There are literally thousands of studies on retention efforts; however, the role of the built environment at the campus level is largely ignored. Using data from 103 universities in the United States with high research activities, we found strong positive associations between three campus qualities—(1) greenness, (2) urbanism, and (3) on-campus living—and student retention and graduation rates after controlling for student selectivity, class size, total undergraduate enrollment, and university type. Overall, this research provides new insight for university administrators, campus planners, and higher education researchers about the significance of the campus built environment in retention efforts.DOWNLOAD
Michael Haggans, a visiting professor in the Center for 21st Century Universities at Georgia Institute of Technology and visiting scholar in the School of Architecture at the University of Minnesota, recently interviewed Amir Hajrasouliha, one of the authors of this article. In the interview, available here, Hajrasouliha, an assistant professor at the Department of City and Regional Planning, Cal Poly State University, San Luis Obispo, discusses the research that led to this article and his conclusions.
Designers and planners believe that design matters and plans are helpful. That is why campus master plans, generally, recommend a set of design and planning actions intended to fulfill a university’s goals and objectives as a higher education institution. A review of different campus master plans shows undeniable similarities among their recommendations. However, the validity of the proposed recommendations has not been tested. Most publications about campus planning/design are by practitioners (Chapman 2006; Coulson, Roberts, and Taylor 2010, 2014; Dober 1996; Kenney, Dumont, and Kenney 2005; Toor and Havlick 2004), and few academic studies verify the default assumptions of campus planning practice. As Dober (1996, p. 12) observed, “Lacking an organized body of research or theory, campus planning is likely to be continued on a pragmatic basis.” Thus, among the many methods employed to foster learning, use of the physical environment is perhaps the most neglected. This research is an attempt to evaluate the role of the campus built environment in two major concerns of universities: student retention and graduation.
Student retention and graduation rates are currently among the most discussed topics in the field of higher education, and they are critical measures of the quality of higher education institutions. Retention and graduation rates are important for students, universities, and society as a whole. They can affect the self-esteem and future career of students, the economy and reputation of the institution, and, overall, the well-being of a generation. The statistics in the United States are not very promising. According to the National Center for Education Statistics (2015), institutional retention of first-time degree-seeking undergraduates at degree-granting postsecondary institutions was around 71 percent from 2006 to 2012. The 2012 graduation rate for first-time, full-time undergraduate students who began their pursuit of a bachelor’s degree at a four-year degree-granting institution and completed the degree within six years was 59 percent.
The literature on student retention focuses on different contributing factors, such as student engagement and involvement (Kuh 2001; Kuh et al. 2008; Quaye and Harper 2014; Roberts and McNeese 2010), student socioeconomic status (Ethington and Smart 1986; Lei and Chuang 2010; Naretto 1995), student expectations (Bank, Biddle, and Slavings 1992; Braxton, Vesper, and Hossler 1995), and institutional characteristics (Braxton and McClendon 2001; Eckles 2010; Lau 2003; Seidman 2005). However, the focus of the research presented here is aligned with the concept of a “supportive learning environment”  proposed by Kenney, Dumont, and Kenney (2005). The supportive learning environment extends beyond the classroom to embrace the entire educational environment. While “supportive learning environment” is a broad construct, the scope of this research is limited to the physical environment outside the classroom that is conducive to meeting students’ social and educational needs. This is understudied territory in the student retention literature and overlaps with the practice of campus planning and design.
Our research question is: Can the physical campus help universities achieve their retention and graduation objectives? There is no established theory in the field of higher education to answer this question, but there is a rich practical understanding among campus planners and designers about how to create a well-designed campus that can support a vital learning environment. To proceed with this research, we used the theoretical framework of the “well-designed campus” (Hajrasouliha 2015) to analyze campus form dimensions. Then, we modeled student retention and graduation rates in view of that framework.
A campus is not a city, a neighborhood, or a block. Therefore, describing and analyzing campus form should be different from analyzing other aspects of the built environment. To construct a theoretical framework for analyzing campus form, a content analysis of 50 randomly selected university campus master plans in the United States was conducted by Hajrasouliha (2015). This analysis showed that there are significant similarities among plans in terms of challenges, objectives, and recommendations. To avoid a subjective definition of the “well-designed” campus, the top 100 common recommendations in the selected master plans were identified. Based on these recommendations, seven dimensions of campus form were suggested:
According to these morphological dimensions, the well-designed campus is conceptualized as a mixed, compact, well-connected, well-structured, inhabited, and green campus in an urbanized setting (figure 1). These dimensions are measurable; therefore, it is possible to test their relationship to the desired outcomes quantitatively.
Figure 1 Morphological Dimensions of the University Campus
We operationalized the morphological dimensions of campus as described in figure 2. Five dimensions were operationalized quantitatively with one or more variables. However, we had to rate two, land-use organization and configuration, qualitatively. 
Figure 2 Operationalizing the Campus Morphological Dimensions
We used structural equation modeling (SEM) to model freshman retention rate and six-year graduation rate in terms of the campus morphological dimensions, accounting for a set of control variables.
This research involved universities in the United States with high or very high research activities according to the 2010 Carnegie Classification; there are a total of 206 such universities. We randomly selected 103 campuses for this research stratified by census regions—Northeast, South, Midwest, and West—and type—Research 1 (very high research activity) and Research 2 (high research activity). Universities that have more than one campus with campuses that are formally very different were not selected. The University of Michigan in Ann Arbor was the only case with this quality in the sample and therefore was replaced by another university.
We had two reasons to constrain our statistical population to research-intensive universities. First, it was essential to control for institutional type since retention rates vary significantly among different types of higher education institutions. Research-intensive universities tend to have a higher retention percentage compared to, for example, community colleges; yet, retention and graduation rates are major concerns for research-intensive universities. Second, in general, research-intensive universities have bigger and more complex campuses and are likely to invest more in their campus master plans. Therefore, the findings of this research may have a larger audience among research university administrators and planners.
For the selected sample, on average, the total enrollment in 2013 was 24,809 students and the campus size was 797 acres. Public universities made up 76 percent of the total; the median founding year was 1875. The average acceptance rate was 58.4 percent with a standard deviation of 23.2. The average freshman retention rate was 85.6 percent with a standard deviation of 9.2. And finally, the average six-year graduation rate was 68.4 percent with a standard deviation of 18.3. Although we focused only on research-intensive universities, there was enough variance in retention and graduation rates for our modeling purposes.
The first step in measuring the morphological dimensions of campus was mapping the figure-ground of all 103 campuses in ArcGIS. We used the base maps of OpenStreetMap in ArcGIS to map main physical features, such as building footprints, campus boundaries, surface parking, pitches, paths, and roads. We then used Google Earth images to increase the accuracy of the base maps. We used the spatial statistic tools in ArcGIS, Space Syntax software (for more information on Space Syntax see Hillier 2007; Hillier and Hanson 1984), and other techniques (described in figure 2) to measure the morphological dimensions. Overall, creating different analytical maps for each campus was the fundamental step in measuring the morphological dimensions. These maps were produced for all 103 cases.
The endogenous (outcome) variables in this study were freshman retention and six-year graduation rates in 2013. The source of these data was the National Center for Education Statistics. We also considered a number of control variables. For quantifying the quality of universities, we took into account student selectivity and university resources. As proxy variables, we used the most common measures in the literature, which are the percentage of classes with fewer than 20 students, the average faculty pay, and the average SAT score (Belfield and Bailey 2011; Black and Smith 2004, 2006; Black, Smith, and Daniel 2005; Daniel, Black, and Smith 1997). To control for institutional characteristics, we considered seven variables: age of university; campus size; research type (Research 1 = 1, Research 2 = 0); university type (dummies for public, private for-profit, private not-for-profit); enrollment profile classification from Carnegie Classification 2010; percentage of undergraduate enrollment; and average total indebtedness of 2013 graduating class from U.S. News & World Report to control for institutional affordability.
We also considered three variables to control for the contextual differences among universities: median household income 2009–2013 at the city level from the U.S. Census Bureau to control for the socioeconomic status of cities; heating and cooling degree days from NOAA’s National Climatic Data Center to control for climate; and crime rates of cities in 2013 from the FBI Uniform Crime Reports.
The modeling process had three steps: first, computing the seven morphological dimensions of the 103 university campuses and collecting the data on outcome and control variables; second, using SEM to identify the interactions among the morphological dimensions; and third, using SEM to evaluate the influence of campus form on the desired outcomes. Here, we present a brief definition of SEM and its application to this research.
Structural equation modeling is a powerful statistical tool because it can account for complex interrelationships among variables where some variables are both cause and effect. Byrne (2010, p. 3) explains the term “structural equation modelling” based on two important aspects of the procedure:
(a) that the causal processes under study are represented by a series of structural (i.e., regression) equations, and (b) that these structural relations can be modelled pictorially to enable a clearer conceptualization of the theory under study. The hypothesized model can then be tested statistically in a simultaneous analysis of the entire system of variables to determine the extent to which it is consistent with the data. If goodness-of-fit is adequate, the model argues for the plausibility of postulated relations among variables; if it is inadequate, the tenability of such relations is rejected.
Figure 3 shows the causal path diagram of one of the SEM models estimated in this research. Causal paths are represented by straight lines with an arrowhead pointing from the cause to the effect. Curved lines with arrowheads at both ends represent correlations. Rectangles represent observed variables. Ovals represent latent variables: variables that are not measured directly, but are estimated in the model from several measured variables.
Figure 3 Modeling Campus Form
We measured the seven morphological dimensions of 103 campuses with high research activities though 13 variables. Basic descriptive statistics—mean and standard deviation—show morphological differences among universities based on their region and type of institution. Specifically, on average, Research 1 (very high research activity) universities in the northeast region obtain superior values for most morphological measures (see figure 4).
Figure 4 The Mean and Standard Deviation of Campus Morphological Measures for All Samples and for Research 1 Universities in the Northeast Region
The interaction among the different morphological variables of university campuses has not been explored in prior studies. We used Amos 22, a SEM software, to model campus morphological dimensions with the observed variables described in figure 2. We created latent variables to represent morphological dimensions based on the proposed hypothesis. We had to slightly modify our original hypothesis to generate the best model (in terms of goodness-of-fit indices). We found significant interaction among the compactness, connectivity, and context dimensions. Instead of creating three distinct latent variables, all related observed variables could be loaded on a broader latent variable that represents the degree of urbanism of a campus. In other words, campuses that are more compact, better connected internally and to their surroundings, and located in a more urban context have a higher degree of urbanism. The other option that we had was creating a second-order latent variable of urbanism based on the three latent variables of compactness, connectivity, and context. However, the first option—directly loading observed variables on the urbanism latent variable—had a better model fit.
Figure 3 shows the path diagram of our proposed hypothesis.  On the left side, the interaction of all dimensions is presented. However, we found no significant interaction between two morphological dimensions (the two qualitatively rated dimensions, configuration and land-use organization) and either of the outcome variables (freshman retention rate and six-year graduation rate). Therefore, we decided to model campus form without these two dimensions. Although we tested for the reliability of these measurements, the possibility of a substantial measurement error contributing to the observed results is likely, since these two dimensions, unlike the other five, were rated qualitatively. It is also very much possible that these factors truly have no significant association with the outcome variables. On the right side of figure 3, the path diagram of the remaining three latent variables is presented.
We used maximum likelihood procedures for estimation and to evaluate model goodness of fit. Because of the relatively small number of sampled universities, we also conducted Bayesian estimates (Riginos and Grace 2008) using Amos for confirmatory purposes since these estimates do not depend on large-sample theory. While using maximum likelihood estimation generated a good model fit, using Bayesian estimates generated a good model fit only when the outliers were removed from the sample. 
The following results were obtained by removing outliers: The structural equation model obtained through maximum likelihood estimation had 29 degrees of freedom and a X2 value of 18.80 with a P value of 0.926. This P value, along with all model fit indicators (CFI is 1 and RMSEA is .000), indicates good model fit. In Bayesian estimation, the Posterior Predictive P value has to be close to 0.5 to have good model fit. This model had a Posterior Predictive P value of 0.51, which indicates good model fit. Figure 5 shows the regression weights in maximum likelihood and Bayesian estimation. All regression paths possessed coefficients with significance level of 0.05 or beyond in both maximum likelihood and Bayesian estimations. Coefficient estimates were close to each other with both techniques, which confirms the model.
Figure 5 The Regression Weights (Maximum Likelihood and Bayesian) in Modeling Campus Form
Figure 6 Modeling Students’ Satisfaction and Learning Outcomes
After modeling campus form through three distinct latent variables, we investigated the relationship between campus form, freshman retention rate, and six-year graduation rate. Figure 6 shows the path diagram of our model. The three latent variables—degree of urbanism, greenness, and campus living—were generated from 10 observed variables according to the model confirmed in the previous step. Similar to the previous step, we used a marker variable strategy to specify the scale of latent variables based on one observed variable. The latent variables are fixed to have means of 0, but their variances are not fixed.
Our hypothesis is that the morphological dimensions (latent variables) can have direct effects on students’ satisfaction with their college experience and their overall academic performance. Also, students’ satisfaction with their college experience can have a direct effect on their graduation. We considered four control variables for this model: (1) the total number of undergraduate enrollments to control for the size of the university; (2) the average SAT score to control for the student selectivity of the university; (3) the percentage of classes with fewer than 20 students to control for the faculty resources of the university; and (4) the Research 1 or Research 2 university dummy variable to control for the level of research activity. We also tested other control variables such as enrollment profile, university type (private or public), climate, crime rate, and the average total indebtedness of graduates; they had no significant effect on either outcome variable. In addition, we assumed that the exogenous variables are not orthogonal. Therefore, we estimated the covariance between all exogenous variables.
Because our sample size was relatively small, similar to the previous step we examined our model using both maximum likelihood and Bayesian estimations. The structural equation model obtained through maximum likelihood estimation had 71 degrees of freedom and a X2 value of 70.206 with a P value of 0.504. This P value, along with all model fit indicators (CFI is 1 and RMSEA is .000), indicates good model fit. The structural equation model obtained through Bayesian estimation had a Posterior Predictive P value of 0.45, which indicates good model fit as well.
Figure 7 shows the direct regression weights with both maximum likelihood and Bayesian estimation. The results show that all three campus form variables have a significant positive correlation with freshman retention rate. To the best of our knowledge, this is the first time that a significant correlation between the morphology of university campuses and freshman retention rates has been reported. One unit increase in the urbanism latent variable (with the range of 1.90) is associated with an increase in freshman retention of 4.8 percent. One unit increase in the greenness latent variable (with the range of 37.75) is associated with an increase in freshman retention of 0.2 percent. Also, one percent increase in on-campus residents is associated with an increase in freshman retention of almost 0.1 percent. Note that even a one percent increase in the freshman retention rate has an important effect, considering the fact that it may change the future of 200 people per year in a university with 20,000 students.
To the best of our knowledge, this is the first time that a significant correlation between the morphology of university campuses and freshman retention rates has been reported.
Figure 7 The Regression Weights (Maximum Likelihood and Bayesian) in Modeling Students’ Satisfaction and Learning Outcomes
The impact of the freshman retention rate on the six-year graduation rate is very strong and significant. A one percent increase in freshman retention can increase the six-year graduation rate by 1.355 percent. Since all variables (three latent variables and four control variables) showed significant impact on the freshman retention rate, and the freshman retention rate has a significant impact on the six-year graduation rate, we can conclude that all variables have a significant indirect impact on the six-year graduation rate. However, only two variables (greenness and campus living) other than freshman retention rate show a significant direct impact on the six-year graduation rate. The total standardized effects of campus living on the graduation rate is .315, and the total standardized effects of greenness is .292 (figure 8). A 10 percent increase in on-campus residents is associated with an increase in the six-year graduation rate of 2.43 percent, considering both direct and indirect effects. Also, a 10 unit increase in the greenness measure is associated with an increase in the six-year graduation rate of 5.58 percent, again considering both direct and indirect effects.
A 10 percent increase in on-campus residents is associated with an increase in the six-year graduation rate of 2.43 percent.
Figure 8 The Total Effects of Exogenous Variables on Six-Year Graduation Rate
It became clear from the literature review that although campus planning and design have received extensive attention from the profession in recent years, this field is understudied in academia. This research is an attempt to provide new insight into the field of campus planning, using the campus environment to address certain institutional missions. We used a sample of 103 research-intensive universities to highlight the association of three campus qualities—urbanism, greenness, and campus living—with the two major concerns of higher education institutions: retention and graduation rates. Because of the limitations of this study,  we are cautious about claiming causality between a “well-designed” campus and students’ retention and graduation. However, the strength of these associations is intriguing.
An interesting finding of this research is that although greenness and urbanism are negatively correlated with each other, both are positively associated with students’ satisfaction with their college experience, controlling for other university qualities. This finding can shed light on a classic debate among campus planners and designers: the dichotomy between a green and pastoral campus and an urban campus. The results show that campuses must have a fair amount of both qualities to get a high design score. A green campus can create a pleasant “college experience” and encourage students to spend time and “socialize” on campus. At the same time, an urban-feeling campus can act as a “supportive environment” for increasing students’ perception of “social connectedness.” These constructs were shown to be associated with student retention in previous studies (Ashar and Skenes 1993; Berger and Braxton 1998; Lounsbury and DeNeui 1995; Naretto 1995; Roberts and Styron 2010). It is important to note that greenness is measured in a quarter-mile buffer around campus buildings and not just on the campus grounds since accessibility is more important than ownership. Therefore, universities located in an urban setting should be sensitive to not only the greenness of their campus, but also the accessibility of local parks and green spaces. Likewise, universities with rural and suburban campuses should plan for and support more activities in their adjacent urban areas.
The other major finding is the strong association of on-campus living with student retention and graduation rates, after controlling for other influential factors. This finding is in accordance with previous studies that have shown that students who live on campus have a greater sense of community and higher retention rates (Lounsbury and DeNeui 1995; Thompson, Samiratedu, and Rafter 1993). As described in the results section, a 10 percent increase in on-campus residents is associated with an increase in the six-year graduation rate of 2.43 percent. This finding suggests that campus housing may not just provide a convenient residence for students, but also largely impact their quality of life and education. Most importantly, improving this aspect of campus form is more feasible and economical than improving greenness or urbanism. We should note that while the number of students living on campus is important, the quality of their living is even more so. While we could not measure the quality of students’ living in university housing on the selected campuses, in the reviewed master plans certain aspects were highlighted. For example, student housing should be close enough to the campus core to make it convenient for students to walk or bike to major campus destinations. Students should also have reasonable housing choices with respect to type, style, and cost. In addition, universities should pursue innovative living-learning communities (LLCs) as a recruitment and retention tool. For example, the University of Utah has launched a plan to recruit the “400 best student entrepreneurs” to live in a $45 million residential building starting fall 2016. The goal is to create a place where student entrepreneurs “live, create, launch.” 
Finally, it is important to understand that we have evaluated a number of broad qualities of the “well-designed” campus and not any specific recommendation. For example, this research does not specifically assess the effect of a recommendation such as “encouraging mixed-use development along a street corridor at the campus border;” however, this specific recommendation may increase the degree of urbanism on campus, which has proved to be a positive quality. If we want to further translate our findings into lessons for practitioners, we should stress those recommendations that are shown to have strong associations with students’ experience and performance. The first and foremost recommendation is to increase campus housing. The second recommendation is to decrease surface parking area, which can increase campus greenness or urbanism or both. And the third recommendation depends on the campus setting. For urban campuses, it is to invest in green spaces on and adjacent to campus. For suburban and rural campuses, it is to encourage infill and mixed-use development on or adjacent to campus.
Overall, the proposed theoretical framework can be related to different research topics in regard to university campuses. For example, research on the impact of university interventions in surrounding neighborhoods is limited. There have been some detailed case studies on campus expansion and neighborhood revitalization in the past decades; some of these projects were successful, some were not. However, whether the morphology of the campus, its surrounding neighborhood, and their physical interaction are the influential factors in the success of university interventions is an unexplored research area. To conduct systematic research in this area, the proposed theoretical framework for analyzing campus form can be applied.
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Amir H. Hajrasouliha, Ph.D., is an assistant professor at the Department of City and Regional Planning, Cal Poly State University, San Luis Obispo. He holds a bachelor’s of architecture from Beheshti University, master’s degrees in city planning and urban design from the University of Tehran and the University of Michigan, and a Ph.D. in urban planning and design from the University of Utah. His main research interest concerns campus planning and urban morphology, using qualitative and quantitative methods together.
Reid Ewing, Ph.D., is a professor of city and metropolitan planning at the University of Utah, associate editor of the Journal of the American Planning Association, and columnist for Planning magazine, writing the bi-monthly column “Research You Can Use.” He holds master’s degrees in engineering and city planning from Harvard University and a Ph.D. in urban planning and transportation systems from the Massachusetts Institute of Technology. A recent citation analysis by Virginia Tech found that his work is the sixth-most highly cited among more than 850 planning academics in the United States.