What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups



This longitudinal study examined factors that contribute to the persistence of underrepresented racial minority (URM) undergraduates in STEM fields. The primary source of data came from the Cooperative Institutional Research Program's 2004 The Freshman Survey (TFS) and 2008 College Senior Survey (CSS). The sample included 3,670 students at 217 institutions who indicated on the TFS that they intended to major in a STEM field, 1,634 of whom were underrepresented minority (URM) students. Findings indicate that Black and Latino undergraduates were significantly less likely to persist in STEM majors than were their White and Asian American counterparts. Background characteristics and college experiences moderated this race effect, suggesting both that pre-college factors may explain some of the observed racial disparities and that individual institutions can take more concrete actions to improve science achievement. Findings from the follow-up analysis of the sample of URMs suggest that institutions can improve URM STEM persistence by increasing the likelihood that those students will engage in key academic experiences: studying frequently with others, participating in undergraduate research, and involvement in academic clubs or organizations. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 51: 555–580, 2014

For nearly a decade, governmental agencies (e.g., AAAS, 2001 and National Academy of Sciences, National Academy of Engineering, & Institute of Medicine, 2007) have maintained that the productivity and strength of the U.S. economy will face a serious decline if no significant action is taken to address the racial disparities in the attainment of post-secondary degrees in science, technology, engineering, and mathematics (STEM) fields. The National Science Foundation (NSF, 2009) reported that although African American and Latino undergraduates were just as likely as their White counterparts to enter college with the intention to major in STEM, they were much less likely to earn a degree in those majors. A National Research Council (NRC, 2011) report, Expanding Underrepresented Minority Participation: America's Science and Technology Talent at the Crossroads, stated that most of the growth in the new jobs will require science and technology skills, and that “those groups that are most underrepresented in S&E are also the fastest growing in the general population” (p. 3). Indeed, they write, the proportion of underrepresented minorities in science and engineering would need to triple to match their share in the population. In an effort to achieve long-term parity in the preparation of a diverse workforce, the NAS recommends a near-term goal of doubling the number of underrepresented minorities receiving undergraduate STEM degrees.

As suggested by these reports, the underrepresentation of racial/ethnic minorities is not necessarily attributable to a lack of interest in science fields, but rather to poor degree completion rates. The latter pattern has been well documented. For example, Huang, Taddese, and Walter (2000) found that African American, Latino, and Native American students—or underrepresented racial minorities (URMs)—had lower persistence rates in science and engineering (26%) than their White and Asian American counterparts (46%). A more recent study conducted by the Higher Education Research Institute (2010) found that 33% of White and 42% of Asian American students at a national sample of institutions completed their bachelor's degree in STEM within 5 years of entering college, compared to only 18% of African American and 22% of Latino students. URMs, then, appear to face unique and persistent challenges when moving along the STEM pipeline. However, according to Mutegi (2013), science education research has been unable to explain how science degree achievement is “racially determined.” Although students' ability and pre-college preparation are important, steady progress through the STEM pipeline also depends on the types of opportunities, experiences, and support students receive while in college (Chang, Eagan, Lin, & Hurtado, 2011; Espinosa, 2011).

Institutions of higher education stand to play an important role in improving the participation rates of key segments of our nation's population in STEM-related careers and subsequently, also contribute to our nation's long-term welfare. Perhaps the most immediate and obvious role that they can play is to do a better job retaining the African American, Native American, and Latino undergraduates who enter college seeking a degree in a STEM field. In the interest of achieving that goal, the main purpose of this study was to identify key individual and institutional factors that either positively or negatively predict STEM degree persistence. We first examined whether or not a student's racial classification contributed to the chances of persisting in a STEM major after 4 years of college, and then examined the extent to which other factors moderated this race effect. To the extent that the effects of race can be explained by other factors, we examined patterns of preparation and experiences that contributed to the persistence of URM students.

Literature Review

Previous studies have identified a number of factors that contribute to the retention of undergraduates in 4-year colleges and universities in general (see for example Nora, Barlow, & Crisp, 2005; Tinto, 1993; Titus, 2004, 2006) and STEM fields in particular (Daempfle, 2003; Elliott, Strenta, Adair, Matier, & Scott, 1996; Grandy, 1998; Seymour & Hewitt, 1997; White, Altschuld, & Lee, 2006). Some of the factors that have been shown to contribute to STEM retention include students' high school academic preparation (Elliott et al., 1996), freshmen academic performance and enrollment in “gatekeeper” courses (Crisp, Nora, & Taggart, 2009), personal contact with faculty (Daempfle, 2003), degree of racial isolation (Seymour & Hewitt, 1997), exposure to racial minority support systems (Grandy, 1998), and an institution's level of selectivity (Chang, Cerna, Han, & Sáenz, 2008; Chang et al., 2011; Espinosa, 2011). Taken together, these studies suggest that completion of a STEM degree requires not only academic preparation but also resilience and capacity to negotiate a complex academic context.

In Seymour and Hewitt's (1997) seminal qualitative study of 425 undergraduates who entered seven universities as STEM majors, the experiences of students who changed majors were compared to the experiences of those who persisted. The researchers found that “the educational experience and the culture of the discipline (as reflected in the attitudes and practices of [STEM] faculty) make a much greater contribution to [STEM] attrition than the individual inadequacies of students or the appeal of other majors” (p. 392). We extend this work by increasing the number and variety of institutions that students attend, using quantitative methods, and by focusing on diversity among STEM aspirants and their achievement of major goals from college entry. Guiding our work are a recent set of studies that have helped to clarify the impact of unique experiences and circumstances on STEM degree completion for underrepresented racial minority (URM) students, as discussed below.

Key Individual Experiences and Institutional Factors

For URM students who pursue undergraduate studies in STEM fields, a combination of environmental and internal psychological factors has been shown to impact persistence. For example, Elliott et al. (1996) found that African American students had far less preparation in pre-college sciences, as demonstrated by lower rates of participating in AP Biology, Chemistry, Physics, and Calculus courses. As Russell and Atwater (2005) noted, a demonstrated competence in science and mathematics at the pre-college level is vital to African American students' successful progress through the science pipeline from high school to college. Other critical factors include receiving family support and teacher encouragement, developing intrinsic motivation, and maintaining perseverance. Likewise, the presence of family support and guidance from faculty mentors are associated with the development of greater academic self-efficacy and success in the sciences for Latino students (Anaya & Cole, 2001; Cole & Espinoza, 2008; Torres & Solberg, 2001).

Beyond individual factors, campus environments play an important role in improving undergraduate success in STEM fields. At the programmatic level, offering undergraduates research opportunities can make a difference not only in attracting and retaining STEM majors, but also in facilitating students' learning in the classroom by introducing them to the benefits of what science research careers might entail (Kinkead, 2003; Lopatto, 2003). URM students who participate in well-structured undergraduate research programs receive added benefits as well, including enhancing their knowledge and comprehension of science (Sabatini, 1997); clarifying graduate school or career plans in the sciences (Hurtado, Cabrera, Lin, Arellano, & Espinosa, 2009; Kardash, 2000; Sabatini, 1997); and obtaining other professional opportunities that develop their scientific self-efficacy (Gándara & Maxwell-Jolly, 1999; Hurtado et al., 2009; Mabrouk & Peters, 2000). According to Carlone and Johnson (2007), URM students who feel, think, behave, and are recognized by meaningful others (e.g., faculty role models) as “science people” are more confident about their academic abilities. Students of color who are more likely to consider science as an important aspect of their self-identity, are also more likely to persist in their STEM major (Chang et al., 2011; Espinosa, 2011).

Several recent studies further illuminate the individual experiences and institutional attributes that can significantly improve URM students' chances of completing an undergraduate degree in a STEM field. In one longitudinal study of biomedical and behavioral science aspirants, Hurtado et al. (2007) examined the impact of college on two key outcomesin the first year of college, sense of belonging and academic adjustment. They found that the relevance of coursework to students' lives had a positive impact on academic and social adjustment for White and Asian students as well as for URMs in the sciences. Although this finding underscores the importance of experiential learning and understanding the application of knowledge for all aspiring scientists, it may also confirm previous studies, which found that URM students often leave the sciences due to a perceived lack of relevance with their social values and desire to improve conditions for their communities (Bonous-Hammarth, 2000). Moreover, campus climate may be at play as well. Hurtado et al. found that perceptions of hostile racial climates were negatively associated with the sense of belonging of all students, whereas such climates hindered the academic adjustment of only URMs. Further, Hurtado et al. found that aspiring scientists of all racial groups were more affected by financial concerns than their non-science counterparts, but URM science students were most strongly affected by such concerns, which further inhibited both their academic and social adjustment. Other national research studies have also identified effects of the campus racial climate on retention in college (Museus, Nichols, & Lambert, 2008; Titus, 2006).

Similarly, in examining key factors that influence career aspirations in science research for students entering their first year of college, Oseguera, Hurtado, Denson, Cerna, and Sáenz (2006) found that entering URM undergraduates reported working more hours during high school and were more likely to expect to work full-time during college than their White and Asian counterparts. These authors argued that having financial concerns and misperceptions about the financial viability of research professions can deter students from choosing a career in STEM fields. In contrast, they also found that participating in research-oriented programs prior to college substantially increased entering URM freshmen's interests in pursuing a scientific research career. Reinforcing these findings, Adedokun, Bessenbacher, Parker, Kirkham, and Burgess (2013) found that research skills and research self-efficacy predict student aspirations for research careers.

Unfortunately, opportunities for participating in research seem to be harder to come by for URM students when in college. In another study drawing from a similar longitudinal data set, Hurtado et al. (2008) found that African American students have significantly lower odds of participating in health science research during college compared to their White counterparts. However, African American students that attended institutions offering formal health science research opportunities to first-year students were much four times more likely to participate in research than students at institutions without such programs. Similar to findings from other studies reported earlier, Hurtado et al. (2008) found that students' concerns about financing college had a significant influence on research participation. African American students who indicated having more serious financial concerns about paying for college were significantly less likely to participate in health science research than their peers who were less concerned about finances.

Another set of studies examined factors that contributed to persisting in a STEM major through a student's first-year of undergraduate study. Chang et al. (2008) found that aspiring to attain a graduate degree increased URM students' likelihood of staying in a biomedical science major through the first-year of college by over 30%. More impressively, joining a pre-professional or departmental club during a student's freshman year increased the likelihood of persisting by more than 150%. This study also found that a URM student had a 30% higher chance of departing from a science major if he or she attended an institution where the average undergraduate combined math and verbal SAT score (the “selectivity” of the institution) was 1,100, versus one with an average of 1,000. In addition, they also found that a 100-point average undergraduate SAT score increase at the institution-level lowered the chances of biomedical persistence by 20% for all students. Similarly, Espinosa (2011) found that after 4 years of college, for every 100 point increase in institutional selectivity, women of color in STEM were almost half as likely as White women to persist in a STEM major (7.6% compared with 14.0%). So, higher institutional selectivity (measured by student achievement scores) negatively affects all science students, but the effect is stronger for URM students. Curiously, this effect does not appear to apply to those students who attended historically Black colleges or universities (HBCUs) in the first year of college; for these students the opposite tended to occur. That is, as the average undergraduate combined SAT score increased, the chances of persisting in STEM for students attending HBCUs also tended to improve (Chang et al., 2008). These studies indicate the moderating effects of selective contexts associated with race and STEM persistence in the first year of college, and with gender in the fourth year of college. The current study extends this work by examining contextual selectivity effects for all STEM aspirants, especially URM students, to the fourth year of college.

One shortcoming concerning some of the studies reviewed here is that they employed single-level statistical techniques that did not account for the multi-level nature of the data (e.g., Cole & Espinoza, 2008; Elliott et al., 1996; Grandy, 1998). Data on the college student experience is by nature multi-level, as students are “nested” within institutions. Analytical techniques that do not account for this nesting are not only less robust but also risk drawing erroneous conclusions due to misestimated standard errors. The present study further adds to the existing literature on undergraduate STEM persistence by utilizing a more robust analytical technique that can account for the multi-level nature of student data. By accounting for the institutional characteristics and student level characteristics separately, this study can more accurately assess the important contextual factors contributing to STEM degree persistence.

Taken together, the findings reported above are nicely captured in Nora et al.'s (2005) model of student persistence and degree attainment. The Nora et al. model is a reformulation of the Tinto model (1993) that brings more clarity to the academic dimensions of the college environment while maintaining social and academic integration as a central tenet. Based on research on underrepresented groups of students, Nora et al.'s integration model includes many of the factors that are likely to influence minority, low-income, and non-traditional student populations in important ways—such as aspects of pre-college socialization environments (school and home environment), financial assistance/need, family support, environmental pull factors (family and work responsibilities), and commuting to college. In reference to the academic and social experiences in college, the model emphasizes such experiences as formal and informal academic interactions with faculty, campus climates (perceptions), validating experiences (from faculty and peers), and mentoring relationships (faculty, peer, and advising staff). The model also emphasizes academic performance, academic/intellectual development, and non-cognitive gains (in psychosocial domains) as intermediate outcomes, which in turn determine persistence in college. In the current study, we incorporated measures from many of these key areas to help explain persistence in STEM at the college students entered as first-time freshman.

Hurtado (2007) suggests that sociological models of college impact should include four measurable domains of institutional and normative constructs: characterizations of the environment focusing on student perceptions of their experiences within the social and academic systems of the collegiate environment; social interactions that capture both the frequency and quality of informal academic and social engagement in college; formal memberships based on both individual interest and how the group determines entry and confers privileges on its members; and perceived social cohesion or the students' own psychological sense of integration in the college community. In multi-institutional studies, it is thus important to include relevant structural characteristics that define distinctions between colleges, such as minority enrollment and selectivity, which may further account for probabilities of STEM retention in particular types of institutions.

Building on studies of STEM student persistence and transitions in the first year (Chang et al., 2008, 2011; Hurtado et al., 2007), we adopted key constructs based on the Nora et al. (2005) integration model to detail the link between persistence in STEM to the fourth-year of college and student experiences at multiple types of 4-year colleges. Specifically, we tested the hypothesis that STEM persistence is not only a function of the characteristics students bring at college entry, but is also affected by participation in formal structures that distinguish undergraduate experiences, the racial dynamics of a college, the continuing influence of family, financial concerns, and student assessments of their own development and competence in their identity as a scientist. We apply this understanding to examine the extent to which racial disparities in science achievement can be explained by those and other factors that can then be applied to improve URM STEM degree persistence.


Research Questions

This study was designed to examine individual and institutional factors that predict 4-year STEM degree persistence, with a focus on underrepresented racial minority students. Two main lines of questioning guided this study. Specifically, we asked:

  1. Among all students who started college with an interest in majoring in a STEM field, does a student's race contribute significantly to the chances that he or she will follow through on these intentions? If so, are the effects of race moderated by high school academic preparation and/or key college experiences?
  2. If there are racial disparities in persistence rates after controlling for pre-college student characteristics, what are the college factors that contribute to the persistence of URM students? That is, what college experiences and institutional characteristics significantly predict the likelihood that a URM student will follow through on his or her freshman year intentions to pursue a degree in STEM?

Data and Sample

Data for this study were drawn from the Cooperative Institutional Research Program (CIRP)'s 2004 The Freshman Survey (TFS) and 2007–2008 College Senior Survey (CSS). The CIRP is a program of data collection and research housed at the Higher Education Research Institute at the University of California, Los Angeles. The TFS and CSS are administered annually by CIRP to college students across the U.S., and each survey collects a wide variety of information about students (see Liu, Ruiz, DeAngelo, & Pryor, 2009 and Sax et al., 2004 for more information about these surveys). The 2004 TFS was administered to first-year students entering college in the summer/fall of 2004, either during freshman orientation or during the first few weeks of the fall term. The 2008 CSS followed up with this same group of students in the spring of or summer after their fourth year in college. The 2008 CSS data were linked to the 2004 TFS data to form a longitudinal dataset that tracked students over their first 4 years of college. The overall longitudinal response rate for the TFS-CSS was 23%. Response weights were applied to the data to reduce nonresponse bias, as is detailed in the analyses section. To the longitudinal database, we added institution-level data from the Integrated Postsecondary Education Data System (IPEDS), drawn from academic year 2006 to 2007.

Grants from the National Institutes of Health (NIH) and National Science Foundation (NSF) provided funds for a targeted sampling strategy for this study. In addition to the large number of institutions that typically participate in CIRP surveys, we supplemented the TFS sample using an NIH grant that allowed for the specific recruitment of students at minority-serving institutions that have strong reputations of graduating undergraduates in the biomedical and behavioral sciences. The funding also allowed us to target students at institutions that have NIH-funded undergraduate research programs. Additional funding from NSF allowed us to expand the regular CSS sample to include students at institutions that have strong reputations for producing bachelor's degrees in STEM. The overall goal of our data collection strategy was to obtain a large and diverse sample of students from underrepresented racial and ethnic groups who were interested in STEM, as well as a set of their White and Asian counterparts for comparison, in order to assess how science achievement is differentiated by race across different types of institutions.

The current study used two samples of students to answer its main research questions. For the first research question, which asked whether a student's race significantly predicts the likelihood of persisting in a STEM degree, we used all students who took both the TFS and CSS (at the same institution), and who indicated on the TFS that they intended to major in a STEM field. This sample included 3,670 students at 217 different institutions. One thousand five hundred twenty-two of the respondents (41.5%) identified as White (58.0% of whom were female), 498 (13.6%) as Asian (59.9% female), 812 (22.1%) as Latino/a (60.0% female), 626 (17.1%) as Black/African American (72.9% female), and 196 (5.3%) as Native American (55.9% female). Overall, the sample was approximately 61.3% female. For our second research question, we restricted the above sample to include only underrepresented racial minority (URM) students—that is, only students who indicated they were Native American, Latino/a or Black/African American. In total, 1,634 students were included in the URM subsample; 64.6% of these students were female. Appendix A shows descriptive statistics for URM students.


Nora et al.'s (2005) theoretical model and the research reviewed earlier informed the selection of the independent variables in the model (see Appendix A). The dependent variable used in this study was dichotomous and represented whether students who graduated or were still enrolled after 4 years of college had followed through with their first-year intentions to pursue a degree in a STEM field (coded 1), or whether they switched majors and completed or continued to pursue a degree in a non-STEM field (coded 0). The selected variables included student demographics and background characteristics, college experiences, and institutional characteristics. Student-level characteristics were grouped into several blocks to aid analysis and interpretation. Specifically, we grouped student background characteristics into (a) demographics (race, sex and socioeconomic status [as proxied by mother's education level]); (b) high school academic preparation (grades, SAT score, high school course taking patterns), and (c) other pre-college characteristics (including degree aspirations, concern about financing higher education, and student assessments of their academic and social strengths). Finally, all college experiences were included as one block, and within this block there were measures corresponding to students' interaction with faculty, assessments of the institutional climate, psychosocial concerns, and social and academic integration and involvement.

In addition to the student-level variables, institution-level variables were also modeled. These included institutional type and control (4-year/university, public/private), institutional selectivity (measured by the average math and verbal SAT score of entering freshmen), percent of students majoring in STEM fields, structural diversity (percent of student body that is Black, Native American or Latino/a), whether an institution is a historically Black college or university (HBCU), proportion of students receiving financial aid and/or federal aid, and institutional size (as measured by total undergraduate FTE).


Missing Data

Before conducting our main analyses, we first addressed missing data. We began by using listwise deletion to remove all cases for which no information was available on the outcome variable, demographic characteristics, and/or dichotomous college experiences (i.e., participation in undergraduate research programs or clubs relating to a major, working full time while in school). For the remaining variables in the model, we analyzed the extent to which missing data occurred. Overall, there was very little missing data. No variable had more than 6% of cases missing, and examination of missing data patterns suggested that missing data occurred at random. The SAT variable had the highest proportion of missing data, at 5.1%. Most variables were missing data in fewer than 1% of cases.

Given the relatively few instances of missing data across the variables used in the analysis, we elected to fill in missing data using the expectation maximization (EM) algorithm. The EM algorithm employs maximum likelihood estimation techniques to impute values for cases with missing data, and because it uses most of the information available in the dataset to produce the imputed values, it is a more robust method of dealing with missing data than listwise deletion or mean replacement (Allison, 2002; Dempster, Laird, & Rubin, 1977; McLachlan & Krishnan, 1997).


Because the longitudinal response rate for the TFS-CSS sample was only 23%, we calculated and applied response weights to the data to adjust for any non-response bias that might be present.1 The aim of this weighting was to adjust our CSS sample of respondents to resemble the original population targeted by the CSS—that is, the TFS participants (Babbie, 2001). All analyses performed for this study were conducted using weighted data.

Multivariate Analyses

The clustered, multi-level nature of our data and the dichotomous outcome variable warranted the use of hierarchical generalized linear modeling (HGLM). HGLM is an ideal statistical technique for this study, as it can separate individual and institutional effects to help determine how individual characteristics interact with institutional contexts to affect STEM major persistence. Further, performing single-level analyses with multi-level data can underestimate the standard errors of model parameters, which can inflate Type-I statistical error (de Leeuw & Meijer, 2008; Raudenbush & Bryk, 2002). To ensure the use of HGLM was justified, we first ran a fully unconditional model, or a model with no predictors specified at level one or level two. This unconditional model allowed us to assess whether there was significant variation in the outcome between institutions, via an examination of the variance of the random effect at level two. That is, the significance of the random effect variance at level two of the unconditional model (the between-institution variance component) allowed us to assess whether students' average probabilities of persisting in STEM majors significantly varied across institutions; if they did, the use of HLM would be justified. For both the whole sample and for the URM subsample, we confirmed that these institutional probabilities of persistence varied significantly, so we proceeded with our modeling course. All of our HGLM analyses were performed using HLM 6.0 (Raudenbush, Bryk, Cheong, & Congdon, 2004).

When using hierarchical modeling such as HGLM, researchers must make choices regarding the centering of variables. Because we were interested in the average effect of each predictor on students' likelihood of persisting in STEM, we chose to grand-mean center all continuous variables, as well as all ordinal categorical variables with more than two categories. Grand-mean centering subtracts the mean of the variable for the entire sample from each individual observation, and allows the model intercept to be more easily interpreted (Raudenbush & Bryk, 2002). Dichotomous variables were left un-centered. In order to easily interpret the results of the final model for URMs, we reported the HGLM results for significant predictors as delta-p statistics, calculated using the formula provided by Petersen (1985). These statistics represent the expected change in probability of persisting in a STEM major (vs. not persisting) that is associated with a one-unit change in the predictor variable. For dichotomous predictor variables, we calculated delta-p using the mean of the sample as an estimate of the probability of STEM persistence for the reference group. For all other predictors, we used the sample mean to estimate the probability of persisting when evaluated at the mean of the predictor variable. For these non-dichotomous predictors, the delta-p statistic represents the expected change in probability associated with one-unit change from the mean (Pampel, 2000).2

Full Sample HGLM Models

To address research question 1 we ran three models predicting STEM persistence using the full sample of undergraduates who indicated on the TFS that they intended to major in a STEM field. Although these models did not contain any level two predictors, they were run in HLM 6.0 to ensure that the clustering of students within institutions was taken into account, thereby avoiding misestimation of standard errors. In our first model, we utilized only race/ethnicity, gender, and mother's level of education (a proxy of socioeconomic status) to predict STEM persistence. In the second, we added variables to the model representing high school academic preparation—grades, SAT score, and indicators of years of STEM course-taking. Finally, we removed the high school preparation variables and added a set of variables representing experiences in college that may contribute to (or detract from) STEM persistence. The general form for each of the full-sample HGLM models was as follows:

display math(1)
display math(2)

where ϕij is the probability of persisting in STEM, there are i = 1, …, I level-1 units (students) nested within j = 1, …, I level-2 units (institutions), and there are Q predictors at level 1 and no predictors at level 2—that is, the level-1 effects are constant across level-2 units. Notationally, there are Q + 1 coefficients at level 1, and:

  • β0j is the intercept at level 1,
  • γq0 is the intercept at level 2;
  • βQj are level-1 coefficients for
  • XQij level-1 predictor variables; and
  • γq0 are level-2 fixed effects.

Appendix C lists each predictor that was included in the three models run on the full sample.

URM-Only HGLM Models

Our second research question concerned the impact of college on URM STEM persistence rates. The model building for this analysis proceeded in several stages, mirroring the full-sample analysis. Specifically, we first modeled STEM persistence with only student background characteristics as predictors. We then added college experiences, and finally institution-level characteristics. For the analyses without institution-level predictors, the general form of the HGLM models were the same as given above in Equations (1 & 2). For the final model, we allowed the intercept of level 1 (β0j) to vary, in order to examine how institutional contexts impact STEM persistence. The general form for the level-1 equation in our final model was the same as shown in Equation (1). The general form of the level-2 equation for the intercept β0j was:

display math(3)

where there are Sq predictors math formula and Sq + 1 coefficients math formula at level 2, uqj is the normally distributed level-2 error, and for all other level-2 predictors,

display math(4)

The specific predictors used in the final URM-only analysis are detailed in our results tables and in Appendix A.


Before presenting and interpreting the results of our analyses, it is important to note some of this study's limitations. First and foremost, our sample included only students who were still enrolled or were graduating after 4 years of college. In other words, students who withdrew or stopped out were not included in the sample, and thus our results apply only to those students who were successful in persisting in the same college they entered as freshmen–though not necessarily in their original intended major. In addition, our study had a relatively low longitudinal response rate (23%), and the extent to which our results are generalizable to a larger group of students may thus be limited. Although we attempted to correct for the nonresponse bias that may have been introduced by the low response rate, our correction was limited to the entering characteristics we had available on the TFS, and may not have taken all relevant factors into consideration. Further, our sample contained relatively few Native American students—just under 200—so our analyses had limited statistical power to detect significant results for this group.

Another limitation of our study is that we define “STEM persistence” as “following through on first-year intentions to major in STEM.” Entering freshmen who take the TFS may not have a good sense of all of the areas of study available to them, and thus some students who initially thought they would major in STEM may fail to follow through on these aspirations because they discovered another field that better aligned with their evolving interests. Still, because our findings (discussed below) mirror previous results, especially regarding differences in persistence rates by race, it appears that our calculation of STEM persistence is capturing an important aspect of movement in the STEM pipeline. More importantly, no other national data set contains measures of students' aspirations upon college entry, so we are uniquely positioned to understand loss of science talent prior to and after students select their major.


Descriptive Statistics: STEM Persistence

Among the aspiring scientists in our overall sample, 62.5% persisted in a STEM major to the fourth year of college. This rate was lower among URM students (58.4%) than it was among Asian American (73.5%) and White (63.5%) students. Disaggregating by URM groups, African Americans had the lowest rate of STEM major persistence (56.5%), followed by Latino/as (58.9%) and Native Americans (62.8%).

Full Sample HGLM Results

To answer research question 1, three separate hierarchical models were conducted using the full sample of undergraduates who indicated on the TFS that they intended to major in a STEM field (see summary of results in Table 1 and detailed results in Appendix B). Model 1 analyzed the effects of racial classification (i.e., self-identification as Native American, Latino, or African American) on 4-year STEM persistence, controlling for two key demographic characteristics (gender and mother's level of education). We found significant effects for both African Americans and Latinos; students who identified as being from either of these groups were significantly less likely to persist in STEM majors than were their White and Asian American counterparts. To examine whether these racial differences in STEM persistence were due to differential high school academic achievement and/or experiences, we next added a set of variables to the model representing high school academic preparation (grades, SAT score, and high school course taking patterns, Model 2). When the high school preparation and experience variables were included as predictors along with the demographic characteristics, the race variables were no longer statistically significant, suggesting that high school preparation moderates the effects of race.

Table 1. Hierarchical generalized linear modeling (HGLM) results for all students, looking at main effects of race
Model Number:All Students (N = 3,670)
  • *Effect significant, p < 0.05, see Appendix C for detailed statistics and lists of variables in each block.
Race main effects (Whites and Asians are reference group)Significant effects?*
Native AmericanaNoNoNo
Black/African AmericanaYesNoNo
Blocks of variables included in the model*
Other demographic characteristics (gender, mother's education)XXX
High school academic preparation X 
College experiences  X

We next examined whether experiences in college might also moderate the negative race effects observed in Model 1, by removing the high school preparation variables from the model and adding in college experiences (Model 3). Encouragingly, we found that college experiences also moderated the effects of race—after taking college experiences into account, the race variables did not exhibit a significant effect on STEM persistence. This indicates that colleges and universities can play a significant role in moderating racial disparities in STEM persistence.

To more accurately identify what key experiences might be for URM undergraduates, we conducted another set of analyses with the sample of only URM students. As described above, the model building for this analysis proceeded in several stages, with student background characteristics entered first, followed by college experiences, and finally by institution-level characteristics. As the results changed relatively little from one stage to the next, the HGLM model for URM students is presented only in its final form.

URM Subsample HGLM Results

Table 2 presents the results from the URM-only HGLM analysis examining the impact of background and college experiences on the likelihood of persisting in STEM majors. Focusing first on student demographic characteristics, we found no significant difference in the probability of STEM persistence when comparing Latinos and Native Americans to African Americans. Student gender and SES (proxied by mother's education) also had no significant effects. Among the high school academic preparation predictors, only one variable was significantly associated with STEM persistence for URM students: SAT score. For a 100-point increase over the mean in combined math and verbal SAT score, our model suggests that URM students will be 6.86 percentage points more likely to persist in a STEM degree. Curiously, high school GPA and the number of years students took mathematics, physical science, and/or biological science in high school did not significantly affect undergraduate STEM persistence over and above other predictors in the model.

Table 2. Hierarchical generalized linear modeling (HGLM) results for URM persistence in a science, technology, engineering or math (STEM) major
 URM Students Only (N = 1,634)
 Log OddsSEΔpSig.
  1. aReference group for race variables is Black/African American.
  2. bReference group for degree aspiration variables is Bachelor's degree only.
Demographic characteristics
Native Americana−0.160.25  
Student's gender−0.080.16  
Mother's Education0.040.04  
High school academic preparation
Average high school grade0.080.06  
Math + Verbal SAT score (in 100-point increments)
Physical science0.030.06  
Biological science−0.020.08  
Participated in Summer Research Program0.160.19  
Other pre-college characteristics
Intend to obtain Master's degree (M.A., M.S., etc.)b0.060.21  
Intend to obtain Ph.D. or Ed.D.b0.010.27  
Intend to obtain M.D., D.O., D.D.S., D.V.M.b−0.460.23−11.500.04
Entering science identity0.060.10  
TFS academic self-concept0.040.010.980.00
TFS social self-concept−0.030.01−0.810.00
Concern about financing college education0.140.12  
College experiences
Worked full-time while attending school−0.390.18−9.740.03
Felt family support to succeed−0.110.13  
Faculty mentoring factor−0.270.09−6.790.00
Asked a professor for advice outside of class−0.080.12  
Felt intimidated by your professors−0.130.13  
Faculty feel that most students here are well-prepared academically0.080.12  
Studied with other students0.600.1213.570.00
Sense of belonging0.170.09  
Positive cross racial interaction−0.120.09  
Negative cross racial interaction−0.210.11  
Hostile racial climate0.010.10  
There is strong competition among most of the students for high grades0.140.09  
Felt overwhelmed by all I had to do0.090.15  
Participated in an undergraduate research program (e.g., MARC, etc.)0.800.2217.380.00
Joined a club or organization related to your major0.400.149.320.01
Worked on a professor's research project0.190.12  
Institutional characteristics
Institutional control (Private keyed higher)0.390.32  
Institutional type (4-year keyed higher)0.070.27  
HBCU (HBCU's keyed higher)−0.200.76  
Selectivity in 100-point increments−0.520.12−13.000.00
Percent of students on financial aid (in 10-point increments)−0.030.06  
Percent of students receiving federal aid (in 10-point increments)−0.030.09  
Indicator of whether institution has any research expenditures (>$0)−0.010.26  
Percent of students majoring in STEM (in 10-point increments)
Percent of student body that is American Indian, Black, or Latino−0.060.10  
Log(Undergraduate FTE)−0.100.18  
Model statistics
Intercept reliability0.09   
Explained variance at level 20.694   
Baseline probability of STEM major persistence0.58   

Three more pre-college variables were significantly associated with the likelihood of persisting in a STEM major. The largest of these concerned the intent to obtain the following profession/clinical science-oriented degrees: M.D., D.O., D.D.S., and D.V.M. URM students who came to college with intentions of pursuing one of these professional degrees were 11.5 percentage points less likely to persist in a STEM field than were those who intended to obtain only a Bachelor's degree. URM students who came in with aspirations for a Master's degree or Ph.D./Ed.D., on the other hand, were no more or less likely than their Bachelor's-aspiring counterparts to persist in STEM majors. Entering students' academic and social self-concepts also significantly predicted the likelihood of STEM persistence. Having a higher academic self-concept when beginning college positively predicted persistence, whereas having a higher social self-concept negatively predicted persistence.

Five college experiences significantly predicted the likelihood of URM students following through on their freshman intentions to major in STEM. The strongest of these predictors was participation in an undergraduate research program. URM students who participated in programs that exposed them to research were 17.4 percentage points more likely to persist in STEM than those who did not. Similarly, a positive though less striking effect was also shown for joining a club or organization related to students' majors. Students who joined such organizations were 9.3 percentage points more likely to persist in STEM than those who did not. Likewise, URM students who studied more frequently with other students were also more likely to persist in STEM; we observed a 13.6 percentage point improvement in chances of persisting for a one-unit increase from the mean.

Two college experiences exhibited negative relationships with persistence. Students who worked full-time while attending school (at any point in their college career) were 9.7 percentage points less likely to follow through with intentions to major in STEM, compared to those who never worked full-time. Second, students who had more mentoring from faculty (as measured by our faculty mentoring factor, see Appendix B) were more likely to have chosen a major outside of STEM than were students who reported lower levels of mentoring activity with faculty. Because this finding seems counterintuitive, we examined it further and found that this factor had no impact until we controlled for student participation in a faculty member's research project. In other words, the initial relationship between the outcome and the faculty mentoring variable was non-significant, suggesting that students who persisted in STEM were just as likely as those who did not persist to participate in a range of mentoring activities with faculty. However, after controlling for participation in research, which is a major determinant for students persisting in STEM, we found that students who persisted in STEM tended to report lower rates of faculty mentoring activities than students who switched to non-STEM fields. Thus, our results seem to show that faculty mentoring on its own is not necessarily associated with STEM persistence, but rather that its relationship with persistence is contingent on whether or not the student participated in research.

At the institution level, two measures of college context were found to be significant predictors of STEM persistence. First, the proportion of the student body majoring in STEM fields at an institution significantly and positively contributed to the average likelihood of STEM persistence at that institution. For a 10-point increase from the mean proportion of undergraduates majoring in STEM, the average likelihood of a URM student persisting in STEM was predicted to increase by 5.6 percentage points. However, on the negative side, institutional selectivity, as measured by the average math plus verbal SAT scores of entering students, negatively predicted STEM persistence. For a 100-point increase from the mean institutional selectivity, the average likelihood of STEM major persistence dropped by 13.0 percentage points. We found no significant effects of institutional control, type, HBCU status, research expenditures, structural diversity, or size. The institutional predictors accounted for 69.4% of the between-institution variance in students' average probability of following through with their initial intentions to major in STEM.


Although the racial disparities in science achievement have been well documented, it is not altogether clear why this problem persists and what institutions of higher education can do about it (Mutegi, 2013). The findings from this study begin to untangle some of those issues and suggest that the effect of race on persistence in a STEM major is largely associated with unequal preparation and access to educational opportunities. While our findings show that identifying as African American or as Latino, compared to identifying as Asian American or White, was associated with a lower likelihood of STEM persistence, the negative effects of race were moderated by both pre-college characteristics and college experiences. In other words, addressing key factors either before students enter college or while they are in college can independently and significantly diminish the racial disparities in STEM achievement.

Regarding pre-college characteristics, we found that across a wide range of institutions, having higher SAT scores and a higher academic self-concept as an entering freshman contributed in positive ways to the persistence of underrepresented racial minority (URM) undergraduates (Latinos, Native Americans, & African Americans) in STEM fields. These findings are consistent with those of previous studies, which have pointed to the importance of students' high school academic preparation (Elliott et al., 1996; Russell & Atwater, 2005). Thus, our findings support ongoing calls for early intervention that build and sustain science self-efficacy. Those interventions have more positive long-term effects when they are reinforced with activities in college that ensure student success (e.g., bridge programs, supplemental instruction, and tutoring, and meta-cognitive study strategies) and assist students through introductory coursework. Further, enhanced support for performing well on tests in terms of curriculum content and test preparation are also likely to reduce the racial disparity in science achievement. Unfortunately, institutions of higher education are arguably less well-positioned to directly address the persistent racial disparities in high school preparation, so the discussion below emphasizes the findings from this study that can be applied more directly by colleges and universities. This focus offers a distinctive contribution to the developing literature and practices on STEM retention, particularly for underrepresented groups. The discussion will conclude with a set of recommendations tied to our findings.

One unique finding from our study regarding pre-college characteristics is that those entering URM freshmen who aspire to obtain a professional/clinical science-oriented degree (M.D., D.O., D.D.S., or D.V.M) were less likely to persist in STEM than were their counterparts who did not report the same degree intentions. This finding is especially noteworthy because professional/clinical degree aspirations are popular among URM students who chose STEM fields as freshmen (almost 30% of our URM STEM aspirant sample reported such degree aspirations at college entry). One explanation for this finding could be that interest in pursuing professional/clinical degrees, especially medicine, tend to be more competitive because those professions are widely viewed as socially prestigious and financially rewarding; they may thus attract and screen out more talent. Another possible explanation could be that entering freshmen interested in obtaining those types of degrees have only a superficial understanding of those related professions and that such aspirations signal a weak interest and commitment to science learning. It will be important to more closely monitor this group of students, as we point out in the recommendations that follow.

A number of our findings show that exposure to unique college experiences can make a significant positive difference in STEM degree attainment for URM undergraduates. Consistent with other studies, we found that URM students who participated in an undergraduate research program increased their chances of obtaining or continuing to progress toward completing a STEM degree, by an impressive 17.4 percentage points. According to Carlone and Johnson's (2007) qualitative study of women of color, students in research programs are typically given the opportunity to engage in the practical application of their coursework, which improves their degree of identification with science through “performance and competence.” As students feel more personally connected to STEM, Carlone and Johnson argue, they are more likely to persist in their respective majors. Students in research programs are also connected to committed faculty who nurture students in ways that enable them to take on progressively complex research tasks and subsequently identify as scientists (Hurtado et al., 2009).

We also found that those URM students who joined a club or organization related to their major significantly improved their chances of persisting. Clubs such as the National Society of Black Engineers (NSBE) and the Society of Women Engineers (SWE) are examples of undergraduate student organizations that revolve around particular STEM majors. These clubs provide their targeted membership with a wide range of academically enriching experiences, which promote socially and academically supportive networks. Perhaps by providing more opportunities to engage meaningfully with other students who share similar academic interests and trajectories, institutions also provide more opportunities for students to study together. Applying peer learning strategies, for example, have been found by Lopez, Nandagopal, Shavelson, Szu, and Penn (2013) to further improve academic performance in STEM courses. Overall, these findings confirm previous calls (Espinosa, 2011) to build a stronger social network of peers in STEM majors.

Further attention to student finances will also be key in organizing interventions for STEM degree persistence. We found that working full-time while attending school is negatively associated with URM students' chances of persisting in a STEM field. Other studies (DeAngelo, Franke, Hurtado, Pryor, & Tran, 2011; Titus, 2006) have indicated that having more financial concerns and fewer resources during college present obstacles for degree completion, but our finding uniquely show that this is also the case for URM STEM aspirants. While working full-time may relieve some financial burdens, it may also put greater strain on students' time to invest in their coursework and science career aspiration. It thus seems that STEM persistence interventions should be structured to relieve students of the burden of working full-time—for example, by providing stipends for work on research projects.

Curiously, our model showed that higher-levels of faculty mentorship were associated with a higher probability of switching to a non-STEM major. Upon further examination, this finding was explained by the fact that participating in faculty research was strongly associated with receiving mentorship; once research experiences were taken into account in our model, lower faculty mentoring was evident among URM STEM persisters. This suggests that faculty mentoring in its current form may be limited at many institutions to specific programs and research experiences. Perhaps for URM students who do not participate in research or special programs, the reason to seek mentoring from faculty might be related to difficulties with coursework; students having difficulties may be more likely to switch out of a STEM major. In any case, institutions will need to more carefully consider faculty's role—beyond providing research opportunities—in improving URM students' chances of achieving their intended academic goals, especially since faculty recognition is an important component of STEM identity development (Carlone & Johnson, 2007).

Lastly, we tested and advanced Seymour and Hewitt's (1997) claim that institutional context makes a difference in URM students' chances of following through with their intent to degree in a STEM field. As Seymour and Hewitt observed in their qualitative study, this effect may be related to the educational experiences and the culture of STEM disciplines, which are especially unique compared to other areas of study. Indeed, we confirmed the importance of institutional context using a much larger sample of STEM aspirants than previous studies, which enabled us to control for students' predispositions while examining variations across different contexts. We found that the percent of students at an institution who are majoring in a STEM field is positively associated with URM students' major persistence. Institutions with larger proportions of STEM majors might have stronger STEM cultures that may provide fewer distractions to, and better support students' STEM related career goals, thereby contributing to the likelihood of a student completing a STEM degree. We also found that the average student body SAT score, or college selectivity, had a negative effect on persisting in a STEM field. A similar effect was documented by Chang et al. (2008) on all first-year biomedical students including White and Asian students, and by Espinosa (2011) on both White women and women of color after 4 years of college.3 Given these findings concerning college selectivity, this issue may have more to do with the context of science instruction and progress within science majors at selective institutions than with the backgrounds of students. Although it is unclear how exactly selectivity or the proportion of science majors affects persistence, the effects are likely a function of the peer culture in science, institutional practices, and faculty roles as institutional agents that foster development. Our findings thus point to the importance of creating an institutional culture of commitment to and success in science.


While we confirmed that URM students are significantly less likely than their counterparts to persist in a STEM major, our findings also suggest several key moves that institutions can adopt to reduce racial disparities and increase the overall number of science degree recipients. Although URM students' pre-college characteristics, especially prior academic preparation, do make a difference in their chances of following through with the intent to complete a degree in a STEM field, we focus below on what colleges and universities can do once students begin their course of study.

First, institutions can provide URM students with more extracurricular opportunities to engage meaningfully in their chosen major. These opportunities can be in the form of well-structured research programs or student organizations that enable students to be more engaged, including major-related extracurricular activities, work on a professor's research project, peer study groups, or other social networks that support access to information and strategies for navigating a STEM major. To have even more of an impact, extracurricular major-related opportunities should also address financial concerns of students to obviate the need to work long hours at jobs off campus. Since engagement in research requires a high level of commitment, working too many hours for pay outside of those commitments can compromise students' level of engagement toward their degree aspiration. Both the National Institute of Health and the National Science Foundation have supported programs that build in these opportunities, including, for example, Minority Access to Research Careers (MARC), Minority Biomedical Research Support (MBRS), and Research Experiences for Undergraduates (REUs). Opportunities to “earn and learn” in STEM are ideal solutions for improving student success.

Second, faculty and departments should more closely monitor the success of those URM students who aspire to obtain a professional/clinical science-oriented graduate degree (M.D., D.O., D.D.S., or D.V.M). Since such degree aspirations are popular among URM undergraduates, yet are associated with lower rates of STEM degree completion, more effort should be made to retain URM students in STEM majors even if aspirations to an M.D., D.O., D.D.S., or D.V.M. are derailed. Perhaps institutions can better leverage professional/clinical degree aspirations to “hook” students into the sciences early on in their studies, and can then broaden their understanding about other careers for STEM graduates. Efforts related to providing more career guidance in STEM fields might improve students' willingness to continue working toward a STEM degree rather than leave the sciences altogether when they abandon their aspiration to pursue a professional/clinical degree.

Third, institutions need to examine more closely how their educational context contributes to or obstructs from completing a STEM degree. Highly selective institutions, ironically, have higher probabilities of degree completion but lower probabilities of STEM retention. By contrast, institutions that enroll large proportions of students who are STEM majors are graduating a larger proportion of those majors, and that success likely helps attract more students interested in pursuing STEM degrees. By encouraging educators to look more closely at different educational environments, and by having institutions engage in self-examination, we may be able to gain greater insights into how to develop stronger and more inclusive “cultures of science.” The complex dynamic of selectivity and STEM persistence should be of great concern because highly selective colleges presumably enroll the most promising STEM students, and typically have more resources to support student learning and advancement in science. One issue at selective institutions that needs more attention is whether and how courses taken during the early years of study (i.e., introductory chemistry, biology and calculus) promote STEM talent so that students persist to the degree. As it stands, introductory courses at more selective institutions tend to serve as “gatekeepers” that screen out more than nurture talent (Gasiewski, Eagan, Garcia, Hurtado, & Chang, 2012), and the courses often encourage competition that discourages students from continuing in the sciences.

Overall, our findings suggest that colleges and universities can make a significant difference in reducing racial disparities in science achievement and do not have to wait idly for high schools to send them more well-prepared students. Much more can be done at the undergraduate level to shape students' experiences and level of engagement in science, and to improve institutional circumstances for students, especially for those underrepresented students who may be diverted from their intended academic goals. The findings from this study identify promising areas for developing structured educational interventions that increase the number of underrepresented racial minority STEM degree recipients. In applying those interventions, institutions will need to pursue them with a long-term commitment in ways that are mindful of the institution's strengths and weaknesses (Henderson, Beach, & Finkelstein, 2011). Fortunately, some of our recommended interventions are already in place on some campuses, so a promising line of inquiry for future research will be to more closely examine their educational efficacy, inclusiveness, and impact on a broader range of students.

This research was supported in part by grants from the National Institute of General Medical Sciences (R01 GMO71968-01 & R01 GMO71968-05), and the National Science Foundation (0757076).


1Our response weights were calculated in two steps. In step 1, we used data from the National Student Clearinghouse and institutional registrars to identify the students that did not complete at least 4 years of higher education. We removed these students from the 2004 TFS data to make the initial sample consist of only those students who persisted for at least 4 years. In step 2, we used the persisting cohort of students and logistic regression to predict the probability of responding to the CSS. Predictor variables came from the 2004 TFS, and included indicators of race, gender, high school achievement, and reasons for attending college (a full list of variables in the model is available upon request). We then used the coefficients from the significant predictors in the model to calculate the probability that a student would respond to the CSS, and these response probabilities were inverted to develop response weights. The general formula for developing a non-response weight is: weight = 1/(probability of response). After calculating response weights, we compared the weighted and un-weighted samples from 2004 to determine whether our weights inappropriately skewed any of the 2004 Freshman Survey variables. After confirming that the weight had not adversely affected the distributions of variables from the 2004 Freshman Survey, we created a final weight that was normalized to account for sample size. This was calculated by dividing each student's response weight by the average population response rate in order to avoid inflating any statistics calculated using the weighted sample.

2As described in Pampel (2000), the expected difference in predicted probabilities for dichotomous predictors can be calculated as follows: (1) Compute the logged odds of the predicted logit for the reference group, L0 = ln(P0/(1 − P0)); we used the mean of the outcome variable for the whole sample as P0; (2) Compute the predicted logit for the keyed group, Lx, by adding L0 to the parameter estimate for the predictor of interest, bx (Lx = L0 + bx); (3) Use the logit Lx to compute the probability for the keyed group, math formula; (4) Subtract the difference in probabilities, Px − P0. For all non-dichotomous variables, we followed the same procedure but substituted the mean of the variable math formula for the reference group and math formula for the keyed group.

3We also found in our HGLM analyses (identical to the URM model, but not shown in this paper) that the effect of selectivity was negative and significant on all students. For a 100-point increase from the mean student body SAT score, the chances of persisting in a STEM field were reduced by over 11 percentage points for all students, including White and Asian American undergraduates. So, the negative effect of selectivity was not limited to just URM students but tends to influence all students.

Appendix A

Descriptive Statistics and Variable Coding (URM Students Only, Student N = 1,634; Institution N = 194)

  1. aIndicates multi-item factor, see Appendix B for more details.

Source: Cooperative Institutional Research Program 2004 Freshman Survey, 2008 College Senior Survey, and 2006 Integrated Postsecondary Data System.

Dependent variable
Persistence in a science, technology, engineering or math major through fourth year of college (0 = no, 1 = yes)0.580.4901
Independent variables
Student background characteristics
Native American (0 = no, 1 = yes)0.120.3301
Latino (0 = no, 1 = yes)0.500.5001
Black/African American (0 = no, 1 = yes)0.380.4901
Student's gender1.650.4812
35.4% Male (1)    
64.6% Female (2)    
Mother's education5.052.0718
7.3% Grammar or less (1)    
5.1% Some HS (2)    
16.4% HS graduate (3)    
4.1% Postsecondary (4)    
18.0% Some college (5)    
28.9% College grad (6)    
3.2% Some grad school (7)    
17.0% Grad degree (8)    
Average high school grade6.811.2528
0.0% D (1)    
0.6% C (2)    
1.0% C+ (3)    
3.1% B− (4)    
11.6% B (5)    
17.1% B+ (6)    
29.0% A− (7)    
37.6% A OR A+ (8)    
Math + Verbal SAT Score (in 100-point increments)11.441.756.116
Years of mathematics in high school5.990.5727
0.0% None (1)    
0.1% 1/2 year (2)    
0.5% 1 year (3)    
1.1% 2 years (4)    
8.5% 3 years (5)    
77.4% 4 years (6)    
12.3% 5+ years (7)    
Years of physical science in high school3.881.2717
6.1% None (1)    
2.7% 1/2 year (2)    
28.8% 1 year (3)    
34.4% 2 years (4)    
16.4% 3 years (5)    
10.5% 4 years (6)    
1.0% 5+ years (7)    
Years of biological science in high school3.741.0517
1.9% None (1)    
2.0% 1/2 year (2)    
43.5% 1 year (3)    
34.6% 2 years (4)    
10.0% 3 years (5)    
6.7% 4 years (6)    
1.3% 5+ years (7)    
Participated in Summer Research Program1.140.3512
85.7% No (1)    
14.3% Yes (2)    
Intend to obtain Master's degree (M.A., M.S., etc.)(0 = no, 1 = yes)0.220.4101
Intend to obtain Ph.D. or Ed.D. (0 = no, 1 = yes)0.290.4501
Intend to obtain M.D., D.O., D.D.S., D.V.M. (0 = no, 1 = yes)0.290.4501
Entering science identitya0.030.85−1.941.86
TFS academic self-concepta51.867.823.8666.92
TFS social self-concepta48.359.3518.0668.14
Concern about financing college education1.970.6313
21.9% None (1)    
59.7% Some (2)    
18.4% Major (3)    
College experiences
Worked full-time while attending school1.220.4112
78.3% No (1)    
21.7% Yes (2)    
Felt family support to succeed2.540.6313
7.6% Not at all (1)    
30.9% Occasionally (2)    
61.6% Frequently (3)    
Faculty mentoring factora−0.030.95−2.011.6
Asked a professor for advice outside of class2.010.6413
19.4% Not at all (1)    
59.3% Occasionally (2)    
21.2% Frequently (3)    
Felt intimidated by your professors1.660.6313
42.4% Not at all (1)    
49.3% Occasionally (2)    
8.3% Frequently (3)    
Faculty feel that most students here are well-prepared academically2.940.6314
1.9% Strongly disagree (1)    
17.5% Disagree (2)    
65.0% Agree (3)    
15.7% Strongly agree (4)    
Studied with other students2.430.5813
4.7% Not at all (1)    
47.0% Occasionally (2)    
48.2% Frequently (3)    
Sense of belonginga0.020.92−3.171.35
Positive cross racial interactiona0.130.91−2.61.4
Negative cross racial interactiona0.070.88−1.012.97
Hostile racial climatea0.160.85−1.312.59
There is strong competition among most of the students for high grades2.900.8014
3.3% Strongly disagree (1)    
27.8% Disagree (2)    
44.4% Agree (3)    
24.5% Strongly agree (4)    
Felt overwhelmed by all I had to do2.270.5813
6.8% Not at all (1)    
59.3% Occasionally (2)    
33.8% Frequently (3)    
Participated in an undergraduate research program (e.g. MARC, etc.)1.210.410.962
79.3% No (1)    
20.7% Yes (2)    
Joined a club or organization related to your major1.600.4912
39.9% No (1)    
60.1% Yes (2)    
Worked on a professor's research project1.510.7013
60.4% Not at all (1)    
27.5% Occasionally (2)    
12.1% Frequently (3)    
Institution-level variables
Institutional control1.560.5012
43.8% Public (1)    
56.2% Private (2)    
Institutional Type1.610.4912
39.2% University (1)    
60.8% Four-year (2)    
90.7% No (1)    
9.3% Yes (2)    
Selectivity (in 100-point increments)11.151.487.815.1
Percent of students on financial aid in 2006 (in 10-point increments)8.011.71010
Percent of students receiving federal aid in 2006 (in 10-point increments)2.781.8109.5
Any research expenditures in 20060.810.3901
Percent of students majoring in STEM in 2006 (in 10-point increments)1.661.5008.9
Percent of student body that is American Indian, Black or Latino in 20062.572.500.289.94
Log (Undergraduate FTE in 2006)8.620.94610.51

Appendix B

Hierarchical Generalized Linear Modeling (HGLM) Results for all students (N = 3,670) persistence in a Science, Technology, Engineering or Math (STEM) major

 Model 1Model 2Model 3
  1. aReference group for race variables is White/Asian.
  2. bReference group for degree aspiration variables is Bachelor's degree only.
Demographic characteristics
Native Americana−0.140.17−0.830.41−0.040.19−0.210.84−0.030.19−0.170.86
Student's gender−0.350.07−4.730.00−0.200.09−2.260.02−0.340.09−3.970.00
Mother's education0.070.023.350.
High school academic preparation
Average high school grade    
Math + Verbal SAT score (in 100-point increments)    
Physical science    
Biological science    
Participated in Summer Research Program    
Other pre-college characteristics
Intend to obtain Master's Degreeb    0.300.112.630.01    
Intend to obtain Ph.D. or Ed.D.b    
Intend to obtain M.D., D.O., D.D.S., D.V.M.b    −0.130.12−1.080.28    
Entering science identity    
TFS academic self-concept    
TFS social self-concept    −0.030.00−7.510.00    
Concern about financing college education    
College experiences
Worked full-time while attending school        −0.510.10−4.920.00
Felt family support to succeed        −0.070.07−1.100.27
Faculty mentoring factor        −0.120.06−2.120.03
Asked a professor for advice outside of class        −0.200.07−2.890.00
Felt intimidated by your professors        −0.070.07−1.000.32
Faculty feel that most students here are well-prepared academically
Studied with other students        0.490.077.220.00
Sense of belonging
Positive cross racial interaction
Negative cross racial interaction        −0.140.07−2.220.03
Hostile racial climate        −0.150.06−2.590.01
There is strong competition among most of the students for high grades
Felt overwhelmed by all I had to do
Participated in an undergraduate research program (e.g., MARC, etc.)        0.720.126.090.00
Joined a club or organization related to your major        0.390.094.260.00
Worked on a professor's research project        0.360.075.490.00

Appendix C

Multi-Item Factors

Scale & ItemsAll Students Factor LoadingsURM Students Factor Loadings


  1. The multi-item factors TFS Academic Self-Concept and Social Self-Concept were created by researchers at CIRP using Item Response Theory (IRT). Academic Self-Concept was constructed using items representing students' self-ratings of their Academic ability, Drive to achieve, Mathematical ability, and Self-confidence (intellectual). Social Self-Concept was constructed using items representing students' self-ratings of their Leadership; Public speaking ability, Self-confidence (social), and Popularity. See Sharkness, DeAngelo & Pryor (2010) and http://www.heri.ucla.edu/PDFs/constructs/Appendix2009.pdf for more details.

Source: Cooperative Institutional Research Program 2004 Freshman Survey and 2008 College Senior Surveys

Entering Science Identity (Freshman Year)aAlpha 0.673Alpha 0.653
Become authority in my own field0.5900.546
Obtain recognition from colleagues0.6940.632
Make theoretical contribution to science0.5790.586
Work to find cure for health problem0.4880.510
dAll items on a 4-point scale, 1 = not important, 4 = essential  
Faculty mentoringbAlpha 0.896Alpha 0.894
Encouragement to pursue graduate/professional study0.6920.684
An opportunity to work on a research project0.5900.567
Advice and guidance about your educational program0.7870.776
Emotional support and encouragement0.7470.753
A letter of recommendation0.6440.628
Help to improve your study skills0.6510.665
Feedback about your academic work (outside of grades)0.7240.722
An opportunity to discuss coursework outside of class0.6560.651
Help in achieving your professional goals0.8250.828
bAll items on a 3-point scale, 1 = not at all, 3 = frequently  
Sense of belongingcAlpha 0.879Alpha .882
I see myself as part of the campus community0.790.786
I feel I am a member of this college0.855.872
I feel I have a sense of belonging to this campus0.8840.881
cAll items on a 4-point scale, 1 = strongly disagree, 4 = strongly agree  
Positive cross-racial interactiondAlpha 0.897Alpha 0.891
Dined or shared a meal0.7790.746
Had meaningful and honest discussions about racial/ethnic relations0.7620.758
Shared personal feelings and problems0.8250.794
Had intellectual discussions outside of class0.8140.802
Studied or prepared for class0.7100.685
Socialized or partied0.7300.725
Attended events sponsored by other racial/ethnic groups0.6180.646
dAll items on a 5-point scale, 1 = never, 5 = very often  
Negative cross-racial interactioneAlpha 0.771Alpha 0.762
Had guarded interactions0.6610.670
Had tense, somewhat hostile interactions0.8100.795
Felt insulted or threatened because of your race/ethnicity0.7210.701
eAll items on a 5-point scale, 1 = never, 5 = very often  
Hostile Racial ClimatefAlpha 0.682Alpha 0.690
I have been singled out because of my race/ethnicity, gender, or sexual0.6980.728
I have heard faculty express stereotypes about racial/ethnic groups in class0.6570.623
There is a lot of racial tension on this campus0.5860.610
fAll items on a 4-point scale, 1 = strongly disagree, 4 = strongly agree