EDUCATION

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RR-1740-SSU 9 780833 096913

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Single Stop U.S.A.’s Community College Initiative was designed to improve the 

well-being of low-income communities by connecting individuals to public benefits 

and other institutional and community resources to address nonacademic barriers 

to college completion. Through offices located on community college campuses, 

Single Stop provides students with a range of free services, including screenings 

and applications for public benefit programs; tax services, financial counseling, 

and legal services; and case management with referrals to a wide variety of 

resources and support programs across the institution and community. This report 

presents an evaluation of the Single Stop program and its impact on students’ 

postsecondary outcomes. The authors examined the Single Stop program at four 

community college systems: Bunker Hill Community College, City University of 

New York, Delgado Community College, and Miami Dade College. The analysis 

indicates that use of Single Stop was associated with improved postsecondary 

outcomes. The findings suggest that access to alternative financial resources from 

government benefit programs alongside a network of institutional and community 

support programs can offer valuable support to college students. 
 

 

 

 

EDUCATION
 

Connecting College 

Students to Alternative 

Sources of Support 

The Single Stop Community College 

Initiative and Postsecondary Outcomes 

Lindsay Daugherty, William R. Johnston, Tiffany Tsai 

Sponsored by Single Stop U.S.A. 
 

 

 

 

 

 

 

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ISBN: 978-0-8330-9691-3 

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Preface
 

Higher education in the United States faces challenges surrounding 

low rates of degree completion, particularly among low-income populations. Students face unmet fnancial needs and a lack of support in 

such critical areas as transportation and child care that can deter their 

chances for success in college. Tis report examines the efectiveness 

of a program that aims to improve college success by facilitating access 

to wraparound supports and alternative sources of fnancial resources. 

Te report should be of interest to policymakers and institutions that 

are looking to programs that can better support college students and 

improve educational outcomes. 

Single Stop U.S.A.’s Community College Initiative assists college students—at no cost to them—with applications for public beneft programs and other wraparound services that can provide support 

for housing, food, taxes, child care, legal services, and other essential 

resources. Under a Social Incentive Fund (SIF) grant provided by the 

Corporation for National and Community Service to New Proft (an 

organization that provides fnancial support to Single Stop U.S.A.), 

Single Stop services were funded at four community college systems: 

Bunker Hill Community College, City University of New York (established as separate sites at six institutions), Delgado Community College, and Miami Dade College. Tis report serves as an independent 

impact evaluation of the SIF-funded program. We examine the relationship between use of Single Stop services and key postsecondary 

outcomes, including credit accumulation and persistence in college. 

A prior implementation study of the program was published by Sara 

iii 
iv Connecting College Students to Alternative Sources of Support 

Goldrick-Rab and colleagues in 2014 and should be referenced for 

fndings regarding implementation. 

Tis research has been conducted by RAND Education, a unit 

of the RAND Corporation, under a contract with Single Stop U.S.A. 

Funding to support the evaluation has been provided by New Proft 

under a Social Incentive Fund grant provided by the Corporation for 

National and Community Service. 
Contents
 

Preface ............................................................................. iii
 

Figures and Tables............................................................... vii
 

Summary .......................................................................... ix
 

Acknowledgments ............................................................. xvii
 

CHAPTER ONE 

Te Importance of Efective College Support Programs .................. 1
 

Te Role of Financial Assistance and Wraparound Service Programs in 


Supporting College Students ................................................ 1
 

Prior Research on Wraparound Support Programs and Financial 


Supports........................................................................ 5
 

Evaluating Single Stop’s Community College Initiative....................... 9
 

CHAPTER TWO 

Describing the Single Stop Program......................................... 11
 

Te Single Stop Model ........................................................... 11
 

Findings from a Prior Implementation Study.................................. 15
 

Variation Across Study Institutions............................................. 19
 

CHAPTER THREE 

Our Approach to Evaluating Single Stop ................................... 37
 

Data................................................................................. 37
 

Methods........................................................................... 44
 

v 
vi Connecting College Students to Alternative Sources of Support 

CHAPTER FOUR 

Single Stop and Postsecondary Outcomes .................................. 51
 

Assessing Baseline Equivalence.................................................. 51
 

Single Stop Use Is Associated with Improved Postsecondary Outcomes... 55
 

Positive Outcomes Are Larger for Certain Types of Students ............... 59
 

Postsecondary Outcomes Vary by Institution ................................. 61
 

CHAPTER FIVE 

Summary of Findings and Implications .................................... 63
 

Single Stop Use Is Associated with Positive Postsecondary Outcomes .... 64
 

Implications of the Findings for Policy and Practice ........................ 68
 

More Research Needed on Single Stop and Related Programs.............. 69
 

Abbreviations..................................................................... 73
 

References......................................................................... 75
 
 

 

 

 

 

 

 

 

 

 

 

 

Figures and Tables
 

Figures 

4.1. Predicted Persistence Rates for Single Stop Users and 


Nonusers .............................................................. 57
 

4.2. Predicted Credit Outcomes for Single Stop Users and 


Nonusers .............................................................. 59
 

Tables 

2.1.	 Characteristics of Study Institutions ............................. 20
 

2.2.	 Characteristics of Single Stop Sites ............................... 22
 

3.1. FTIC/Freshman Characteristics by School: Fall 2014 to 


Spring 2015 .......................................................... 42
 

4.1. Baseline Characteristics of All Single Stop Users Before 


and After Matching ................................................. 52
 

4.2. Baseline Characteristics of Students Receiving Beneft 


Screenings Before and After Matching............................ 53
 

4.3. Baseline Characteristics of Students Receiving Tax 


Services Before and After Matching.............................. 54
 

4.4.	 Estimates of the Relationship Between Single Stop Use 


and Persistence ...................................................... 56
 

4.5. Estimates of the Relationship Between Single Stop Use and 


Credit Accumulation ................................................ 58
 

4.6. Estimates of the Relationship Between Single Stop Use 


and Postsecondary Outcomes by Student Subgroup ........... 60
 

4.7. Estimates of the Relationship Between Single Stop Use and 


Postsecondary Outcomes by Institution .......................... 62
 

vii 

Summary
 

Te success rates of community college students are low, with fewer 

than one-third of students graduating or transferring within three 

years (U.S. Department of Education, National Center for Education 

Statistics, 2015). Community colleges are searching for ways to better 

support their students and improve success rates. Yet colleges are often 

ill-equipped to deal with the range of nonacademic barriers to college 

completion that their student populations face. Advising departments 

are underresourced and focused on academic issues (Gallagher, 2010; 

Key Findings 

• In a study of frst-time-in-college students at four community college systems during fall 2014, Single Stop use was associated with an increase in college persistence of at least 3 percentage points. 

• Single Stop users attempted more credits than comparable 

students who did not use Single Stop. 

• Use of Single Stop’s tax assistance services was associated 

with particularly positive outcomes in terms of persistence and 

credits earned. 

• Findings were particularly positive for Single Stop users who 

were adult learners (age 25 and older), independent students, 

and nonwhite students. 

• Single Stop use was associated with improved postsecondary 

outcomes at all but one of the institutions in the study. 

ix 
 

 

 

 

 

 

 

 

 

 

x Connecting College Students to Alternative Sources of Support 

Karp, 2013). Institutional and community support services are often 

dispersed, and it can be challenging for students to navigate the broad 

range of options (Karp, O’Gara, and Hughes, 2008; Nodine et al., 

2012). Many of the issues that students face are fnancial in nature, yet 

there are few programs that help students to access alternative sources of 

fnancial support through public beneft programs that provide access 

to food stamps, health care coverage, housing subsidies, and other 

essential resources. Financial programs, such as aid and tax credits, can 

help to cover costs but leave about half the price of college uncovered 

(Calahan and Perna, 2015). For students with low incomes, this unmet 

need can present a signifcant obstacle to attaining higher education 

and associated improvements in life circumstances. To address this 

shortcoming, there has been a call for improved access to and use of 

benefts among students (Goldrick-Rab, Broton, and Eisenberg, 2015; 

U.S. Department of Housing and Urban Development, 2015). 

Tis study examined Single Stop U.S.A.’s Community College 

Initiative, a program designed to improve the well-being of low-income 

communities by connecting individuals to public benefts and other 

institutional and community resources to address nonacademic barriers to college completion. Trough ofces located on community 

college campuses, Single Stop provides students with a range of free 

services, including screenings and applications for public beneft programs; tax services, fnancial counseling, and legal services; and case 

management with referrals to a wide variety of resources and support 

programs across the institution and community. Tis report presents 

an evaluation of the Single Stop program and its impact on students’ 

postsecondary outcomes. 

Programs that provide wraparound services (e.g., mental health 

counseling, social service programs) to college students have the potential to improve postsecondary outcomes by helping students address nonacademic barriers to success and facilitating access to alternative sources 

of fnancial support. Studies of wraparound support programs suggest 

that they lead to improved postsecondary outcomes (Castleman and 

Goodman, forthcoming; Scrivener et al., 2008; Scrivener et al., 2015). 

Te research also shows that fnancial support programs play an important role in student success (see Dynarski and Scott-Clayton, 2013, for 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Summary xi 

a review). In addition, programs that help college students access existing resources and complete important administrative requirements have 

been shown to positively afect student outcomes (Castleman and Page, 

2016; Castleman, Page, and Schooley, 2014). However, Single Stop’s 

program difers from these other programs in a number of important 

ways, including its accessibility to all students in an institution, its focus 

on nonacademic supports, and its particular focus on facilitating access 

to public benefts. Tis report helps to build evidence on an approach 

to student support that shows the potential to improve postsecondary 

outcomes for community college students. 

Study Approach 

We examined the Single Stop program at four community college 

systems: Bunker Hill Community College, City University of New 

York, Delgado Community College, and Miami Dade College. Our 

analysis—which used data on program use from Single Stop’s database and administrative data from the respective institution—focuses 

on frst-time-in-college students in fall 2014. We examined the 

relationship between Single Stop use and postsecondary outcomes 

through two methodological approaches: multiple regression and 

coarsened exact matching. Tese approaches allowed us to compare 

Single Stop users with their peers at the same institutions who are 

similar in terms of demographics and fnancial resources. We examined fve postsecondary outcomes: persistence into a second semester 

(one-term persistence), persistence into a second year (one-year persistence), credits attempted in the 2014–2015 academic year, credits 

earned in the 2014–2015 year, and ratio of earned to attempted credits. In addition, we analyzed outcomes for three defnitions of Single 

Stop use. We frst looked at all Single Stop users, individuals who 

were registered as clients with Single Stop. We then looked at individuals who received two of the primary services ofered by Single 

Stop, beneft screenings and tax services. In addition to examining 

the outcomes for all students in our sample, we conducted several 

subgroup analyses. We calculated institution-specifc estimates to 
xii Connecting College Students to Alternative Sources of Support 

determine whether the results are consistent despite variation in implementation and context. We also looked at particular student subgroups 

to determine whether the program impact is greater for certain types of 

students. Specifcally, we examined estimates for adult learners (25 or 

greater), students of varying races and ethnicities, and student dependent status. 

Single Stop Use and College Success 

Our analysis indicates that use of Single Stop was associated with 

improved postsecondary outcomes. Students who used Single Stop 

were more likely to persist into their second and third semesters of 

college relative to similar students who did not receive Single Stop services. Tese results were consistent across methodological approaches 

and robust to changes in model specifcations. Tere was also a positive 

association between Single Stop use and attempted and earned credits, 

with results varying somewhat across models. 

We examined outcomes for all Single Stop users, outcomes for 

individuals who received beneft screenings, and outcomes for individuals who received tax services. Across the full sample, the results 

were positive for all three groups of Single Stop users. Te results for 

students who received tax services were particularly large; students who 

used these services were estimated to persist at rates nearly 15 percentage points higher than comparable students who did not receive tax 

services. When we examined results by institution, the fndings were 

consistently positive for two of the institutions, mixed for one of the 

institutions (i.e., large, positive estimates for credits but no signifcant 

estimates for persistence), and null for one of the institutions (i.e., neither persistence nor credit estimates were statistically signifcant). Tese 

results suggest that aspects of implementation or unaccounted for differences in the student populations or other contextual factors might 

be related to the efectiveness of Single Stop in improving postsecondary outcomes. When we examined the estimates for various student 

subgroups, we found that the results were more positive and statistically signifcant for adult learners (ages 25 and older), independent stu­
Summary xiii 

dents, and nonwhite students. Tis aligned with expectations, because 

these students might have been more likely to qualify for public benefts and tax credits. 

Together, these fndings suggest that having a “one-stop shop” for 

nonacademic wraparound services and alternative sources of fnancial 

support can play a valuable role in promoting student success in college. Students can beneft from an ofce that assesses student needs, 

directs students to available resources, assists with application processes, and brings valuable services to campus. Te positive outcomes 

of the program were consistent across program services, suggesting that 

students might have benefted regardless of the particular services they 

used. Variation in outcomes across student subgroups suggests that 

programs such as Single Stop might be particularly benefcial to older, 

independent students and nonwhite students. However, variation in 

the results by institution suggests that implementation and institutional context might have an impact on the ability of the Single Stop 

program to deliver outcomes for students. Given limitations in data 

and research design, we could not pinpoint the reasons for diferences 

across institutions. 

Limitations and Future Research 

Our fndings provide evidence of a strong relationship between use 

of Single Stop and postsecondary outcomes and represent important 

preliminary evidence for the impact of this coordinated approach to 

wraparound services. We are hesitant to conclude that these estimates 

indicate that Single Stop caused the outcomes, because the methodology used in this study does not permit such causal inferences. We 

accounted for many of the relevant observable diferences between 

Single Stop users and nonusers in our methodological approaches, but 

there might be other factors that are related to Single Stop use and 

postsecondary outcomes that are not accounted for. For example, we 

were unable to account for motivation in our analysis, and we were 

limited in our ability to incorporate measures of fnancial need. Given 

these limitations, we are cautious in our interpretation of the fnd­
xiv Connecting College Students to Alternative Sources of Support 

ings. More-rigorous studies of impact can provide additional evidence 

on the efectiveness of programs like Single Stop; experimental studies of the Single Stop program are currently ongoing. In addition, the 

return on investment of the program should be assessed and compared 

with other programs intended to support college students to determine 

which programs should be scaled to improve postsecondary success 

rates. 

Single Stop’s Community College Initiative is consistently associated with positive outcomes for college students. A next step would be 

to investigate how the program achieves these outcomes. More research 

is needed to unpack the program’s mechanisms and understand how 

students are using various services and why the program might be 

efective. First, we are unable to examine some activities because of 

data limitations. Single Stop’s data system does not collect data on all 

activities provided by Single Stop staf, and other services are tracked 

with varying reliability, so we cannot examine the relationship of these 

services to student outcomes. A more complete accounting of activities 

would be valuable in identifying the relative efectiveness of diferent 

services and understanding how combinations of services may work 

together to improve outcomes. Tis research could help Single Stop 

and institutions to refne their eforts and focus on the services with 

the highest impact. 

It would also be useful to understand more about what the program delivers in terms of fnancial value to students. For example, 

Single Stop makes a strong efort to collect data on confrmed benefts for clients (e.g., beneft amount is reported through letters from 

some public beneft providers), but the total fnancial value is difcult to determine because not all clients who receive these benefts are 

confrmed in the system; therefore, the estimate might be lower than 

the actual value. Future analysis that includes government data on the 

recipients of public benefts would provide more-reliable evidence on 

the value of Single Stop services in terms of benefts delivered. Tese 

data would also improve analytic methods by allowing researchers to 

account for pretreatment receipt of benefts. 

Finally, it would be valuable to understand more about how implementation and context are related to outcomes. Single Stop’s national 
Summary xv 

ofce is providing sites with increasing autonomy, so implementing 

outreach and student services and integration into the institution may 

vary to a great extent. Additional cross-site analysis that incorporates 

measures of implementation could provide evidence around best practices that afect student outcomes. 

Acknowledgments
 

We would like to thank the sponsor of this project, Single Stop U.S.A. 

Funding for the project was provided through a grant from the Corporation for National and Community Service, distributed to Single 

Stop through New Proft. We appreciate the support of Single Stop 

staf with data transfer, program information, and project oversight. 

We also thank the institutional research departments at participating institutions for data transfer. We thank Ben Master, an external 

reviewer, and Cathy Stasz for their valuable feedback in reviewing this 

document. Te authors alone are responsible for any errors within. 

xvii 

CHAPTER ONE 

The Importance of Effective College Support 

Programs 

The Role of Financial Assistance and Wraparound Service 

Programs in Supporting College Students 

College completion is a challenge for the U.S. postsecondary system. 

Fewer than two-thirds of enrollees at four-year colleges graduate within 

six years, and fewer than one-third of students at two-year colleges 

graduate or transfer within three years (U.S. Department of Education, National Center for Education Statistics, 2015). Completion rates 

are a particular challenge for low-income students. Only 45 percent of 

college enrollees in the bottom income quartile attained a degree or 

certifcate within six years of enrollment, compared with 68 percent 

of college enrollees in the top income quartile (Radford et al., 2010). 

Although academic preparation plays an important role in determining 

whether students will succeed in college, students also face a number of 

nonacademic barriers to success. According to the Community College 

Research Center (2013), these nonacademic barriers include such concrete barriers as transportation issues and lack of child care, as well as 

more-subtle barriers, such as a lack of social capital to navigate college 

bureaucracies or a lack of confdence in one’s ability to succeed. 

Te departments responsible for advising and support services 

at many community colleges are not equipped to efectively serve the 

nonacademic needs of their students. Advising is often underresourced, 

with student-to-adviser ratios as high as 1,500 to 1 (Gallagher, 2010). 

Advisers are primarily trained to deal with academic issues, while such 

nonacademic support services as fnancial aid and mental health ser1 
2 Connecting College Students to Alternative Sources of Support 

vices are often managed under diferent departments and are scattered across ofces throughout the institution (Karp, 2013). Students 

and advisers struggle to navigate the complex web of resources that 

many institutions have available to meet the needs of low-income students (Karp, O’Gara, and Hughes, 2008; Nodine et al., 2012). Students might beneft from more-efective ways to access the full range 

of wraparound services (e.g., mental health counseling, social service 

programs) and programs that use a systematic approach to connect 

individuals to the resources they need. 

Many wraparound supports address fnancial issues for lowincome students, providing assistance with needs that include food, 

housing, health care, and transportation. Financial aid and tax credits play an important role in helping to cover costs, but national data 

indicate that grants and loans from federal, state, and local programs 

cover only 51 percent of the costs of college (Calahan and Perna, 2015). 

Students and their families are typically required to cover the remaining costs, and low-income families often have insufcient resources to 

cover the costs, referred to as unmet fnancial need. In 2012, students in 

the lowest income quartile faced an average of $8,221 in unmet fnancial need, compared with a surplus of $13,950 for students in the top 

income quartile, and these fnancial barriers for low-income students 

have been growing; the average amount of unmet need for the poorest 

students doubled between 1990 and 2012 (Calahan and Perna, 2015). 

Unmet fnancial need can negatively afect college success in 

several ways. If students are unable to cover the costs of tuition and 

other living expenses, then they may choose to reduce their level of 

enrollment or withdraw from college. Students who are facing fnancial barriers may also be more likely to seek outside employment while 

enrolled. More than half of all students enrolled at community colleges 

work more than 20 hours per week (Saunders, 2015). Studies suggest 

that outside employment during college is associated with lower levels 

of academic success (Ehrenberg and Sherman, 1987; Levin, MonteroHernandez, and Cerven, 2010; McCormick, Moore, and Kuh, 2010). 

Finally, students facing unmet fnancial need might also face challenges with meeting their basic living needs, such as housing and food. 

Tere are physical and psychological consequences associated with 
 

 

The Importance of Effective College Support Programs 3 

food and housing insecurity (Evans, Wells, and Moch, 2003; GoldrickRab, Broton, and Eisenberg, 2015; Melchior et al., 2009), which might 

harm academic performance. For example, Maroto, Snelling, and 

Linck (2015) found that food insecurity is associated with lower grade 

point averages (GPAs) among community college students. A 2010 

survey of City University of New York (CUNY) students found that 

more than two in fve reported housing-insecurity issues, including not 

having enough money to pay rent and being evicted (Tsui et al., 2011). 

Approximately the same number reported challenges with food insecurity, and many students reported facing both (Freudenberg et al., 2011). 

To address concerns about unmet fnancial need among college 

students, governments can ofer benefts that act as alternative sources 

of fnancial support. Public beneft programs that students could 

beneft from include (1) Supplemental Nutrition Assistance Program 

(SNAP), the primary food stamp program; (2) Women, Infants, and 

Children (WIC), a supplemental nutrition program for low-income 

pregnant women and young children; (3) Medicaid, health insurance 

for low-income individuals; (4) housing assistance through the Housing Choice Voucher Program; (5) Temporary Assistance for Needy 

Families (TANF), the cash assistance program commonly known as 

welfare; (6) the Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI) programs, which provide cash assistance to disabled individuals; (7) child care assistance; (8) unemployment insurance; (9) state and local transportation assistance programs 

for low-income and disabled individuals; (10) utility assistance for lowincome individuals through the Low Income Home Energy Assistance 

Program; and (11) tax credits, including the Earned Income Tax Credit 

for low-income individuals and the American Opportunity Tax Credit 

for college enrollees. Students who meet eligibility criteria and complete applications can receive cash and noncash benefts from these programs that can be used to cover college costs or other living expenses. 

However, despite the abundance and apparent value of public 

beneft programs as alternative sources of fnancial support, they are 

underutilized by students. A survey of ten community colleges across 

the country found that only one in fve students with low levels of food 

security received food stamps, and only one in fve students with low 
4 Connecting College Students to Alternative Sources of Support 

levels of housing security received public housing support (GoldrickRab, Broton, and Eisenberg, 2015). Underutilization of public benefts 

is not unique to college students; estimates indicate that a quarter of 

eligible families across the United States do not access any of the public 

benefts they qualify for (Zedlewski et al., 2006). 

Tere are a number of reasons individuals might not access public 

benefts, including ineligibility because of program constraints, a lack 

of information about eligibility and the processes for application, and 

stigma associated with the use of public benefts. Even when students 

are well informed about and eligible for public beneft programs, the 

process of registering for benefts can be challenging and resource 

intensive. Individuals might need to visit a number of diferent ofces 

and complete a range of applications, and low-income students who 

are busy with school, work, and other life responsibilities might not 

have the time to navigate these complex processes. To address barriers 

to the use of public benefts among college students, stakeholders have 

called for eforts to improve students’ access to benefts and increase 

use among students (Goldrick-Rab, Broton, and Eisenberg, 2015; U.S. 

Department of Housing and Urban Development, 2015). 

Single Stop U.S.A.’s Community College Initiative (hereafter 

referred to as Single Stop) is a program that was created to act as a onestop shop for wraparound services, with a particular focus on addressing, through public beneft programs, fnancial barriers that many 

low-income students face. By embedding ofces on community college 

campuses and stafng them with site coordinators who can provide 

diferentiated support based on student need, the program facilitates 

access to public beneft programs and other institutional and community resources. Single Stop’s primary services include assistance with 

screening and application for public beneft programs; provision of free 

tax services, fnancial counseling, and legal services; and case management to connect students to other programs and resources that are 

needed for support (e.g., mental health counseling, textbook-funding 

programs). Single Stop’s efort to ofer comprehensive beneft screenings to college students is unique; we are aware of no other programs 
 

 

 

 

 

 

 

 

 

 

The Importance of Effective College Support Programs 5 

that provide this level of service.1 And although colleges often ofer 

wraparound services, the Single Stop model is unique in pulling all of 

these resources together with the beneft screenings in a single place. 

By facilitating access to public beneft programs and institutional 

and community resources, Single Stop aims to address nonacademic 

barriers to college and help students feel supported. When fnancial 

barriers are overcome and students feel supported, we expect to see 

improved postsecondary outcomes, including increased course-taking 

and course success, as well as improved rates of college persistence 

and graduation. Single Stop also aims to more broadly enhance beneft access by providing individuals with lifelong knowledge and skills 

around beneft program use and encouraging the development of networks within colleges that are designed to better support a range of 

student needs. 

Prior Research on Wraparound Support Programs and 

Financial Supports 

Tere are many emerging programs that ofer wraparound supports 

for college students, and the evidence regarding the efectiveness of 

these programs is growing. For example, the Opening Doors intervention placed students in learning communities, provided students 

with fnancial aid, and enhanced student services through advising 

and monitoring. A series of experimental studies demonstrated that the 

program had positive impacts on time of enrollment and credit accumulation (Brock and Ritchburg-Hayes, 2006; Scrivener, Bloom, et al., 

2008; Scrivener and Au, 2007; Scrivener and Pih, 2007). Another 

1 Tere are a few programs at colleges that focus on improving access to a particular beneft, but these programs are distinct from Single Stop in that they do not ofer comprehensive 

benefts screening and focus on one need rather than attempting to act as a one-stop shop 

for many potential needs. An example of a narrower intervention focused on public benefts 

for college students, described by the U.S. Department of Housing and Urban Development 

(2015), is a partnership between the Tacoma Housing Authority and Tacoma Community 

College to provide housing to students who meet certain criteria. In addition to an exclusive 

focus on housing, the program is substantially smaller than the Single Stop program, serving 

just 21 families in the fall 2014 semester. 
6 Connecting College Students to Alternative Sources of Support 

multifaceted support intervention that was recently evaluated was the 

Accelerated Study in Associate Programs (ASAP) program, designed 

to enhance advising and fnancial aid resources, as well as requiring 

linked coursework and instruction in study skills. An experimental 

evaluation of the program conducted on CUNY campuses found that 

it led to increased credit accumulation and degree completion (Scrivener, Weiss, et al., 2015). 

A second set of recently evaluated programs initiate wraparound 

supports even earlier, targeting services to students in both high school 

and college. Te Bottom Line and College Possible programs provide 

one-on-one coaching at the high school and college levels around a 

range of academic and nonacademic topics, such as fnancial literacy, 

college application and transfer, and career search. Several evaluations 

of Bottom Line indicated positive impacts of the high school component on the decision to enroll, college choice, enrollment intensity, 

and persistence (Barr and Castleman, 2016; Castleman and Goodman, 

forthcoming). A recent experimental study of the College Possible program found similarly positive impacts on college enrollment for the 

high school component of the program (Avery, 2013). Tere has not 

yet been any research published on the components of these programs 

that serve college students. 

While wraparound support programs often focus on providing 

high-touch case management services to students, another group of 

emerging interventions are designed to support college enrollment and 

success through low-touch, low-cost information-sharing. Tese interventions rely on text messaging to target information to students at key 

times, encouraging students to engage in enrollment-related activities 

or reach out to student support providers for additional high-touch 

assistance. To the degree that these interventions can connect students 

to important wraparound services with the institution, they have the 

potential to address nonacademic barriers to success. In experimental 

studies, these text-messaging interventions have demonstrated positive 

impacts on enrollment and persistence (Castleman and Page, 2016; 

Castleman, Page, and Schooley, 2014). However, the evaluated interventions largely focused on preenrollment outreach and were primarily 
The Importance of Effective College Support Programs 7 

focused on admissions and enrollment requirements rather than connecting students to wraparound supports. 

Overall, the emerging research on programs that connect college 

students to wraparound supports indicate positive impacts on postsecondary outcomes. However, the programs evaluated in the literature 

difered in important ways from the Single Stop program. Programs 

such as Opening Doors and Bottom Line require applications from students and focus eforts on small cohorts of students who are applying to 

a number of diferent colleges. ASAP was also targeted to specifc students within the CUNY system, limited to full-time students with citizen status; summer assistance is budget dependent, and students must 

attend all ASAP programming to receive fnancial benefts. Students 

participating in these programs were required to meet such criteria as 

“being interested in enrolling in a four-year bachelor degree program” 

or being “enrolled full-time.” In contrast, Single Stop sites are often 

accessible to all students in a college. Marketing of the program and 

services may be targeted toward certain populations (e.g., frst-time-incollege [FTIC] students, low-income students, students in other support programs), but the program is intended to provide an ofce for 

support to all students rather than an intensive cohort-based support 

program. Another area of distinction between the programs described 

earlier is use of various supports. While the other programs focus on 

regular, ongoing support to students at similar levels of intensity across 

a cohort of participants, Single Stop is designed to provide services that 

vary in type and intensity on an as-needed basis. For example, some 

students may visit the ofce a single time and receive a referral for one 

particular need, while other students may regularly work with site staf

to apply for a range of beneft programs and address other potential 

needs. Finally, Single Stop focuses primarily on nonacademic wraparound support, while many of the previously evaluated programs provide support around academic and nonacademic issues. 

Because Single Stop’s wraparound supports are largely focused 

on addressing fnancial barriers to college success by providing students with access to public benefts, the broad literature on the role 

of fnancial support in improving postsecondary outcomes is also relevant. Our review of the research on state and federal fnancial aid pro­
 

 

 

8 Connecting College Students to Alternative Sources of Support 

grams found strong evidence regarding the impacts of fnancial aid on 

enrollment, with college enrollment rates increasing by approximately 

4 percent for every $1,000 of grant aid (Dynarski and Scott-Clayton, 

2013). Somewhat fewer studies have examined the impact of fnancial aid on postenrollment outcomes, such as persistence, credit accumulation, and graduation, and the fndings across studies are mixed. 

Some studies found no impact of fnancial aid programs on persistence (Bruce and Carruthers, 2014; Scott-Clayton, 2011; Sjoquist and 

Winters, 2012), while others found a positive impact (Bettinger, 2004; 

Dynarski, 2008; Goldrick-Rab et al., 2016; Richburg-Hayes et al., 

2009). With regard to credit accumulation and college GPA, several 

studies found positive impacts (Bartik, Hershbein, and Lachowska, 

2015; Scott-Clayton, 2011). Looking across studies, Dynarski and 

Scott-Clayton (2013) argued that the positive impacts of fnancial aid 

programs on postenrollment outcomes might be limited to those programs with explicit academic requirements. We might anticipate participation in public beneft programs such as Single Stop, as alternative 

sources of fnancial support, to have similar impacts on postsecondary 

outcomes. 

Te process of applying for public benefts can be complex, and 

one of the central services that Single Stop provides is assistance with 

public beneft enrollment. A recent study on the Free Application 

for Federal Student Aid (FAFSA) process provided evidence on the 

potential value of eforts to assist with complex application processes 

for government assistance. Bettinger et al. (2012) evaluated an H&R 

Block program that helped low-income families complete FAFSA as 

part of the company’s tax assistance services. By prepopulating information based on tax returns and walking students and their families 

through the forms, the program aimed to connect more students to 

the resources that were available to them. A randomized control study 

of the program found that it led to higher rates of Pell Grant receipt, 

higher rates of enrollment, and increased time in college (Bettinger 

et al., 2012). Single Stop aims to achieve similar outcomes through its 

simplifcation of the application process for public benefts. 

To our knowledge, there have been no studies that examine the 

impact of programs that facilitate access to public benefts for college 
 

 

 

 

 

 

 

 

 

 

 

 

 

The Importance of Effective College Support Programs 9 

students. Several recent reports described the Single Stop program 

and assessed the implementation of the program at various campuses 

(Goldrick-Rab, Broton, and Gates, 2013; Goldrick-Rab, Broton, and 

Frank, 2014). Researchers found that Single Stop users who received 

public benefts averaged approximately $5,400 in benefts received, 

but the studies did not attempt to attribute outcomes to Single Stop, 

and they did not examine postsecondary outcomes. Te U.S. Department of Housing and Urban Development identifed Single Stop’s 

program as a model that could be replicated to address beneft-access 

issues for students (U.S. Department of Housing and Urban Development, 2015). However, the report did not identify any research on the 

impacts of the program. 

Evaluating Single Stop’s Community College Initiative 

Tis study was conducted to assess the efectiveness of the Single Stop 

Community College Initiative. We examined whether students who 

accessed Single Stop services had improved postsecondary outcomes, 

adding to the broad literature on the impact of wraparound service 

programs and fnancial supports. Specifcally, we addressed the following research questions: 

1.	 Is use of Single Stop associated with improved postsecondary 

outcomes in terms of persistence and credit accumulation? 

2.	 Is use of a public beneft screening associated with improved 

postsecondary outcomes in terms of persistence and credit accumulation? 

3.	 Is use of tax services associated with improved postsecondary 

outcomes in terms of persistence and credit accumulation? 

4.	 Do the relationships between Single Stop use and postsecondary outcomes vary across student subgroups? 

5.	 Do the relationships between Single Stop use and postsecondary outcomes vary across institutions? 
10 Connecting College Students to Alternative Sources of Support 

Te analysis focused on FTIC students entering four community college systems in fall 2014: Bunker Hill Community College 

(BHCC), CUNY, Delgado Community College, and Miami Dade 

College (MDC). Single Stop has sites at six community college sites 

at CUNY and on three campuses at MDC, and we include all campuses with Single Stop sites in our analysis. We compared students who 

used Single Stop services with similar students who did not use Single 

Stop services through a matching approach. Te analysis included 

data from the Single Stop program, administrative data systems at the 

four community college systems, and national college data from the 

National Student Clearinghouse (NSC). We examined outcomes for 

three semesters (fall 2014 through fall 2015), including one-semester 

and one-year persistence, credits attempted and earned, and the ratio 

of earned to attempted credits. 

We next provide some additional detail about the Single Stop 

model and describe variation in implementation and context across 

the four community colleges. Tis is followed by a discussion of the 

data and methods for the study. After presenting the results, we conclude with a discussion of the implications of our fndings and study 

limitations. 
CHAPTER TWO 

Describing the Single Stop Program 

In this chapter, we start by describing the Single Stop model and summarizing fndings from a prior study that assessed the implementation of Single Stop (Goldrick-Rab, Broton, and Frank, 2014). We then 

discuss variation in context and implementation across Single Stop 

sites. Focusing on the four community college systems included in this 

study, we provide information about the student population, operational details for the sites, use of various Single Stop services, and the 

availability of benefts. 

The Single Stop Model 

Single Stop U.S.A. was ofcially established as a national organization 

in 2007, although the program began providing services nearly a decade 

earlier. Te program frst initiated sites in community-based organizations, such as food pantries and health clinics, and now operates more 

than 132 sites across the United States, serving more than 200,000 

individuals in 2015. In 2009, the program launched its Community 

College Initiative with just three pilot sites. Te colleges selected for the 

launch of the program were chosen based on characteristics of the student population, especially fnancial need and the size of the institution 

(Goldrick-Rab, Broton, and Frank, 2014). As the program matured, 

it served a broader set of institutions and expanded to 31 sites across 

nine states in 2015. Program data suggest that 41,760 households were 

served by Single Stop in the 2015 calendar year across the 31 sites. 

11 
12 Connecting College Students to Alternative Sources of Support 

Program Services 

Single Stop establishes an ofce on a college campus that is intended to 

provide a range of services at no cost to students, acting as a one-stop 

shop for support related to fnancial and nonfnancial needs. Students 

entering the ofce meet with a site coordinator who collects basic information about the student to assess needs and register the student in the 

program’s case management system. Te site coordinators then have 

information and tools and provide active and diferentiated case management to connect students to a wide range of programs and services, 

including the following: 

•	 Beneft screening and application: Te most common service 

provided by Single Stop is screening for public benefts through a 

tool called the Benefts Enrollment Network (BEN). By collecting some basic information from students, the tool can determine 

likely eligibility for a range of public programs, including TANF, 

SNAP, Medicare, and housing assistance programs. After determining whether students are eligible, site coordinators will work 

with students to prepare and submit applications and in some 

cases to confrm beneft receipt. 

•	 Tax preparation: Te Single Stop program has established relationships with local tax service providers to come to campus and 

provide one-on-one assistance with tax preparation for students. 

Tax assistance ensures that students maximize returns through 

the tax credits they qualify for (e.g., Earned Income Tax Credit). 

•	 Financial counseling: Similar to tax preparation, Single Stop 

contracts with fnancial counseling organizations to provide oneon-one assistance with fnancial planning services, working with 

students on such issues as debt reduction and credit improvement. 

In some cases, the fnancial advisers may also ofer group workshops on particular issues of interest. 

•	 Legal services: Local legal service providers also contract with 

Single Stop to provide one-on-one legal advising to students. 

Attorneys assist students with a range of noncriminal legal issues 

such as immigration, child support issues, eviction, and beneft 

cases. 
 

 

 

 

 

 

 

Describing the Single Stop Program 13 

•	 Case management and referrals to wraparound support services: Single Stop site coordinators aim to be broadly informed 

about other resources that are available to support students, 

including both on-campus resources such as counseling and 

fnancial aid and of-campus resources such as child care, transportation, and mental health services. Site coordinators can then 

refer students to these programs depending on needs. Beyond 

providing referrals, Single Stop ofces have facilitated the provision of particular on-campus services, such as food pantries and 

health insurance enrollment. 

Between 2010 and 2012, tax preparation services formed the 

greatest portion of confrmed benefts for students across Single Stop’s 

community college sites, accounting for approximately one-third of all 

confrmed benefts (Goldrick-Rab, Broton, and Gates, 2013). According to program staf, tax services are particularly valuable in terms of 

confrmed benefts because students save a potentially pricey tax preparation fee, and a substantial percentage of Single Stop users qualify for 

the Earned Income Tax Credit, allowing sites to confrm a substantial 

beneft and making it possible for students to complete the FAFSA and 

apply for fnancial aid. Other public beneft programs tend to have 

more eligibility and application requirements, and in many cases it can 

be hard for site coordinators to confrm that benefts have been received 

because the coordinators must rely on student self-reporting. Health 

insurance benefts accounted for the second-largest portion of Single 

Stop’s confrmed benefts between 2010 and 2012, followed by food 

stamps, legal services, and fnancial services (Goldrick-Rab, Broton, 

and Gates, 2013). 

Key Program Resources 

Site coordinators are a critical resource for the program and have 

responsibility for advising students, working with a network of 

institutional and community stakeholders to identify resources and 

market the program, and coordinating with the national ofce. 

According to Goldrick-Rab, Broton, and Frank (2014), these individuals are expected to have experience and education in social work or 
 

 

 

 

 

 

 

 

 

 

14 Connecting College Students to Alternative Sources of Support 

counseling, although they are not required to have experience working on college campuses. Other skills considered in the application 

process include communication skills, commitment to students with 

low incomes, experience with computers, and bilingual abilities. Site 

coordinators receive training from the national ofce on the specifc 

activities they engage in and strategies for success. Te trainings are 

workshop-based and interactive, focusing on opportunities to prepare 

site coordinators to deal with various scenarios that they might face 

with students (Goldrick-Rab, Broton, and Frank, 2014). 

Technology is another important resource for the Single Stop program, with BEN acting as a tool to facilitate case management and 

quickly screen students for a wide range of beneft programs. Students 

are entered into the BEN system the frst time they visit the Single Stop 

ofce or receive services from Single Stop. Te system tracks data on 

student characteristics, services provided and referrals made to services, 

and benefts confrmed. With regard to beneft screening, the system 

frst uses a short list of background questions to identify federal, state, 

and local benefts that an individual might qualify for. BEN then ofers 

more-detailed screenings for the beneft programs fagged in the initial 

screening. When students are identifed as eligible through the more 

detailed screening, site coordinators can work with them on application materials, and these can sometimes be fled electronically. 

Relationships with the institution and external stakeholders were 

also mentioned by Single Stop staf in a recent implementation report 

as a key resource (Goldrick-Rab, Broton, and Frank, 2014). Tese relationships serve two purposes: broadening the network of resources and 

support programs for students and providing marketing and outreach 

for the Single Stop program. Site coordinators work to build relationships with the institution, government agencies, and community organizations to identify additional resources for students and learn more 

about the programs. Tese eforts broaden the network of support 

and improve the guidance that coordinators are able to provide. With 

regard to marketing and outreach, Single Stop has focused on developing relationships with faculty, advisers, and other program staf in the 

institution. 
Describing the Single Stop Program 15 

Outreach to Students 

Students may hear about Single Stop through formal outreach or informal word of mouth from school staf and peers. Tese informational 

campaigns aim to inform students of the services provided by Single 

Stop and reduce the general stigma around the use of public benefts. 

To advertise the program, Single Stop posts signs and other program 

resources around the campus, uses social media, and holds informational sessions in classrooms and at school events, such as new student 

orientations. In addition, program staf network with instructors and 

campus staf (e.g., those in the registrar and fnancial aid ofces) to 

facilitate referrals to Single Stop. Some sites maintain outreach campaigns throughout the year, although many concentrate outreach at 

particular times, such as early in the semester (especially the fall semester) and at times when specifc services are particularly relevant, such 

as deadlines for tax fling and FAFSA submission (in April and June, 

respectively). 

Outreach is generally provided to all students, although some colleges specifcally target new students and students who might be more 

likely to be eligible for public benefts, including those in economicopportunity programs. However, there are no specifc requirements for 

meeting with a Single Stop site coordinator beyond enrollment in the 

college, and in some cases (e.g., tax services) Single Stop assistance is 

also available to an enrollee’s family. Depending on the service, the 

program may allow for both walk-ins and appointments, but there is 

always staf available during ofce hours to provide students with some 

level of assistance. 

Findings from a Prior Implementation Study 

An implementation study of Single Stop was conducted in winter 2013 

and spring 2014 across 11 sites, including sites at CUNY, Delgado, and 

MDC (Goldrick-Rab, Broton, and Frank, 2014). In addition to helping us describe the Single Stop program, the report had several fndings 

that were useful to consider in the design of our study and the inter­
 

 

 

 

 

 

 

 

 

 

 

16 Connecting College Students to Alternative Sources of Support 

pretation of results. We describe these important fndings below and 

discuss the implications for the study. 

Community colleges face substantial challenges with supporting the nonacademic needs of their students, especially those 

in poverty, and Single Stop was viewed as extremely valuable in 

providing a structured way of addressing these needs by connecting students to a network of resources and services. Goldrick-Rab, 

Broton, and Frank (2014) found that although institutions did have 

some resources available to support the nonacademic needs of their 

students prior to establishing a Single Stop site, they did not have a 

systematic way of pulling these resources together. By providing a onestop location for assistance, and by marketing the program broadly to 

students, Single Stop reduced barriers to accessing these wraparound 

supports. According to the researchers, “the greatest accolades for 

Single Stop came when people spoke of the program’s case management services,” whereby the individual needs of students were assessed 

and “triaged” with the appropriate supports. In addition to providing an essential service to individual students, school administrators 

felt that the Single Stop had helped their institutions to better understand the importance of wraparound supports for ensuring student 

success in college. Te fndings from Goldrick-Rab, Broton, and Frank 

(2014) indicated whether evidence on student outcomes aligned with 

the anecdotal evidence on Single Stop’s efectiveness. However, because 

we compared students within an institution who did and did not use 

Single Stop, we were unable to examine the institution-wide impacts of 

a program like Single Stop. 

Some stakeholders believe that case management services are 

extremely important and are underemphasized as a core component of the Single Stop model. Based on a long history of povertyreduction eforts, the national Single Stop ofce believes that the program will be most efective if site staf focus their eforts on ensuring 

that as many students as possible receive the formal services provided by 

Single Stop, especially beneft screenings. Te BEN system was designed 

with a focus on tracking these major services, and evaluation and funding were primarily tied to metrics on beneft screenings. Individualized 

case-management eforts, while useful, were reportedly viewed as less 
 

 

 

 

 

 

 

 

 

 

 

Describing the Single Stop Program 17 

cost-efective by the national ofce. Site staf and school administrators, 

on the other hand, reported concerns that case-management services 

had been undervalued and should be incorporated into evaluation and 

funding to a greater extent. In this study, we captured the value of casemanagement services in our primary measure of treatment as we examined outcomes for all students who interacted with Single Stop, averaging across various services received. We were able to examine outcomes 

for students who received two of Single Stop’s primary services—beneft 

screenings and tax assistance—but we were unable to assess the use and 

usefulness of case-management services because data are not systematically collected on these services in the BEN system. 

Variation in service delivery across institutions may have 

implications for evaluations of program impact. Although the 

national ofce trained site staf and set the standards for how services 

should be delivered in some respects, implementation of these services 

varied in some important ways that may drive variation in student outcomes across institutions. For example, some sites screened as many 

students as possible, regardless of whether the students were likely to be 

eligible for benefts, and this broad screening was often done by individuals with little training, as opposed to other core staf. Other sites 

only screened students who were likely to be eligible, and the screening was accompanied by a one-on-one discussion with a trained staf

member to provide case-management support. Te estimated impact 

of a beneft screening is likely to be larger in institutions where the 

services are targeted to students in need. We examine some of these 

areas of variation for our study institutions in the next section, but we 

are limited in our ability to relate outcomes to aspects of implementation because we were unable to collect implementation data for our 

institutions. 

Variation in data-collection practices across institutions may 

have implications for evaluations of program impact. GoldrickRab, Broton, and Frank (2014) found that sites used the BEN system 

in varying ways to track data. For example, some sites conducted initial 

beneft screenings on paper and did not enter students into the BEN 

system unless they were likely to be eligible for benefts. Institutions 

were incentivized to do this because they were sometimes evaluated 
18 Connecting College Students to Alternative Sources of Support 

and funded based on the proportion of screenings that resulted in confrmed benefts. Referrals were also reported diferently across institutions, with some institutions recording all referrals made and others 

reporting only those referrals where it could be confrmed that a student followed through. Te underreporting of beneft screenings and 

referrals means that some students may be included in the comparison 

group when in fact they did receive some level of treatment by Single 

Stop, and, to the degree that these students may have benefted from 

these services, estimates of the programs impact may be biased downward. In addition, it is unclear what treatment is being measured when 

these data elements represent diferent levels of treatment across institutions. In response to this fnding, the national Single Stop ofce provided additional training in summer 2014 to the institutions included 

in this study to ensure that data were being collected in a standard way. 

Given this, we have somewhat greater confdence in the quality of data 

collected in the 2014–2015 academic year for our study institutions. In 

the next chapter, we discuss several methodological decisions we made 

to address concerns about BEN data quality. 

Te Single Stop model is evolving as the program is scaled 

to a larger set of institutions. Tere were a number of changes to 

the Single Stop program that took place over recent years, including 

changes to the funding model, changes to training, and changes to the 

services provided. When the early Single Stop sites were established, the 

national ofce played a central role in identifying funding and holding 

sites accountable for meeting certain goals in terms of implementation 

and impact. Because of Single Stop’s role as an intermediary between 

funders and institutions, there were incentives to ensure that institutions were conforming to a common model and focusing on similar 

goals. As Single Stop grows to serve a larger group of institutions, these 

institutions are encouraged to take the leading role in establishing the 

sites, ensuring both sustainable funding and integration into the institution’s structure for providing student supports. In terms of training, 

the Single Stop model has moved to more of a consultancy, ofering 

a range of services to provide support to institutions as they establish 

and maintain their sites, as opposed to the previous focus on holding 

sites accountable to funders and monitoring performance. According 
Describing the Single Stop Program 19 

to the Goldrick-Rab, Broton, and Frank (2014), Single Stop was also 

adapting BEN and other services to more systematically service a larger 

group of students within each institution and track the broader range 

of case-management services that were provided at many sites. Our 

study focused on sites that were established early on under the initial 

models for funding, training, and services, so our fndings are specifc 

to this model. Additional research might be needed to determine how 

implementation and outcomes difer as Single Stop has evolved. 

Variation Across Study Institutions 

Te institutions in this study were expected to establish and run Single 

Stop sites according to standards and programmatic requirements set 

out in Single Stop’s site manual and their grant agreements. For example, the institutions needed to ofer beneft screening and some level of 

wraparound services support, and they were required to use the BEN 

system for screening and case management. Although the core services 

were provided at all of the institutions, the availability of services varied 

because of community resources and grant funding. Tere might also 

have been variation in student experiences across institutions because 

diferences in the context (e.g., student population, the generosity of 

state beneft programs), implementation (e.g., targeted outreach, stafing, ofce location), and access to additional wraparound services inside 

and outside the institution (e.g., grants for textbooks, food pantries). 

Although a full implementation study was outside the scope of 

our research, we did have access to some information that shed light 

on context and implementation across sites. To describe the institutional context, we used the Integrated Postsecondary Education Data 

System (IPEDS) to access data on student characteristics, college costs, 

and student success rates (Table 2.1). In addition, study data from the 

BEN system and institutions allowed us to describe how recipients 

of Single Stop services compared with the overall student population 

and to examine the use of specifc services among Single Stop users 

(Table 2.2). Finally, the national Single Stop ofce collected information from site coordinators in summer 2015 on a variety of diferent 
Table 2.1Characteristics of Study Institutions

CUNY 

Borough of CUNY CUNY CUNY CUNY 

Characteristic BHCC Manhattan Bronx Hostos Kingsborough LaGuardia 

Student characteristics

Total 14,253 26,606 11,506 6,985 17,758 20,231 

enrollment 

Female 57% 57% 56% 65% 54% 57% 

White 26% 10% 2% 2% 32% 11% 

Black 27% 27% 28% 26% 29% 18% 

Hispanic 23% 44% 64% 64% 21% 45% 

Other race/ 24% 19% 8% 8% 18% 26% ethnicity
 

Age 25+ 47% 22% 34% 30% 20% 29% 

Part-time 68% 36% 41% 42% 42% 46% enrollees 

Expenses and fnancial aid

Total expenses $15,984 $24,802 $24,838 $24,839 $24,834 $24,850 

(in state, without family) 
Table 2.1—Continued 

CUNY 

Borough of CUNY CUNY CUNY CUNY 

Characteristic BHCC Manhattan Bronx Hostos Kingsborough LaGuardia 

% receiving 51% 68% 68% 68% 49% 48% 

Pell Average Pell $3,233 $4,089 $3,934 $4,030 $4,048 $3,893 

received 

Success rates

2nd year 67% 65% 61% 60% 67% 66% 

retention full-
time students
 2nd year 52% 54% 50% 57% 47% 39% 

retention part-
time students
 

Graduation 11% 16% 11% 13% 24% 16% 

rate (150% 
time)
 Transfer-out 18% 18% 13% 11% 15% 12% rate
 

SOURCE: Data on student characteristics, expenses, fnancial aid, and success rates were obtaineDepartment of Education, National Center for Education Statistics). 
NOTES: Student characteristics refer to all enrollees in fall 2014. Expenses and fnancial aid refer Retention rates were calculated for FTIC students in fall 2013 returning in fall 2014. Graduation 

full-time students in the fall 2011 cohort and are three-year graduation rates. CUNY campuses areport as separate institutions. N/A = not available.
 
Table 2.2Characteristics of Single Stop Sites

CUNY 

Borough of CUNY CUNY CUNY CUNY 

Characteristics BHCC Manhattan Bronx Hostos Kingsborough LaGuardia Q

Student characteristics

Total Single 7,593 8,654 4,939 8,333 7,542 7,678 

Stop users Female 62% 63% 70% 69% 63% 62% 

White 18% 9% 1% 1% 20% 9% Black 36% 41% 37% 32% 48% 24% 

Hispanic 23% 33% 55% 59% 20% 44% Other race/ 23% 16% 7% 8% 13% 23% ethnicity
 Age 25+ 49% 47% 55% 60% 49% 57% Part-time 43% 21% 18% 24% 14% 19% 

enrollees 

Operational details

Program January January January January January January 

launch 2012 2010 2010 2010 2009 2010 

FTE core staff 1 2 1 1 1 1 

(spring 2015) 
Table 2.2—Continued 

CUNY 

Borough of CUNY CUNY CUNY CUNY 

Characteristics BHCC Manhattan Bronx Hostos Kingsborough LaGuardia Q

FTE support 2 2 2 2 1 1.5 staff (spring 
2015)
 

Offce hours 40 45 40 44 40 41 

per week
 

Total Single 3,371 2,613 1,055 2,933 2,587 2,517 

Stop users 
(summer 2014–
fall 2015)
 

Use of services(summer 2014–fall 2015)

Screened for 82% 89% 83% 83% 96% 84% 

benefts 

Received tax 45% 61% 49% 42% 34% 44% 

services Received legal 0% 10% 13% 10% 4% 6% services Received 7% 7% 11% 8% 5% 8% fnancial services Received 52% 71% 63% 88% 56% 70% 

referrals 
Table 2.2—Continued 

CUNY 

Borough of CUNY CUNY CUNY CUNY 

Characteristics BHCC Manhattan Bronx Hostos Kingsborough LaGuardia Q

State beneft program characteristics

Benefts 

generosity ranking Medicaideligibility limit, ages 19–20 

Medicaideligibility limit, ages 21–64 

Extensions to SNAP student eligibility 

2nd 4th 4th 4th 4th 4th 

highest highest highest highest highest highest 

150% 150% 150% 150% 150% 150% 

133% 133% 133% 133% 133% 133% 

Yes No No No No No 

SOURCES: Data on characteristics of Single Stop users were drawn from Single Stop’s BEN data sidentifed as a Single Stop client. Data on use of specifc Single Stop services were limited to new 

2014 to August 2015) to capture the suite of services obtained in the frst year as a Single Stop c

on state beneft programs were provided by Single Stop in fall 2015 and refer to the 2014–2015 

rankings come from Sauter, Hess, and Frohlich, 2014. 

NOTES: Percentages for the Medicaid eligibility limit refer to the maximum income allowable fo

federal poverty level. There are a number of federal extensions to SNAP student eligibility, such having a child. Extensions to SNAP student eligibility data refers to additional extensions offereextensions. FTE = full-time equivalent.

a Two of the three existing MDC sites opened in October 2010. The third site opened in Octobe
 

   

Describing the Single Stop Program 25 

aspects of implementation at the sites, and these site descriptions were 

used to provide additional context (Table 2.2). 

We summarize key details around context and implementation 

across our study institutions in four areas: 

•	 Population served: Characteristics of the student population 

served by a Single Stop site are determined by the makeup of 

the institutional population, the targeting of Single Stop services 

to particular groups of students, and individual decisions to seek 

Single Stop services. We present data on the characteristics of the 

overall student population and Single Stop users. We also discuss 

information provided by the sites regarding outreach and the targeting of services and the overall penetration rate of the program 

(i.e., the percentage of students served). 

•	 Operational details: Te quality and availability of Single Stop 

services might have varied across sites because of implementation 

decisions that include stafng, integration of Single Stop into the 

institution, and fdelity to the Single Stop operational standards 

set by the national ofce. Although we did not have the implementation data that allowed us to directly examine quality or 

fdelity of services, we provide available information on program 

history, stafng, and ofce hours. 

•	 Use of services: Although all Single Stop ofces provide the full 

range of services, some sites might have placed a greater emphasis 

on certain services, or students might have chosen to use Single 

Stop in diferent ways. We provide information on the proportion 

of Single Stop users reported to have received various services at 

each institution.1 

•	 Features of public beneft programs: Te features of public beneft programs varied across states and localities and might have 

afected the eligibility of college students, the generosity of benefts, and the complexity of processes undertaken to receive ben1 In Chapter Tree, we discuss concerns about potential data-quality issues around the 

tracking of particular services. 
26 Connecting College Students to Alternative Sources of Support 

efts. We provide some detail on eligibility and the generosity of 

benefts for the state in which an institution is located. 

Given data limitations and the design of the study, we were not 

able to attribute variation in outcomes to particular aspects of context or implementation. However, by highlighting potential sources 

of cross-institution variation, we can start to understand the degree 

of variation in context and implementation across Single Stop institutions, and this may help uncover potential explanations for variation 

in outcomes that can be explored in future research. In addition, this 

information sheds light on the representativeness of our study institutions of community colleges across the country. 

Bunker Hill Community College 

Population Served 

BHCC is a two-year public institution located in Massachusetts. As 

Table 2.1 indicates, enrollment was more than 14,000 students in fall 

2014. Te student population was diverse by race and ethnicity, and 

nearly half of the enrollees were adult learners (age 25 or older). More 

than two-thirds of students were enrolled part time, the highest of 

all institutions in the study. Te total estimated costs for all expenses 

in the 2014–2015 academic year were nearly $16,000. To help cover 

the costs of college, more than half of BHCC students received Pell 

Grants. Te average Pell Grant accounted for approximately 20 percent 

of total expenses. In 2014, the institution reported one-year persistence 

rates of 67 percent for full-time students and 52 percent for part-time 

students, as well as three-year graduation rates of 11 percent. 

According to Single Stop data, BHCC has served 7,593 Single 

Stop clients (Table 2.2). Single Stop users were similar to the overall 

student population in terms of gender and age but appear to be slightly 

more likely to be nonwhite and much more likely to be a full-time student. Te program served 3,371 new clients during the study period, 

representing nearly a quarter of all enrollees in 2014–2015. 
Describing the Single Stop Program 27 

Operational Details 

Te Single Stop program at BHCC was established in January 2012, 

so the program had been in place for a little more than two years when 

our study sample entered in fall 2014. Te program was housed under 

the Student Services Department, and the ofce was open Monday 

through Friday, 9 a.m. to 5 p.m., for a total of 40 hours per week. Te 

ofce had three full-time equivalent staf members, including one core 

staf member and two support staf. 

Outreach at BHCC was largely targeted to new students at the 

institution, as well as student leaders who could spread the word 

through peer networks and at college events. Te program also engaged 

with staf in other student support programs and faculty to facilitate 

referrals to the program. Program staf reported that referrals from faculty and other students were the most commonly reported way that 

students heard about the program. 

Use of Services 

Approximately 82 percent of Single Stop users were screened for benefts (Table 2.2). All beneft screenings at BHCC were done on-site, 

and screenings were spread evenly across the academic year. Fewer than 

half of all students received tax services. Data on legal and fnancial 

services and referrals might potentially be underreported across Single 

Stop sites, but more than half of all Single Stop users at BHCC were 

reported as having received at least one referral. Only 7 percent of new 

Single Stop users received fnancial services in 2014–2015, and no students were reported to have received legal services at BHCC during the 

study period. 

Features of Beneft Programs 

Massachusetts provided relatively generous benefts for its residents 

and open eligibility for the benefts when compared with other states. 

Income eligibility limits for SNAP, Medicaid, and TANF were relatively high. Restrictions on food stamps for students were lifted entirely 

for Massachusetts community college students as long as they were 

enrolled in a credit degree or certifcate program that the college determines will increase employability. Te state extended Medicaid eligibil­
28 Connecting College Students to Alternative Sources of Support 

ity to adults without children ages 19–64 and ofered a limited general 

assistance program to provide cash benefts to low-income individuals. 

City University of New York 

Population Served 

CUNY is a public college with seven two-year community college campuses, of which six had up-and-running Single Stop sites at the time 

this evaluation began (the seventh opened its Single Stop site in 2014). 

Across the six campuses, there were nearly 100,000 students enrolled 

in fall 2014. Te student populations varied by campus in race and 

ethnicity and age. For example, at Hostos, nearly two-thirds of students in fall 2014 were Hispanic, 2 percent were white, and 30 percent 

were adult learners (see Table 2.1). Race and ethnicity at Kingsborough 

were more mixed, and only 20 percent of students were adult learners. 

Approximately 40 percent of students were enrolled part time across 

campuses. Total expenses for in-state CUNY students living without 

their families were nearly $25,000. Te percentage of students using 

Pell Grants varied from 48 percent at CUNY LaGuardia to 68 percent 

at several other CUNY campuses. On-time graduation rates within 

three years varied from 13 to 24 percent across campuses, and transfer 

rates also varied. 

According to Single Stop data, the CUNY sites have served more 

than 42,000 clients (Table 2.2). Across CUNY campuses, Single Stop 

users were more likely to be female, more likely to be black and less 

likely to be Hispanic, and more likely to be age 25 or older. Single Stop 

users were also less likely to be part-time students across campuses. 

Approximately 13,000 new students were served by the program in 

2014–2015, representing approximately 13 percent of all enrollees at 

the CUNY campuses with Single Stop sites. Penetration rates for the 

year ranged from 9 to 15 percent across the campuses, with the exception of Hostos, where new Single Stop clients accounted for 42 percent 

of all enrollees. 

Operational Details 

Te CUNY Single Stop ofces were among the frst established under 

the Community College Initiative. Te Kingsborough program was 
Describing the Single Stop Program 29 

established in January 2009, and the other campuses created Single 

Stop ofces in January 2010. Te management of the program fell 

under diferent ofces according to campus, with four campuses locating the program under Student Afairs and the other two locating the 

program under Enrollment Management. Te sites ranged from two to 

four staf members, although the most common stafng arrangement 

included one core staf member and two support staf. Te ofce hours 

varied from 40 to 45 hours per week, with ofces typically open from 

9 a.m. to 5 p.m. 

Outreach across the CUNY campuses was largely provided to 

the full student population, although in some cases campuses provided additional outreach to new students. Queensborough was the 

exception by having exclusively targeted Single Stop to new students 

and students who were participating in specialized support programs, 

such as College Discovery and CUNYStart. Te Kingsborough and 

Hostos campuses relied on faculty referrals for outreach in 2014–2015, 

although faculty referrals did not play a major role at the other campuses at that time. 

Services Used 

Across all CUNY sites, 87 percent of new Single Stop clients were 

screened for benefts (Table 2.2). However, screening rates varied substantially across campuses, from 84 percent at LaGuardia to 96 percent 

at Kingsborough. Te percentage of Single Stop users who received 

tax services in 2014–2015 also varied across campuses, from a low of 

34 percent at CUNY Kingsborough to 61 percent at the Borough of 

Manhattan campus. Although somewhat less reliable, the data on other 

Single Stop services also indicate variation across campuses, particularly with regard to referrals. Across most CUNY campuses, the majority of students received at least one referral. However, CUNY Queensborough was the exception; there, just one-third of Single Stop users 

received referrals. Te percentage of Single Stop users receiving fnancial and legal services was approximately 8 percent each across CUNY 

campuses. Single Stop users were slightly more likely to be reported as 

receiving fnancial or legal services at the Bronx and Queensborough 

campuses and somewhat less likely at the Kingsborough campus. 
30 Connecting College Students to Alternative Sources of Support 

Features of Beneft Programs 

Like Massachusetts, New York provided relatively generous benefts, 

ranked the fourth most generous among U.S. states in January 2014, 

according to 24/7 Wall St. rankings (Sauter, Hess, and Frohlich, 2014). 

Income eligibility limits for SNAP, Medicaid, and TANF were relatively high. New York did not ofer any additional exceptions to students for SNAP, but the state ofered the Family Assistance program 

that provided TANF-like cash benefts to low-income individuals who 

did not qualify for TANF, such as individuals without children. In 

addition, the state had extended Medicaid eligibility to adults without 

children, ages 19–64. 

Delgado Community College 

Population Served 

Delgado Community College is a two-year public institution located 

in New Orleans, Louisiana. Delgado enrolled approximately 17,000 

students in fall 2014, of which 45 percent were African American and 

32 percent were white (see Table 2.1). Approximately half of the enrollees were adult learners and more than half were enrolled part time. 

Te total estimated costs for all expenses were more than $17,000 for 

an in-state student living without his or her family. More than half 

of Delgado students received Pell Grants, and the average Pell Grant 

accounted for approximately 30 percent of total expenses. Retention 

rates into the second year for full-time and part-time students were 65 

and 54 percent, respectively. Approximately one in six students earned 

a degree or certifcate within three years, and 18 percent transferred to 

other institutions. 

According to Single Stop data, Delgado’s site has served 3,742 

students (Table 2.2). Similar to CUNY, Single Stop users at Delgado 

were more likely to be female, more likely to be black, more likely to 

be age 25 or older, and less likely to be a part-time student relative 

to the overall student population. Tere were more than 1,400 new 

Single Stop clients in the study period, accounting for 8 percent of total 

enrollment. 
Describing the Single Stop Program 31 

Operational Details 

Te Single Stop program at Delgado was established in January 2012. 

Te program was housed under the Student Afairs department and 

had three staf members, including one core staf member and two 

support staf, Te ofce was open Monday through Friday, 9 a.m. to 5 

p.m., for a total of 40 hours each week. 

Although outreach at Delgado was targeted to all students at the 

institution, there were additional outreach eforts focused on freshman 

and students identifed as being at risk. Single Stop also worked with 

specialized programs (e.g., the Women’s Center, Allied Health, and 

the TRIO program) to provide outreach, and there were eforts under 

way during the 2014–2015 academic year to develop a referral system 

between Single Stop and other student services. Program staf reported 

that word of mouth and classroom visits were the most common way 

students heard about the program. 

Services Used 

Among Single Stop users at Delgado, 88 percent were screened for benefts and 41 percent received tax services (Table 2.2). Tese rates of service use are similar to those for other institutions. However, with nearly 

one-third of all Single Stop users receiving legal services in 2014–2015, 

Delgado Single Stop users were much more likely to have been reported 

as receiving legal services. Tis fnding is likely related to the fact that 

the legal services provider at Delgado was very inexpensive and ofered 

many more service hours than legal providers elsewhere. Te percentage of Single Stop users reported as having received fnancial services 

and referrals was also higher at Delgado than any of the other campuses. Notably, Delgado was the frst site that had site coordinators 

cross-trained to do fnancial counseling, and this likely contributed to 

the higher rates of fnancial services and referrals on this campus. 

Features of Beneft Programs 

Louisiana was less generous with benefts relative to Massachusetts and 

New York, ranked the tenth least generous in January 2014 among 

U.S. states. Te maximum income limits for SNAP, Medicaid, and 

TANF were the lowest among the four states represented in this study. 

Te state did not extend Medicaid to individuals ages 19–20 or to 
32 Connecting College Students to Alternative Sources of Support 

individuals ages 21–64 without children, as the states of Massachusetts 

and New York did. Tere were no additional eligibility exceptions for 

students for SNAP beyond those set by federal rules. 

Miami Dade College 

Population Served 

Although MDC has traditionally been a two-year public institution, 

many community colleges in Florida began to ofer baccalaureate 

degrees during the past decade. At the time of the study, MDC was 

ofcially classifed as a public institution that ofers four-year degrees, 

as well as degrees and certifcates that can be earned in two years or 

less. MDC enrolled nearly 100,000 students in fall 2014, and more 

than two-thirds of the student population was Hispanic (Table 2.1). 

Only a third of MDC enrollees were adult learners, and approximately 

40 percent were part-time students. Te total estimated costs for MDC 

were the highest of all of the study colleges, at more than $37,000 

for in-state students living apart from their families. More than half 

of all students received Pell Grants, although the average Pell grant 

accounted for only 10 percent of total expenses. Approximately onethird of all MDC students earned a degree or certifcate, and 10 percent transferred to other institutions. 

According to Single Stop data, the MDC sites have served more 

than 22,000 clients (Table 2.2). Single Stop users at MDC were similar 

to the overall population in terms of gender and age. However, Single 

Stop users at MDC were somewhat more likely to be black. And similar to all institutions in the study, MDC Single Stop users were substantially less likely to be part-time students. MDC registered 6,817 

new clients in 2014–2015, representing a penetration rate of approximately 10 percent. 

Operational Details 

Tree MDC campuses had Single Stop ofces, with ofces opened 

on two campuses in October 2010 and at a third campus in October 

2011. Te program was housed under the Student Services division, 

with some centralized management across the three campuses and 

some cross-campus variation in the way that services were delivered. 
Describing the Single Stop Program 33 

Te hours that Single Stop ofces were open varied across campuses; 

two provided services 52 hours per week and one provided services 42 

hours per week. 

Outreach at MDC was primarily targeted to new students at the 

institution through mandatory orientation sessions. Te program also 

engaged with staf in other student support programs and with faculty 

to facilitate referrals to the program. Program staf reported that the 

orientations, referrals from faculty and other students, and classroom 

presentations were the most commonly reported sources of information about the program. 

Services Used 

Approximately 91 percent of new Single Stop users in 2014–2015 at 

MDC were screened for benefts, among the highest of our study sites 

(Table 2.2). However, only 14 percent of new Single Stop users received 

tax services, the lowest rate of any institution in the study. Reported 

rates of referrals and use of legal services were also lower at MDC than 

at most of the other institutions in the study. Use of fnancial services 

among new MDC Single Stop clients was similar to rates reported for 

BHCC and the CUNY campuses. 

Features of Beneft Programs 

According to 24/7 Wall St.’s January 2014 rankings (Sauter, Hess, 

and Frohlich, 2014), Florida was the ffth least-generous state in the 

United States in terms of benefts. Te state had relatively low maximum income thresholds for SNAP, Medicaid, and TANF eligibility. 

Although Medicaid was available to students ages 19–20, they must 

have had an income of less than 30 percent of the federal poverty line 

for eligibility. Medicaid was not expanded to poor individuals ages 

21–64 without children, as was done in New York and Massachusetts. 

Florida did extend SNAP to students who qualifed for TANF. 

Summary 

Many aspects of Single Stop are common across colleges, but there was 

variation in implementation across sites. For example, BHCC, MDC, 

and certain CUNY campuses focused outreach primarily on new students, while Delgado and other CUNY campuses targeted the full stu­
34 Connecting College Students to Alternative Sources of Support 

dent population. Tere is also variation in penetration rates—or the 

number of new clients served in 2014–2015 as a percentage of total 

enrollment—with CUNY Hostos and BHCC accounting for penetration rates of 42 and 24 percent, respectively. All of the other study 

institutions had new Single Stop client populations that accounted for 

just 8 to 14 percent of all enrollees. Beneft screening was the most 

common service across Single Stop sites, with approximately 85 percent of all Single Stop users receiving screenings. However, there were 

outliers, including CUNY Kingsborough, where 96 percent of Single 

Stop users received beneft screenings. Te percentage of Single Stop 

users who received tax assistance also varied widely, from just 14 percent at MDC to nearly 61 percent at CUNY Borough of Manhattan. 

Stafng was relatively similar across sites, ranging from two to four 

staf members per Single Stop ofce. 

Variation in context across institutions might also have afected 

the experiences students had with Single Stop. For example, even 

though the sites were stafed similarly, the sizes of the institutions 

varied. Te staf-to-student ratio across institutions ranged from one 

Single Stop staf member per 2,328 students at the CUNY Hostos site 

to one staf member per 11,107 students at MDC. In addition, some 

institutions had larger low-income populations, so a greater proportion 

of students at these institutions might have been able to beneft from 

Single Stop services. 

Given that our Single Stop sites are located in diferent states, they 

also face widely varying beneft programs in terms of eligibility and 

generosity. Students at the CUNY campuses and BHCC were eligible 

for more benefts, and the benefts they received were likely to be more 

generous than those available to students at Delgado and MDC. In 

addition, Single Stop sites rely on support resources within the institution and from external organizations to supplement the major services 

they provide. We did not have data on access to these additional wraparound supports, but they likely vary across sites. 

It is also useful to compare the students in our study institutions 

with community college enrollees nationally to assess the generalizability of our fndings. We found that our study institutions are somewhat 

comparable to the national population in terms of gender and age, 
Describing the Single Stop Program 35 

according to data from the National Center for Education Statistics 

for 2014–2015 (U.S. Department of Education, National Center for 

Education Statistics, 2015). Approximately 56 percent of enrollees at 

our study institutions were female, compared with 57 percent of all 

community college enrollees nationally. Adult learners (age 25 or older) 

accounted for 31 percent of our study institution enrollment, slightly 

lower than the 38 percent in community colleges nationally. However, 

our study institutions had substantially larger minority populations 

relative to the national community college population; 51 percent of all 

community college students were white compared with just 13 percent 

of students across our study institutions. In addition, our study institutions had somewhat fewer part-time enrollees than the national average 

(44 percent versus 60 percent nationally). Students in the study institutions were representative of the nation in terms of federal grant aid; 

56 percent of students in the study institutions received Pell Grants, 

compared with 56 percent nationwide. In terms of costs for tuition 

and room and board, CUNY and MDC were somewhat more expensive than the $16,370 national average across community colleges (for 

a full-time student living of campus without their families). Finally, 

success rates at our study institutions were slightly lower than those 

nationwide. One-year persistence rates for frst-time full-time students 

were 57 percent across our institutions (excluding MDC), compared 

with 60 percent of public community colleges nationwide. Graduation 

rates were 14 percent and 20 percent, respectively. So although our 

study institutions are similar to the national community college population in some aspects, there were some diferences in terms of racial 

and ethnic makeup, enrollment intensity, and success rates. 

CHAPTER THREE 

Our Approach to Evaluating Single Stop 

Data 

We used two primary sources of data for our analysis of postsecondary outcomes: program data on students’ receipt of Single Stop services and administrative data on student enrollment and performance 

in college. Single Stop program data are collected as part of normal 

site operations and stored in Single Stop’s BEN system. As described 

earlier, BEN is a proprietary beneft-screening and case-management 

tool that identifes the benefts for which a student is eligible and tracks 

interactions between the student and Single Stop. Administrative student record data were collected from the four partner college systems 

by Single Stop and provided to RAND for this evaluation. To compile their administrative data fles, institutions supplemented data from 

their student data systems with NSC data that track enrollment for 

students outside our study colleges. 

Single Stop Data 

Te data in the BEN system were collected by site coordinators and 

included three types of information: data collected at client intake, 

such as contact information, student identifcations, and demographics; data on services provided to students, including beneft screenings 

and referrals to various programs; and data collected during followup regarding the confrmation of benefts for which the client was 

approved or denied. Te data extracted from BEN were structured into 

two data fles. Te client fle contained student-level information, such 

as age, gender, ethnicity, campus, and answers to the beneft screener 

37 
38 Connecting College Students to Alternative Sources of Support 

for those who received the screening. Tese data were static in BEN’s 

data structure, although staf might have updated client information 

at any time and overwritten old information. Te event fle contained 

information about Single Stop services, with a unique record for each 

service and a time stamp for when the services were received. Terefore, 

a given client might have had multiple records in the event fle depending on the number of services received and recorded in the BEN platform. For tax, legal, and fnancial services, Single Stop contracted with 

on-site and of-site services providers, and these providers were responsible for collecting and providing data on the students who received 

their services. Single Stop staf then backflled these data into the BEN 

system, so there was a lag in the recording of these data. Given this lag, 

we waited until all external provider data had been recorded in BEN 

before extracting the data for our analysis. 

In addition to documenting students served and services provided, 

site coordinators attempted to track benefts received. Te processes for 

confrming benefts varied by beneft program and site. In some states, 

certain beneft programs allowed for electronic applications, and when 

Single Stop staf assisted with applications, they might have received 

confrmations noting receipt of benefts. However, it is more common 

for Single Stop staf to rely on students to self-report benefts. Despite 

eforts of the site coordinators to follow up with students and collect 

this information, there are substantial missing data and likely errors in 

self-reported data. 

Goldrick-Rab, Broton, and Frank (2014) found inconsistencies 

in how data were entered into the BEN system across sites, noting 

incidences of underreporting for a number of Single Stop services. In 

summer 2014, Single Stop’s evaluation team conducted additional training and monitoring of the study institutions in response to the fndings from that implementation study. Te institutions were instructed 

to ensure that every student who was served by Single Stop in any way 

was entered into the BEN client fle and that every beneft screening 

was accounted for regardless of a student’s likelihood of eligibility for 

benefts. Single Stop also worked with the staf to improve processes 

for collection and transfer of third-party data from tax service providers. Because of these eforts, the Single Stop evaluation team reported 
Our Approach to Evaluating Single Stop 39 

confdence in the reliability of three data elements: (1) whether an 

individual had any interaction with the Single Stop program (as indicated by a record in the BEN client fle), (2) whether the Single Stop 

client received a beneft screening (indicated in the BEN event fle), and 

(3) whether the Single Stop client had received tax assistance services 

(indicated in the BEN event fle). Concerns remained about the quality of other data elements, including legal and fnancial service records 

that were collected through external providers, referrals of students to 

various wraparound services, and the confrmation of benefts received. 

So although we could identify all students who were served by Single 

Stop with some certainty, we were limited in our ability to estimate 

outcomes associated with many of the specifc services provided by 

Single Stop, including those that were not tracked reliably (legal and 

fnancial services, referrals, confrmed benefts) and those not tracked 

at all (some case-management activities). 

Institutional Administrative Data 

Te data provided by the four community colleges included institutional administrative data, such as student demographics, fnancial aid 

data, and enrollment and academic data from fall 2014 to fall 2015. 

Tese data were primarily drawn from student information systems 

that warehouse student academic data at the institutions. In some 

cases, fnancial aid data were stored in a separate database and were 

merged with the other student data. Institutions were responsible for 

matching institutional administrative student records with Single Stop 

BEN records according to student identifcation, date of birth, phone 

number, frst letter of frst name, and frst letter of last name. Te institutions then provided Single Stop and RAND with deidentifed data. 

In addition to data from internal student information systems, 

the institutions provided data derived from the NSC fles they received, 

which documented the enrollment of their students at other colleges 

across the United States in spring 2015 and fall 2015. NSC’s Student 

Tracker system provided the most-comprehensive data on postsecondary enrollment that are available to date, including semester-level 

enrollment data for approximately 94 percent of all institutions, with 

near-universal coverage of student enrollment at public colleges and 
40 Connecting College Students to Alternative Sources of Support 

private not-for-proft colleges. Tere are substantial gaps in coverage in 

NSC data for for-proft institutions, so students who transfer to these 

institutions are reported as nonpersisting students. However, we have 

no reason to suspect that Single Stop users would have enrolled in these 

colleges at diferent rates from their peers, so the missing data might 

not have afected results. Using these data, the four institutions created indicators for enrollment at any institution and/or transfer in the 

spring 2015 and fall 2015 semesters for each of their fall 2014 enrollees. 

Defning the Sample 

According to fndings from Goldrick-Rab, Broton, and Frank (2014) 

and discussions with the Single Stop evaluation team, there were concerns about substantial measurement error in program use data prior 

to summer 2014. Given this, it was challenging to identify which students were Single Stop users when looking at earlier semesters, and the 

misidentifcation of Single Stop users as non–Single Stop users would 

likely lead to downward bias in estimates of the program’s impacts. As 

a result of these data issues, we decided to focus our analysis on students who enrolled at the study institutions only after data began to 

be reliably collected. Our analysis focused on FTIC students (i.e., frsttime freshmen) enrolled in fall 2014, limiting the total sample size to 

34,487 across our four institutions. Although this restriction allowed 

us to have greater confdence in the validity of our estimates, it limited 

the representativeness of our results to FTIC students. 

Key Data Elements 

Use of Single Stop 

Our main research question of interest was whether interaction with 

the Single Stop program, regardless of the type and intensity of services 

received, helps support improved postsecondary outcomes, such as persistence and credit accumulation. We are therefore interested in examining outcomes for all students who interacted with the Single Stop 

program. As our primary defnition of Single Stop use, we included 

all clients who were registered in the BEN system prior to September 1, 2015. Tis broad defnition of treatment allowed us to examine 
Our Approach to Evaluating Single Stop 41 

outcomes for all students served by Single Stop and accounted for the 

full range of formal services and informal assistance that students have 

access to as part of the Single Stop program. Data on our sample of 

FTIC students are presented in Table 3.1. According to this broadest 

defnition of Single Stop use, the percentage of fall 2014 FTIC students interacting with Single Stop varied from 6 percent at Delgado to 

23 percent at MDC. 

In addition to examining the relationship between Single Stop 

and postsecondary outcomes across all served students, we examined 

outcomes for students who used particular Single Stop services, including beneft-screenings and tax-preparation services. As Table 3.1 indicates, a larger percentage of our sample received beneft screening than 

tax services at all four of the community colleges. Of those enrolled 

FTIC in fall 2014 at BHCC, 19 percent of students received beneft 

screenings and 4 percent received tax services. For CUNY, 9 percent of 

2014 FTIC students received beneft screenings and 4 percent received 

tax services. At Delgado, the percentages of 2014 FTIC students who 

received beneft screening and tax services were 5 percent and 1 percent, respectively. MDC’s Single Stop site provided 22 percent of FTIC 

students with screenings but provided tax services to fewer than 1 percent of FTIC students. We did not conduct analysis of fnancial counseling, legal services, and referral data because of the reporting issues 

described in the previous section. 

Postsecondary Outcomes 

Using data from fall 2014 through fall 2015, we examined fve measures of academic performance as our key outcomes: one-semester persistence, one-year persistence, credits attempted, credits earned, and 

the ratio of attempted to earned credits. Persistence is defned as enrollment in any college in spring 2015 and fall 2015; NSC data accounted 

for enrollment at 94 percent of all colleges in the United States. Students are considered to have persisted if they were enrolled at any college for at least one day in any given semester. According to Table 3.1, 

one-semester persistence rates ranged from 73 percent at Delgado to 

85 percent at MDC. One-year persistence rates ranged from 36 percent at MDC to 64 percent at CUNY. 
42 Connecting College Students to Alternative Sources of Support 

Table 3.1 

FTIC/Freshman Characteristics by School: Fall 2014 to Spring 2015 

Characteristic BHCC CUNY Delgado MDC Total 

Student characteristics 

Enrollment 2,050 15,885 2,720 8,356 34,587 

Female 52.2% 51.7% 58.3% 51.8% 52.4% 

Average age 22.3 20.9 22.9 20.4 21.0 

White 18.2% 12.8% 31.7% 5.5% 12.8% 

Black 24.8% 29.9% 54.3% 17.8% 28.3% 

Hispanic 31.7% 42.0% 0.2% 70.4% 45.5% 

Asian 8.9% 14.9% 2.7% 1.1% 9.3% 

1st generation 71.5% N/A 23.1% 46.6% 44.8% 

Citizen 94.3% 90.5% 98.3% 92.4% 92.0% 

Financial aid receipt 72.6% 72.4% 81.3% 77.7% 74.6% 

Annual personal $3,481 $3,432 $6,627 $3,240 $3,680 

income 

Annual household $23,867 $27,471 $34,116 $37,281 $30,659 

income 

Have dependents 17.6% 10.4 % 27.3% 8.3% 11.7% 

Dependent 67.5% 80.0% 61.5% 85.5% 79.5% 

Outcomes 

Persistence into 81.7% 81.8% 72.8% 85.1% 81.9% 

spring 2015 

Persistence into fall 59.8% 62.4% 42.4% 43.9% 55.5% 

2015 

Spring 2015 credits 19.5 18.1 22.8 23.6 20.3 

attempted 

Spring 2015 credits 15.7 15.3 14.7 18.8 16.4 

passed 

Ratio of passed/ 79.3% 81.4% 62.1% 78.7% 78.9% 

completed 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Our Approach to Evaluating Single Stop 43 

Table 3.1—Continued 

Characteristic BHCC CUNY Delgado MDC Total 

Single Stop use 

Single Stop user 21.1% 10.5% 5.7% 22.9% 14.4% 

Screened for 

benefts 

19.3% 9.4% 5.1% 21.9% 13.3% 

Received tax services 4.2% 2.3% 0.9% 0.6% 1.8% 

SOURCE: Data on student characteristics, fnancial aid, and outcomes are from the 

authors’ analysis of Single Stop’s BEN data. 

NOTE: N/A = not available. 

Administrative records provided information on attempted and 

earned credits at the college where each Single Stop site is located. Our 

outcome measures account for all credits attempted and earned in the 

2014–2015 academic year; we did not include credits earned prior to 

enrollment in fall 2014 or in fall 2015. Table 3.1 shows that the average number of credits attempted ranged from 18.1 at CUNY to 23.6 at 

MDC, and the average number of credits earned ranged from 14.7 at 

Delgado to 18.8 at MDC. 

Other Key Variables 

Te basic student demographic characteristics included in the study are 

gender, age, race, citizenship, and frst in family to attend college. Student gender, citizenship, and frst in family to attend college were created 

as binary variables. Only those who were citizens or permanent residents 

were categorized as having citizenship. As indicated in Table 3.1, more 

than 50 percent of students in the sample were female across all institutions. Te average age of students in the sample ranged from 20.4 at 

MDC to 22.9 at Delgado, indicating that the sample was not restricted 

to 18-year-old students coming straight out of high school and includes 

adult learners. Te sample was primarily made up of minority students; 

only 13 percent of students were white across institutions. Te percentage of students reported as frst-generation college-goers ranged from 

23 percent at Delgado to 72 percent at BHCC. More than 90 percent 

of the FTIC students were citizens. 
44 Connecting College Students to Alternative Sources of Support 

We also accounted for fnancial status and other aspects of family 

makeup from information provided by students through the FAFSA. 

Specifcally, we included fnancial aid receipt, personal income, household income, dependent status, and number of dependents. Financial aid receipt and dependent status were created as binary variables. 

Personal income, household income, and number of dependents 

were reported as continuous variables. It is important to note that we 

encountered substantial missing data for some of these variables. Students are not required to fle the FAFSA, and those who do complete 

the FAFSA are not required to answer every question, so this led to 

missing data. To address missing fnancial data, we created indicator 

variables to identify individuals with missing data and replaced missing values with zero values. 

Across the institutions, at least 70 percent of students received 

fnancial aid. Household income averaged $30,659 across the sample, 

with the highest incomes among MDC students. Personal income averaged just $3,680. CUNY and MDC had substantially more students 

who were dependents relative to BHCC and Delgado. Te percentage of students with dependents also varied across institutions, ranging 

from just 8 percent at MDC to 27 percent at Delgado. 

We also included high school GPA as a proxy for prior academic 

achievement. Te diferent community college systems utilized diferent measurement scales for GPA, with some using a traditional fourpoint scale and others using a 100-point scale, so we binned students 

into deciles based on their relative rankings at their respective campuses. We did not report GPAs in Table 3.1 because of a lack of comparability of data across campuses. 

Methods 

To examine the impact of the Single Stop program, we cannot simply 

compare outcomes for Single Stop users and non–Single Stop users. 

Students did not use Single Stop services at random; for example, students with unmet fnancial need were probably more likely to seek 

out Single Stop services than students without fnancial issues. Tis 

nonrandomness could lead to biased estimates if the students who 
 

 

Our Approach to Evaluating Single Stop 45 

used Single Stop services were diferent from nonusers in ways that are 

related to postsecondary outcomes. Tis selection bias could be positive 

or negative. For example, if more academically motivated students take 

the initiative to seek out Single Stop services because they are more 

committed to doing what it takes to stay in college, we may expect 

that these more academically motivated students will have better postsecondary outcomes. If we do not account for these diferences in academic motivation, our estimates of impact might be biased positively. 

On the other hand, if Single Stop’s outreach eforts deliberately target 

more-disadvantaged students, and if disadvantaged students are less 

likely to succeed in college, we might anticipate a negative bias if we do 

not account for all relevant aspects of disadvantage. 

To account for these potential biases, we used two methodological 

approaches: multiple regression analysis and coarsened exact matching 

(CEM) (Iacus, King, and Porro, 2011). We describe the key components of each method below. 

Multiple Regression Analysis 

We frst conducted multiple regression analysis, the most commonly 

used selection bias adjustment method. Te covariance models adjust 

for preexisting diferences between Single Stop and non–Single Stop 

students, including, as controls in the model, all observable confounding variables that capture preexisting diferences between those students. Given that this approach relies on explicit, model-based controls 

to adjust estimates, it is primarily useful for addressing overt bias. Te 

resultant models produce estimates of the diferences between Single 

Stop and non–Single Stop students, after controlling for (i.e., holding constant) preexisting observable diferences (Rosenbaum, 2002). 

Unfortunately, considerable research suggests that this method rarely 

produces estimates that are unbiased and efcient (Achen, 1986) 

because other, unaccounted for diferences may afect results. As such, 

this approach served simply as a baseline against which to compare 

more-sophisticated methods. 

In our multivariate regression, we estimated regression models of 

the form Yis = β0 + β1Tis + β2Xis + ηs + eis, where Yis represents our 

outcomes of interest (e.g., persistence) for student i in school s, and Ti is 
 

 

 

 

 

 

46 Connecting College Students to Alternative Sources of Support 

an indicator variable for receiving Single Stop services. Te variable Xis 

represents a vector of observable baseline covariates at the student level 

that have been found in previous research to be associated with postsecondary success and might also be related to the take-up of Single Stop 

services. Tese covariates include age, race and ethnicity, gender, fnancial aid receipt, household income, dependency status, and whether 

the student is responsible for dependents. Finally, the model includes a 

vector of campus-level fxed efects ηs and a student-level error term eis. 

Coarsened Exact Matching 

CEM is a recently developed method of establishing baseline equivalence between treated and control groups in observational studies.1 

CEM represents a data preprocessing step that essentially prunes some 

unmatched observations from the data so that the remaining data have 

better balance on key variables (Blackwell et al., 2009). A key feature 

of CEM is that the bounds of imbalance on matching variables are 

determined a priori, thus ensuring balance on all variables used in the 

matching procedure. One key diference between CEM and propensity 

score methods is that CEM is monotonic imbalance-reducing, which 

means that the adjustment of balance on one variable has no efect on 

the balance on another variable. For example, requiring matches be 

from the same income category will not reduce one’s ability to also 

have matched observations with similar age or gender characteristics. 

We implemented CEM in three steps. Te frst step involved 

coarsening the variables into researcher-defned bins, thus constricting 

the amount of possible imbalance that is allowed to exist postmatching. 

We used the following variables in the CEM algorithm, which were 

coarsened into bins as follows: 

• gender (one bin for female, one for male) 

•	 age (eight bins based on cut points at ages 18, 20, 22, 25, 30, 35, 

and 40) 

Although much of the literature about CEM, as well as some prior empirical work implementing the method, uses terminology related to causal inference, such as treatment, efect, 

and so on, we deliberately avoid such terms whenever possible so that this study is strictly 

observational in nature. 

1 
Our Approach to Evaluating Single Stop 47 

•	 race and ethnicity (four bins—white, black, Hispanic, and Asian) 

•	 campus of attendance (11 bins based on the 11 campuses in the 

study) 

• household income2 (8 bins based on cut points at $10,000, 

$20,000, $30,000, $40,000, $50,000, $75,000, and $100,000) 

•	 fnancial aid receipt (one bin for yes, one bin for no) 

• high school GPA (ten bins based on the students’ decile ranks at 

their respective campuses) 

• dependency status (one bin for dependent, one bin for independent) 

• dependents (one bin for students responsible for dependents, one 

bin for those who are not) 

•	 frst in family to attend college (one bin for yes, one bin for no). 

Second, all observations were placed into strata based on the variable binning strategy described above, within which matches were 

made. 

Tird, observations were given weights based on their locations in 

strata that contained matches of both Single Stop users and nonusers. 

Individuals who were in unmatched strata were given weights of zero, 

and those in matched strata were given weights as follows: Single Stop 

users (the treatment group) were given a weight of 1.0, and non–Single 

Stop users (the control group) were given proportional weights so that 

their sum added up to 1.0. In other words, if there is a particular stratum that has one Single Stop user and fve non–Single Stop users, the 

nonusers are given weights of 0.2. Individuals in unmatched strata are 

given weight of 0 and were essentially excluded from the analysis. Tus, 

the estimates derived in the subsequent analysis apply only to the segment of the population that was successfully matched (Iacus, King, 

and Porro, 2009). 

2 Variables that were derived from students’ FAFSA flings, such household income, fnancial aid receipt, dependency status, and dependent care, all had nontrivial levels of missing 

data, which underscores a key advantage to using CEM. Observations with partially missing 

data were preserved in the sample, as CEM treats missing values as a discrete measure of a 

variable that can be used when creating matches (Blackwell et al., 2009). 
48 Connecting College Students to Alternative Sources of Support 

Assessing Baseline Equivalence 

To ensure that the estimates of our analysis were unbiased, we had 

to assume that our treatment and weighted comparison groups were 

similar across all observable and unobservable characteristics. We 

assessed the baseline equivalence of key observable characteristics 

across the treatment and comparison groups to test this assumption, 

comparing the means of the two groups of students after applying the 

CEM weights to ensure that diferences on all baseline covariates were 

no greater than 0.25 standard deviations (Ho et al., 2007). We also 

conducted statistical tests of baseline equivalence by regressing each 

covariate on the treatment variable using the CEM weights, following 

the recent empirical work by Jenkins et al. (2016). 

Second, the exclusion of some observable measures of family background allowed for post hoc balance-checking based on these measures. Because it is not possible to obtain defnitive proof of balance 

on unobservable family characteristics, the removal of what observable data are available allows for the postmatching balance-checking 

on these variables. Postmatching imbalance on observable information 

is thus suggestive of imbalance on unobservable information. Furthermore, if imbalance on observable information is detected, then these 

variables can be incorporated as predictors in the analytic model. 

Estimating Outcomes 

Matching methods such as CEM are not statistical estimators of outcomes but rather preprocessing algorithms that ensure baseline equivalence. Terefore, an additional analytic step is required to estimate 

the outcomes of interest. Once the weights were applied and baseline 

equivalence was assessed, we estimated the associations between Single 

Stop services and postsecondary outcomes via linear regression techniques. We looked at fve outcomes for three defnitions of treatment, 

for a total of 14 estimates.3 For each of these outcome-treatment combinations, we estimated a weighted regression model in which the outTere are technically 15 combinations of treatment multiplied by outcome, but we do not 

present results for the combination of tax service (treatment) and one-semester persistence 

(outcome) because in this case the outcome likely occurred before the treatment, so the outcome is likely to be endogenous. 

3 
Our Approach to Evaluating Single Stop 49 

come was regressed on an indicator of receipt of Single Stop service. 

We ran a number of diferent regression models that included various 

combinations of observable demographic and fnancial characteristics, 

as well as campus-level fxed efects. Given a lack substantive diferences in results across models, and our inclusion of the full range of 

variables in the matching process that ensured strong balance across 

observables, we focused on the results from the basic regression model 

with no additional controls. 

In addition to looking at outcomes for the full population of 

Single Stop users, we estimated associations for several diferent subgroups of students to see whether the program was particularly benefcial for some demographic subgroups. Specifcally, we estimated separate models for younger and older students (based on a cutof at 25 

years of age), students who were dependents and independents, and 

students who were white and nonwhite. We also examined institutionlevel variation in the outcomes associated with Single Stop use. 

Key Assumptions and Limitations 

A key limitation of our analytic approach relates to the fnite information we have available about students at baseline. Although we assume 

that the observable characteristics included in our matching approach 

account for all possible factors that may infuence selection into Single 

Stop, there may be unobservable characteristics driving selection that 

we are unable to account for. For example, some institutions target services to particularly vulnerable students, such as those who are homeless. Our rough measures of income from FAFSA data might not have 

fully accounted for these vulnerabilities, in which case our estimates 

would be lower than the true impact of Single Stop. On the opposite 

side, if we think that there are aspects of motivation that are not captured in high school GPA, we might not have fully accounted for the 

role of motivation in determining use of Single Stop. In this case, our 

estimates would be higher than the true impact of Single Stop. 

Te assumption that all selection is accounted for is untestable, 

so we cannot be 100 percent confdent that our results represent causal 

estimates of the impact of Single Stop services. Nonetheless, we can 

assess the model’s sensitivity to omitted variable bias by deliberately 
50 Connecting College Students to Alternative Sources of Support 

excluding observable measures from the CEM process and then assess 

balance afterward. Postmatching imbalance on observable information is therefore suggestive of imbalance on unobservable information. Furthermore, if imbalance on observable information is detected, 

then these variables can be incorporated as predictors in the analytic 

models (Sharkey, 2012). In the present study, we conducted this post 

hoc balance test on the students’ citizenship status. 

Another limitation to our analysis is our inability to generalize 

to the larger population of community college students because of our 

focus on FTIC students at specifc institutions. Although the focus on 

FTIC students allowed us to eliminate the measurement error in treatment because of BEN data issues prior to summer 2014, we cannot 

speak to the outcomes related to Single Stop use for returning students, 

a substantial portion of the community college population. Te exclusion of non-FTIC students from the analysis may lead to underestimations of the overall impact of Single Stop if non-FTIC students were 

most likely to have benefted from use of Single Stop. Our matching 

approach resulted in further limitations to the sample, because Single 

Stop users who could not be matched to similar students within the 

institution were dropped from the analysis. So our results are limited 

in that they speak to the outcomes related to Single Stop use for FTIC 

students for which matching was possible. 
CHAPTER FOUR 

Single Stop and Postsecondary Outcomes 

In this chapter, we present the results of a series of statistical models 

that show the association between Single Stop use and fve academic 

outcomes for FTIC community college students. We found a consistently positive association between use of Single Stop services and the 

postsecondary outcomes, and this association held whether we were 

looking at the broadest defnition of Single Stop use or specifc services, 

such as beneft screenings and tax return support. Prior to presenting 

these fndings, we begin by assessing the level of baseline equivalence 

of Single Stop users (the treatment group) and non–Single Stop users 

(the control group) across our key variables of interest. 

Assessing Baseline Equivalence 

In Tables 4.1–4.3, we present baseline descriptive statistics for students who used Single Stop services compared with those who did not. 

Table 4.1 contains data for the use of any Single Stop services, while 

Tables 4.2 and 4.3 focus on recipients of beneft screenings and tax 

services. In each table, the three columns on the left present the raw, 

unweighted means and calculated diferences between students who 

were treated and students who were not treated, while the three columns on the right present the group characteristics and diferences 

after creating a matched comparison group that looks more similar 

to Single Stop users across observable characteristics. Diferences are 

shown in standard-deviation units, and asterisks next to these measures indicate a statistically signifcant diference. 

51 
Table 4.1Baseline Characteristics of All Single Stop Users Before and After Matching

Unweighted (Pre-CEM) 

Treatment Control Treatment 

Student Characteristics (n = 4,163) (n = 24,848) Differencea (n = 2,477) 

Female 0.565 0.517 0.096*** 0.557 

Average age 21.1 21.0 0.018 20.1 White 0.083 0.136 0.158*** 0.050 

Black 0.245 0.285 0.088 0.238 

Hispanic 0.537 0.442 0.190*** 0.641 

Asian 0.064 0.098 0.116*** 0.056 

Financial aid receipt 0.830 0.732 0.225*** 0.832 

Household income ($) 27,584 31,218 0.101*** 27,858 

High school GPA decile 5.623 5.363 0.090*** 5.686 

Have dependents 0.112 0.117 0.015 0.005 

Dependent 0.783 0.780 0.007* 0.893 

First in family in college 0.495 0.436 0.118*** 0.402 

Citizen 0.930 0.918 0.044** 0.929 

a Differences are reported in standard deviation units. *** p < 0.001, ** p < 0.01, * p < 0.05. 
Table 4.2Baseline Characteristics of Students Receiving Beneft Screenings Before and After M

Differences Prior to Matching 

Treatment Control Treatment 

Student Characteristics (n = 3,863) (n = 25,146) Differencea (n = 2,383) 

Female 0.566 0.517 0.098*** 0.556 

Average age 21.0 21.0 0.000 20.1 

White 0.083 0.135 0.155*** 0.048 Black 0.270 0.285 0.033 0.238 Hispanic 0.541 0.442 0.198*** 0.642 Asian 0.063 0.098 0.120*** 0.054 Financial aid receipt 0.834 0.733 0.232*** 0.840 Household income ($) 27,942 31,114 0.088*** 27,704 High school GPA decile 5.628 5.364 0.091*** 5.691 

Have dependents 0.107 0.118 0.034 0.045 Dependent 0.791 0.796 0.012 0.897 First in family in college 0.493 0.438 0.110*** 0.429 Citizen 0.933 0.917 0.058** 0.933 

a Differences are reported in standard deviation units. *** p < 0.001, ** p < 0.01, * p < 0.05. 
Table 4.3Baseline Characteristics of Students Receiving Tax Services Before and After Matchin

Unweighted Mean 

Treatment Control Treatment 

Student Characteristics (n = 530) (n = 28,479) Differencea (n = 312) 

Female 0.557 0.523 0.068 0.561 Average age 23.3 21.0 0.416*** 22.7 White 0.098 0.129 0.092* 0.064 Black 0.372 0.282 0.199*** 0.372 Hispanic 0.394 0.456 0.124** 0.468 Asian 0.100 0.093 0.024 0.064 Financial aid receipt 0.805 0.745 0.137** 0.804 Household income ($) 17,361 30,920 0.376*** 16,362 

High school GPA decile 5.558 5.540 0.006 5.393 Have dependents 0.140 0.116 0.074 0.071 Student is a dependent 0.612 0.799 0.463*** 0.762 First in family in college 0.573 0.446 0.255** 0.573 Citizen 0.881 0.920 0.143** 0.891 

a Differences are reported in standard deviation units. *** p < 0.001, ** p < 0.01, * p < 0.05. 
Single Stop and Postsecondary Outcomes 55 

In Table 4.1, the unweighted means indicate that Single Stop users 

were diferent from nonusers on almost all observable characteristics. 

Single Stop users were more likely to be female, less likely to be white, 

more likely to be Hispanic, more likely to be the frst in their families 

to go to college, and less likely to be citizens compared with their classmates who did not use Single Stop. With the exception of gender, all 

of these diferences in observable characteristics suggest that we might 

expect lower rates of postsecondary success among Single Stop users. 

Tere were also notable diferences along fnancial indicators, because 

Single Stop users were more likely to have received fnancial aid and 

had lower average household incomes. 

Tese baseline diferences in the unweighted data underscore the 

need to account for possible selection bias through the use of CEM. 

When conducting CEM based on the “any service” defnition of Single 

Stop treatment, the CEM process resulted in 13,003 possible strata, 

with 1,620 of them containing Single Stop users and nonusers. Individuals from the other strata were dropped from the analysis, resulting 

in an analytic sample of 2,477 in the treatment group and 7,144 in the 

comparison group (Table 4.1). 

Te second set of values in Tables 4.1–4.3 allows us to assess the 

success of CEM. It is apparent that Single Stop users and nonusers 

were more similar on observed covariates after weighting, and there 

were no longer statistically signifcant diferences between the treatment and comparison groups. Furthermore, balance improved for the 

citizenship and FTIC indicators, even though these variables were not 

used in the matching algorithm, lending additional support to claim 

that that Single Stop users and nonusers were similar across observable 

and unobservable characteristics after implementing CEM. 

Single Stop Use Is Associated with Improved 

Postsecondary Outcomes 

In Table 4.4, we present the results of models that estimate fve diferent postsecondary outcomes as associated with three diferent forms 

of Single Stop services. We frst discuss our results for persistence and 
56 Connecting College Students to Alternative Sources of Support 

Table 4.4 

Estimates of the Relationship Between Single Stop Use and Persistence 

One-Semester Persistence One-Year Persistence 

Unweighted 

Regression CEM 

Unweighted 

Regression CEM 

Single Stop user 0.068*** 0.033*** 0.055*** 0.031** 

Beneft screening 0.067*** 0.030*** 0.049*** 0.028* 

Tax service – – 0.157*** 0.144*** 

NOTE: *** p < 0.001, ** p < 0.01, * p < 0.05. 

then examine our results on credit accumulation. Under each outcome, 

we discuss the results for users of any Single Stop service, recipients of 

a beneft screening, and recipients of tax assistance. 

Single Stop Users Persist Longer in College 

Te results in Table 4.4 indicate that use of Single Stop is associated 

with increased persistence into the following semester and the following 

year. Although the estimates from our CEM analysis were somewhat 

more conservative than those estimated from unweighted multivariate 

regression, all of the results were positive and statistically signifcant. 

When we looked at students who received particular Single Stop services, we also found consistently positive results. Te estimate for use 

of tax services is particularly large; students receiving tax services are 

estimated to persist at rates that are 14 to 15 percentage points higher 

than those of comparable students who did not receive tax services (p < 

0.001).1 

To aid in the interpretation of these model estimates, we also calculated the conditional outcome means, which we refer to as the preWe also assessed whether tax services were independently benefcial even when controlling for the receipt of other services, but the models did not suggest any sort of interaction 

efect between the tax services and other types of Single Stop support. Results are available 

from the authors on request. 

1 
Single Stop and Postsecondary Outcomes 57 

dicted persistence rates, based on the estimates from our model results.2 

Tese persistence rates are presented in Figure 4.1, which shows the 

estimated percentage of Single Stop users and nonusers who persisted 

into a second semester and a second year of college enrollment. Te 

vertical lines protruding from the top of the bars represent the 95 percent confdence intervals. Tis fgure underscores the consistent positive association between persistence in community college and Single 

Stop services, showing a one-semester persistence rate that was approximately 3 percent higher and a one-year persistence rate that was 2.4 percent and 4.5 percent higher depending on the Single Stop service being 

considered. 

Figure 4.1 

Predicted Persistence Rates for Single Stop Users and Nonusers 

NOTE: Column values represent average predicted values conditional on Single Stop 

services and using CEM weights. 

RAND RR1740-4.1 

Tese conditional outcomes are also known as marginal means and are calculated using 

the margins command in Stata. 

2 
58 Connecting College Students to Alternative Sources of Support 

Findings for Single Stop Use and Credit Accumulation Are Mixed 

Similar to persistence rates, we found positive associations between 

Single Stop use and credits attempted and earned, although the results 

were not all statistically signifcant (Table 4.5). Te multivariate results 

were larger and more likely to be signifcant, but we focus on the CEM 

results given our greater confdence in the results of that model. Te 

estimates indicate that Single Stop users attempted 0.38 more credits 

on average relative to nonusers (p < 0.05). Te coefcient on earned 

credits was similar to that for attempted credits, but the relationship 

was nonsignifcant. And there was no relationship between Single Stop 

use and the proportion of attempted credits earned. Results for beneft 

screenings are similar to those for Single Stop users overall, with positive signifcant results for credits attempted and positive nonsignifcant 

results for credits earned. Users of tax services were estimated to earn 

more than one additional credit in the academic year relative to similar students who did not use tax services (p < 0.05). Te estimates for 

attempted credits and the ratio of earned to attempted credits were 

positive but not statistically signifcant. 

In Figure 4.2, we display average predicted values for Single Stop 

users and nonusers based on the results of each treatment condition. 

Tis plot confrms the diferences presented in Table 4.4, with Single 

Stop users averaging between 0.4 and 1.1 more credits attempted and 

Table 4.5 

Estimates of the Relationship Between Single Stop Use and Credit 

Accumulation 

Credits Attempted Credits Earned 

Ratio of Earned to 

Attempted 

Unweighted 

Regression CEM 

Unweighted 

Regression CEM 

Unweighted 

Regression CEM 

Single Stop 

user 

1.112*** 0.388* 1.170*** 0.332 0.014** −0.001 

Beneft 

screening 

1.171*** 0.345* 1.199*** 0.269 0.013* −0.003 

Tax service 1.158*** 0.855 2.034*** 1.212* 0.058*** 0.023 

NOTE: *** p < 0.001, ** p < 0.01, * p < 0.05. 
 

Figure 4.2 

Predicted Credit Outcomes for Single Stop Users and Nonusers 

Single Stop and Postsecondary Outcomes 59 

NOTE: Column values represent average predicted values conditional on Single Stop 

services and using CEM weights. 

RAND RR1740-4.2 

between 0.5 and 1.6 more credits earned, depending on the treatment 

condition being considered. 

Positive Outcomes Are Larger for Certain Types of 

Students 

In Table 4.6, we present the results for diferent subgroups of students, 

focusing on the estimates from our CEM models. In particular, we 

were interested in understanding whether results varied for adult learners (those who were 25 or older), independent students, and minority students. We found that the positive relationships between Single 

Stop user and persistence were primarily found for older, independent 

students and minority students. Relationships with credit outcomes 

were only statistically signifcant for adult learners. Estimates for beneft screenings are similar to those for overall Single Stop use, with a 

few exceptions. White students who received a beneft screening were 
60 Connecting College Students to Alternative Sources of Support 

Table 4.6 

Estimates of the Relationship Between Single Stop Use and Postsecondary 

Outcomes by Student Subgroup 

Ratio of 

One- Credits 

Semester One-Year Credits Credits Earned to 

Persistence Persistence Attempted Earned Attempted 

Single Stop user 

Older (>25) 0.121*** 0.154*** 2.782*** 2.953*** 0.014 

Younger 0.025*** 0.02 0.234 0.141 −0.003 

(≤25) 

Dependent 0.018* 0.012 0.296 0.194 −0.002 

Independent 0.100*** 0.116** 1.196 1.222 −0.009 

White −0.007 0.037 1.522 1.194 −0.033 

Nonwhite 0.035*** 0.031** 0.33 0.294 −0.001 

Beneft screening 

Older (>25) 0.118*** 0.172*** 2.613*** 2.870*** 0.027 

Younger 0.022** 0.015 0.207 0.087 −0.006 

(≤25) 

Dependent 0.018* 0.008 0.185 0.06 −0.005 

Independent 0.090** 0.126*** 0.989 1.617* 0.03 

White −0.001 0.028 1.957* 1.808 −0.015 

Nonwhite 0.031*** 0.028* 0.267 0.2 −0.002 

Tax service 

Older (>25) — 0.262*** 2.283 2.333 0 

Younger — 0.116*** 0.535 0.951 0.028 

(≤25) 

Dependent — 0.084* 1.107 1.483* 0.027 

Independent — 0.200*** 0.704 -0.16 −0.008 

White — 0.321** −0.074 1.025 −0.059 

Nonwhite — 0.133*** 0.907 1.211* 0.029 

NOTE: Estimates are from the CEM model. *** p < 0.001, ** p < 0.01, * p < 0.05. 
Single Stop and Postsecondary Outcomes 61 

estimated to have attempted nearly two additional credits (p < 0.05), 

and independent students were estimated to have earned an additional 

1.6 credits (p < 0.05). With regard to use of tax services, there was 

a strong relationship with persistence outcomes for all student subgroups, with more-positive relationships for adult learners and independent students. Interestingly, there was a more positive relationship 

between receiving tax services and persistence for white students than 

for minority students, in contrast to what we saw for the estimates 

for all Single Stop users. For the most part, the relationships between 

use of tax services and credit outcomes were nonsignifcant. However, 

dependent students were estimated to have earned an additional 1.5 

credits (p < 0.05), and nonwhite tax service students were estimated to 

have earned an additional 1.2 credits (p < 0.05). 

Postsecondary Outcomes Vary by Institution 

When we conducted subgroup analyses by institution, we found varying results (Table 4.7). Te estimated outcomes for CUNY were consistently positive and statistically signifcant, with the exception of the 

ratio of earned-to-attempted credits. Single Stop users at CUNY persisted at rates that were 6 percentage points higher than comparable 

students who did not use Single Stop (p < 0.001). Tese students also 

attempted and earned more than one additional credit on average (p < 

0.001). When looking at results for the specifc services, it appears that 

the credit outcomes are related to beneft screenings but not tax outcomes, while both beneft screenings and tax services are related to 

improved persistence outcomes. 

We also found large, positive estimates across all outcomes for 

Delgado; however, because of small sample sizes, some of the estimates 

were not statistically signifcant. Single Stop users at Delgado persisted 

into a second year at rates that were 11 percentage points higher relative 

to nonusers (p < 0.05) and attempted 1.3 additional credits (p < 0.05). 

Although we found positive estimates for both persistence and credit 

outcomes with regard to beneft screenings and tax services, only the 

persistence results for these specifc services were positive. 
62 Connecting College Students to Alternative Sources of Support 

Table 4.7 

Estimates of the Relationship Between Single Stop Use and Postsecondary 

Outcomes by Institution 

Ratio of 

Credits 

One-Semester One-Year Credits Credits Earned to 

Persistence Persistence Attempted Earned Attempted 

Single Stop user 

BHCC −0.058 −0.026 2.234 3.763* 0.075 

CUNY 0.063*** 0.060*** 1.314*** 1.222** 0.003 

Delgado 0.146** 0.113* 1.373* 1.681 0.044 

MDC 0.000 0.000 −0.281 −0.354 −0.007 

Beneft screening 

BHCC −0.059 −0.003 −0.889 −0.308 −0.007 

CUNY 0.065*** 0.058** 1.420*** 1.295** 0.003 

Delgado 0.139** 0.117* 1.038 1.170 0.039 

MDC −0.001 −0.003 −0.271 −0.368 −0.008 

Tax service 

BHCC — 0.120 2.392** 3.521 0.079 

CUNY — 0.160*** 0.604 0.931 0.017 

Delgado — 0.403** 2.251 0.232 −0.066 

MDC — −0.091 0.265 0.934 0.042 

NOTE: Estimates are from unconditional model. *** p < 0.001, ** p < 0.01, * p < 0.05. 

Results for persistence and credit outcomes diverged for BHCC. 

Estimates for persistence were negative and nonsignifcant, but estimates for credits were large and positive. Single Stop users at BHCC 

were estimated to have earned nearly four additional credits relative 

to similar students who did not use Single Stop (p < 0.05). At MDC, 

we found no relationship between Single Stop use and postsecondary 

outcomes. 
CHAPTER FIVE 

Summary of Findings and Implications 

Community colleges are struggling to improve completion rates, and 

they are looking to better ways to support their students and ensure 

success. Students face a range of nonacademic barriers to success, and 

with the costs of college rising, many of these nonacademic barriers to success are fnancial in nature. Yet community colleges are not 

equipped to address the range of challenges that students face. Advising departments are typically underresourced and focused on academic 

issues. Colleges and community organizations might ofer a variety of 

resources to address nonacademic barriers, yet students have difculties navigating the network of resources and accessing the assistance 

they need. And although there are fnancial aid ofces in every college, there are few resources devoted to accessing alternative sources 

of fnancial support, including the resources ofered through public 

beneft programs. Programs such as Single Stop U.S.A.’s Community 

College Initiative aim to ofer students a better way to access public 

beneft programs and the broad network of wraparound services that 

institutions and community organizations provide. 

Te literature indicates that programs providing wraparound services are successful in improving college outcomes for students (Avery, 

2013; Castleman and Goodman, forthcoming; Scrivener, Bloom, 

et al., 2008; Scrivener, Weiss, et al., 2015), as are interventions that 

aim to connect students to fnancial support (Bartik, Hershbein, and 

Lachowska, 2015; Bettinger, 2004; Dynarski, 2008; Richburg-Hayes 

et al., 2009; Scott-Clayton, 2011) and interventions that provide 

information about or assist with complex application processes (Bet63 
64 Connecting College Students to Alternative Sources of Support 

tinger et al., 2012; Castleman and Page, 2016; Castleman, Page, and 

Schooley, 2014). However, there is limited research on programs such 

as Single Stop, which focuses exclusively on nonacademic wraparound 

support and facilitates access to public benefts and other resources for 

low-income students. Tis report contributes to the literature by providing evidence on a unique support program that could be scaled to 

community colleges across the United States in an efort to address 

nonacademic barriers to student success. 

Single Stop Use Is Associated with Positive Postsecondary 

Outcomes 

We found that students who interacted with Single Stop demonstrated 

improved postsecondary outcomes relative to their peers who were 

not clients of Single Stop. Single Stop users were at least 3 percentage 

points more likely to persist into a second year of community college 

compared with similar students who did not interact with Single Stop. 

In addition, Single Stop users attempted at least one additional credit 

in their freshman years. 

Tese fndings align with prior work that shows positive impacts 

for interventions that connect students to fnancial resources or provide advising support, with the estimates falling in the range of those 

found in the literature. Brock and Richberg-Hayes (2006) found that 

a Louisiana scholarship program increased persistence rates by 6.5 percent, while Bettinger (2004) estimated a reduction of 3 to 4 percentage points in dropout rates for those who received Pell Grants. An 

intervention that involved direct contact “nudges” for frst-year community college students was found to have somewhat stronger impacts, 

increasing persistence rates by 12 to 14 percentage points (Castleman 

and Page, 2016). 

We were unable to examine some of the services that Single Stop 

provides—screenings and referrals for tax, fnancial, and legal services 

are tracked fairly well, but additional services, such as referrals for substance abuse, are not tracked as easily. Many of the case-management 

activities were not tracked, and reporting from external fnancial and 
Summary of Findings and Implications 65 

legal providers was not reliable according to Single Stop’s evaluation 

ofce. However, after eforts to improve data reliability in response to 

fndings on these data issues (Goldrick-Rab, Broton, and Frank, 2014), 

we were able to examine the subset of Single Stop users who used the 

two most common services provided by Single Stop: beneft screenings 

and tax assistance services. We found positive relationships between 

postsecondary outcomes and use of these two major services, with relationship between tax services and postsecondary outcomes being particularly strong. 

It is important to consider why tax services were found to have a 

particularly strong relationship with postsecondary outcomes. According to Single Stop staf, tax services can be particularly productive in 

terms of benefts confrmed, meaning that nearly every low-income 

student who participates in tax assistance receives some money through 

the Earned Income Tax Credit. Beneft screenings, on the other hand, 

require students to meet strict eligibility requirements and complete 

complex applications, so only a portion of students qualify for and 

receive the benefts. In addition, whereas application for public benefts may carry stigma for some individuals, there is little to no stigma 

associated with fling taxes. Another factor may be that tax fling is 

required for FAFSA and Pell applications, so when taxes are fled, students can also apply for those. In addition to a higher likelihood of 

confrmed benefts, variation in outcome may be driven by the timing 

of these services. Tax services were largely provided to students in the 

spring semester, so most students must have already persisted at least 

one semester to have received these services. 

Our analysis of outcomes by student subgroup suggests that 

Single Stop may ofer particularly large benefts for adult learners (age 

25 and older), independent students, and nonwhite students. Although 

we did not explicitly test for diferences across student subgroups, the 

estimates were substantially larger and more likely to be statistically 

signifcant for these students. Tis aligns with what we might anticipate given that these students are more likely to be eligible for public 

benefts and may have a greater need for such resources as housing 

and child care. Tis suggests that our focus on FTIC students might 

have resulted in estimates that were lower than the true efect across 
66 Connecting College Students to Alternative Sources of Support 

community college students, because those most likely to beneft from 

Single Stop were somewhat underrepresented among FTIC students. 

Institution-specifc analysis indicated variation in the relationship between Single Stop use and postsecondary outcomes. Single Stop 

users at CUNY and Delgado were found to have higher rates of persistence and increased credit accumulation across most of our models. 

Results for BHCC Single Stop users were mixed, with strong positive 

results for credits earned, while there was no statistically signifcant 

relationship with persistence. And there were no positive results found 

for MDC. 

Our discussion in Chapter Two suggests that there are a number 

of reasons we might have expected variation in outcomes across Single 

Stop sites. Our institutions varied in terms of the population served. 

At BHCC and MDC, more than one-ffth of all FTIC students were 

served by Single Stop (Table 3.1). Te CUNY and Delgado sites served 

to just 10.5 and 5.7 percent of their FTIC students, respectively. To 

the degree that CUNY and Delgado sites are providing more-intensive 

services to a smaller number of students, we might expect to see morepositive outcomes for the students served. Even if all of the sites were 

afecting a small group of high-needs students in a similar way, eforts 

to broadly provide outreach to and screen students regardless of potential need at institutions like BHCC and MDC might have reduced 

the average estimates across all students served. On the other hand, 

spreading time and resources across many students may afect the ability of sites to efectively serve their highest-needs students. Additional 

analysis examining subgroups of students and assessing the package 

of services students receive could provide evidence on the question of 

whether the percentage of students served is related to variation in outcomes across institutions. 

Te makeup of institutional populations and Single Stop clientele 

also varied across campuses. For example, the student population at 

Delgado included a greater percentage of adult learners, and students 

were more likely to have dependents (Tables 2.1 and 3.1). Further, Delgado and several of the CUNY sites served Single Stop client populations that were more than 50 percent adult learners (Table 2.2). Because 

there were more-positive outcomes for these students, we might expect 
Summary of Findings and Implications 67 

the average impact across Single Stop users at those sites. However, 

BHCC had an older FTIC population and more students with dependents relative to CUNY (Table 3.1), yet CUNY had more consistently 

positive outcomes, so age and family makeup might not account for all 

of the variation across institutions. 

Te sites also varied in terms of operational details. For example, 

the CUNY sites are the oldest among those examined, so these sites 

might have had more time to integrate into the institution and map the 

full network of resources. In addition, the Single Stop staf-to-student 

ratio was much higher at MDC than at the other institutions. However, without systematic data on implementation, we were unable to 

directly examine variation in how the sites are run and how they are 

integrated into the overall institution. 

Single Stop users varied across sites in their take-up of specifc 

Single Stop services (Tables 2.2 and 3.1). BHCC provided beneft 

screenings and tax services to the greatest percentage of FTIC students, 

and MDC also provided beneft screenings to many FTIC students. In 

terms of tax services, MDC was somewhat of an outlier in having provided tax services to just 22 percent of all Single Stop users, with many 

of the other sites providing tax services to more than twice as many 

Single Stop users. Given that we found a large positive relationship 

between tax services and postsecondary outcomes, we might anticipate a somewhat smaller improvement in outcomes at MDC relative to 

the other institutions. According to data recorded by the institutions 

(which might underidentify use because of incomplete reporting), the 

site at Delgado provided a greater percentage of all Single Stop users 

with legal and fnancial services in 2014–2015 and was more likely to 

have provided Single Stop users with at least one referral (Table 2.2). 

Tere were limited data on services provided, so we could not describe 

the full treatment provided to Single Stop users in terms of type and 

intensity of services, and we could not assess the efect of providing a 

particular service in isolation. 

Finally, the support and beneft program landscape varied across 

sites. Sites in states such as Massachusetts and New York have access 

to generous public programs, while opportunities are limited in other 

states. We might therefore have expected BHCC and CUNY to have 
68 Connecting College Students to Alternative Sources of Support 

the most positive outcomes. Access to public beneft programs does 

not, however, explain Delgado’s strongly positive outcomes given a lack 

of generous benefts in Louisiana. It may be that CUNY and Delgado 

also benefted from the institutional and community resources available beyond those that Single Stop provides, allowing site staf to more 

efectively provide case-management support; we were unable to examine this directly with available data. 

Tis report provides important evidence that is suggestive of positive impacts for Single Stop on college outcomes. It should be noted that 

concluding a causal relationship is premature. Te primary concern 

is omitted variables that confound the observed association between 

Single Stop use and student outcomes. For example, more-motivated 

students might not only be more likely to seek out services from a program like Single Stop, but they may also be more likely to persist and 

accumulate more credits. Without accounting for this motivation, our 

estimates may overstate the benefts of the Single Stop program. On 

the other hand, our ability to account for level of fnancial need was 

limited because of substantial missing data and relatively few fnancial 

indicators. Assuming that Single Stop attracts the students with the 

greatest level of fnancial need, we might not be accounting for this 

need, and our estimates might understate the benefts of the Single 

Stop program. 

Implications of the Findings for Policy and Practice 

Although there were limitations to our analysis, this report provides 

important evidence on the value of a program that connects students 

to a network of support programs and provides access to public benefts as an alternative source of fnancial support. Tese fndings suggest important next steps for policy and practice. We found that community college students who use Single Stop are likely to experience 

improved postsecondary success. Tese fndings suggest that access 

to alternative fnancial resources from government beneft programs 

alongside a network of institutional and community support programs 

can ofer valuable support to college students. Institutions should con­
Summary of Findings and Implications 69 

sider how they might ofer programs such as Single Stop to create a 

central location for students to access wraparound supports and to provide students with greater access to government beneft programs and 

other critical services. Even in the cases where institutions do not want 

to ofer additional services, such as beneft screenings, Single Stop’s 

model of a one-stop shop may be useful to consider as a means of integrating existing resources in the institution and reducing the complexity of processes that students must undertake to obtain fnancial and 

nonfnancial support. Our results cannot, however, speak to the efectiveness of providing individual services. Although positive outcomes 

were observed for individuals who received beneft screenings and tax 

services, we could not account for the other services received and the 

outcomes that could be attributed to these other services. If institutions 

are interested in implementing specifc services rather than developing 

a one-stop shop, this report cannot speak to the impacts of implementing these services in isolation. 

To facilitate the work of programs such as Single Stop, it is important for policymakers to consider how government beneft programs 

might be made more accessible to students. By modifying eligibility 

requirements and simplifying application requirements, public beneft 

programs can be a more accessible source of support for more college 

students. In addition, comprehensive tracking of beneft recipients and 

data-sharing with programs such as Single Stop can help improve coordination of services while also supporting research around the use of 

beneft programs by students. If programs such as Single Stop work 

collaboratively with the government, institutions, and community 

organizations to improve access to public benefts and other support 

resources, college students will have the support needed to overcome 

many barriers to success in college. 

More Research Needed on Single Stop and Related 

Programs 

Tis report is just a frst step to understanding how Single Stop and 

similar programs might provide benefts to community college stu­
70 Connecting College Students to Alternative Sources of Support 

dents and whether institutions across the United States should adopt 

similar programs. Our analytic methods were limited at accounting for 

selection bias, so future studies might employ experimental approaches 

to estimate the true impact of the program. In addition, our decision 

to limit analysis to FTIC Single Stop users who could be matched to 

similar FTIC students who did not use Single Stop limited the generalizability of our fndings. Our student subgroup fndings suggest that 

non-FTIC students may be more likely to beneft from Single Stop, 

so the estimates in this report may underestimate the true impact of 

Single Stop across all community college students. 

To help our understanding of how and why programs such as 

Single Stop’s Community College Initiative may lead to improved 

postsecondary outcomes, future studies should seek to unpack the 

mechanisms through which the various Single Stop services may translate into greater persistence rates and higher levels of credit completion. 

Institutions have considerable autonomy in how they implement the 

program and how the program is wrapped into the larger set of institutional programs and structures. For example, some institutions rely 

heavily on faculty referrals to drive student trafc to Single Stop, while 

other institutions do not make explicit eforts to involve faculty in the 

program. Additional research that examines a larger set of Single Stop 

institutions and exploits cross-institution variation in the data could 

be useful in understanding more about the role of implementation. In 

addition, more research on implementation can help to identify best 

practices at the institutions experiencing better outcomes and drive 

program improvement by making changes at other sites. 

It is also important to investigate what drives student outcomes: Is 

it the receipt of fnancial resources because of Single Stop? Or do other 

aspects of the support that Single Stop provides (e.g., feeling welcome 

at an institution) also play a role in driving student outcomes? Better 

data are needed on the fnancial resources and receipt of government 

benefts among users and nonusers to account for the contribution of 

Single Stop in terms of additional fnancial resources. Tis could help 

Single Stop sites and similar programs focus resources on the eforts 

that are most likely to drive improved student outcomes. Additional 

data on benefts received may shed light on our fnding that tax assis­
Summary of Findings and Implications 71 

tance services were associated with more-positive outcomes relative to 

beneft screenings, as well as the hypothesis of Single Stop staf that the 

greater efectiveness is due to a greater likelihood of having received 

confrmed benefts. However, it may also have been the timing of the 

service or the types of students who accessed the service. In general, 

additional research is necessary to assess the isolated value of tax services and other services that Single Stop provides. Our study cannot 

speak to the efectiveness of individual services because incomplete 

data on the range of services provided. 

Finally, interventions such as Single Stop are not the only interventions that have been developed to improve student success in college. We described a range of other programs—including wraparound 

support programs that combine academic and nonacademic support, 

fnancial aid programs, and informational interventions—that help 

students facilitate completion of registration processes and access to 

support services. Tese interventions varied in terms of estimated 

impacts, and they likely difer in cost as well. To determine which 

interventions should be adopted by institutions and scaled across the 

country to address student success in college, it is critical to conduct 

cost-efectiveness analysis to determine which programs among the 

universe of student-support interventions improve student outcomes at 

the lowest cost. 

Abbreviations
 

ASAP Accelerated Study in Associate Programs 

BEN Benefts Enrollment Network 

BHCC Bunker Hill Community College 

CEM coarsened exact matching 

CUNY City University of New York 

FAFSA Free Application for Federal Student Aid 

FTIC frst time in college 

GPA grade point average 

IPEDS Integrated Postsecondary Education Data 

System 

MDC Miami Dade College 

NSC National Student Clearinghouse 

SNAP Supplemental Nutrition Assistance Program 

TANF Temporary Assistance for Needy Families 

73 

     

   

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