Difference-Education Improves First-generation Students’ Grades Throughout

College and Increases Enrollment in Diversity Courses

Nicole M. Stephens

Kellogg School of Management, Northwestern University

MarYam G. Hamedani

Center for Social Psychological Answers to Real-world Questions (SPARQ),

Stanford University

Sarah S. M. Townsend

Marshall School of Business, University of Southern California

Mesmin Destin

Department of Psychology, Northwestern University; and School of Education and Social

Policy, Northwestern University

Author Note

Correspondence concerning this article should be addressed to Nicole M.

Stephens, Kellogg School of Management, Northwestern University, 2211 Campus

Drive, Evanston, IL, 60208. E-mail: n-stephens@kellogg.northwestern.edu

Word Count: 3131

1

Abstract

We examined the long-term academic and behavioral outcomes of a difference-

education intervention delivered during incoming students’ transition to a selective

college. Nearly four years after delivering the initial intervention, we found that first-

generation students who participated in the difference-education intervention earned

higher grades than those in the control intervention. They also took more courses about

diversity (e.g., Class and Culture), an experience that could help them learn more about

social difference. This study contributes to the literature on intervention science by

demonstrating for the first time that educating first-generation students about social

difference can provide long-term academic benefits that persist until graduation. This

study also shows one important behavioral change that difference-education initiates (i.e.,

enrolling in courses about diversity), which could be a key process that helps sustain

students’ new theory of social difference over time.

Keywords: social class; first-generation; academic performance; higher education;

intervention; diversity.

2

Difference-Education Improves First-generation Students’ Grades Throughout

College and Increases Enrollment in Diversity Courses

American universities typically promote middle- to upper-class cultural norms

and ways of being a student (Bernstein, 1974; Bourdieu & Passeron, 1990; Stephens,

Markus, & Phillips, 2014). As a result, first-generation students (i.e., students whose

parents do not have four-year degrees) confront threats to their sense of fit and academic

empowerment in college (Covarrubias & Fryberg, 2015; Goudeau & Croizet, 2017),

which can undermine their performance and success (Stephens, Fryberg, Markus,

Johnson, & Covarrubias, 2012). Previous research showed that a difference-education

intervention, which helps students understand how their different social class

backgrounds matter in college, reduced the social class achievement gap by empowering

first-generation students to seek out more campus resources (Stephens, Hamedani, &

Destin, 2014). This study investigates whether the academic performance benefits of

difference-education persist over time, and identifies one key behavioral change that

could help to sustain the intervention’s benefits.

The social psychological literature on intervention science theorizes that

seemingly small interventions can change students’ long-term outcomes by giving them a

new “lay theory” or way of construing their experiences in school (Cohen & Sherman,

2014; Wilson, 2011; Yeager & Walton, 2011). For example, “belongingness”

interventions provide students with a theory that difficulty is normal and transient

(Walton & Cohen, 2007, 2011), and “mindset” interventions provide students with a

theory that abilities are malleable (Blackwell, Trzesniewski, & Dweck, 2007). This new

3

theory can initiate a set of self-reinforcing or recursive processes, in which the theory

produces a change in experience or behavior that further reinforces or amplifies the

theory. When struggling with a difficult assignment, students who had a belonging

intervention may be less likely to think that they do not belong if they understand that

adversity is normal and temporary. Adaptive experiences like these can then become self-

reinforcing, producing a positive feedback cycle and strengthening students’ new lay

theory of behavior (cf. Miller, Dannals, & Zlate, 2017).

Following this logic, we theorize that difference-education also operates through a

recursive process. Difference-education teaches students a contextual and asset-based

understanding or lay theory of social difference (Stephens, Hamedani, & Townsend,

2017). That is, difference-education helps students understand that their differences or

experiences of feeling different in their current environment (1) are contextual (i.e., a

product of their different backgrounds) and (2) can serve as assets or strengths (i.e., not

only as obstacles to overcome). If this contextual and asset-based theory of social

difference operates as theorized and initiates a recursive process of change for first-

generation college students, then it should continue to shape how they make sense of and

respond to the situations they encounter throughout college. Specifically, it should lead

first-generation students to be more open to and comfortable with experiences of

difference.

In follow up research to our initial difference-education intervention, we sought to

identify what might fuel these kinds of recursive processes (Stephens, Townsend,

Hamedani, Destin, & Manzo, 2015). Nearly two years after the intervention, when asked

to give a speech about how their backgrounds matter in college in a lab study, we found

4

that difference-education (vs. control) participants more often used this new lay theory of

difference to make sense of their experiences. Specifically, they were more willing to

discuss how different aspects of their backgrounds (e.g., family) impacted their college

experiences. We also found that, at a physiological level, first-generation students in the

difference-education condition were more comfortable when talking about social

difference. Building on this work in the lab, the current study investigates another

possible component of the recursive process by examining students’ actual behavior in

the real world. Beyond feeling more comfortable talking about difference, do first-

generation students also seek out ways to learn more about it?

We investigated two long-term outcomes: students’ academic performance (i.e.,

GPA’s) upon graduation and their tendency to seek out courses about diversity or social

difference. First, since we theorize that students’ new understanding of social difference

builds over time, we predicted that academic performance benefits among first-

generation students in the difference-education intervention would persist throughout

college. Second, given that first-generation students are especially likely to encounter

obstacles in college that can be addressed by using the new theory of social difference

(Stephens et al., 2012), we predicted that first-generation students in the difference-

education (vs. control) condition would seek out opportunities to learn about difference

by enrolling in more diversity courses.

Method

5

We analyzed the end-of-college academic outcomes and course selection of

students who participated in our difference-education intervention. We collected grades,

honors attainment,1 and course history from the university registrar.

Participants. In the initial intervention (Stephens, Hamedani, et al., 2014), given

the small number of first-generation students in the population of incoming students at

the selective university, we recruited as many incoming first-generation students as

possible for our study. We then recruited a comparable number of continuing-generation

students as our comparison group.

Participants are the same 134 intervention participants reported in the GPA

analyses of Stephens, Hamedani, et al. (2014). Eight participants had missing data for

end-of-college GPA—either because they dropped out or had not yet completed college.

The final sample included 126 participants. Importantly, the eight missing participants

were both first-generation and continuing-generation, and they were distributed across

conditions. Specifically, they were two first-generation and two continuing-generation

students in the difference-education condition and three first-generation and one

continuing-generation student in the control condition.

Of these remaining 126 participants, 55 were first-generation (i.e., neither parent

had a 4-year college degree), and 71 were continuing-generation (i.e., at least one parent

had a 4-year college degree). The majority of first-generation students (63.64%) were low

income (i.e., Pell recipients), compared with a minority of continuing-generation students

(9.86%), 2 (1, N = 126) = 40.33, p < .001.

1 We also had data on student dropout, course withdrawal, and disciplinary action. These incidents were too infrequent to analyze (e.g., only 8 of 134 students withdrew from a course; Agresti, 2007; Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996).

6

Participants’ race/ethnicity did not differ significantly according to their

generation status. A series of chi-square analyses comparing generation status in each of

the five racial categories revealed that none of the racial categories significantly differed

by generation status, White: χ2 (1, N = 126) = 0.09, p = .77; Asian/Asian American: χ2

(1, N = 126) = 0.61, p = .43; African American: χ2 (1, N = 126) = 1.18, p = .28; Latino:

χ2 (1, N = 126) = 0.44, p = .51; and Native American: χ2 (1, N = 126) = 0.78, p = .38.

Among first-generation students, 50.91% self-identified as White, 18.18% as

Asian/Asian American, 10.91% as African American, 20.00% as Latino, and 0.00% as

Native American. Among continuing-generation students, 53.52% self-identified as

White, 23.94% as Asian/Asian American, 5.63% as African American, 15.49% as Latino,

and 1.41% as Native American.

Covariates. In GPA analyses, we included the same five covariates as the end-of-

first year follow-up study (Stephens, Hamedani, et al., 2014).2 To ensure that the effects

resulted from the intervention rather than from preexisting differences in students’

academic skills or demographic characteristics, we controlled for high school GPA,

highest SAT scores, gender (male = 0; female = 1), family income (0 = not low SES; 1 =

low SES; based on Pell status), and race and ethnicity. To control for race and ethnicity,

we created a dummy variable (disadvantaged race = 0; advantaged race = 1). Given the

relationship between race and academic performance in the U.S. (Kao, 1995; Steele,

2010), African Americans, Latinos, and Native Americans were classified as

2 In the Supplementary Materials, we report all of our primary results without any covariates included in the analyses. The pattern of results across dependent measures is unchanged, but the strength of the results differs. In particular, the results for academic outcomes become weaker and are no longer significant. In contrast, the results for diversity courses become stronger and more significant.

7

disadvantaged, whereas Whites and Asians/Asian Americans were classified as

advantaged.

In addition, to ensure that any observed GPA benefits of difference-education

could not be attributed to participants’ course selection, we added two covariates. First,

we controlled for the difficulty of participants’ course selection.3 Second, given that first-

generation students are often underrepresented in STEM courses and these courses can

particularly threatening for them (Bozick & Ingels, 2008; Schneider, Swanson, & Riegle-

Crumb, 1998), we also controlled for the number of these courses. We report all

measures, manipulations, and exclusions in this study.

Results

Academic Performance. To evaluate the long-term impact of difference-

education, we examined students’ end-of-college cumulative GPAs. Since four

participants graduated in three rather than four years, we used their end-of-third year

cumulative grades (i.e., final grades upon graduation) as their end-of-college GPAs. For

all analyses involving grades, we report raw means, rather than estimated marginal

means, to make the actual observed differences between conditions clear.

A 2 (intervention condition: difference-education vs. control) x 2 (generation

status: first- vs. continuing-generation) ANCOVA with the covariates mentioned above

revealed a main effect for intervention condition, F(1, 115) = 4.73, p = .03, ηp2 = .04,

3 A proxy for course difficulty, this variable represents the mean disciplinary GPA of all of the courses that a student completed throughout college. We first calculated the mean GPA for each of the 102 disciplines from which students took courses (e.g., physics or economics). Then, for each participant, we calculated the mean discipline GPA for each of the four years and averaged those to create one composite variable. For example, if a student took two physics courses and two economics courses during their first year, and if the average GPA received in a physics course was 3.1 and the average GPA received in an economics course was 3.3, then the student’s mean disciplinary GPA for that year would be 3.2. We assume that courses from disciplines that have lower GPAs on average are more difficult (i.e., more difficult to earn a high grade) than those from disciplines that have higher GPAs on average.

8

such that participants in the difference-education intervention (M = 3.49, SD = 0.32)

performed better than those in the control intervention (M = 3.42, SD = 0.33). Supporting

our hypotheses, this main effect was qualified by a marginally significant condition by

generation status interaction, F(1, 115) = 3.18, p = .08, ηp2 = .03. As shown in Figure 1,

first-generation students in the difference-education intervention (M = 3.45, SD = 0.32)

earned higher end-of-college grades than first-generation students in the control condition

(M = 3.31, SD = 0.34), F(1, 115) = 6.70, p = .01, ηp2 = .06. Conversely, continuing-

generation students in the difference-education intervention (M = 3.53, SD = 0.32) did

not differ from those in the control (M = 3.50, SD = 0.31), F(1, 115) = 0.09, p = .77, ηp2

= .001. Given the small effect size for the generation status by condition interaction

reported above (ηp2 = .03), this study is underpowered (i.e., 49.92%).

Although in the expected direction, there was not a significant social class

achievement gap in the control condition, F(1, 115) = 1.66, p = .20, ηp2 = .01

(continuing-generation M = 3.50, SD = 0.31; first-generation M = 3.31, SD = 0.34).4

To supplement the grades analysis, we examined the impact of difference-

education on receiving academic honors (0 = no; 1 = yes).5 We conducted a logistic

regression analysis with generation-status, intervention condition, and their interaction as

predictors with the same covariates as the GPA analyses. We found a significant main

effect of condition, such that participants in the difference-education condition (30.64%)

more often obtained academic honors than those in the control (20.31%), Wald χ2 (1, N =

126) = 4.62, p = .03. This finding is consistent with our prediction that difference-

4 As reported in the Supplementary Materials, the social class achievement gap becomes significant when covariates are no longer included. 5 "Honors" is based on GPA. Each college (e.g., Humanities) awards their graduating seniors with honors status based on the GPA’s in the top percentage of students in that college.

9

education would benefit first-generation students academically: more than twice as many

first-generation students in the difference-education intervention (25.00%) earned honors

compared to those in the control (11.11%). However, given that difference-education also

benefitted continuing-generation students (difference-education: 35.29%; control:

27.03%), the predicted generation status by condition interaction did not reach

significance, Wald χ2 (1, N = 126) = 1.84, p = .18.

Enrollment in Diversity Courses. We next examined whether, as hypothesized,

difference-education increased first-generation students’ tendency to seek out diversity

courses. We classified courses as diversity-relevant if the title of the course focused on

content that would increase students’ understanding of diversity or social difference

broadly defined. To determine whether each course met this criterion, we reviewed how a

wide range of universities classifies a course as a “diversity course” for their curricula

requirements. Informed by this research, the courses classified as diversity-relevant

ranged from those that focused on social difference as a function of social class,

race/ethnicity, gender, culture (e.g., Class and Culture; Black Social and Political Life) to

those that focused on general diversity topics (e.g., Stereotyping and Prejudice; Global

Inequalities). We used the title of the course to determine its topic, rather than the course

description, because we sought to include only courses that had diversity as a primary

focus and that signaled that focus to students. For all analyses involving diversity courses,

we report raw means, rather than estimated marginal means, to make the observed

differences between conditions clear.

A 2 (intervention condition) x 2 (generation status) ANCOVA predicting number

of diversity courses, and controlling for gender, race, and mean disciplinary GPA,

10

revealed no significant main effect of condition, F(1, 119) = 0.18, p = .67, ηp2 = .001.

There was a marginally significant main effect of generation status such that first-

generation students (M = 3.36, SD = 4.39) enrolled in more diversity courses than

continuing-generation students (M = 2.18, SD = 2.97), F(1, 119) = 3.50, p = .06, ηp2

= .03.6 Supporting our hypotheses, a significant generation status by intervention

condition interaction emerged, F(1, 119) = 5.41, p = .02, ηp2 = .04. Specifically, first-

generation students in the difference-education intervention (M = 4.36, SD = 5.48)7

enrolled in marginally more diversity-relevant classes than first-generation students in the

control (M = 2.33, SD = 2.60), F(1, 119) = 3.35, p = .07, ηp2 = .03. Continuing-

generation students did not significantly differ in enrollment in diversity courses across

conditions (difference-education: M = 1.47, SD = 2.49; control M = 2.84, SD = 3.25),

F(1, 119) = 2.11, p = .15, ηp2 = .02.

We also examined students’ area of major choice upon graduation to ensure that

choosing a particular major was not the pathway that dictated the increased tendency

among first-generation students to take more diversity courses in the difference-education

condition compared to the control. As shown in the Supplementary Materials, our

analyses suggest that choice of major does not explain our diversity course results.

General Discussion

6 To ensure that this increase in diversity-relevant classes was not because students took fewer STEM courses, we conducted a 2 (intervention condition) x 2 (generation status) ANCOVA controlling for gender and race with STEM courses as the dependent measure. There was neither a main effect of condition, F(1, 120) = 0.15, p = .70, ηp2 = .001, generation status, F(1, 120) = 0.97, p = .33, ηp2 = .01, nor a generation status x condition interaction, F(1, 120) = 0.68, p = .41, ηp2 = .01.7 We examined why the standard deviation was higher among first-generation students in the difference-education condition compared to students in the other conditions. We found that there was one outlier who took 28 diversity courses in this condition. We redid the analyses excluding this outlier and found that the results remained mostly unchanged. We report these results in the Supplementary Materials.

11

We examined the long-term academic and behavioral outcomes of a one-hour

difference-education intervention delivered during students’ transition to a selective

college. We asked: (1) do the academic performance benefits of difference-education

observed among first-generation students persist throughout their four years in college,

and (2) do first-generation students who participated in the difference-education

intervention actively seek out opportunities to learn more about difference? This study

suggests that the answer is yes and contributes intervention science in two key ways.

First, this study demonstrates that educating first-generation students about social

difference can provide long-term academic benefits. Even upon graduation, first-

generation students who participated in a brief difference-education program four years

earlier fared better academically (e.g., earned higher grades) than their peers in the

control intervention. This suggests that the contextual and asset-based theory of social

difference that students gained likely initiates a series of recursive processes that persist

over time to shape their long-term outcomes.

Second, this study shows one important behavioral change that difference-

education initiates, which could be a key process that helps to sustain students’ new

theory of social difference over time. For first-generation students, learning about how

difference matters in the intervention translated into a behavioral change—i.e., they took

marginally more diversity-relevant courses than their peers in the control condition.

While we did not demonstrate the full recursive cycle here, this finding suggests that

students sought out additional educational opportunities that are consistent with and

should serve to expand the new theory of social difference that they gained in the

intervention. This finding contributes to a growing body of literature that documents

12

specific behavioral pathways through which intervention effects are maintained over time

(Miller et al., 2017).

Despite these contributions, this study also has limitations. First, although the

pattern of results is consistent with our hypotheses, some findings are marginally

significant and/or not robust without the covariates (see Supplementary Materials). We

speculate that this is because the study is underpowered at 49%. Second, although we

document a new behavior (i.e., enrolling in diversity courses) that may help sustain the

recursive processes through which difference-education operates, this behavioral change

did not statistically account for the observed academic performance benefits. This was

not surprising, however, as we would not expect that learning more about difference

would directly predict improved grades. Rather, it is more likely that maintaining this

new theory of social difference leads to other psychological experiences (e.g., increased

fit and empowerment) and adaptive behaviors (e.g., seeking resources) that can directly

improve grades (see Stephens, Hamedani, et al., 2014).

Difference-education is an effective way to produce long-term change in first-

generation college students’ experiences and outcomes in higher education. Educating

students about difference can reduce social class achievement gaps throughout students’

college careers and also spark an interest in learning about diversity. With this new

theory of social difference in mind, first-generation students should be better equipped to

thrive not only in college, but also in their future endeavors as they transition to graduate

school or enter professional workplaces.

13

Figure 1. Mean end-of-college cumulative grade point average (GPA) as a function of

generation status and intervention condition. Error bars show standard errors of the mean.

First-Generation Students Continuing-Generation Students3.2

3.25

3.3

3.35

3.4

3.45

3.5

3.55

Difference-education ConditionControl Condition

End

-of-C

olle

ge G

PA

14

Figure 2. Mean number of diversity-relevant courses taken throughout college as a

function of generation status and intervention condition. Error bars show standard errors

of the mean.

First-Generation Students Continuing-Generation Students0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Difference-education ConditionControl Condition

Tota

l Num

ber o

f Div

ersi

ty C

ours

es

15

References

Agresti, A. (2007). An Introduction to Categorical Data Analysis. Hoboken, NJ: Wiley.

Bernstein, B.B. (1974). Class, codes and control. (Vol. 2). London, England: Routledge.

Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence

predict achievement across an adolescent transition: A longitudinal study and an

intervention. Child Development, 78(1), 246–263. doi:10.1111/j.1467-8624.2007.00995.x

Bourdieu, P., & Passeron, J. C. (1990). Reproduction in education, society, and culture. London,

England: Sage Publishing.

Bozick, R., & Ingels, S. J. (2008). Mathematics coursetaking and achievement at the end of high

school: Evidence from the Educational Longitudinal Study of 2002 (Report No. NCES

2008-319). Washington, D.C.: National Center for Education Statistics.

Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: Self-affirmation and social

psychology intervention. Annual Review of Psychology, 65(1), 333–371. doi:10.1146/

annurev-psych-010213-115137

Covarrubias, R., & Fryberg, S. A. (2015). Movin’ on up (to college): First-generation college

students’ experiences with family achievement guilt. Cultural Diversity and Ethnic

Minority Psychology, 21(3), 420–429. doi:10.1037/a0037844

Goudeau, S., & Croizet, J. C. (2017). Hidden advantages and disadvantages of social class.

Psychological Science, 28(2), 162–170. doi:10.1177/0956797616676600

Miller, D. T., Dannals, J. E., & Zlatev, J. J. (2017). Behavioral processes in long-lag

intervention studies: Peering into the white box. Perspectives in Psychological Science,

12(3), 454–467. doi:10.1177/1745691616681645

Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation

study of the number of events per variable in logistic regression analysis. Journal of

Clinical Epidemiology, 49, 1373–9. doi: 10.1016/S0895-4356(96)00236-3

16

Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S., & Covarrubias, R. (2012).

Unseen disadvantage: How American universities' focus on independence

undermines the academic performance of first-generation college students. Journal of

Personality and Social Psychology, 102(6), 1178–1197. doi:10.1037/a0027143

Stephens, N.M., Hamedani, M. H., & Destin, M. (2014). Closing the social-class achievement

gap: A difference-education intervention improves first-generation students' academic

performance and all students' college transition. Psychological Science, 25(4), 943–953.

doi:10.1177/0956797613518349

Stephens, N. M., Markus, H. R., & Phillips, T. (2014). Social class culture cycles: How three

gateway contexts shape selves and fuel inequality. Annual Review of Psychology, 65(1),

611–634. doi:10.1146/annurev-psych-010213-115143

Stephens, N. M., Townsend, S. S. M., Hamedani, M. G., Destin, M., & Manzo, V. (2015). A

difference-education intervention equips first-generation college students to thrive in the

face of stressful college situations. Psychological Science, 26(10), 1556–1566. doi:

10.1177/0956797615593501

Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and

achievement. Journal of Personality and Social Psychology, 92(1), 82–96. doi:

10.1037/0022-3514.92.1.82

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic

and health outcomes of minority students. Science, 331(1), 1447–1451. doi:10.1126/

science.1198364

Wilson, T. D. (2011). Redirect: The surprising new science of psychological change. New York,

NY: Little, Brown and company.

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re

not magic. Review of Educational Research, 81(2), 267–301. doi:10.3102/

0034654311405999

17

Supplementary Materials

Additional Analyses:

Academic Performance Without Covariates: For end-of-college grades, the pattern of results was the same but the results were

no longer significant without the covariates. A 2 (intervention condition: difference-education vs. control) x 2 (generation status: first- vs. continuing-generation) ANCOVA revealed neither a significant main effect for intervention condition, F(1, 122) = 2.19, p = .14, ηp2 = .018; nor a condition by generation status interaction, F(1, 122) = 0.84, p = .36, ηp2 = .007. Follow up contrasts revealed that first-generation students in the difference-education intervention (M = 3.45, SD = 0.32) did not significantly differ from first-generation students in the control condition (M = 3.31, SD = 0.34), F(1, 122) = 2.55, p = .11, ηp2 = .02. Likewise, continuing-generation students in the difference-education intervention (M = 3.53, SD = 0.32) did not significantly differ from continuing-generation students in the control (M = 3.50, SD = 0.31), F(1, 122) = 0.18, p = .67, ηp2 = .001.

Social Class Achievement Gap Without Covariates: In the control condition, the nonsignificant social class achievement gap reported

in the text is significant without the covariates. Specifically, continuing-generation students (M = 3.50, SD = 0.31) had higher end-of-college grades compared to first-generation students (M = 3.31, SD = 0.34), F(1, 122) = 5.11, p = .03,ηp2 = .04. In the difference-education condition, this social class achievement gap was no longer significant, F(1, 122) = .91, p = .34, ηp2 = .007.

Academic Honors Without Covariates: For academic honors, the pattern of results was the same but the results were no

longer significant without the covariates. A logistic regression analysis with generation-status, intervention condition, and their interaction as predictors revealed neither a main effect of condition, Wald χ2 (1, N = 126) = 1.70, p = .19, nor a generation status x condition interaction, Wald χ2 (1, N = 126) = 0.42, p = .52.

Diversity Courses Without Covariates: For diversity courses, the results follow the same pattern and become more

significant without the covariates. A 2 (intervention condition) x 2 (generation status) ANCOVA revealed no significant main effect of condition, F(1, 122) = 0.26, p = .61, ηp2 = .002. Supporting our hypotheses, a significant generation status by intervention condition interaction emerged, F(1, 122) = 6.91, p = .01, ηp2 = .05. Specifically, first-generation students in the difference-education intervention (M = 4.36, SD = 5.48) enrolled in more diversity-relevant classes than first-generation students in the control (M = 2.33, SD = 2.60), F(1, 122) = 4.37, p = .04, ηp2 = .035. Continuing-generation students did not significantly differ in enrollment in diversity courses across conditions (difference-education: M = 1.47, SD = 2.49; control M = 2.84, SD = 3.25), F(1, 122) = 2.57, p = .11, ηp2 = .021.

18

Diversity Courses Without Outlier:For diversity courses, the results are mostly unchanged by excluding this outlier.

A 2 (intervention condition) x 2 (generation status) ANCOVA revealed no significant main effect of condition, F(1, 122) = 0.07, p = .79, ηp2 = .001. However, consistent with the results reported in the text, a significant generation status by intervention condition interaction emerged, F(1, 122) = 4.97, p = .03, ηp2 = .04. First-generation students showed the same pattern across conditions, but did not take significantly more diversity-relevant courses in the difference-education (M = 3.48, SD = 2.98) compared to control condition (M = 2.33, SD = 2.60), F(1, 122) = 1.71, p = .19, ηp2 = .014. Continuing-generation students enrolled in marginally fewer diversity courses in the difference-education (M = 1.47, SD = 2.49) compared to control condition (M = 2.84, SD = 3.25), F(1, 122) = 3.62, p = .06, ηp2 = .03.

Choice of Major:We examined students’ majors upon graduation to ensure that choosing a

particular major was not the pathway that dictated their increased tendency to take diversity courses. Given the infrequent occurrence of specific majors such as mathematics or Spanish, we did not have a sufficient sample size to conduct a fine-grained analysis of students’ choice of each of these specific majors (i.e., the occurrence of each major would have been far too infrequent to examine with logistic regression). To examine choice of major, we therefore classified majors as falling into one of three broad categories: STEM (e.g., biological sciences), languages and the arts (e.g., theatre, Spanish), and social sciences (e.g., economics).

We conducted a multinomial logistic regression predicting these three major choices with generation-status, intervention condition, and their interaction as predictors. Given the small sample size, the model was not able to accommodate control variables for race and gender. Including these variables led to errors that reduced the reliability of the results. The overall model was significant, χ2 (6, N = 122) = 15.60, p = .02, indicating that the variables include in the model are better than an intercept-only model in predicting major choice. For the overall model, there was neither a main effect of condition, Wald χ2 (2, N = 122) = 2.67, p = .26, nor a generation status x condition interaction, Wald χ2 (2, N = 122) = 1.96, p = .38. However, suggesting that first-generation students prefer different majors than continuing-generation students, there was a main effect of generation status, Wald χ2 (2, N = 122) = 9.23, p = .01. Specifically, compared to continuing-generation students, first-generation students were less likely to major in STEM compared to the social sciences, OR = .14, 95% CI [.03, .55], p = .005. Again, compared to continuing-generation students, first-generation students were less likely to major in arts and language compared to the social sciences, OR = .22, 95% CI [.05, .95], p = .04. This main effect of generation status does not explain the tendency among first-generation students to take more diversity courses in the difference-education condition compared to the control.

19