |
| Field Intervention Start Date | Before March 16, 2020 | After October 07, 2020 |
| Field Intervention End Date | Before June 30, 2020 | After December 20, 2020 |
| Field Experimental Design (Public) | Before The study intends to evaluate how providing implicit bias training to higher education instructors impacts students' outcomes. We will offer---in the spring of 2020---an in-person implicit bias training to a randomly selected sample of instructors teaching courses at the Math, Reading and Writing departments at Portland Community College (PCC). The training is designed to expose faculty members to their own implicit biases and provide them with tools to adjust their automatic pattern of thinking with the ultimate goal of mitigating any biased behavior. This treatment--- implemented by psychologists---will be based on scientific evidence and previous research results and it will adopt a non-judgmental approach that focuses on the recipients’ self-interest and organizational interest. Follow up emails will be sent bi-weekly to remind instructors of training content to raise awareness about potentially biased behavior. We will then evaluate the impact of interacting with instructors exposed to training on students' outcomes. | After The study intends to evaluate how providing implicit bias training to higher education instructors impacts students' outcomes. We will offer---in the fall of 2020---an implicit bias training to a randomly selected sample of instructors teaching courses at the Fall 2020 term in Reynolds Community College. The training is designed to expose faculty members to their own implicit biases and provide them with tools to adjust their automatic pattern of thinking with the ultimate goal of mitigating any biased behavior. This treatment--- implemented by psychologists---will be based on scientific evidence and previous research results and it will adopt a non-judgmental approach that focuses on the recipients’ self-interest and organizational interest. Follow up emails will be sent at most bi-weekly to remind instructors of training content to raise awareness about potentially biased behavior. We will then evaluate the impact of interacting with instructors exposed to training on students' outcomes. Due to schools’ adjustments to online education in March 2020, we will pilot the study using an online format in Fall 2020 at Reynolds Community College, randomizing across instructors from all departments. |
| Field Randomization Unit | Before We will randomize at the individual---instructor---level. We will block randomization at the department level. | After We will randomize at the individual---instructor---level. |
| Field Planned Number of Clusters | Before We will include about 300 instructors in our randomization. This number represents all instructors teaching courses at the Math, Reading and Writing departments at Portland Community College (PCC) in the Spring of 2020. | After For the online pilot at Reynolds community college in Fall 2020, we will stratify based on faculty’s baseline survey completion such that half of the survey completers are assigned to treatment and half are assigned to the control group. |
| Field Planned Number of Observations | Before We will have information on 300 instructors (unit of randomization). We will also have information on approximately 10,500 students. Each students is enrolled, on average, in 1.8 courses at the Math, Writing and Reading Departments. | After For the online pilot at Reynolds community college in Fall 2020, we will include 328 instructors across all departments in our randomization. We can observe for these instructors across 1,000 classes and we also have information on over 6,000 students. |
| Field Sample size (or number of clusters) by treatment arms | Before We will have 150 instructors in the treatment group and 150 instructors in the control group. | After For the online pilot at Reynolds community college in Fall 2020, we will have 164 instructors assigned to treatment and 164 assigned to the control group. |
| Field Power calculation: Minimum Detectable Effect Size for Main Outcomes | Before | After Using administrative information from previous terms, we were able to simulate the Minimum Detectable Effect considering different measures of students’ performance. Our power calculations suggest that we will be able to detect an impact 3 percent of a standard deviation for outcomes measured at the student-class level (e.g., grade) and from 13 to 18 percent of a standard deviation for outcomes measured at the instructor-class level (e.g., black-white grade gap, hispanic-white grade gap). To compute MDEs, we assumed a significance level of 5 percent and an 80 percent power for the overall treatment. |
| Field | Before | After |
|---|---|---|
| Field Document | Before |
After
PreanalysisPlan_MC-LS-MB-DD_reynolds.pdf
MD5:
b0c82c8247cc7dbd0da07bfe8b67682b
SHA1:
547bfaad8d5031107a7f66eb18cb646087b04472
|
| Field Title | Before | After Analysis Plan |
| Field | Value |
|---|---|
| Field Document |
Value
PreanalysisPlan_MC-LS-MB-DD.pdf
MD5:
5389556c37db576d1c59bc83a16d151c
SHA1:
2c29b61924d7ed15413e1ac3cf3ec6fa1ffde936
|
| Field Title | Value Analysis Plan |