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Abstract

Despite evidence for the academic and psychological benefits of racial/ethnic minority (REM) students enrolling in minority-serving institutions, REM college students’ graduation rates have been consistently lower for White students across institutions. The current study examined REM graduation rates and associated risk factors in a campus with no-racial/ethnic majority (NRM). The findings indicated that (1) the graduation rates of REM (Black and Latinx) students were higher at the NRM institution than national averages, with little disparity in NRM graduation rates among racial/ethnic groups, (2) REM female students graduated at higher rates than Asian and White male students, and (3) self-reported stress and registration status predicted college completion. Moreover, traditional predictors, such as household income, gender, and parental education, were not associated with college completion. A separate analysis of graduation rates at colleges high versus low in racial/ethnic diversity also revealed little graduation disparity among racial/ethnic groups when diversity is relatively high. The results support the hypothesis that REM students thrive academically in educational settings where no single racial majority exists.

Keywords

race ethnicity college graduation racial/ethnic minority urban higher education

Introduction

Racial/ethnic minority (REM) students graduate from colleges and universities at significantly lower rates than White students (Seidman, 2005). In Predominantly White Institutions (PWIs), REM students are more likely to experience racism and discrimination, as well as a lack of social, academic, and financial support, which reduces their sense of belonging and intention to continue their education (Gloria et al., 2005; Hurtado & Carter, 1997; Hurtado & Kamimura, 2003; Seidman, 2005). Indeed, REM students at PWIs often experience “minority status stress” triggered by their membership in a socially stigmatized group, which negatively affects their mental health and willingness to continue (Contrada et al., 2001; Gloria et al., 2005). By contrast, in settings where relatively large numbers of REM students are enrolled, such as at Historically Black Colleges and Universities (HBCUs) or Hispanic Serving Institutions (HSIs), REM students have more frequent and meaningful interactions with faculty (Wells-Lawson, 1994), more positive self-image, greater racial pride, higher educational aspiration (Contreras & Contreras, 2015; Gurin & Epps, 1975; Ward & Zarate, 2015), and more favorable adjustment skills (Fleming, 1984). Nevertheless, REM students at HBCUs and HSIs are even less likely to graduate from college compared with their REM counterparts at PWIs (Contreras & Contreras, 2015; Flores & Park, 2013; but see Kim & Conrad, 2006), perhaps due to the former’s relatively lower SAT scores, weaker high school records, and less financial or parental resources (Allen, 1992; Benítez & DeAro, 2004; Flores & Park, 2013; Kim & Conrad, 2006; Seidman, 2005).

What factors affect the college completion rates of REM students? In the current study, we provide pilot data for a new hypothesis: REM students thrive academically in educational settings where no single racial majority exists. We consider here the case of a 4-year institution with no racial/ethnic majority (NRM). We examined the graduation rates of White, Black, Latinx, and Asian students at the NRM institution, compared those rates with national averages, and explored potential factors that contribute to the NRM graduation rates, including parental education, socioeconomic status, gender, registration status, mental health, and financial support.

Disparities in College Education

Although the number of Black and Latinx students entering college has grown significantly in the past 50 years, McGlyn(2014) showed the graduation rates of Black (27.62%) and Latinx (19.81%) students are still significantly lower than White (43.87%) and Asian (59.35%) students. A large body of literature has documented similar racial/ethnic disparities in higher education, yet without consensus on the factors that drive these disparities (Adelman, 2006; Bowen et al., 2009; Camburn, 1990). One difficulty with previous studies is that the effects of racial/ethnic composition on college retention have mostly been observed only in either PWIs or minority-serving institutions (Adelman, 1999; Allen, 1985, 1992; Astin, 1991; Attewell et al., 2011; Bean, 1980; Benitez & DeAro, 2004; Berger & Milem, 1999; Buchman & DiPrete, 2006; Flores & Park, 2013; Ishitani & DesJardins, 2003; Kim & Conrad, 2006; Rector et al., 2001; Rincón, & Rincâon, 2008; Stoecker et al., 1988; Wells, 2008). Due to the demographics of the US population, samples often contain large proportions of White participants (>85%) but relatively few REM participants (<15% combined; Adelman, 2006; Astin, 1991; Attewell et al., 2011; Bean, 1980; Berger & Milem, 1999; Buchman & DiPrete, 2006; Ishitani & DesJardins, 2003; Rector et al., 2001; Rincón & Rincâon, 2008; Stoecker et al., 1988; Wells, 2008). Moreover, students who transferred and received bachelors’ degrees at other institutions are typically considered dropouts and excluded from many studies, which creates unreliable graduation estimates (Attewell et al., 2011; Berger & Milem, 1999; Buchman & DiPrete, 2006; Contreras & Contreras, 2015; Flores & Park, 2013; Ishitani & DesJardins, 2003; Rector et al., 2001; Rincón & Rincâon, 2008; Stoecker et al., 1988). Finally, although some risk and protective factors such as gender, socioeconomic status, and parental education have been well studied (Adelman, 1999; Astin, 1993; Buchman & DiPrete, 2006; Ewert, 2012; Ishitani & DesJardins, 2003; Pascarella & Terenzini, 1980; Strodtbeck et al., 1957; Tierney, 1999; Tinto, 1975, 1993), other factors, such as stress, satisfaction in college, hours of study, hours of work, length of commute, immigration status, have been under-examined (Ackerman & Gross, 2003; Adams et al., 2016; Bradburn & Carroll, 2002; Ishitani & DesJardins, 2003; Martin & Midgley, 2010 ).

Purpose of the Current Study

The current study employed Astin’s (1984) I-E-O model to examine protective and risk factors among racial minority and majority students at an NRM campus. Astin’s model is particularly useful when comparing intrinsic factors, such as race/ethnicity and gender, to experiential factors, such as stress, satisfaction, and registration status. We compared graduation rates of the four main racial groups at the NRM institution (Black, Latinx, White, and Asian) to national averages, while addressing the limitations identified in previous studies. For example, to provide a better estimate of academic success, the current study included students who discontinued their enrollment in their initial college but received an undergraduate degree at other institutions. We also focused the study on eight-year college completion rates to provide a more complete window on REM graduation (Snyder et al., 2018).

Method

Participants

The NRM is a large urban commuter-based institution in Harlem, New York City offering low in-state tuition and comprised of over 16,500 students, including 630 (3.8%) doctoral, 2,441 (14.8%) masters, and 13,445 (81.4%) undergraduate students (Silverman, 2016). The student body has an equal representation of males (49%) and females (51%) with relatively equal numbers of students in each demographic group (White 18%; Black 23%; Latinx 32%; Asian 18%) (see Table 1). The NRM faculty is less diverse than the study body (White 44%; Black 20%; Latinx 17%; Asian 12%), but much more diverse than the average faculty at US degree-granting postsecondary institutions (White 75%; Black 6%; Latinx 6%; Asian 12%; Hussar et al., 2020). We investigated two samples. The first sample consists of all undergraduate students (n =12,960) enrolled in the NRM during the fall semester of 2009. The second sample is a subset from the larger sample of 514 students in an introductory psychology class in 2009 who completed a 21-item survey of sociodemographic characteristics (e.g., household income, parents’ education, place of birth) and college-related experience (e.g., student employment, source of financial support, full or part-time registration status, length of commute, stress, hours of study) (see Online Appendix A).

Table 1.

Demographics and Graduation Rates of the 2009 Undergraduate Cohort (n = 12,960) and Survey Sample (n = 514) at NRM.

Variables	Cohort		Survey
Variables	Total	Number (percent) graduated	Total	Number (percent) graduated
Total	12,960	9,683 (74.71)	514	344 (66.9)
Gender
Female	6,660	5,167 (77.58)	280	192 (68.57)
Male	6,300	4,516 (71.68)	234	152 (64.96)
^aRace/ethnicity
Latinx	4,180	3,060 (73.21)	207	141 (68.11)
Black	2,930	2,133 (72.80)	116	78 (67.24)
Asian	2,285	1,814 (79.39)	116	76 (65.52)East: 30 (73.17)South: 31 (64.68)Other: 12 (54.54)
White	2,293	1,708 (74.49)	70	47 (67.14)
Year in college
Freshmen in 2009	4,226	2,564 (60.67)	356	241 (67.7)
Sophomore in 2009	3,171	2,363 (74.52)	107	69 (64.5)
Junior in 2009	3,058	2,496 (81.62)	37	25 (67.6)
Senior in 2009	2,505	2,260 (90.22)	14	9 (64.3)

Note. ^aData of Nonresident alien and Native Americans were omitted.

Measures

Institutional records of the NRM. The NMR’s Office of Institutional Research (OIR) provided graduation, academic, and demographic data for all students in the sample.

National records. National Student Clearinghouse (NSC) records were used to provide transfer and graduation data on students who left the NRM but received an undergraduate or higher degree elsewhere. Thus, the total graduation rate was calculated after merging data from the OIR and NSC records. National averages of college graduation rates were retrieved from the NSC reports Signature 12 Supplement: Completing College: A National View of Students Attainment Rates by Race and Ethnicity-Fall 2012 Cohort, and the National Center for Education Statistic (NCES) report, Digest of Education Statistics 2016.

Input-Environment-Output (I-E-O) variables. We employed in I-E-O analyses (Ahmad et al., 2012; Callahan et al., 2017; Patterson et al., 2012; Renn & Reason, 2013) 8 input variables (race, gender, place of birth, father’s education, mother’s education, sibling’s college enrollment, sibling’s college graduation, and household income), 13 environment variables (students’ expectation of “college premium” (Rampell, 2013), resources for financial support, employment status, hours of work, hours of study, length of commute, stress, registration status, satisfaction in academic service and tutoring, satisfaction in computer lab services, satisfaction in library services, and satisfaction in teaching), and 1 output variable (college completion).

Data Analysis

Eleven variables contained at least one missing value, 224 students failed to answer at least one question, and a total of 389 (3.44%) among 11,308 values were unrecorded (see Table 2). Missing data were handled by either matching with the sociodemographic OIR data or using multiple imputations. Missing race identifiers reduced the survey sample for regression analyses from 514 to 509. The racial groups in both samples were re-categorized into four groups (see Table 3). To prevent multicollinearity, collinearities among all variables were tested, and VIF was controlled below 3.0. Nonetheless, a singularity in Hessian matrix and a possible quasi-complete separation were detected in multinominal logistic regression analyses. By removing two variables, place of birth and registration status, both errors were corrected.

Table 2.

Descriptions of Missing Data From Survey Sample (n = 514) at NRM.

		Missing
	Valid	Count	%	Method*
Input
Race/ethnicity	491	23	4.5	OIR
Place of birth	485	29	5.6	OIR, MI
Environment
Hours of work	512	2	0.4	MI
Hours of study	509	5	1	MI
Stress	491	23	4.5	MI
Financial resource – self/family saving	511	3	0.6	MI
Loan/Grant	511	3	0.6	MI
Satisfaction in academic service/tutoring	373	41	27.4	MI
In computer lab service	450	64	12.5	MI
In library service	444	70	13.6	MI
In teaching	491	23	4.5	MI

Note. MI = multiple imputations, OIR = the cohort data from the office of institutional research.

Table 3.

Graduation Rates (%) of Freshmen Cohorts in the NCES, NSC, and NRM Samples.

Variables	NCES 2009 cohort	NSC 2010 cohort	NRM 2009 cohort
Gender
Female	62.1	58.3	65.46
Male	56.2	51.5	55.54
Race/ethnicity
White	63.3	67.2	60.89
Black	39.5	45.9	57.59
Latinx	53.6	54.9	58.30
Asian	73	71.7	65.77
Overall	59.4	54.8	60.67

Data analysis comprised four phases. First, the 2009 cohort (n = 12,960) and survey (n = 514) samples were analyzed by gender, race/ethnicity, and year in college to observe general graduation patterns at the NRM. Second, the graduation rates of students who were freshmen in 2009 (n = 4,226) were compared with the national freshmen graduation rates reported by the NSC and NCES. Third, to identify factors that affect college completion at the NRM, binary logistic regression analyses were conducted separately with well-established and under-examined variables (n = 509). Finally, to identify relative risk factors associated with REM students, multi-nominal logistic regressions (NOMREG) were conducted using White and Asian as reference groups and race/ethnicity as the dependent variable (n = 509). To ensure ample statistical power in logistic regression analyses with many independent variables (see Peduzzi et al., 1996), two separate logistic regression analyses, one with input and another with environment variables, were conducted in the third and fourth phases.

Results

Graduation rates. Analyses of graduation rates revealed that among the 12,960 students enrolled at NRM in the 2009 fall semester, 74.71% (n = 9,683) graduated by the end of 2017, 63.42% (n = 8,219) from the NRM, and 11.29% (n = 1,464) from their transferred institutions. Female students graduated at a higher rate than male students, and Asian students graduated with the highest rate, followed by White, Latinx, and Black students (see Table 1). Analysis of survey students (n=514) were generally in line with these results: 66.93% (n = 344) graduated by the end of 2017, 49.81% (n = 256) from the NRM and 17.12% (n = 88) from their transferred institutions. Female students graduated at higher rates than male students. However, in the survey group, Latinx students graduated at the highest rate, followed by Black, White, and Asian students (see Table 1). Discrepancies in graduation rates were found among Asian subgroups, with South Asian and Other Asian students struggling more than Latinx and Black students to complete their undergraduate degrees.

Among students who began NRM as freshmen in 2009 (n = 4,226), 60.67% graduated either from the NRM or the college where they transferred. The NCES and NSC analyzed 6-year graduation rates; thus, we expected the 8-year rates calculated at the NRM to be elevated by 1.9% to 3.8% (see Horn, 2011). The NCES report for the 2009 cohort (Snyder et al., 2018) revealed a six-year graduation rate of 59.4%. The NSC report for the 2012 cohort (n = 2,823,578; Shapiro et al., 2017) showed a six-year graduation rate of 54% (including 42% graduating from their starting college and 12.8% graduating from transfer colleges). The graduation rate of Black students was 18.08 points higher at the NRM compared with NCES data and 11.69 points higher compared with NCS data. The graduation rate of Latinx at NRM was 4.7 points (NCES) and 3.4 points (NSC) higher. On the other hand, Asian and White students’ graduation rates at NRM were 2.41 and 7.23 points lower, respectively (see Table 3).

The upshot is that NRM showed significantly smaller disparities among racial/ethnic groups in graduation rates compared with national averages (see Figure 2). We found common patterns among the NSC, NCES, and NRM data sets. Asian female students graduated at the highest rate, whereas Black male students graduated at the lowest rate. Differences in graduation rates among racial/ethnic groups were the largest among males. Yet two patterns stood out in the NRM as distinct from NCES and NSC: 1). Black and Latinx females graduated at higher rates than males of all races, including Asian and White (see Figure 1); 2). Differences in graduation rates were larger between genders (9.92) than among racial/ethnic groups (8.18) (see Figure 2). Of course, national data sets include some NRM schools, making the comparison imprecise. To provide a deeper understanding of the role of racial/ethnic diversity in college graduation, we compared six-year graduation rates among 34 mid-sized colleges (∼20,000 students) that are relatively high (approximately 40% of the student population is White) versus low (60% White) in racial/ethnic diversity (NCES survey, February 26, 2021). A summary of graduation rates by racial/ethnic status appears in Table 4. As one can see, the lower the racial/ethnic diversity of the student population, the greater the disparity in graduation rates of Latinx and, especially, Black students relative to White students. Indeed, Black students at schools with relatively low racial/ethnic diversity have a 16-point lower graduation rate on average than White students. By contrast, Black, Latinx and White students have roughly the same graduation rate at mid-sized institutions with relatively high racial/ethnic diversity. These results are in concert with the comparison of the NRM with national data.

Figure 1.

Rates of Student Graduation Separately by Each Combination of Gender-Race/Ethnicity Group in the National Student Clearing House (NSC), National Center for Education Statistic (NCES), and No-Racial/Ethnic Majority (NRM) Samples.

Figure 2.

Disparities in Graduation Rates (Expressed as Graduation Range) for Racial/Ethnic Groups (Combined and Separately by Gender) and Gender in the No-Racial/Ethnic Majority (NRM) Campus, National Student Clearing House (NSC), and National Center for Education Statistic (NCES) Samples.

Table 4.

Six-Year Graduation Rates in High Versus Low Racial/Ethnic Diversity Colleges.

Colleges	Student population	Graduation rates
Colleges	Student population	Black	Latinx	White	Asian
High racial/ethnic diversity colleges (∼40% White)
Montclair State	16,812	67.2	65.5	68.0	63.0
Arizona State Downtown Phoenix	11,503	40.9	52.0	59.4	73.3
The University of New Mexico	17,132	42.9	53.0	59.4	72.9
The University of Colorado Denver	11,892	78.3	78.2	59.4	81.0
The University of California, Santa Cruz	17,792	69.9	72.4	59.4	80.5
The University of California, Riverside	20,574	74.5	74.5	59.4	80.0
Georgia State	26,489	58.5	58.2	59.4	55.8
The University of North Carolina at Greensboro	16,238	63.7	57.3	59.4	71.2
The University of Texas at Dallas	19,837	57.1	61.5	59.4	80.0
Florida Atlantic	23,265	52.1	50.3	59.4	70.1
The University of Maryland, Baltimore	11,156	73.5	69.1	59.4	73.1
George Mason	25,508	73.7	69.2	59.5	73.3
Old Dominion University	19,194	53.1	45.7	59.5	55.7
Virginia Commonwealth	22,497	66.8	63.9	59.5	75.1
The University of Massachusetts Boston	12,229	43.4	45.4	59.5	60.7
San Francisco State	25,799	61.3	55.3	53.0	62.7
San Diego State	30,018	67.4	71.0	71.0	78.2
The University of Nevada, Las Vegas	24,707	26.3	41.5	49.0	53.4
California State	12,728	36.5	48.9	48.2	62.8
The University of California, Santa Barbara	23,063	68.0	80.9	84.9	83.9

Average graduation rate	19,422	58.8	60.7	60.3	70.3
REM graduation gap		−1.5	0.4	–	10.0
Low racial/ethnic diversity colleges (∼60% White)
The University of Massachusetts Amherst	23,202	73.7	70.2	83.5	83.6
The University of Massachusetts Lowell	13,385	19.6	54.0	64.0	63.0
Bridgewater State University	9,312	56.8	50.0	63.0	37.5
Eastern Michigan	15,325	29.9	42.7	52.6	59.2
Wayne State University	17,114	23.4	39.0	52.9	62.8
Oakland University	15,476	32.4	41.7	60.2	60.0
The University of Colorado Boulder	29,753	52.1	63.3	70.2	71.4
The University of Nevada Reno	17,513	49.1	52.9	64.1	66.0
The University of Oregon	18,923	65.9	70.2	74.8	81.7
Portland State	18,541	41.7	45.9	48.3	57.9
The University of Utah	23,396	50.0	47.2	58.6	63.7
Clemson	19,564	63.4	88.8	85.1	90.6
Georgia Southern	22,239	48.5	40.8	51.8	44.3
The University of North Carolina	18,946	85.8	89.9	91.3	92.8
Average graduation rate	18,764	49.4	56.9	65.7	66.7
REM graduation gap		−16.3	-8.9	-	1.0

Data: NCES survey, Integrated Postsecondary Education Data System, February 26, 2021.

Factors affecting graduation. Binary logistic regression models were used to estimate the effects of input and environment variables on graduation at the NRM, the well-established variables entered in Block 1, and the under-examined variables in Block 2. Tables 5 and 6 summarize the logistic regression coefficients, the standard errors associated with coefficients, and odds ratios for the predictors.

Table 5.

Input Variables Predicting College Completion at NRM (n = 509; Models 1 and 2).

	Model 1			Model 2
Input (parameter coding*)	B	SE	OR	B	SE	OR
Block 1.
Race/ethnicity
Black (0)	−0.05	0.32	0.95	−0.05	0.32	0.96
Latinx (0)	−0.1	0.3	0.91	−0.12	0.3	0.89
Asian (0)	0.03	0.33	1.03	0.10	0.33	1.11
Male (1)	−0.17	0.19	0.84	−0.18	0.19	0.84
Father’s education	−0.02	0.06	0.98	−0.03	0.06	0.98
Mother’s education	0.06	0.07	1.07	0.07	0.07	1.08
Household income	0.05	0.08	1.05	0.06	0.08	1.06
Block 2.
No siblings in college (1)				−0.27	0.22	0.76
US born (1)				−0.09	0.19	0.92
Constant	0.63	0.64	1.88	0.76	0.66	2.15

Note: B=logistic regression coefficients; SE= standard error; OR=odds ratio.

Note: Parameter coding of categorical variables were indicated in the parenthesis.

Table 6.

Environment Variables Predicting College Completion at NRM (n = 509; Models 3 and 4).

	Model 3			Model 4
	B	SE	OR*	B	SE	OR*
Environment (parameter coding)*Block 1.
College premium	−0.21	0.12	0.81	−0.23	0.13	0.8
Finance- family/self (0)	−0.16	0.23	0.86	−0.16	0.24	0.85
System/institution (0)	0.43	0.31	1.54	0.51	0.32	1.66
Part-time registration (1)	−1.15**	0.3	0.32**	−1.26**	0.3	0.29**
Constant	1.34	0.33	3.88
Block 2.
Hours of working				0.04	0.14	1.04
Hours of studying				0.15	0.11	1.17
Employment (0)				−0.08	0.09	0.93
Commuting length				0.03	0.34	1.03
Stress				−0.32*	0.16	0.73*
Satisfaction
In advising /tutoring				0.12	0.11	1.13
In computer lab				−0.01	0.10	1
In library				0.17	0.10	1.18
In teaching				−0.07	0.12	0.94
Constant	1.35	0.33	3.88	1.21	0.94	3.36

Note: B=logistic regression coefficients; SE= standard error; OR=odds ratio

Parameter coding of categorical variables were indicated in the parenthesis.

*p < .05. **p < .001.

The regression analyses revealed that none of the input variables predicted students’ graduation in either Model 1 (Nagelkerke R² = 7% variance explained), χ²(7) = 2.559, ns, or Model 2 (12% variance), χ²(7) = 4.44, ns (see Table 5). However, the environment variables were statistically significant in both Model 3 (53.6% variance), χ²(4) = 20.1638, p < 0.001, and Model 4 (92.4% variance), χ²(13) = 35.155, p < 0.001 (see Table 6 ).

Both models correctly classified 68.18% of cases. Here, the well-established factors, such as parental education, household income, gender, and financial support, which have been known to be good predictors in previous studies, did not predict graduation. Only registration status and perceived stress predicted college completion. Table 6 shows that part-time registration and an increase in perceived stress were both associated with a reduction in the likelihood of graduation. A part-time student was 71% less likely to graduate than a full-time student (Model 4); every unit increase in part-time status led to a decrease in the log-odd for graduation of 1.15 units. A student who perceived more stress in college relative to high school was 27% less likely to graduate than students who perceived less stress (see Table 5); every unit increase in stress led to a decrease in the log-odd for graduation of 0.32 units.

Risk factors associated with REM students. NOMREG of input variables (Models 1 and 2) uncovered significant relative risks (or protections) for REM students compared with White or Asian students, χ²(20) = 74.343, p < 0.001 (53% variance explained), in parental education, χ²(4) = 14.093, p < 0.001, household income, χ²(4) = 31.277, p < 0.001, and gender, χ²(4) = 11.097, p < 0.05 (see Table 7).

Table 7.

Multinomial Logistic Regression Results Linking Input Variables to Students’ Race/Ethnicity at NRM (n = 509; Models 1 and 2).

		Model 1 Reference group: White			Model 2 Reference group: Asian
Group*	Input variables	B	SE	OR	B	SE	OR
1	Father’s education				0.09	0.11	1.09
	Mother’s education				0.35**	0.12	1.43**
	Household income				−0.62**	0.14	0.54**
	Gender (male)				0.94**	0.32	2.55**
	Siblings’ college enrollment (0)				−0.08	0.32	0.93
2	Father’s education	−0.14	0.11	0.87	−0.06	0.09	0.94
	Mother’s education	−0.06	0.12	0.94	0.30**	0.10	1.34**
	Household income	0.39**	0.14	1.47**	−0.24*	0.11	0.79*
	Gender (male)	−0.22	0.31	0.8	0.71**	0.28	2.04**
	Siblings’ college enrollment (0)	0.1	0.31	1.1	0.02	0.27	1.02
3	Father’s education	−0.23*	0.10	0.8*	−0.14	0.08	0.87
	Mother’s education	−0.22*	0.11	0.8*	0.13	0.08	1.14
	Household income	0.63**	0.14	1.89**	0.01	0.09	1.01
	Gender (male)	−0.48	0.29	0.62	0.46	0.24	1.58
	Siblings’ college enrollment (0)	0.27	0.3	1.32	0.2	0.24	1.22
4	Father’s education	−0.09	0.11	0.92
	Mother’s education	−0.35**	0.12	0.70**
	Household income	0.62**	0.14	1.87**
	Gender (male)	−0.94**	0.32	0.39**
	Siblings’ college enrollment (0)	0.08	0.32	1.08

Note: B=logistic regression coefficients; SE= standard error; OR=odds ratio.

1 = White, 2 = Black, 3 = Latinx, 4 = Asian.

*p < .05. **p < .001.

Parameter coding of categorical variables was indicated in the parenthesis.

When White was used as the reference group, Black students revealed higher household incomes; Latinx students had lower parental education and higher household incomes. When Asian was used as the reference group, Black students had higher parental education, lower household income, and a higher ratio of male students. Latinx and Asian students did not differ on any input variable.

NOMREG of environment variables (Models 3 and 4) uncovered significant relative risks (or protections) for REM students compared with White or Asian students, χ²(48) = 115.72, p < 0.001 (22% variance explained), in hours of study, χ²(4) = 29.64, p < 0.001, length of commute, χ²(4) = 22.21, p < 0.001, financing college with self/family saving, χ²(4) = 10.44, p < 0.05, and financing college with loan or grant χ²(4) = 10.28, p < 0.055 (see Table 8). When White was used as the reference group, REM students demonstrated higher expectations of college premium and fewer study hours. Latinx students were also less likely to finance college with self/family savings but more likely to use loans or grants. When Asians were used as the reference group, REM students were found to study and commute for fewer hours; Latinx students were more likely to finance college with loans or grants.

Table 8.

Multinomial Logistic Regression Results Linking Environment Variables to Students’ Race/Ethnicity at NRM (n = 509; Models 3 and 4).

		Model 3 Reference Group: White			Model 4 Reference Group: Asian
Group*	Environment & output	B	SE	OR	B	SE	OR
1	College premium				−0.44	0.21	0.64
	Hours of working				0.13	0.22	1.14
	Hours of studying				0.10	0.16	1.11
	Length of commuting				−0.53**	0.14	0.59**
	Stress				−0.18	0.25	0.84
	Satisfaction with advising				−0.01	0.21	0.99
	With computer lab				−0.17	0.22	0.84
	With library				0.05	0.26	1.06
	With teaching				0.37	0.20	1.44
	Finance by self or family (0)				−0.73	0.46	0.48
	By loan or grant (0)				0.39	0.41	1.47
	Employment (0)				0.44	0.55	1.55
	Graduation (0)				0.06	0.34	1.06
2	College premium	0.55*	0.21	1.72*	0.11	0.18	1.11
	Hours of working	−0.02	0.21	0.98	0.11	0.18	1.12
	Hours of studying	−0.47**	0.17	0.63**	−0.36**	0.15	0.7**
	Length of commuting	0.13	0.13	1.14	−0.4**	0.12	0.67**
	Stress	0.11	0.24	1.11	−0.07	0.21	0.93
	Satisfaction with advising	0.01	0.22	1.01	0.003	0.18	1
	With computer lab	0.15	0.18	1.17	−0.02	0.18	0.98
	With library	0.01	0.22	1.01	0.06	0.17	1.07
	With teaching	−0.05	0.20	0.96	0.32	0.17	1.38
	Finance by self or family (0)	0.36	0.47	1.44	−0.37	0.35	0.69
	By loan or grant (0)	−0.47	0.42	0.63	−0.09	0.39	0.92
	Employment (0)	−0.33	0.55	0.72	0.11	0.46	1.11
	Graduation (0)	−0.14	0.34	0.87	−0.08	0.29	0.92
3	College premium	0.44*	0.2	1.55*	0	0.16	1
	Hours of working	−0.05	0.21	0.95	0.08	0.17	1.08
	Hours of studying	−0.63**	0.16	0.53**	−0.53**	0.14	0.59**
	Length of commuting	0.15	0.13	1.16	−0.38**	0.11	0.68**
	Stress	0.11	0.24	1.12	−0.06	0.19	0.94
	Satisfaction with advising	−0.04	0.23	0.97	−0.04	0.15	0.96
	With computer lab	0.14	0.18	1.15	−−0.04	0.15	0.97
	With library	−0.01	0.26	0.99	0.05	0.16	1.05
	With teaching	−0.18	0.19	0.84	0.19	0.15	1.21
	Finance by self or family (0)	0.95*	0.43	2.6*	0.22	0.29	1.25
	By loan or grant (0)	−1.19**	0.43	0.30**	−0.8**	0.39	0.45**
	Employment (0)	0.04	0.52	1.04	0.48	0.44	1.61
	Graduation (0)	−0.23	0.32	0.79	0.17	0.26	0.84
4	College premium	0.44	0.21	1.55	–	–	–
	Hours of working	−0.13	0.22	0.88	–	–	–
	Hours of studying	−0.10	0.16	0.90	–	–	–
	Length of commuting	0.53**	0.14	1.69**	–	–	–
	Stress	0.18	0.25	1.19	–	–	–
	Satisfaction with advising	0.01	0.21	1.01	–	–	–
	With computer lab	0.17	0.22	1.19	–	–	–
	With library	−0.05	0.26	0.95	–	–	–
	With teaching	−0.36	0.20	0.69	–	–	–
	Finance by self or family (0)	0.73	0.46	2.08	–	–	–
	By loan or grant (0)	−0.39	0.41	0.68	–	–	–
	Employment (0)	−0.44	0.55	0.64	–	–	–
	Graduation (0)	−0.05	0.34	0.94	–	–	–

Note: B=logistic regression coefficients; SE= standard error; OR=odds ratio.

1 = White, 2 = Black, 3 = Latinx, 4 = Asian. Note: *p < .05,; , ** p < .001.

Parameter coding of categorical variables was indicated in the parenthesis.

Discussion

College graduation rates of REM students are significantly lower than those of White students across PWIs, HSBCs, and HSIs (Fletcher & Tienda, 2010; Kao & Thompson, 2003; Snyder et al., 2018). The current study examined the hypothesis that REM students thrive academically in educational settings where no single racial/ethnic majority exists (see Seidman, 2005; Umbach & Kuh, 2003). We examined graduation rates and predictive factors for REM students in an urban NRM institution. We found that college completion of REM students in the NRM (58%) was comparable to the average graduation rates of White students in PWIs (63%), despite appreciably less racial/ethnic diversity among the NRM faculty (Hussar et al., 2020). Moreover, in contrast to national averages, we found little disparity in average graduation rates among racial/ethnic groups in the NRM sample. Indeed, female students REM outperformed Asian and White male students at the NRM, and REM students had a similar social and financial status to White students, including parental education, household income, and immigration status. Traditional input factors such as social and economic background, immigration status, and gender played no role in predicting college completion at the NRM. Instead, two experiential variables explained NRM graduation: perceived stress and registration status. We found similar results in a separate analysis of graduation rates in mid-sized colleges that are high versus low in racial/ethnic diversity (Table 4). The results suggest that the absence of a single racial/ethnic minority among students in college may alleviate REM disparities in graduation and create an environment in which the individual’s college experience has a more powerful impact on completion than demographic, cultural, or economic factors.

Risk Factors in College Completion

Minority status stress. We speculate that one possible explanation for success of REM students in NRM institutions is lower minority status stress – the perception of an unwelcoming campus environment, direct experiences of discrimination, within-group pressure, and a sense of academic disadvantage – relative to PWIs, HBSCs, or HSIs, which in turn might contribute to the elevated graduation rates among these students (Arbona et al., 2018; Smedley et al., 1993; Shahid et al., 2018). Studies show that minority status stress is positively associated with depression symptoms (Arbona et al., 2014; Rodriguez et al., 2000; Saldaña, 1994; Smedley et al., 1993; Wei et al., 2011) and negatively associated with the sense of belonging (Hurtado & Carter, 1997), including intention to remain in college (Hurtado & Kamimura, 2003, Wei et al., 2011). The role of minority status stress may be particularly acute among certain groups – such as Black males – who suffer disproportionately from systemic racism (Fields, 2014). Considering that minority status stress is triggered not only by racial discrimination but also from within-group pressures (Arbona et al., 2014; Rodriguez et al., 2000; Saldaña, 1994), the absence of a racial/ethnic majority on campus might reduce experiences of discrimination from a majority group, reduce the pressure from one’s own racial/ethnic group whose number is dominant on campus, and provide greater feelings of hope in REM students (Chang & Banks, 2007). Moreover, from a critical race perspective, the NRM campus may provide a context for post-traumatic growth, perhaps empowering REM students to attenuate previous experiences of systemic racism and discrimination (Ault, 2017).

Perceived stress. Perceived stress has generated mixed results in previous studies. In some studies, stress had negative associations with academic performance (Chartrand & Harmon, 1992) and college retention (Perrine, 1999; Saunders-Scott et al., 2017); in other studies, stress affected neither college performance (Petrie & Stoever, 1997) nor intention to stay in college (Sandier, 2000). Indeed, in one study (Zajacova et al., 2005), stress was associated positively with student retention. In the present study, we found that students who perceived less stress in college than high school were on average relatively more likely to graduate. Although speculative, stress might affect students’ abilities to cope with the transition from high school to college. We also found that commuting time and hours of study were associated with stress, which suggests that longer commutes and extended study in college compared with the high school may elevate perceived stress. Unfortunately, the influence of student racial/ethnic diversity on both perceived stress and minority status stress could not be assessed in the present study. Future studies are tasked with comparing student stress levels in NRMs relative to PWIs, HBSCs, and HSIs.

Registration status. The present study found that students at the NRM who registered part-time were less likely on average to graduate than students who registered full time. Stratton et al. (2007) noted that protective/risk factors affect part-time students differently from full-time students: Parental education, academic grade, and household characteristics (such as marital and parental status) had relatively weaker associations with the part-time whereas race/ethnicity had relatively stronger associations. In the current study, we found no significant differences on average between part- and full-time students in terms of household income, parental education, employment status, race/ethnicity, or gender. Instead, we found that students who registered full-time were more likely on average to use loans or grants, less likely to use self or family savings, and commuted longer distances than part-time students. Since financial resources and commuting length were not associated with college graduation, the registration status affected students’ college completion independently from the influence of other input and environment variables.

Atypicality in racial stratification. In our NRM sample, more than 80% of the students had household incomes below the national median (U.S. Census Bureau, 2016), regardless of racial/ethnic status. Interestingly, household income for over 90% of the White NRM students was below the national median, more than 60% were below the poverty threshold, and the poverty rate for the Asian NRM students exceeded that for the REM students (see Figure 3).

Figure 3.

Percentage of Total Household Income in Each of Five Income Categories, Across All Students (n = 509) in the No-Racial/Ethnic Majority (NRM) Campus and Separately for Each of Four Racial/Ethnic Groups.

Are White and Asian students economically and socially marginalized in this setting? The economic risk factors operating in White and Asian groups might be expected to lower graduation rates at the NRM, at least relative to the NCES and NSC rates (Fuligni & Witkow, 2004; Kao & Thompson, 2003). Yet despite the socioeconomic disadvantages of White and Asian NRM students, their average graduation rates were similar to the national averages (as were REM NRM students). Additionally, unlike previous studies (DesJardins et al., 2002; Hu & St. John, 2001, Ishitani & DesJardins, 2003; Whalen et al., 2010), access to financial aid from public sources, such as grants or loans, did not affect graduation rates at the NRM. We conclude that low socioeconomic status among NRM students did not depress graduation rates.

Limitations and Directions for Future Research

Several limitations constrain our conclusions. First, since our primary analysis focused on a single NRM institution, we can only infer that the absence of a racial/ethnic majority contributed to successful college completion. In this regard, comparisons of graduation rates in the NRM with other large urban commuter-based institutions in New York City are instructive. For example, the two public baccalaureate institutions in Brooklyn, New York are distinct in both student populations and graduation rates: The one with no racial/ethnic majority (White 28.5%; Black 21.9%; Latinx 23.4%; Asian 21.0%), like the NRM studied here, reveals a relatively high graduation rate (54%), whereas the one primarily serving a single racial/ethnic minority population (White 1.4%; Black 73.4%; Latinx 17.9%; Asian 2.9%) reveals a substantially lower graduation rate (14%). The patterns, and those found in institutions with relatively high racial/ethnic diversity (see Table 4), are consistent with our thesis that the absence of a single racial/ethnic majority among the students contributes to improved college completion. Composition of the faculty, staff, and administrators in the NRM schools may further influence student success. To extend generalizability, future research must systematically investigate additional examples of NRM institutions and explore how diversity among faculty, staff, and administrators may contribute to a more equitable learning environment for students (Seidman, 2005).

Second, although linking the eight-year-old survey data to the current institutional data enabled us to observe final graduation rates, as opposed to retention rates, and investigate a wide range of factors, it hindered the comparison of outcomes with studies examining six-year graduation rates. Moreover, we did not evaluate our survey instrument's reliability or construct validity, relying instead on face validity, content validity, and ecological validity. Also, no specific method was used to assess or improve the internal consistency of the survey since each construct was evaluated with an individual item. Future studies are encouraged to implement standardized measures with established validity and reliability to allow researchers to compare the outcomes of different studies using the same measures. Finally, by collapsing survey responses from East, South, and Other Asian students (to align with the institutional data), we sacrificed an opportunity to observe each Asian subgroup separately. The same is true of Latinx subgroups. A separate analysis in future studies will enable researchers to identify differential risk factors to different subgroups.

Conclusions

Findings from the present study suggest that the academic success of REM college students may be significantly enhanced in educational settings where no single racial/ethnic majority exists. In this environment, risk factors affecting college completion may shift from traditional background variables to experiential variables such as perceived stress. Most encouraging was the finding that graduation rates in a student body residing in an economically underserved community were comparable to national averages.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Namhee Stokvis

Supplemental material

Supplemental material for this article is available online.

Author Biographies

Namhee Stokvis studied and practiced psychology in South Korea and completed her graduate education at the City College of New York and Rutgers University. Dr. Stokvis has been working with culturally diverse and economically disadvantaged clientele since 2013. Her interest is to apply philosophy and practice of Buddhism to psychotherapy targeting clients who are ethnic minority and in poverty. Her academic works regarding Buddhism and psychotherapy have been presented in a couple of international conferences.

Robert D. Melara is a psychophysicist and cognitive neuroscientist with 30 years of experience as an investigator or normal and impaired processes of attention in humans. His research is supported by grants from NIDA, OASAS, and NCI. For the past 14 years he has served as the chairperson of the Department of Psychology at the City College of New York. In this capacity he has launched a host of local initiatives to facilitate the success of racial/ethnic minority students, including Student Success course where community college transfers to senior college explore the ways in which various psychological, societal, cultural, and historical factors impact their academic experiences and outcomes.

Prabal K. De is a Professor of Economics in the Colin Powell School of the City College and a doctoral faculty at the Graduate Center of the City University of New York. His recent research focuses on access, outcome, and disparities in health and education. His research has been funded by National Cancer Institute and the Murphy Institute, among others. He holds a Ph.D. in Economics from New York University.

References

Ackerman

Gross

(2003). Is time pressure all bad? Measuring between free time availability and student performance perceptions. Marketing Education Review, 13(2), 21–32. https://doi.org/10.1080/10528008.2003.11488825

Adams

Meyers

Beidas

(2016). The relationship between financial strain, perceived stress, psychological symptoms, and academic and social integration in undergraduate students. Journal of American College Health, 64(5), 362–370.

Adelman

(2006). The toolbox revisited: Paths to degree completion from high school through college . Office of Vocational and Adult Education, U.S. Department of Education. http://hdl.handle.net/2027/mdp.39015069291808

Ahmad

Anantharaman

Ismail

(2012). Students’ motivation, perceived environment and professional commitment: An application of Astin’s college impact Model. Accounting Education, 21(2), 187–208. https://doi.org/10.1080/09639284.2011.603472

Allen

(1992). The color of success: African-American college student outcomes at predominantly white and historically black public colleges and universities. Harvard Educational Review, 62(1), 26–44.

Allen

(1985). Black student, white campus: Structural, interpersonal, and psychological correlates of success. Journal of Negro Education, 54(2), 134–147. https://doi.org/10.2307/2294928

Arbona

Fan

Olvera

(2018). College stress, minority status stress, depression, grades, and persistence intentions among Hispanic female students: A mediation model. Hispanic Journal of Behavioral Sciences, 40(4), 414–430. https://doi.org/10.1177/0739986318799077

Arbona

Jimenez

Tracey

(2014). Minority stress, ethnic identity, and depression among Latino/a college students. Journal of Counseling Psychology, 61(1), 162–168. https://doi.org/10.1037/a0034914

Astin

A. W.

(1991). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. American Council on Education/Oryx Press.

10.

Astin

A. W.

(1993). What matters in college: Four critical years revisited. Jossey-Bass.

11.

Astin

A. W.

(1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25, 297–308.

12.

Attewell

Heil

Reisel

(2011). Competing explanations of undergraduate noncompletion. American Educational Research Journal, 48(3), 536–559. https://doi.org/10.3102/0002831210392018

13.

Ault

S. M.

(2017). Queens speak - A youth participatory action research project: Exploring critical post-traumatic growth among Black girls within the school to prison pipeline [Unpublished doctoral dissertation]. University of San Francisco. https://repository.usfca.edu/diss/348

14.

Bean

(1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187. http://dx.doi.org/10.1007/BF00976194

15.

Benítez

DeAro

(2004). Realizing student success at Hispanic‐serving institutions. New Directions for Community Colleges, 2004(127), 35–48.

16.

Berger

Braxton

(1998). Revising Tinto’s interactionalist theory of student departure through theory elaboration: Examining the role of organizational attributes in the persistence process. Research in Higher Education, 39(2), 103–119.

17.

Berger

Milem

(1999). The role of student involvement and perceptions of integration in a causal model of student persistence. Research in Higher Education, 40(6), 641–664.

18.

Bowen

W. G.

Chingos

McPherson

M. S.

(2009). Crossing the finish line: Completing college at America’s public universities. Princeton University Press.

19.

Bradburn

Carroll

(2002). Short-term enrollment in postsecondary education (NCES 2003-153), U.S. Department of Education.

20.

Buchman

DiPrete

(2006). The growing female advantage in college completion: The role of family background and academic achievement. American Sociological Review, 71(4), 515–541.

21.

Callahan

Ladue

Baber

Sexton

Van Der Hoeven Kraft

Zamani-Gallaher

(2017). Theoretical perspectives on increasing recruitment and retention of underrepresented students in the Geosciences. Journal of Geoscience Education, 65(4), 563–576. https://doi.org/10.5408/16-238.1

22.

Camburn

(1990). College completion among students from high schools located in large metropolitan areas. American Journal of Education, 98(4), 551–569. https://doi.org/10.1086/443976

23.

Chang

E. C.

Banks

K. H.

(2007). The color and texture of hope: Some preliminary findings and implications for hope theory and counseling among diverse racial/ethnic groups. Cultural Diversity and Ethnic Minority Psychology, 13(2), 94–103. https://doi.org/10.1037/1099-9809.13.2.94

24.

Chartrand

Harmon

(1992). An empirical test of a model of nontraditional student adjustment. Journal of Counseling Psychology, 39(2), 193–202. https://doi.org/10.1037/0022-0167.39.2.193

25.

Cohen

(1969). Immigrants and the schools: A review of research (ERIC-IRCD Urban Disadvantages Series, Number 8), Clearinghouse on Urban Disadvantaged, Teachers College, Columbia University.

26.

Contrada

Ashmore

Gary

Coups

Egeth

Sewell

, Ewell, K., Goyal, T., & Chasse

(2001). Measures of ethnicity‐related stress: Psychometric properties, ethnic group differences, and associations with well‐being 1. Journal of Applied Social Psychology, 31(9), 1775–1820. https://doi.org/10.1111/j.1559-1816.2001.tb00205.x

27.

Contreras

(2015). Raising the bar for Hispanic serving institution: An analysis of college completion and success rates, Journal of Hispanic Higher Education, 14(2), 151–170.

28.

DesJardins

Ahlburg

McCall

(2002). Simulating the longitudinal effects of changes in financial aid on student departure from College. The Journal of Human Resources, 37(3), 653–656.

29.

Durkheim

(1951). Suicide (J. A. Spaulding & G. Simpson, Trans.). The Free Press .

30.

Ewert

(2012). Fewer diplomas for men: The influence of college experiences on the gender gap in college graduation. The Journal of Higher Education, 83(6), 824–850. https://doi.org/10.1080/00221546.2012.11777271

31.

Fields

A. R.

(2014). The effects of systemic racism on the academic achievement of African American male adolescents [Unpublished doctoral dissertation]. Western Michigan University. https://scholarworks.wmich.edu/dissertations/374

32.

Fleming

(1984). Blacks in college (1st ed.). Jossey-Bass Publishers.

33.

Fletcher

Tienda

(2010

). Race and ethnic differences in college achievement: Does high school attended matter?

The Annals of the American Academy of Political and Social Science, 627(1), 144–166.

34.

Flores

Park

(2013). Race, ethnicity, and college success: Examining the continued significance of the minority-serving institution. Educational Researcher, 42(3), 115–128. http://www.jstor.org.proxy.libraries.rutgers.edu/stable/23462375

35.

Fuligni

Witkow

(2004). The postsecondary educational progress of youth from immigrant families. Journal of Research on Adolescence, 14(2), 159–183. https://doi.org/10.1111/j.1532-7795.2004.01402002.x

36.

Gloria

Castellanos

Lopez

Rosales

(2005). An examination of academic nonpersistence decisions of Latino undergraduates. Hispanic Journal of Behavioral Sciences, 27(2), 202–223. https://doi.org/10.1177/0739986305275098

37.

Gurin

Epps

(1975). Black consciousness, identity, and achievement: A study of students in historically Black colleges. Wiley.

38.

Horn

(2011). Tracking students to 200 percent of normal time: Effect on institutional graduation rates (Issue Brief- NCES 2011221). National Center for Education Statistics. U.S. Department of Education.

39.

Horn

Berger

(2004). College persistence on the rise? Changes in 5-year degree completion and postsecondary persistence rates between 1994 and 2000 (NCES 2005-156). U.S. Department of Education.

40.

Kuh

(2003). Diversity experiences and college student learning and personal development. Journal of College Student Development, 44(3), 320–334. https://doi.org/10.1353/csd.2003.0026

41.

St. John

E. P.

(2001). Student persistence in a public higher education system: Understanding racial and ethnic differences. Journal of Higher Education, 72(3), 265–286.

42.

Hurtado

Carter

(1997). Effects of college transition and perceptions of the campus racial climate on Latino college students’ sense of belonging. Sociology of Education, 70(4), 324–345.

43.

Hurtado

Kamimura

(2003). Latina/o retention in four-year institutions. In Castellanos, J and Jones, L (Eds.). The majority in the minority: Expanding representation of Latina/o faculty, administrators and students in higher education (pp. 139–150). Stylus.

44.

Hussar

Zhang

Hein

Wang

Roberts

Cui

Smith

Bullock Mann

Barmer

Dilig

(2020). The condition of education 2020 (NCES 2020-144). U.S. Department of Education. National Center for Education Statistics. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020144

45.

Ishitani

DesJardins

(2003). A longitudinal investigation of dropout from college in the United States. Journal of College Student Retention: Research, Theory & Practice, 4(2), 173–201.

46.

Kao

Thompson

(2003). Racial and ethnic stratification in educational achievement and attainment. Annual Review of Sociology, 29(2003), 417–442.

47.

Keller

Tillman

(2008). Post-secondary educational attainment of immigrant and native youth. Social Forces, 87(1), 121–152. https://doi.org/10.1353/sof.0.0104.

48.

Kim

Conrad

(2006). The impact of historically Black colleges and universities on the academic success of African-American students. Research in Higher Education, 47(4), 399–427.

49.

Martin

Midgley

(2010). Immigration in America 2010. Population Bulletin, I–6. http://search.proquest.com/docview/1041268594/

50.

McGlynn

A. P.

(2014, November 17). Degree attainment and college-going rates increase but gaps remain. The Hispanic Outlook in Higher Education, 25, 19–21. https://search-proquest com.proxy.libraries.rutgers.edu/docview/1628553737?accountid =13626

51.

Neville

Heppner

Thye

(2004). The relations among general and race-related stressors and psychoeducational adjustment in Black students attending predominantly White institutions. Journal of Black Studies, 34(4), 599–618.

52.

Pascarella

Terenzini

(1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. The Journal of Higher Education, 51(1), 60–75.

53.

Patterson

Krouse

Roy

(2012). Student outcomes of distance learning in nursing education: An integrative review. CIN: Computers, Informatics, Nursing, 30(9), 475–488. https://doi.org/10.1097/NXN.0b013e3182573ad4

54.

Peduzzi

Concato

Kemper

Holford.

Feinstein

(1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 49(12), 1373–1379.

55.

Perrine

R. M.

(1999). Stress and college persistence as a function of attachment style. Journal of the First Year Experience and Students in Transition, 11(1), 25–38.

56.

Petrie

T. A.

Stoever

(1997). Academic and nonacademic predictors of female student- athletes' academic performance. Journal of College Student Development, 38(6), 599–608.

57.

Rampell

(2013, February 19). College premium: Better pay, better prospects. http://economix.blogs.nytimes.com/2013/02/19/college-premium-better-pay-betterprospects/

58.

Rector

Johnson

Fagan

(2001). Understanding differences in Black and White child poverty rates. The Heritage Foundation.

59.

Renn

Reason

(2013). College students in the United States characteristics, experiences, and outcomes. Jossey-Bass.

60.

Rincón

Rincâon

(2008). Undocumented immigrants and higher education sí se puede! LFB Scholarly Pub Wells.

61.

Rodriguez

Myers

H. F.

Morris

J. K.

Cardoza

(2000). Latino college student adjustment: Does an increased presence offset minority status and acculturative stresses? Journal of Applied Social Psychology, 30(7), 1523–1550.

62.

Saldaña

(1994). Acculturative Stress: Minority status and distress. Hispanic Journal of Behavioral Sciences, 16(2), 116–128. https://doi.org/10.1177/07399863940162002

63.

Sandier

(2000a). Career decision-making self-efficacy, perceived stress Acculturative stress, minority status and distress, and an integrated model of student performance: A structural model of finances, attitudes, behavior, and career development. Research in Higher Education, 41(5), 537–578.

64.

Saunders-Scott

Braley

Stennes-Spidahl

(2017). Traditional and psychological factors associated with academic success: Investigating best predictors of college retention. Motivation and Emotion, 42(4), 459–465.

65.

Seidman

(2005). Minority student retention: Resources for practitioners. New Directions for Institutional Research, 125, 7–24.

66.

Shahid

Nelson

Cardemil

(2018). Lift every voice: Exploring the stressors and coping mechanisms of Black college women attending predominantly White institutions. Journal of Black Psychology, 44(1), 3–24.

67.

Shapiro

Dundar

Huie

Wakhungu

Yuan

, . . . . Hwang

A. (2017, April). Completing college: A national view of student attainment rates by race and ethnicity – Fall 2010 cohort (Signature Report No. 12b). National Student Clearinghouse Research Center.

68.

Shapiro

Dundar

Wakhungu

Yuan

Nathan

Hwang

(2016). Completing college: A national view of student attainment rates – Fall 2010 cohort (Signature Report No. 12). National Student Clearinghouse Research Center.

69.

Silverman

(2016). City facts fall 2017. https://www.ccny.cuny.edu/sites/default/files/institutionalresearch/upload/City_ Facts_Fall_2017_WEB.pdf

70.

Slanger

Berg

Fisk

Hanson

(2015). A longitudinal cohort study of student motivational factors related to academic success and retention using the college student inventory. Journal of College Student Retention: Research, Theory & Practice, 17(3), 278–302.

71.

Smedley

Myers

Harrell

(1993). Minority-status stresses and the college adjustment of ethnic minority freshmen. The Journal of Higher Education, 64(4), 434–452.

72.

Snyder

de Brey

Dillow

(2016). Digest of education statistics 2015 (NCES 2016-014). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

73.

Snyder

de Brey

Dillow

(2018). Digest of education statistics 2016 (NCES 2017-094). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

74.

Stoecker

Pascarella

E. T.

Wolfle

L. M.

(1988). Persistence in higher education: A 9-year test of a theoretical model. Journal of College Student Development, 29(3), 196–209.

75.

Stratton

O’Toole

Wetzel

(2007). Are the factors affecting dropout behavior related to initial enrollment intensity for college undergraduates? Research in Higher Education, 48(4), 453–485.

76.

Strodtbeck

Mcdonald

Rosen

(1957). Evaluation of occupations: A reflection of Jewish and Italian mobility differences. American Sociological Review, 22(2), 546–553. http://search.proquest.com/docview/1289866625/

77.

Tierney

(1999). Models of minority college-going and retention: Cultural integrity versus cultural suicide. Journal of Negro Education, 68(1), 80–91. https://doi.org/10.2307/2668211

78.

Tinto

(1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press

79.

Tinto

(1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. http://doi.org/10.3102/00346543045001089

80.

Tracking Black student graduation rates at HBCUs (2014, November 24). https://www.jbhe.com/2014/11/tracking-black-student-graduation-rates-at-hbcus/

81.

Umbach

P. D.

Kuh

G. D.

(2003, May). Student experiences with diversity at liberal arts colleges: Another claim for distinctiveness [Paper presentation]. Association for Institutional Research, Tampa, Florida, United States.

82.

U.S. Census Bureau. (2016, September 13). National average wage. https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-households.html

83.

Ward

Zarate

(2015). The influence of campus racial climate on graduate student attitudes about the benefits of diversity. The Review of Higher Education, 38(4), 589–617.

84.

Wei

Liao

(2011). Stress and college persistence attitudes among African American, Asian American, & Latino students: Perception of university environment as mediator. Cultural Diversity & Ethnic Minority Psychology, 17(2), 195–205.

85.

Wells

(2008). Social and cultural capital, race and ethnicity, and college student retention. Journal of College Student Retention: Research, Theory & Practice, 10(2), 103–128.

86.

Wells-Lawson

M. I.

(1994). The effects of race and type of institution on the college experiences of Black and White undergraduate students attending 30 predominantly Black and predominantly White colleges and universities [Paper presentation]. Annual Meeting of the American Educational Research Association, New Orleans, LA, United States.

87.

Whalen

Saunders

Shelley

(2010). Leveraging what we know to enhance short-term and long-term retention of university students. Journal of College Student Retention: Research, Theory & Practice, 11(3), 407–430.

88.

Zajacova

Lynch

Espenshade

(2005). Self-efficacy, stress, and academic success in college. Research in Higher Education, 46(6), 677–706. https://doi.org/10.1007/s11162-004-41

College Completion in a No-Racial/Ethnic Majority Campus

Abstract

Keywords

Introduction

Disparities in College Education

Purpose of the Current Study

Method

Participants

Measures

Data Analysis

Results

Discussion

Risk Factors in College Completion

Limitations and Directions for Future Research

Conclusions

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Supplemental material

Author Biographies

References