The Influence of the Federal Financial Aid Verification Process on College Student Access

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Title:
The Influence of the Federal Financial Aid Verification Process on College Student Access
Physical Description:
1 online resource (113 p.)
Language:
english
Creator:
Doeble, Gina B
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Doctorate ( Ed.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Higher Education Administration, Human Development and Organizational Studies in Education
Committee Chair:
CAMPBELL,DALE FRANKLIN
Committee Co-Chair:
VILLARREAL,PEDRO
Committee Members:
SANDEEN,CARL A
LEVERTY,LYNN H

Subjects

Subjects / Keywords:
access -- aid -- bureaucracy -- college -- financial -- government -- red -- regulation -- tape -- verification
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
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Higher Education Administration thesis, Ed.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
The federal government is the largest source of financial aid, providing billions of dollars annually to students needing financial assistance in order to gain access to higher education. To ensure accountability over these funds, many rules and regulations have been established that effect the institution and the student. Numerous steps must be completed to process a students federal aid. Submitting the federal financial aid application is only the first step. Navigating through the process can be overwhelming, leaving many students discouraged and frustrated. This study focused on the federal financial aid regulation, known as the verification process. To determine if this federal requirement influences college access, student enrollment patterns were examined.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Gina B Doeble.
Thesis:
Thesis (Ed.D.)--University of Florida, 2014.
Local:
Adviser: CAMPBELL,DALE FRANKLIN.
Local:
Co-adviser: VILLARREAL,PEDRO.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2014
System ID:
UFE0046610:00001


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THE INFLUENCE OF THE FEDERAL FINANCIAL AID VERIFICATION PROCESS ON COLLEGE STUDENT ACCESS By GINA BARATTA DOEBLE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF EDUCATION UNIVERSITY OF FLORIDA 2014

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2014 Gina Baratta Doeble

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To my husband Lance ; and to our children, Taylor and Bree

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ACKNOWLEDGMENTS I wish to thank my husband, Lance and my two daughters, Taylor and Bree for their support and encouragement. I am eternally gratefully for their endless patience. I also thank my colleagues. Without their support and motivation I would not be where I am today. I thank my supervisory committee chair, Dale Campbell, for his leadership and continued encouragement. His passion for success kept me focused. I thank the education faculty and other committee members I had the honor to work with for sharing their experiences. 4

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TABLE OF CONTENTS page ACKNOWLEDGM ENTS ...............................................................................................................4 LIST OF TABLES ...........................................................................................................................7 LIST OF FIGURE S .........................................................................................................................8 ABSTRACT .....................................................................................................................................9 CHAPTER 1 INTRODUCTION ..................................................................................................................10 Statement of Problem .............................................................................................................12 Purpose of Study .....................................................................................................................14 Research Question ..................................................................................................................16 Research Hypotheses ..............................................................................................................16 Accessing Financial Aid Funds ..............................................................................................16 Verification Process ................................................................................................................17 Significance of the Study ........................................................................................................19 2 LITERATURE REVIEW .......................................................................................................20 Higher Education and Financial Aid: A Historical Perspective .............................................20 Financial Aids Impact on Enrollment, Persistence, and Degree Attainment .................23 Investment in Financia l Aid ............................................................................................24 Financial Aids Impact on Low Income and Minority Students .....................................26 Existing Barriers that Inhibit Financial Aid Applications ......................................................29 Red Tape Theory .............................................................................................................33 Rule Inception Red Tape .................................................................................................38 Rule Evolved Red Tape ..................................................................................................38 Red Tape and Performance .............................................................................................40 Red Tape Research ..........................................................................................................42 3 METHODOLOGY .................................................................................................................45 Purpose of Study .....................................................................................................................45 Research Question ..................................................................................................................46 Study Setting ...................................................................................................................46 Research Desig n ..............................................................................................................47 Population ........................................................................................................................47 Data Collection ................................................................................................................49 Data Analysis ...................................................................................................................51 Research Hypotheses ..............................................................................................................52 Limitations ..............................................................................................................................52 5

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4 RESULTS ...............................................................................................................................54 Descriptive Statistics ..............................................................................................................55 Age ..................................................................................................................................56 Gender .............................................................................................................................57 Race .................................................................................................................................58 Socio Economic Status ....................................................................................................59 Month FASFA Submitted ................................................................................................59 Resear ch Hypothesis One .......................................................................................................60 Research Hypothesis Two ......................................................................................................63 Research Hypothesis Three ....................................................................................................60 5 CONCLUSION .......................................................................................................................97 Discussion of Re sults ..............................................................................................................97 Enrollment and Verification ............................................................................................98 Two Year Trends ...........................................................................................................100 Verification and Award Amount .....................................................................................98 Verification and Red Tape ............................................................................................101 Directions for Future Research .............................................................................................103 Implications for Higher Education .......................................................................................104 Instituional Level ...........................................................................................................104 Higher Education Research ...........................................................................................105 Federal Level .................................................................................................................105 APPENDIX A HIGHER EDUCATION AND FINANCIAL AID LEGISLATIVE HISTORY .................106 B FINANCIAL AID PROCES FLOWCHART .......................................................................107 C ACCESSING FINANCIAL AID FUNDS FLOW CHART ................................................108 REFERENCES ............................................................................................................................109 BIOGRAPHICAL SKETCH .......................................................................................................113 6

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LIST OF TABLES Table page 41 Total population descript i ve statistics ................................................................................66 42 Selected for verification descriptive statistics ....................................................................69 43 Completed verification: descriptive statistics ....................................................................72 44 Did not complete verification: descriptive statistics ..........................................................75 45 Selected for Verification: frequency ..................................................................................78 46 Selected for verification and enrolled: frequency ..............................................................78 47 Omnibus tests of model coefficients: logistic regression ..................................................79 48 Classification table: logistic regre ssion .............................................................................79 49 Variables in the equation: logistic regression ....................................................................80 410 Selected for verification completion percentages: descriptive statistics ............................80 411 Completed verification enrolled percentages: descriptive statistics ..................................83 412 Did not complete verification enrolled percentages: descriptive statistics ........................85 413 Students completing verification: frequency .....................................................................88 414 Students completing verification and enrolled: frequency ................................................88 415 Omnibus tests of model coefficients: logistic regression ..................................................88 416 Classification table: logistic regression .............................................................................89 417 Variables in the equation: logistic regression ....................................................................90 418 Moderating variables logistic regression variables in the equation ...................................91 419 Estimated family contribu tion: frequency of change .........................................................93 420 Pre and post estimated family contribution: descriptive statistics .....................................94 421 Pre and post estimated family contribution percent change ..............................................95 7

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LIST OF FIGURES Figure page 41 Mean age: aggregate descriptive statistics comparison .....................................................96 42 Gender: aggregate descriptive sstatistics comparison........................................................96 43 Race/ethnicity: aggregate descriptive statistics comparison ..............................................96 B 1 Financial Aid Process Flowchar t .....................................................................................107 C 1 Flowchart for accessing financial aid funds .....................................................................108 8

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education THE INFLUENCE OF THE FEDERAL FINANCIAL AID VERIFICATION PROCESS ON COLLEGE STUDENT ACCESS By Gina Baratta Doeble May 2014 Chair: Dale Campbell Major: Higher Education Administration The federal government is the largest source of financial aid, providing billions of dollars annually to students needing financial assistance in order to gain access to higher education. To ensure accountability over these funds, many rules and regulations have been established that effect the institution and the student. Numerous steps must be completed to process a students federal aid. Submitting the federal financial aid application is only the first step. Navigating through the process can be overwhelming, leaving many students discouraged and frustrated. Th is study focused on the federal financial aid regulation, known as the verification process. To determine if this federal requirement influences college access, student enrollment patterns were examined. 9

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CHAPTER 1 INTRODUCTION Student Financial aid programs are intended to remove financial barriers for students wishing to purs ue post secondary education. The federal government is the largest source of financi al aid providing b illions of dollars annually to students in need of financial assistance. Federal financial aid is administered by the U.S. Department of Education, Federal Student Aid Office. Their core mission is to ensure that all eligible individuals benefit from federal financial assistance through grants, workstudy, or loans. Every year, the Federal Student Aid Office provides billions in aid to nearly 1 6 million postsecondary students and their families ( NASFAA, 2011a). Despite this annual investment, financial barriers persist; many requirements and regulations must be adhered to, to ensure student eligibility. The purpose of the regulations is to ensure dollars are distributed appropriately; unfortunately, they can make students experience of an already challenging process even more frustrating ( The Institute of College Access and Success, 2010). Regulations and rules are not unique to the federal financial aid programs. Although regulations and rules are perceived by many Americans as ne gative or surrounded by red tape, this organizational model prevails today. Federal bureaucracies regulate almost all aspects of American life such as interacting with schools, hospitals or government offices. For example, The Federal Reserve controls in terest rates; the Federal Deposit Insurance Corporation (FDIC) insures bank deposits ; and the Food and Drug Administration (FDA) determines wh ich drugs doctors can prescribe (Dye, 2010) Since public policies are rarely self executing, bureaucracies are es sentially the creators of policy. They are responsible for developing the procedures and rules for implementing the goals of policy. Congress typically announces the goals of a policy in broad terms, sets up an administrative apparatus, and leaves to the b ureaucracy the task of 10

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working out the details of the program (Edwards, Wattenberg & Lineberry, 2004). Therefore, policy implementation can be defined as the stage of policymaking between the establishment of a policy and the consequences of the policy for the people whom it affects. Unfortunately these rules may end up creating new obstacles to effective and efficient governing Federal regulators issue thousands of pages of rules and regulations, conduct inspections and investigations of complaints, hold hearings, require submission of forms, and levy fines and penalties. Each year nearly 4,000 new rules are added to American life. The Environmental Protection Agency is the leader in making new rules, followed by the Internal Revenue Service (Dye, 2010). These r egulations cost money but the true cost is hard to determine. These costs are passed on to Americans through the direct costs of compliance and the indirect economic costs of devoting resources to compliance; this may add up to $1 trillion per year (Dye, 2010). Economists believe these regulations stymie innovation and productivity while driving up the cost of living (Dye, 2010). H ow much government intervention is necessary is an ongoing debate between proponents of laissez faire (leave it alone) and those who argue that continual and intense government monitoring is necessary to protect the consumer. Social researchers over the years have p rovided their thoughts on bureaucracies. Robert K. Merton (1910 2003), a distinguished American sociologist who coined the terms role model and self fulfilling prophecy provided his insights on the effect on individuals through his description of burea ucratic personality. For Merton, bureaucratic structures and procedures are established to get certain things done, but sometimes they become ends in themselves. When this happens, we may see the emergence of the bureaucratic virtuoso, a functionary who closely adheres to all the rules and procedures but hardly accomplishes anything of significance. 11

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Statement of Problem S ince the U.S. Department of Education is the largest provider of student financial aid in the United States, to obtain access to these funds students are subjected to the adherence of numerous federal regulations. F ederal financial aid programs are no different than other governing agencies discussed above and have been criticized for red tape, paper trails and piles of rules. The National Association of Student Financial Aid Administrators (NASFAA) is a nonprofit membership organization that represents nearly 20,000 financial aid professionals at 2,800 colleges, universities, and career schools across the country. Each year, financial aid professionals help more than 16 million students receive funding for post secondary education (NASFAA, 2011a) One of the many roles of NASFAA is to serve the financial aid community through regulatory analysis. Results from a 2010 NASFAA survey concluded financial aid offices and the student services they provide are being strained by increasing regulatory and administrative burdens. The survey f urther demonstrated that 9 of 10 of more than 1,000 responding NASFAA members reported having fewer resources to dedicate to critical student services that promote college access and success (NASFAA, 2011a). Services that are falling short due because of t he heightened workload are face to face counseling, extra attention for target student populations and outreach efforts The primary reason cited for such critical shortages w ere regulatory/compliance workload. Other factors included the following: Greater numbers of aid applicants More applicants needing their application to be updated due to changes in family finances More applications needing to be verified Compliance with new, complex year round Pell Grant regulations New regulations unrelated t o the student aid programs These findings align with a recent NASFAA review of student aid regulatory language, which found a 40% increase (in word count) of federal regulations governing the student aid programs 12

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over the last decade (NASFAA, 2011b). Jus tin Draeger (2011), President of NASFAA said At some point, we must stop and ask to what extent federal regulations and requirements are either hindering college access and success or increasing costs for students . As mentioned earlier, the government ag ency responsible for administering the federal financial aid programs the Federal Student Aid (FSA) office. The FSA office is responsible for developing distributi ng, and processing the Free Application for Federal Student Aid (FAFSA), the fundamental qualifying form used for all federal student aid distribution programs The FASFA form is also used for many state, regional, and private student aid programs (Office of Federal Student Aid, 2013). Additionally, the FSA office is responsible for enforcing the financial aid rules and regulations required by the Higher Education Act and the U.S. Department of Education and for man aging the outstanding federal student loan portfolio (Office of Federal Student Aid 2013). The FSA offices core mission is to ensure that all eligible Americans benefit from federal financial assistance through grants, loans or work study programs f or education beyond high school (Office of Federal Student Aid, 2013) The FSA office administers the programs compris ing the nation's largest source of student financial aid, in addition to overseeing $864 billion in outstanding student loans The Federal St udent Aid team is committed to making education beyond high school more attainable for all Americans, regardless of socioeconomic status (Office of Federal Student Aid, 2013) Financial aid plays a vital role in removing financial barriers for students by providing billions of dollars of federal funds. In addition, various studies document the importance of financial aid in college persistence and degree attainment. Studies include Michael Haynes (2008), Mike MacCallum (2008), and Katherine Bird (2006). In addition, numerous reports 13

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annually describ e the substantial investment governments, colleges and universities dedicate to financial aid programs to remove financial barriers for students who otherwise cannot afford college. Some of these key reports ar e summarized in a NASFAA series (2005). The U.S. Department of Education, National Center for Education Statistics also collects data used to prepare annual reports such as Integrated Postsecondary Education Data System (IPEDS) and the National Postseconda ry Student Aid Study (NPSAS). In addition, the U.S. Department of Education, Office of Postsecondary Education prepares annual reports used to quantify the substantial investment of federal financial aid funds. However, only a handful of studies have speci fically addressed whether the additional requirements have an influence on student access to higher education, such as the report issued by the Institute of College Access and Success in July 2010. These reports and studies are discussed further in the lit erature review. Purpose of Study Ensuring access to higher education is critical to the completion agenda. The completion agenda as described by OBanion (2013) is doubling the number of students in the next decade who obtain a higher education degree or c ertificate. President Barack Obama in his February 24, 2009 address stated: half of the students who begin college never finish. This is a prescription for economic decline, because we know countries that out teach us today will out compete us tomorrow. That is why it will be the goal of this administration to ensure that every child has access to a complete and competitive education (OBanion, 2013) Federal financial aid is provided to students in need of financial assistance to increase access to st udents who cannot afford college. However, since the federal government invests billions of dollars annually in providing financial assistance to students, a number of rules, regulations, and procedures accompany the funds. The lack of information regardin g the effects of these federal requirements on college access is regrettable as they may be discouraging 14

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students from completing the process to obtain the needed funds. These federal requirements are complicated and add to an already intimidating and time consuming process that may cause students to delay enrolling in higher education; or even worse, never enroll at all. In addition, institutions spend inordinate time and money adhering to these federal regulations further reducing the resources available to help students navigate through the financial aid process. Lack of adherence to federal regulations can also damage the institution. Institutions found negligent in their duties to award federal funds to eligible students may face severe fines, penaltie s or loss of eligibility to participate in the federal financial aid program. Two colleges in Florida were cited for granting financial aid to students de emed ineligible by the Department of Education during a federal audit. Florida State College at Jacks onville expects to pay the federal government $4.7 million for problems stemming from grants and loans mistakenly awarded to students over a 2year period. The money includes a $515,000 penalty for improperly issued student loans. College officials attribu ted the mistakes to overextended financial aid workers who struggled to keep up with increasing enrollment ( DeSantis, 2012). S tate College of Florida was found to have awarded federal financial aid to 1,948 ineligible students during the 200809 year. The U.S. Department of Education noted significant deficiencies in the colleges handling of federal financial aid disbursements to students, and required reimbursement (Kennedy, 2011). State College of Florida must repay $3.2 million plus interest in federal financial aid that it erroneously granted to students. Justin Draeger (2011), president of NASFAA, said the sheer size and scope of federal regulations and other administrative burdens have pushed financial aid offices to the breaking point . As for stu dents, getting the required paperwork in order can be a challenge and not all 15

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make it to the end (The Institute of College Access and Success, 2010) Therefore, the purpose of th is study was to determine if the verification regulation influences college access Red Tape Theory was used as the conceptual framework to evaluate the verification process and the potential influence it has on college access. Research Question T o what extent does the financial aid verification process influence college student access? Research Hypotheses 1. There is no significant change in the amount of financial aid students receive as a result of being selected for verification. 2. There is no significant relationship between being selected for verification versus not being selected for verification on enrollment. 3. There is no significant relationship between completing verification versus not completing verification on enrollment. Accessing Financial Aid Funds There are many steps to the financial aid process. The first step is comp leting the Free Application for Federal Student Aid (FAFSA). This application may be accessed online and submitted electronically to the U.S. Department of Education. The Department of Education uses the information to calculate a students estimated family contribution (EFC). The EFC is the amount student s are able to contribute toward their education. The EFC and the cost of attendance are used to determin e need for financial aid. Once this has been completed, the student and the institution receive the information from the Department of E ducation. The institution then create s a financial aid package based on this information and sends an award letter notif ying students of the financial aid they are eligible t o receive. The student can either accept or reject the financial aid offer. Financial aid will be disbursed to the student after the 16

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student has registered and classes have begun: typically this occurs within the first 2 weeks of classes. However, after r eceiving the students FASFA the Department of Education randomly flag s applications for the institution to verify the data submitted on the FASFA Almost all applicants flagged are PELL eligible and are the lowest income students who may have little support to help them through the verification process (The Institute of College Access and Success, 2010). The verification process is just one of many federal regulations applicants are subjected to in determining their eligibility. The verification proce ss can add up to 8 weeks (depending on the time of year) of additional processing time before awarding the student s financial aid (Figure B 1, Figure C 1). Verification Process The federal regulation requiring verification was adopted in the 198687 award year. This regulation was promulgated because of concern about fraud and abuse and concern that information reported by individuals may not be accurate. Since amounts of eligible aid are based on financial data submitted by individuals the U.S. Department of Education decided it was prudent to verify the information submitted. Since the passage of this regulation known as the verification process, institutions must check the accuracy of information a student or students parent has given, for those applica nts flagged by the U.S. Department of Education. The regulations require colleges and universities to verify, or confirm, the data reported by students and their parent(s) on the FAFSA. Information is verified by requesting additional documentation or a si gned statement attesting to the accuracy of the data. The intent of the verification process is also to ensure that eligible students receive all the federal financial aid to which they are entitled ; and to prevent ineligible students from receiving financ ial aid for which they do not qualify. 17

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The federal regulations do not prohibit institutions from using thirdparty providers to assist in the verification process. However, the institution is responsible for ensuring compliance with the regulations and wi ll be held liable regardless if it uses a third party provider. In addition to the verification process, institutions are also required to resolve conflicting information even if the student was not selected for verification. Although the regulations provi de some guidance on the verification process, institutions are also required to establish written policies and procedures for which the regulations allow institutional discretion or flexibility. Overall, t he purpose of the verification process is to ensure the integrity of the financial aid program to the taxpayer. The main reasons for being selected for verification include random selection, the FAFSA submitted was incomplete, the FAFSA contains estimated information, or the data provided on the FAFSA are inconsistent. If selected, the verification process must be completed before financial aid can be awarded. T he institutions Office of Student Financial Aid may be required to verify the following data elements from the FAFSA: Adjusted g ross i ncome (parent and student, if the student is dependent) Taxes paid (parent and student, if the student is dependent) Income e arned from w ork (for nontax filers) Certain untaxed i ncome i tems (parent and student, if the student is dependent) Household s ize Number in c ollege (excluding parents for a dependent student) Receipt of f ood s tamps/SNAP benefit Child s upport paid Any other inconsistent or conflicting information Before July 1, 2012, the federal requirements only required the institution to verify 30% of the students selected for verification by the Department of Education. The verification regulations have recently undergone major changes. Effective July 1, 2012, by federal requirement, institutions must verify all students selected for verification. The Department of Educations ultimate goal in the future is to target those students most prone to error. Over the 18

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next several years, a risk model will be developed to refine the selection criteria. It is too early to tell the overall impact of these changes to student enrollment and financial resources, but it could have significant implications and is an area for future research. Sign ificance of the Study The study is expected to make at least two contributions to the area of financial aid First, the study will contribute to the expanding knowledge base regarding the role financial aid serves in removing financial barriers to students attending post secondary education. As more is known about the relationship of the federal requirements such as the verification process and the influence on student access it will be possible to more clearly understand whether these requirements are ser ving the intended purpose or are merely red tape, which will be explored further in the literature review. Second, this study aimed to determine the return on investment to the taxpayer for institution s to administer the verification process. This was determined by reviewing the change in financial a i d award amounts as a result of the verification process. Results have the potential to reveal significant financial implications to the institution, the federal government, and the taxpayer. 19

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CHAPTER 2 LI TERATURE REVIEW T his literature review provides an analysis and examination of the existing literature regarding federal financial aid In addition, since Red Tape Theory was used as the conceptual framework to evaluate the verification process an overview of the existing literature on Red Tape Theory and the impact on government performance is provided. After a review of the literature, f our common themes emerged regarding financial aid : financial aids impact on enrollment, persistence and degree attainment; investment in financial aid; financial aids impact to low income and minority students; and barriers that may inhibit financial aid application by students Higher Education and Financial Aid: A Historical Perspective To gain a better understan ding of the importance of higher education in our society, it is helpful to be familiar with the history of higher education and how financial aid has evolved since its inception. Appendix A gives a quick summary of the legislative history pertaining to the federal financial aid regulations. It is equally important to have a basic understanding of the financial aid process of gaining access to federal financial aid funds; therefore a brief overview of this process is provided. From the beginning, federal st udent aid policy has been shaped by a commitment to access. The legacy of access to higher education is deeply ingrained in our public values. The democratization of college opportunities in the United States can be traced through two centuries from the la ndgrant college movement and the establishment of state universities in the l9th century to The Servicemen's Readjustment Act (GI Bill), establishment of community college systems, and explosion of enrollments after World War II. Major phases in the growt h of higher education have extended access to new groups in society (Gladieux, 1995) 20

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Federal student aid has clearly been an important force in shaping American postsecondary education since World War II. The first wave of support for higher education in stitutions began in 1862, when President Lincoln signed into law the land grant bill (Morrill Act). This bill gave states federal lands to establish Land Grant Colleges and the Reserve Officers Training Corp (ROTC) ( Keesey 2013) By 1890, publicly suppo rted institutions of higher education had been established in every state, offering programs in agriculture, engineering, home economics, and teacher training They also forged the expansion of services to the community, with agricultural and general exten sion divisions ( Cohen & Brawer, 2008) During the first half of the 20th century, student tuition and fees made up the bulk of the operating costs for community colleges, while a limited amount of outside funding was derived from the public school budget. After World War II, and with the passing of Roosevelts GI Bill, federal funds began to flow into Americas community colleges sparking another rapid growth period. Community colleges grew from 521 in 1934 to 650 by 1948 (Cohen & Brawer, 2008) The GI Bil l of 1944 provided the first large scale financial aid packages, and made it possible for students to be reimbursed for their tuition and living expenses. This also marked the beginning of the cultural shift with the non wealthy being granted access to hig her education. During this period, the institutional emphasis was on providing training for professionals in local businesses and industries ( Cohen & Brawer, 2008). To cope with the increased number of community colleges, states began to independently develop funding strategies. No federal framework was provided; therefore, methods of funding were left up to the states, allowing them to respond individually to institu tional needs, program requirements, workforce projections, and state fiscal capacities (Mullin & Honeyman, 2007) This created much variation among states, and in many places, access and affordability remained 21

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key issues. By 1959, the trend across the stat es was to reduce local funding and increase tuition and fees, creating added burdens on low income students (Cohen & Brawer, 2008) The Basic Educational Opportunity Grant (Pell grant) legislation was proposed and passed in the early 1970s as a means of i ncreasing higher education access to low income and first generation students. Senator Claiborne Pell, along with other leaders in Congress, promoted education as vital to the sustainability of the nation, and proposed legislation to provide need based in come to undergraduate students. Pell grants promoted access to higher education by granting federal funds to students based on their expected family contribution (EFC). This afforded lower income and nontraditional students with higher education opportunit ies. Pell Grants continue to provide the largest single source of federal financial aid ( Mellow & Heelan, 2008) For nearly four decades, American college attainment rates remained steady, leading the world with roughly 39% of American adults holding a 2 year or 4 year degree (Matthews, 2009) Around the turn of the 21st century, however, college attainment rates began to show significant increases in almost every industrialized country in the world, except the U.S. (Matthews, 2009) This, coupled with the 2008 Great Recession, forced lawmakers to once again look at higher education as a means of bolstering the economy and preparing a workforce capable of meeting the global demands of the rapidly evolving 21st century knowledge economy. In 2009, President Obama proposed spending $12 billion to improve courses, programs, and facilities at community colleges so they can produce an additional five million graduates by 2020 and reestablish Americans as leaders in higher education ( United States Office of the Pr ess Secretary 2009) The 2011 federal budget proposed continued support of higher education by increasing Pell grants; reforming the student loan program; providing student loan debt forgiveness; increasing 22

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funds to support Science, Technology, Engineering, and Mathematics (STEM) education; reforming the job training system to encourage innovation; and increasing support to minority serving institutions ( United States Office of Management and Budget, 2010) Financial Aids Impact on Enrollment, Persistence and Degree Attainment Several studies examined the impact of financial aid on enrollment, persistence, and degree attainment. Although the existing literature on the impact of financial aid on enrollment, student persistence, and degree attainment indica tes a relationship, the literature disagrees on how much of an impact financial aid really has. For example, The Impact of Financial Aid on Postsecondary Persistence (Haynes, 2008) suggests student borrowing has a negative impact on student retention. A si milar study by MacCallum (2008) examined the variations of policies and processes in financial aid offices and related those variations to student outcomes. MacCallum (2008) sought to determine if the variations in institutional polices and processing of f inancial aid such as verification requirements or student academic progress policies would have an effect on financial aid students. More specifically, MacCallums study was designed to ascertain if financial aid delivery, financial aid policies, and institutional support of the financial aid office impacted the enrollment rate, retention, and success of the student. He found factors suggesting that financial aid does have an impact on student retention and success. For example, the length of time to proces s financial aid was positively related to retention; however, verification beyond the minimum requirements was inversely related to enrollment (MacCallum, 2008). Like Haynes study, MacCallum (2008) said loan borrowing had a direct effect on student retent ion. For example, in institutions that limited the amount students were allowed to borrow, the institutions tended to have lower retention rates. Conversely, Baird (2006), examined student enrollment patterns and how other factors such as student preparat ion (rather than tuition costs or financial aid policy) affected student 23

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enrollment. Baird (2006) concluded that, despite extensive literature on factors affecting student enrollment, a consensus has yet to emerge. Bairds study examines the effects of tui tion, financial aid, student preparation, supply (defined as the number of colleges available to students), labor market conditions, family, and community background on student enrollment. Baird cited studies by Ellewood and Kane (2000) and Cameron and Hec kman (2001), indicating that high school achievement is the most important factor explaining college enrollments concluding that financial aid polices have a marginal impact on enrollment decisions. In contrast, Bairds study indicated that neither tuition costs nor financial aid have a measurable effect on student enrollment. Investment in Financial Aid Both the state and federal government provide financial assistance to students in the form of grants, scholarships, or loans. This annual investment is sig nificant and is in the billions of dollars. T he number of students in need of financial assistance to attend a higher education institution has also increased. Total financial aid awarded to students has grown at a faster rate than tuition and fees, with t he largest growth in the number of students receiving Pell grants (NASFAA, 2011 b ). In addition, large gains in student loan borrowing have been reported. What this means for an institution of higher education is the increase d number of students receiving f inancial aid has outpaced the increase in student enrollment. For the 2009 10 year, Pell grants distributed from the federal government totaled $29 billion (U.S. Department of Education, 2010). The U.S. Department of Education (2011) compared the percentag es of students receiving some form of financial assistance in 199596 versus 200708 year. The report shows a 12.7 to 15.6% increase of students receiving financial aid, depending on institutional type. The largest increase was found in public 2year colle ges. In addition, the average amount of financial aid doubled. The investment in financial aid is published annually in numerous reports. These 24

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reports provide valuable information and statistics regarding the substantial investment governments, colleges and universities dedicate to financial aid programs to remove financial barriers for students who otherwise cannot afford college. Some of these key reports are summarized by NASFAA, which include Trends in Student Aid, Trends in Student Pricing, and an An nual Survey of Colleges conducted by the College Board. The U.S. Department of Education, National Center for Education Statistics also collects data from higher education institutions, to prepare annual reports. The data are collected through surveys such as Integrated Postsecondary Education Data System (IPEDS) and the National Postsecondary Student Aid Study (NPSAS). In addition the U.S. Department of Education, Office of Postsecondary Education prepares annual reports such as the Pell Grant End of Year Report and the Federal Campus Based Programs Data Book. A recent report, Trends in Student Aid 2011, by the College Board Advocacy & Policy Center, reported that during the 2010 11 academic year, $227.2 billion in financial aid was distributed to students and students borrowed an additional $7.9 billion. This represents a 141% increase over the 2005 06 academic year. Pell grant recipients also increased 75% from the 200506 academic year to the 2010 11 academic year, representing $34.8 million dollars dist ributed for Pell Grants (College Board Advocacy & Policy Center, 2011). No doubt the federal government has provided financial resources to millions of students pursuing a post secondary education. This substantial investment, after all, is to provide for a stronger society. As evidenced in Education Pays 2010 (College Board Advocacy & Policy Center, 2010) higher education improves peoples lives, makes our economy more efficient, and contributes to a more equitable society. However, the federal aid program s are not easily accessible to all. Unfortunately, too many low income and first generation students still choose 25

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not to enroll because of financial barriers (College Board Advocacy & Policy Center, 2011). It seems counter intuitive that the neediest stude nts undergo additional scrutiny in order to be awarded financial aid. Financial Aids Impact on Low Income and Minority Students Debates over student aid policy have typically centered on whether policy changes would hinder or expand access for disadvanta ged students. Above all, the problem of unequal opportunity has proved more difficult than anyone anticipated in the early years of the Higher Education Act. In the late 1960s and early 1970s, widely cited reports from the Bureau of the Census showed that a collegeage youth from a family with an income over $15,000 was nearly five times more likely to be enrolled in higher education than one from a family with an income of less than $3,000 (Gladieux, 1995). The financial aid polices were to address these g aps by removing financial barriers, to promote equal access. The National Center for Education Statistics (2012) reported that among recent high school graduates, the gap between college age young people from the lowest income range (34.82%) and the highe st income range (64.5%) according to the U.S. Census categories in 1975 was 29.7%. Today the gap remains at 29.9% with 52.3% attending from the lowest income category and 82.2% from the highest income category. These figures may suggest improved in access to college opportunities during this period, but the more certain point is that large gaps stubbornly persist. Other statistics, although improving, continue to show socioeconomic disparities in access to higher education programs among minority students. Although, an overall enrollment of all racial/ethnic groups increased, the enrollment rate of minority students still lags behind that of whites (NCES, 2012). The National Center for Education Statistics (NCES) report indicated an increase between 1980 and 2010 for each racial/ethnic group some 8.5 million or 83% of the 26

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undergraduate enrollment of U.S. residents were White, compared with 9.0 million or 70% in 2000. By 2010, the number of White students had grown to 10.9 million, but the percentage had decr eased to 62% The number of Black undergraduate students who were U.S. residents increased 163 % between 1980 and 2010, from 1.0 million (10% ) to 2.7 million students (15% ). Hispanic and Asian/Pacific Islander enrollments increased 487 and 337 % respectivel y, from 1980 to 2010. In 1980, Hispanics and Asians/Pacific Islanders represented 4 and 2% of enrollment, respectively, compared to 14 and 6 % in 2010. American Indian/Alaska Native enrollment increased from 78,000 to 179,000 students from 1980 to 2010 (1% of total enrollment in each year) (NCES, 2012) In addition to the statistics studies have been conducted regarding the impact financial aid has on low income and minority students A study conducted by Kennamer, Katsinas, and Schumacker (2011), revealed that the increases in federal student aid have not kept pace with the rising costs of tuition and enrollments, thus negatively affecting student retention. The student population most affected by unmet need is low income and minority students. Studies on retention ( Tinto, 1997; Zhao and Kuh, 2004) indicate that students who are more engaged or participate in learning communities are more likely to succeed and more likely to stay in college. Students who have unmet needs and are otherwise unable to afford college often times work in order to fill the gap and are therefore unable to participate in college activities. In fact, studies from the American Association of Community Colleges have shown that most community college students work while attending college (Kennamer, Katsinas, and Schumacker, 2011). For most community college students, college activities and events are not something these students have the luxury of participating in. Although federal financial aid is intended so nobody wanting to attend college is denied the opportunity on the basis of finances, the inability of federal aid 27

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programs to keep pace with tuition is negatively impacting student success (Kennamer, Katsinas and Schumacker, 2011). To further expand on Kennamer, Katsinas, and Shcumackers 2011 findings, Long and Riley (2007) argue that the financial aid policy has shifted its emphasis from providing low income and minority students with sufficient financial a id to defraying costs for middle and upper income families because of the increase in merit based aid versus need based aid. Long and Riley (2007) said that low income and minority students, especially those of color, are more likely to face substantial u nmet needs after considering income from all sources. Their study identified three barriers to college access: cost, academic preparation, and the complexity of admissions and financial aid processes. Their study, however, focused on costs and the effectiv eness of financial aid policy in addressing affordability. Long and Riley said a shift in financial aid policy over the last 15 years increasing emphasis on merit based aid, has negatively impacted the neediest students. The amount of unmet need has nearly doubled from $3,092 in 19951996 to $6,726 in 20032004, with 79% of low income students with unmet need compared to only 13% of highincome students (Long & Riley, 2007). Overall, Long and Riley (2007) concluded that policymakers need to reexamine and re main attentive to the needs of providing access to low income and minority students. They also urged policy makers to simplify the financial aid application process, to increase student access and success (Long & Riley, 2007). A different type of study, co nducted by Chen and Desjardins (2010), examined socioeconomic differences in financial aid and investigated whether differences existed by race and ethnicity. Overall the study focused on the ways financial aid influences dropout risks among students from different racial and ethnic backgrounds (Chen & Desjardins, 2010). To 28

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accomplish this, the researchers further explored the various types and amounts of aid each group received. Their study aimed to inform policy makers developing financial aid policies to improve equal opportunity in higher education. Their study showed that equalizing educational opportunity for minority and low income students is still a challenge, and gaps between minorities and Whites could be narrowed to improve access to financial a id. Despite the significant annual investment by the federal government, many students still experience unmet need. Such gaps in opportunity, and the failure of student aid policies to close them, should probably not come as a surprise. Federal student aid, in its conception, was primarily about helping those who otherwise might not have access to higher education. However, federal policies have become as much (or more) about relieving the economic burden for those who would probably pursue postsecondary pr ograms without such aid. Moreover, state tuition, subsidy, and funding policies are at least as important in determining patterns of enrollment and access as what the federal government can achieve through its investment in student aid. Existing Barriers that Inhibit Financial Aid Applications The federal financial aid process has been described as a complex bureaucratic process that is difficult for students to navigate. In addition, the administrative burden placed on institutions to adhere to the many f ederal requirements is overwhelming, leaving many financial aid offices frustrated and unable to provide necessary help to students. Despite the annual investment to financial aid programs, financial barriers still persist in the form of misperceptions, an d lack of communication regarding the financial aid process and the other requirements that must be adhered to after completing the FAFSA. Attempts have been made to gain a deeper understanding of the perceptions regarding financial aid and the process. 29

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Pe rna (2011) explored high school students perceptions of financial aid and how those perceptions influence collegerelated behaviors. Her case study represented fifteen high schools in five different states. Pernas (2011) study revealed most students and parents have a general awareness that financial aid is available based on financial need and other criteria, however these perceptions and expectations of aid are informed by the characteristics of the school. For example, in low and middle resource schoo ls, where family resources and college enrollment rates are lower, substantially fewer families are certain about the availability of financial aid to pay for college. Moreover, these schools generally do not have the resources, particularly in terms of sc hool provided counseling, to educate students and parents about the availability of financial aid (Perna, 2011). Perna (2011) also noted the limited literature regarding students perceptions of the financial aid process. Among the limitations of prior res earch wa s the practice of considering only actual amounts of financial aid rather than students knowledge or perceptions of aid. Despite the belief that information about financial aid promotes college enrollment, little is known about the most effective content, timing and/or modes of delivering messages about financial aid (Perna, 2011). The importance of communicating the financial process and also provi di ng accurate information was a consistent theme that emerged from a study conducted by the College Board in 2010. The College Board is a not forprofit association commissioned to learn more about students and parents knowledge about the importance of college and how to pay for it. The study, Cracking the Student Aid Code: Parent and Student Perspecti ves on Paying for College (2010), found that the lack of information and understanding of college financing is a barrier that is difficult to overcome for many students and families. The system in place today is complex 30

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and makes planning and saving for college feel like cracking an impenetrable code (College Board, 2010). Although many students have aspirations to attend college, their dream and the reality often diverge when they attempt to navigate the financial aid process. Through the College Boards r esearch, it was determined that c ommunicating financial aid information at the right time and in ways that can be easily understood is critical to removing barriers to attending college. One such study looked at timing and the factors associated with students delaying submission of the FAFSA. The study, conducted by Andrew LaManque (2009), assumes students who complete the FAFSA early versus students who complete the FAFSA late are more likely to be successful in college. LaManque (2009) hypothesized that early filing of the FAFSA was related to the knowledge the student has about college. The implication is that the more knowledgeable students are about college, the financial aid application process, and the benefits of applying early, the more likely they are to complete their FAFSA early (LaManque, 2009). Variation in FAFSA filing was directly related to the students knowledge about financial aid. A significant number of students (85%) who filed early said they received information from their high school guidance counselor. This finding supports Pernas (2011) study and the importance of high schools in educating prospective students about college. Perna (2011) also found that whe re family resources and college enrollment rates are lower, substantially fewer families are certain about the availability of financial aid to pay for college The LaManque study provided data that may help institutions with their outreach efforts and show them where to focus their resources to promote early submission of the FAF SA. F ederal financial aid programs are intended to remove financial barriers to students students may access higher education to obtain the necessary skills to become productive 31

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citizens. However, for students to access these funds numerous requirements must be verified to ensure student eligibility and the onus is on the institution to ensure compliance. In addition, many students are unfamiliar with the process and each step they take is a leap of faith that their efforts are worthwhile (The Institute for College Access and Success, 2010). A study conducted by the Institute of College Access and Success (ICAS) examined students who originally submitted a FAFSA but did not complete the process. The study used financial aid data from thirteen community c olleges in California. They found evidence that students selected for verification, who may otherwise be eligible to receive financial aid, did not complete the process. In addition, those students selected for verification were 7% less likely to receive g rants, resulting in 1,200 students who would have received Pell Grants (ICAS, 2010). The study also determined that all colleges were verifying more students than required by federal regulations, increasing the overall cost to the institution and the overa ll processing time of financial aid applications. The thirteen colleges collectively spent between $1.7 million and $2.5 million attempting to verify student information and 63% of students selected for verification saw no change in their financial aid award. In addition, students and financial aid administrators were surveyed regarding the verification process. Many students said they had to drop classes because the verification process took too long. Overall, financial aid administrators said students el igibility did not change much, and it is unfortunate that students are subjected to the process at all. The ICAS study raises questions about the validity of the verification process. Further study is needed as the ICAS study was isolated to California. Ma cCallums (2008) study also indicated that the financial aid verification process was inversely related to enrollment. Although the literature is mixed about the effect of financial aid on enrollment, persistence, and degree 32

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attainment, it is consistent th at students who cannot pay for education will not attend or end up dropping out. The literature is also consistent about the complexity of the financial aid process and the possible effects it may have on delaying financial aid application and student acce ss. Therefore it is important to understand the unintended consequences that financial aid requirements, such as the verification process has on student access. Although, it is important to protect public dollars it should not negatively impact students. U nfortunately, the current process may discourage completion of the financial aid application and ultimately discourage enrollment. The entire process of completing the FAFSA ; submi tting additional forms for verification purposes ; and determin ing eligibilit y barring no omission s mistakes or delays may take months. Potential students will weigh costs and time versus benefits to determine if applying for financial aid is worthwhile. If the financial aid process is deemed too complicated, time consuming or la borious the time and hassle are likely to be higher than the benefits. The complexities of existing financial aid policies make it difficult for those most in need of help (College Board Advocacy & Policy Center, 2011). Red Tape Theory Although the word bureaucracy does have negative connotations, benefits to the proverbial red tape associated with bureaucracy do exist. For example, bureaucratic regulations that pertain to the FDA take appropriate precautions to safeguard the health of Americans. Even though bureaucracies are typically impersonal, this can be viewed as promoting equal treatment and discouraging favoritism. Bureaucracies have also been criticized for the amount of documentation that is needed to navigate through regulat ory compliance. H owever the paper trail documents the process so that, if problems arise, data exist for analysis and correction. On the other hand, r egulations may be viewed as rules designed to control certain actions by people or as a distinctive state ment by the government against the people or a specific 33

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industry ; even though regulations a re portrayed as a protection for the people. When unexpected situations arise, adherence to rules may inhibit the actions needed to achieve overall goals. Critics of bureaucracy argue that mountains of paper and rules only slow an organization's capacity to achieve stated goals, while costing taxpayers both time and money. The effects of regulation cannot easily be quantified in terms of cost of goods and i t is diffic ult to assess whether the regulation is causing more harm than good. Is the verification process a bureaucratic process that is merely red tape? To answer this question, it is important to understand Red Tape Theory. Many relate red tape with rules, reg ulations, government, time delays, paperwork and overall frustration. Goodsell (2000) said red tape is the leading pejorative symbol of government bureaucracy in the English language representing the inefficient workings of government. Of the several bure aucratic problems, red tape is perhaps the most pervasive and damaging because red tape is assumed to make public organizations more arthritic and self serving, less able to achieve their core missions, and less responsive to users (Brewer & Walker, 2009). This widespread notion of red tape has sparked interest in the research community and has elevated red tape to a researchable phenomenon of public and private administration. So where did the term redtape come from and what does it actually mean ? Many re searchers have begun study ing the phenomenon. The term red tape was actually coined from the color of the cord that bound government documents together in the early centuries. Red tape has been defined differently throughout the years. Barry Bozeman is a p rofessor of public policy and his research focuses on public management, organization theory, and sc i ence and technology policy. He is also known for his research on red tape theory and bureaucracy. Bozeman provides a summary of the various definitions of red tape that has been used throughout the literature 34

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Bozeman refers to Herbert Kaufman who is widely mentioned throughout the literature on red tape. Although, Kaufman never gives an actual definition of red tape he says when people rail against red t ape, they mean that they are subjected to too many constraints, which many of the constraints seem pointless, and that agencies seem to take forever to act (Bozeman, 1993). Ve xation, constraint and delay are common elements of red tape. P rior research has historically viewed red tape as organizational pathology and has been described by researchers as follows : Buchanan (1975) : excessive constraints that are largely structural in nature Rosenfeld (1984) : red tape is the sum of government g uidelines, procedures, and forms that as are perceived as excessive, unwieldy, or pointless in relation to official decision and policy. Baldwin (1990) : defines formal and informal red tape F ormal red tape pertains to burdensome procedures ; and informal r ed tape concerns constraints by external factors such as the media, public opinion, and political parties Although definitions of red tape vary, there are common elements These include excessive or meaningless paperwork ; high degree of formalization and c onstraint, unnecessary rules, procedures and regulations; inefficiency; unjustifiable delays; and as a result frustration and vexation. Red tape has a negative connotation tenor and most would not see why it is needed or see the be nefits to red tape. Goods ell (1987) said red tape is the most universal rejection symbol but also a classic condensation symbol because it incorporates a vast array of subjectively held feelings. Most explanations of red tape in government result from an emphasis o n accountabili ty. I n the public administration literature provided by Kaufman, red tape may be frustrating, but it sometimes provides social benefits. I t does not spring up because of incompetence or malice of bureaucrats but rather to ensure that government processes are accountable and meet the demands of citizens and interest groups. However, this process protection gives rise to red tape. Kaufman points out that red tape could be avoided if we were willing to reduce the checks and 35

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safeguards now imposed. H owever if we were to do away with the safeguards we would be appalled by the resurgence of the evils and follies it currently prevents (Bozeman, 1993). In addition, rules and procedures are fallible and error prone and some redundancy might be beneficial. Looking at regulations through this perspective raises the question: when are extensive rules and procedures considered red tape and when are they justified and beneficial. Bozeman construct ed a theory of good and bad rules in bureaucracies and other organizations. Rules are essential to bureaucratic organizations, creating the basis for stability, continuity, equity, and many other valued attributes (Goods e ll, 2000). Bozeman distinguishes between white tape as the good rules that provide a benefit despite delay s and frustrations and red tape ; as the dysfunctional rules that fail to help or cause much mischief. Bozeman defined several types of r ed tape, lay ing the foundation for future studies attempt ing to measure the impact of red tape on organizations. Bozeman said it is often not the number of rules, regulations and procedures that cause problems T he time and energy u sed by an organization to comply with the rules is the problem. Therefore red tape can be measured as the delays in the organizations c ore activities. Bozeman provides the following formula for measuring red tape: Rule Sum: the total number of written rules, procedures, and regulations in force for an organization. Compliance requirement: total resources (time, people, and money) required formally to comply with a rule or regulation. Compliance burden: total resources (time, money, and people) actually expended in complying with a rule. Rule density : the compliance burden associated with a set of rules and is defined as: the percentage of the compliance burden (resources devoted to complying with all its rule) to total resources expended. 36

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When determining the impact of red tape on an organization, it is also important to understand the reason a rule wa s created and whom it affects. T his functional object of a rule, according to Bozeman is the reason a rule was created, the problem it seeks to solve, the opportunity it exploits and rule efficacy ( the extent to which a given rule addresses effectively the functional object for which it was de signed ) Since red tape affects both the organization enforcing the regulations and the individuals using the services, red tape is viewed through two different lenses. Viewing red tape through the lens of an organization is called organizational red tape and is defined as rules, regulations and procedures that remain in force and entail a compliance burden for the organization, but have no efficacy for the rules functional object (Bozeman, 1993). Viewing red tape through the lens of a stakeholder is call ed stakeholder red tape and is defined as organizational rules, regulations, and procedures that remain in force and entail a compliance burden, but serve no object valued by a given stakeholder group (Bozeman, 1993) A complicating factor for researchers studying red tape is that rules may help some people but hurt others. Rules and regulations are not inherently good or bad for everyone. A rule may be red tape for one and useful for another (Bozeman, 1993). Therefore red tape is subject dependent R ules and procedures have multiple impacts. This view is shared by other researchers Kaufman said One mans red tape is anothers treasured procedural safeguard and Waldo said One mans red tape is another mans system (Bozeman, 1993). Rules may also ach ieve certain values and undermine others or be reasonably useful but have high opportunity costs (Goodsell, 2000). Bozemans research defined two types of red tape : r ule i nception r ed t ape ( rules born bad and dysfunctional from the origin ) These rules h ave a compliance burden, while not addressing 37

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a functional object. The second type is r ule e volved r ed t ape ( rules functional at one time that have turned bad) Rule Inception Red Tape Inadequate comprehension: insufficient understanding of the problem a t hand Self aggrandizement and illegitimate reasons: serves an individual or group only Negative sum compromise: a rule established to serve so many diverse functional objectives that the net result is to produce compliance burden, but not enhance any of the functional objects Over control: the most common since a common response to uncertainty and ambiguity is to seek control through formalization Negative sum process: organizational democracy becomes such a source of red tape that the value of partici pation ends up serving no purpose Rule Evolved Red Tape Rule drift: meaning and spirit get lost in the organization I t is just a ritual and no one knows the purpose it serves Rule entropy: special case of rule drift R ules get passed down through levels of the organization or individuals Change in implementation: rule stays essentially the same but is implemented in different manners Change in functional object: functional object renders the rule obsolete Change in rules efficacy: functional obje ct does not change but circumstances change that mitigate the rules usefulness Rule strain: organizations with high rule density create strain and inefficient use of resources R ules that are good but too abundant have a negative effect Accretion: rule s that build a top one another I f inconsistent the net effect is damaging R ules have an impact that is more than the sum of their parts Misapplication: rules may be difficult to interpret or apply because they are written poorly or are not communicated to those enforcing the rules Rules and regulations may be generated i nternally or externally. Understanding the sources of red tape is important for an organization. For instance external sources are likely to 38

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lead to customer defection and client dissat isfaction. E xternally imposed rules may become internally imposed red tape (Bozeman, 1993). That is the original source of rules is not necessarily the original source of the red tape. Bozeman provides four sources of red tape: Ordinary red tape: red tape that originates inside the organization and has external impacts on clients Intraorganizational red tape: red tape originates inside the organization and impacts internal constituents External control red tape: red tape that originates externally but has internal organizational impacts Pass through ted tape: red tape that originates externally and has its chief impact on the client or customer with the organization simply passing it along by implementing. Bozemans study (1993) using delays as a measure f ound that government owned entities or private organizations with government ties exhibited more red tape This suggests that the chief cause is not government ownership but rather external political authority. Government ownership is positively related to ordinary red tape, external control red tape and pass through red tape (Bozeman, 1993). Government organizations have inherent attributes that make them more likely to be subjected to red tape : external control, homogeneity and number of stakeholders. Typically they have a large number of external controllers exerting legitimate influence and providing rules. The most common causes of redtape in government organizations are accretion, inadequate comprehension, and rule strain. Accretion is problemat ic because the procedural safeguards almost invariably involve cross cutting goals and produce a natural tug of war between agencies I nadequate comprehension is often problematic too, because those interpreting or applying a procedural safeguard can easi ly lose sight of the rule. However, most important is rule strain : the sheer number of procedural safeguards produce an extensive compliance burden and for organizations with limited resources (Bozeman, 1993). Educational institutions 39

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experience rule strai n, because they have a higher level of formalization and procedural regularity. Red Tape and Perfo r mance H igh levels of governmental performance are obviously important to society at large. Thus we must understand t he effect of red tape on governmental pe rformance S uch knowledge can be used to improve public service to better society. Brewer and Walker (2009) examined the effects of red tape on governmental performance and whether these effects vary across the constructs dimensions. The principal source of their data was a survey, which provided perceptual measures of red tape. The major contribution of the study was that it examined the various dimensions of red tape and performance. For instance they examined whether internal red tape results in quali ty and efficiency losses and whether external red tape lowers customer satisfaction and perceptions (Brewer & walker, 2009). The y also present ed evidence showing that red tape is a subject dependent concept consistent with Bozemans formulation of stakeho lder red tape. Brewer and Walker found that an aggregate measure of red tape reduces governmental performance as expected ; but the relationship disappears when a full range of control variables are entered. They concluded that the red tape myth may be somewhat overblown and its potential impact on governmental performance may be slightly over estimated, indicating that red tape does not have any salient effects on efficiency. Their study also found that stakeholder perceptions are important. However, Brewer and Walker confirmed that the one size fits all reforms typically introduced and adopted by governments are unlikely to achieve desired results. Federal regulations exceed 165,000 pages and the desire for greater efficiency in government is one of the most compelling reasons for administrative reforms such as rule simplification and paperwork reduction (Luttner, 2012) Federal agencies through executive 40

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order have been charged with improving the retrospective review process by providing meaningful measu res of regulations and performance. Randall Lutter a scholar at Resources for the Future studied the impact of four regulatory agencies: Environmental Protection Agency (EPA), the Food and Drug Administration (FDA), the National Highway Safety Traffic Adm inistration (NHSTA) and the Securities and Exchange Commission (SEC). Luttner (2012) analyzed recent reports on retrospective review and found little evidence of progress toward improving measurement of results I n fact they consistently failed to produce useful measures of regulations and performance. A lso little information was provided to determine if benefits exceeded cost. The study also concluded that it was impossible to judge how well existing regulation s work, even by the agencies own reports on retrospective review. C hallenges with the retrospective review : agencies lack of impartiality in reviewing their own regulations, inappropriately narrow focus, and failure to promote steps to better measure reg ulations actual benefits versus costs (Luttner, 2012). Lutt ner offered suggestions in improving the retrospective review process saying a wholesale approach focused on regulatory programs rather than individual regulations may provide more useful and eff icient measures of effectiveness. Pandey and Coursey (2007) offered a model that tests red tape for organizational effectiveness. Through their model, they argued that red tape is directly related to organizational effectiveness and the culture of the or ganization can moderate this impact on performance (Bozeman & Feeney, 2012). This is consistent with Walker and Brewers finding that different programs and services report variances in red tape and can have different effects, depending on the perceptions of red tape. 41

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Despite many government reforms aimed to reduce red tape (in the United States and abroad), the literature barely addresses the relationship between red tape and performance. This disconnect is so important that Rainey and Steinbauer (1999) refer to the lack of tests in red tape on organizational performance as the elephant in the room (Bozeman & Feeney, 2012, p. 115). It is regrettable that the literature on red tape theory and its impact on performance for individuals and organizations are limited. Red tape may impede efficiencies within organizations and affects employee and customer satisfaction, by having employees focused on tasks that create excessive paperwork that serve no functional purpose, but create delays and reduce performance (Bozeman & Feeney, 2012). If an organization has a high level of red tape, it may cause systemic negative effects on performance. Red Tape Research Studies of red tape usually involve a survey instrument asking respondents to assess the level of red tape in their organizations. Criticisms of red tape research include overreliance on survey data. Another criticism is the overemphasis of organizational effectiveness as a negative outcome of red tape, while not taking into account other important factors of r ed tape such as accountability, transparency, equity, and fairness (Feeney, 2012). Researchers of red tape recognize gaps in the research and have considered ways to improve measures and data. For example, Feeney (2012) examined how language used in survey questions may influence responses about perceptions of red tape. Feeneys (2012) study investigated whether question wording triggers an overall negative response and how the word usage might be related to perceived red tape. Feeney used a survey instrume nt that randomly assigned four types of red tape measures. When respondents took the survey, they were randomly assigned one of these measures. Feeneys study concluded that question wording and definitions provided in the red tape questionnaire influenced respondents assessments of organizational red tape. Feeney 42

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(2012) said when no definition is provided, higher levels of red tape are reported, because respondents have a broader definition of red tape, and the term red tape elicits strong negative connotations. Feeney (2012) said, to strengthen the validity of red tape research, future red tape measures should eliminate the term red tape and replace it with terms such as rules and regulations. Although the research has produced some mixed results an d the validity of red tape measures has been questioned researchers of red tape have surmounted these challenges to offer a significant amount of empirical work in a short time (Riccucci, 2012). Despite general recognition of the importance of red tape i n organizations behaviors and impacts and the lack of empirical rigor s ome red tape studies report no impact on government performance, while other research disagrees Red tape is universally seen as something problematic that must be overcome. In fact, the concept of red tape seems fundamentally based on the notion that red tape has corrosive effects on governmental performance (Brewer & Walker). Unfortunately measuring the impact of regulations on society as a whole to determine if they are white tape or red tape (as defined by Bozeman) has proved challenging. Debates will continue about whether government intervention is necessary through regulatory requirements. The political left proposes to combat red tape by decentralizing public service and instilling an entrepreneurial spirit in government T he political right offers a harsher set of remedies that includes wholesale deregulation of business and increased contracting and privatization of public services (Brewer & Walker 2009). M uch research on red tape is still needed especially because previous research is neither deep nor rich enough (Riccucci, 2012). Joseph Russo (2010) a director of student financial aid strategies at University of Notre Dame since 1978, has testified before the United St ates Congress on financial aid policy. Russo 43

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indicates that the promulgation of regulations, however well intentioned, has resulted in enormous administrative and financial burdens. The regulations create conundrums, because their purpose does not always l ine up with those of the institution (Russo, 2010). So, is the federal financial aid regulations red tape, as defined by Bozeman? Have students accessing federal financial funds fallen victim to red tape? This study addressed these questions and the ques tion proposed by NASFAA president Justin Draeger ; At some point, we must stop and ask to what extent federal regulations and requirements are either hindering college access and success or increasing costs for students To answer these questions this study applied red tape theory to federal financial aid regulations, by examining the verification process. The verification process, although intended to ensure that students are eligible to receive financial aid, a dds another step to an already intimidating process that adds substantial amount of time and money in processing students financial aid, while leaving many students feeling frustrated This process has added significantly to the cost of administration and is the primary reason for the confusion and complexity, and the discouragement many applicants experience (Russo, 2010). Drawing on the literature on red tape theory this study contributes to the knowledge on red tape theory. This study also contributes to the knowledge on the effectiveness of the federal regulation in verifying financial aid applicants information. To determine if the verification process influences college access two aspects of the process were examined. The first aspect examined the change in the financial aid award amount as a result of the verification process, to ascertain whether the verification process was beneficial in determining the amount a student was eligible to receive. The second aspect examined student access, by examining enr ollment patterns for students subjected to the verification process. 44

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CHAPTER 3 METHODOLOGY In the previous section, an introduction to the research was presented. This included a description of the studys research problem the research purpose and research hypotheses that directed the data analysis A brief overview of the history of financial aid and the financial aid process was also provided. In addition, a review of relevant literature related to financial aid and red tape theory established a b ackground support ing the study. The intent of this section is to describe the methodology that was utilized for the research effort. Included in the section is a description of the study setting, research design, study sample, and data collection methods, procedures, and analysis efforts. The focus of the analysis was on financial aid students selected for verification by the U.S. Department of Education for the 201112 and 201213 academic years and enrollment status for the respective fall terms. Purpose of Study Th is research study aimed to determine if the verification regulation influences college access by examining the effect on college student access. To accomplish this, student enrollment patterns were examined. This study was built on a study con ducted in 2010 by the Institute of College Access and Success (ICAS), which showed evidence that students selected for verification, who may otherwise be eligible to receive financial aid, did not complete the process. The ICAS study analyzed data for students who received a Pell eligible EFC versus those who actually received Pell and categorized the results by those selected for verification versus those not selected for verification. This study expanded on the ICAS study by comparing enrollment patterns for those selected versus not selected for verification to determine if the verification process hinders college access. 45

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Research Question The conceptual framework for the research question arose from a review of the literature on red tape theory. In addition, t here is relatively little research on students who begin the financial aid process but do not see it to the end. T his stu d y focused on the influence of the financial aid verification process on college access by examining student enrollment patt erns. RQ1: To what extent does the financial aid verification process i nfluence college student access? To address the research question, several analyses were required. The first analysis determined whether students selected for verification had a change in the ir financial aid award amount as a result of being selected for verification Essentially, as a result of being selected for verification, did the financial aid amount the student was eligible to receive increase, decrease, or stay the same? The ne xt analysis determined if s tudents selected for financial aid verification are less likely to enroll in college Essentially are students selected for verification less likely to enroll compared to students not selected for verification? The last analys is determined the likelihood of students enrolling upon completion of verification For students completing the process, additional factors (such as gender, age, race, socioeconomic status and the month the FASFA was submitted) were also analyzed, to deter mine if any relationship existed using those variables as predictors of enrollment Study Setting In its broadest conceptualization, this study was intended to address the population of students selected for verification in the United States. However, the vast diversity of educational institutions and financial aid process and other related variables would make for a monumental undertaking. Therefore, it was necessary to delimit the setting from which a sample for the study w as drawn. The community colleges traditional open door philosophy encourages all students 46

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who have graduated high school, obtained a GED, or is 18 years or older to enter college providing access to students who never dreamed of attending (OBanion, 2013). S uccess and survival of the community college is seen critically linked to the sustainability of the nations workforce and economy. The refore, this study sample included students attending a public community college, who applied for federal financial aid by subm itting a FASFA during the 201112 and 201213 academic years and enrolled for the respective fall term. Research Design Th is study used a correlational research design described by Dooley ( 2001 ) measuring the independent variable rather than setting it. F urther, the design used a cross sectional correlation, measuring the independent and dependent variables at the same time. Therefore, the study plan involve d gathering information about the student. No manipulation of the variables by the researcher was possible; instead any determined differences stem from differences in results in the measurement efforts according to age, gender race, socioeconomic status, and the month the FASFA was submitted. Population In Florida community and state colleges are the primary point of access to higher education, with 66% of the states high school graduates pursuing postsecondary education beginning at a Florida college (Florida Department of Education, 2012). Therefore this study focus ed on a public college in the Sta te of Florida. The c ollege selected for this study was established in 1961. With 679 full time and part time employees and 16,000 credit and noncredit students taking classes each year, the business of the college makes a significant impact on the local e conomy. S tudents are most likely to enter the local workforce after completing their college studies. 47

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The c ollege selected has seen the e ffects of changes in the economy with more students pursuing higher education. Although college enrollment decreased 10% for the Fall 2012 term, overall enrollment increased 52% over the 2009 to 2011 academic years Financial aid students represent ed 50% of the student population for the F all 2011 term and 48% for Fall 2012. The data reflect that, although student enrollment increased 52% over a three year period, financial aid applicants increased 173% for the same time frame. The college serves nearly 24,000 students annually and also serves the largest geographic region in the state of Florida covering 5,700 square miles. To ensure access to its students, it has three full service campuses and a center in the most populated counties. The institution has a traditional open door college mission and prides itself on welcoming all students with a high school dipl oma or equivalent. The institution is dedicated to meeting students at their academic level and providing a true pathway to educational success. The college was among 5 of 28 colleges that began offering bachelor s degree as part of a State Pilot Program t o bolster and support Floridas economic productivity and competitiveness by increasing access to affordable baccalaureate degrees. Most of its students are traditional undergraduate students enrolling in the Associate of Arts programs. The composition of the student body is as follows: Full Time 33%; Part Time 67% 64.2% ; students > 24 years old, 35.8% Female, 59.7%; Male 39.4% White, 58.0% ; Hispanic/Latino, 22.9% ; African American, 10.9% ; Other minorities, 3.5% Students who submitted a FASFA for the 201112 and 201213 academic year w ere used in order to determine the population size. Since, the U.S. Department of Education selects students for verification through a random selection process, no further random assignment of 48

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the population was needed. The entire population meeting the specified criteria was examined during the study Although data were pulled from one institution, due to the diverse geographic region the college serves and the diversity in the student populati on, the data analysis yielded sufficient representation from various groups for the purposes of this study. Data Collection T he intent of this study was to determine the impact of federal financial aid regulations on college student access. There are numerous federal financial aid regulations. To narrow the focus, this study was limited to requirements pertaining to verification of student information by the institution. Enrollment was used to measure student access. Therefore, this study attempted to follow the financial aid process from submission of the FAFSA by the student through the ultimate goal of enrollment, to determine whether the verification proc ess hinders access Previously collected data were used to measure two independent variable s one dependent variable, and five moderator (demographic) variables. These are outlined below. Independent variable : Verification the independent variable in this study, w as obtained by collecting information on whether the student was selected by the Department of Education for verification. This information was obtained by gathering data previously collected by the participating institution. The population was di vided into t hree groups : students selected for verification students selected for verification who completed the process and students selected for verification who did not complete the process Two separate analyses were performed using students selected for verification and students who completed verification as the independent variables. Dependent variable : The dependent variable in this study was enrollment. Enrollment in this study was used as the measure for access. This information was obtained by gathering data 49

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previously collected by the participating institution. The dependent variable, enrollment was cross referenced with the independent variable groups. Moderator variables : In addition to the above independent and dependent variables, five secondary independent or moderator variables w ere considered. According to Dooley ( 2001), a moderator variable adjusts the casual connection between other variables. This occurs when the effect of one variable depends on the level of another. Th us, modera tor variables can determine the extent to which the relationship between two major variables is influenced by secondary factors. In this study the moderator variables of age, gender, race, socioeconomic status, and the month the FASFA was submitted. Data gathering p lans : To test these hypotheses, I drew on secondary sources of data already collected by the institution. The data include d information from the institutions student database as well as the financial aid database, in order to follow the student s progress through the financial aid process Data collected for this study included, but were not limited to information such as the institutions total FASFAs, students verification flag, student demographics, student enrollment data, and amount of financial aid award per student. These were the central data sets of the study. To gather the necessary data elements to conduct this study, a review of the computer system the college uses for processing financial aid was necessary. The college uses the Bann er computer system, recognized a s a global leader in educationfocused services, technologies and expertise. The Banner computer system is designed specifically for higher education institutions and has been around since 1968. Banner has 2,400 customers a nd is used in forty countries. Banner is used by both public and private institutions from associate s degree granting institutions to research institutions. O f the t op fifty public c olleges and universities on the 2012 50

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US News and World Report's rankings 92% are Banner clients. In addition, 70% of the twenty l argest degree granting c ollege and university c ampuses in the United States are Banner clients To access data from the Banner system, a computer program was developed to extract the necessary data elements. An i ntroductory letter from the researcher asking for the institutions cooperation was sent to the college The letter described the research and its importance and asked for the support of the appropriate administrator. Data Analysis Sever al types of analysis were used for this study. First, descriptive statistics on the population were provided based on age, gender, r ace, socioeconomic status, and month the FASFA was submitted. Second, to determine the impact on student access and the veri fication process, a logistical regression analysis was used to compare enrollment rates for students selected versus not selected for verification. Logistical regression was used to determine if enrollment patterns were influenced by being selected for ver ification. Logistical regression was chosen for this study as it does not assume a linear relationship between dependent and independent variables, but rather calculates the probability of success in the form of an odds ratio. In addition, logistical regre ssion provides data to determine the strength and relationship of the variables (in this study, verification and enrollment). Logistical regression analysis was also used to compare enrollment rates for students who completed verification. This data was fu rther analyzed using variables for prediction based on age, gender, race, socioeconomic status, and month the FASFA was submitted, to determine any significant relationship between completing verification and those variables. The last analysis used descrip tive statistics providing the frequency of change in the amount of the award the student received as a result of being selected for verification. 51

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Research Hypotheses Sp ecific hypotheses tested are shown below in null form : Ho1 and Ho2 were tested at a minimum significance level of .05. Ho1: There is no significant difference in the amount of financial aid a student receives after v erification Ho2: There is no significant difference between being selected versus not being selected for verification on enrollment. Ho3: There is no significant difference between completing versus not completing verification on enrollment. A secure database w as created to organize the inf ormation collected. Student and institution identity was not be revealed and names of participants were not used in any reports. To ensure trustworthiness, credibility and rigor of the study, data were collected using triangulation This means information was obtained f rom multiple sources such as institution financ ial aid staff, Department of Education, and documentation and literature. In addition, results of the data w ere analyzed and discussed with another member to ensure the same conclusions were drawn from the data gathered. Limitations Aspects of the research design limit the conclusions that may be drawn from the study. There are several limitations to the study. First, the study was limited to an analysis of students who applied for financial aid at a public community/state college in Florida and therefore limits the generalizability While the study sample should be quite diverse, the fact remains that certain segments of th is population w ere not i ncluded. As at any other institution, students attending a public community/state college represent a diverse group. However, differing institutional policies and resources may impact the results. A nother potential limitation is that this study only consider ed the financial aid verification process and not other federal eligibility requirements 52

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that could impact students eligibility to receive financial aid, such as academic progress standards pertaining to grade point average (GPA) and progression requirements (pace of completion). In addition the use of regression analysis is limited to the variable analyzed and does not consider other factors that may also influence students decisions to enroll such as personal factors, including but not limited to, marriage, pregnancy, unforeseen health issues, death, job or relocation. T his study also did not consider the students knowledge of the financial aid process which could have an impact on the results of the data. 53

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C HAPTER 4 RESULTS This chapter provides an overview of the findings from the statistical examination of the study, showing the results of the data gathered as outlined in Chapter 3. The purpose of this study aimed determine the influence the verification regulation has on c ollege access. In order to address this issue the following research question was developed: To what extent does the financial aid verification process influence college student access? To address the research question several hypotheses were developed. All hypotheses are stated in null form. The first hypothesis, involved comparing the change in the financial aid award amount as a result of being selected for verification utilizing descriptive statistics and the frequency of change. In order to measure s tudent access for the second and third hypotheses enrollment patterns were used. The second hypothesis, enrollment patterns were not significantly associated with being selected for verification were compared to students not selected for verification The third hypothesis, completing verification was also not signif icantly associated to enrollment. In addition, all students who completed the verification process were further analyzed to determine if age, gender, race, socio economic status, and the month t he FASFA was submitted was significant to predict completion of verification. To test the research question and resulting hypotheses, logistical regression analysis was employed. All tests and analysis were conducted utilizing data from a public state coll ege for the 201112 and 201213 academic years fall term only. For the statistical analysis, the statistical software package for the social sciences SPSS version 21 was used. The descriptive statistics and regression results for all hypotheses are shown i n order. 54

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Descriptive Statistics Aggregate data ( N =17,991) for the 201112 academic year and ( N =17,409) for the 201213 academic year of students selected versus not selected for verification appear normally distributed w ith no large deviations (Table 45 ). Students verification status f or the 201112 academic year were students selected for verification ( n=7,239), and students not selected for verification ( n =10,752) (Table 4 5) and f or the 201213 academic year students selected for verification ( n =6,720) and students not selected for verification ( n=10,689) (Table 4 5) S tudents selected for verification in the 201112 academic year had an overall mean age of ( M =26.31), 63.7% of students were female and 51.3% were white. This is consistent with the entir e population that shows the overall mean age of ( M =25.72), 63.6% are female and 52.8% are white (Figures 4 1 through 43). Similarly, the student demographic data for the 2012 13 academic year for students selected for verification had an overall mean age of (M =2 5.18), 63.3% of the students selected for verification were female and 46.8% were white. These percentages are again consistent with the demographic information of the entire population (Figures 4 1 through 43). A further breakdown of the student demographic data is provided in three categories, students selected for verification that completed the process, students who completed verification and enrollment status in the fall term and students who did not complete verification and enrollment status in the fall term. Student demographic information includes, age, gender, race, socio economic status, and month the FASFA was submitted. Table 4 13 provides data for students selected for verification and completion status. For 201112, students completing verification ( N =4,456), and students not completing verification ( N = 2,783). For 201213, students completing verification ( N =3,508) and students not completing verification ( N =3,212). 55

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Table 4 11 provides demographic data for students who completed the verification process and their enrollment status in the fall term. For 201112, students enrolled ( N =3,663), and students not enrolled ( N =793). For 201213, students enrolled ( N =2,860), and students not enrolled ( N =648). Table 4 12 provides demographic information for students who did not complete verification and enrollment status in the fall. For 201112, students enrolled ( N =689), and students not enrolled ( N =2,094). For 2012 13, students enrolled ( N =662), and students not enrolled ( N =2,550). An analysis of each demographic is provided below. Age Selected and completed verification status. The categories assigned for the various age groups were determined utilizing how data is captured in regards to age on the Integrated Postsecondary Education Data System (IPEDS) report. For 201112, the overall mean age for completed verification ( M =26.83), and not completed verification ( M =25.46) (Table 4 2). For 201213, the overall mean age for completed verification ( M = 25.70), and not completed verification ( M =2 4.62) (Table 4 2) D ata also revealed that students above the age of 25 have a higher completion percentage with the age range of 20 to 21, representing the lowest completion of 56.1 and 44.3% for both the 201112 and 201213 years (Table 4 10). Completed verif ication and e nroll ment. For 201112, the overall mean age of completed verification ( M =26.83), enrolled ( M =26.70), and did not enroll ( M =27.42) (Table 4 2). For 2012 13, the overall mean age of completed verification ( M =25.7), enrolled ( M =25.5), and not enrolled ( M =26.61) (Table 4 3). Overall, students who completed verification and did not enroll were typically a year older than students who did enroll. The data also shows that students who are nineteen and below have the highest enrollment percentage compared to the other age categories (Table 4 11). 56

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Did n ot c omplete verification and enrollment. The overall mean age of students not completing verification ( M =25.46), enrolled ( M =23.20), and did not enroll ( M =26.20) (Table 4 4). For 2012 13, the overall mean age for students not completing verification ( M =24.62), enrolled ( M =22.70), and not enrolled ( M =25.11) (Table 44). For 2011 12 and 201213 years students in the 18 to 19 age category represented the largest portion of the population that did not complet e verification, 22.9 and 26.2% for the respective years (Table 42). However, students in the 18 to 19 category are among the highest who still end up enrolling than those reported in other age categories, 32.2 and 28.9% for 2011 12 and 201213 respectivel y (Table 4 12). Gender Selected and completed verification status For both the 20112012 and 201213 academic years females represented the larger portion of being selected for verification 63.7 and 63.3% as compared to 35.4 and 35.8% for males (Table 42). This is consistent with the aggregate data as females represent approximately 63% of the total population for both years. However, females and males completed verification at a similar rate. For 2011 12, 63% of females completed while 59.1% of males com pleted. Similarly for 2012 13, 52.4% of females completed compared to 52.3% of males completed (Table 410). This suggests that gender is not a factor in prediction for completing the verification process. Completed verification and e nroll ment For both the 201112 and 201213 academic years females represented the larger portion of completed verification and enrolling 64.6 and 62.6%, compared to 34.4 and 36.7% for males (Table 43). However, the percentage of females and males who complete verification and enroll are similar with males slightly higher than females. For 201112, 81.6% of females completed and enrolled while 83.2% of males completed and enrolled. Similarly for 201213, 80.4% of females completed and enrolled compared to 83.5% of males compl eted and enrolled (Table 4 11). 57

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Did not complete verification and enrollment. Consistent with other categories, for both 201112 and 201213, females represented a larger portion of the population who did not complete verification (Table 4 4). However, the percentage of males who did not complete verification and still enrolled is higher than females. For 201112, 26.0% of males who did not complete verification still enrolled versus 24.0% of females. Similarly for 2012 13, 24.3% of males who did not comple te verification still enrolled compared to 18.5% of females (Table 4 12). Race Selected and completed verification status. Whites represent the majority of the selected for verification population for both the 201112 and 201213 years at 51.3 and 46.8% re spectively (Table 4 2). The completion percentage for each race is provided in Table 410 and reveals that Whites, Hispanics and African American completed at nearly the same rate for both years. Completed verification and e nroll ment Whites represent th e majority of the completed and enrolled population for both the 201112 and 201213 years, 51.3 and 48.3% respectively (Table 4 3). The completion and enrollment percentage for each race is provided in Table 411 and reveals that African Americans have t he lowest completed and enrolled percentage, 76.6 and 77.5% for the 201112 and 201213 years. Hispanics are among the highest percentage, 82.3 and 79.4% for the respective years. Did n ot c omplete verification and enrollment Whites represent the majority of the population who did not complete verification for both the 201112 and 201213 years (Table 4 4). Table 412 provides the breakdown by race for students who did not complete verification and enrollment status. African Americans are among the lowest percentage of enrollment, 21.0 and 18.6% for the 201112 and 201213 years respectively. On the other hand Hispanics are 58

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among the highest percentage that still enroll even though they did not complete verification, 25.6 and 21.3% for the respective years (Table 4 12). Socio Economic Status Selected and completed verification status. Adjusted gross income (AGI) as reported on the FASFA was used in categorizing socioeconomic status. These categories were divided into four groups, less than $25,000, $25,000$49,999, $50,000$74,999 and greater than $75,000. AGI of less than $25,000 represented the largest population selected for verification, 48.0 and 52.7% for the 201112 and 201213 years (Table 4 2). AGI for $50,000$74,999, represents the largest complet ion percentage of 68.3 and 61.6% for the respective years (Table 410). Comparing this group to the AGI of less than $25,000, those in the higher income range completed between 8 to 11% higher. Completed verification and e nroll ment AGI of less than $25,000 represented the largest population of completed and enrolled at 46.7 and 50.0% for the 201112 and 201213 years (Table 4 3). AGI greater than $75,000, represents the largest completion percentage of 86.7 and 96.8% for the respective years (Table 411). Did not c omplete verification and e nroll ment AGI of less than $25,000 represented the largest population of students who did not complete verification (Table 44). The income bracket with the largest percentage that still enrolled without completing verification was the greater than $75,000 category, 31.3 and 23.8% for 201112 and 201213 respectively. (Table 4 12). The AGI range $25,000$49,999 had the lowest enrollment percentage for both years 19.3 and 14.7% (Table 412). Month FASFA Submitted Selected and completed verification status. D ata revealed the number of students selected for verification was consistent regardless of the month the FASFA was submitted (Table 59

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42). However, data also revealed that students who submitted the FASFA after J une had a 10% lower completion percentage (Table 4 10). Completed verification and e nroll ment Students who submitted the FASFA prior to June, completed verification, and enrolled represented the majority of the population (Table 43). Students that submitted the FASFA after June and completed verification enrolled at a lower percentage, 14.4 and 9.1% lower versus submitting the FASFA prior to June (Table 4 11). Did n ot c omplete verification and enrollment Table 4 4 provides the breakdown by month the FASFA was submitted and how many students did not complete verification. While Table 4 12 provides the comparisons by month the FASFA was submitted and whether students enrolled even though they did not complete verification. Data revealed students submi tting the FASFA in July and August that did not complete the verification process had higher percentages of enrollment as compared to the prior months. For instance, in the 201112 year 38.1% of students who submitted the FASFA in August and did not comple te verification enrolled versus 12.7% who submitted the FASFA in January (Table 412). Likewise for 201213, 31.1% of students who submitted the FASFA in August and did not complete verification enrolled versus 16.2% who submitted the FASFA in January (Table 4 12). Research Hypothesis One The research problem guiding this hypothesis examined the impact of the verification process on the amount of federal financial aid the student was eligible to receive. This analysis was accomplished by comparing the stude nts estimated family contribution (EFC) before and after verification. The EFC is the amount the student is able to contribute toward his or her education. A cad emic years 201112 and 201213 were used for data analysis The population consisted of student s who submitted the FASFA before September 1st of the academic year and completed verification The t otal population of students who completed verification for the 201160

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12 academic year ( N = 4,456) and the 201213 academic year ( N =3,508) w ere divided into t hree groups : decrease in EFC, increase in EFC, and no change to EFC. The frequency of the change in EFC was recorded for each group. R esults were consistent for both academic years. For the frequency of EFC outcome after verification, 29 cases in Fall 2011 and 22 cases in Fall 2012 did not fit in any of the three categories (d ecreased, i ncreased, and no c hange ). This was because there were null cases for EFC for either the pre or post data. For the 2011 12 academic year, of the 4,456 students selected w ho completed verification, 4,220 (94.7%) experienced no change in EFC after completing verification. For the 2012 13 academic year, of the 3,508 students selected who completed verification, 3,331 (95.0%) experienced no change in EFC after completing verif ication (Table 419). These results suggest that after the verification process was completed, the information originally provided was correct, resulting in no change to the students EFC. In addition, the EFC amount before and after verification w as compar ed for each year. During the 201112 year the pre verification amount ( M =2,147.32) and post verification amount ( M =2,130.93) resulted in a net change of .76% (Table 4 20) For the 201213 academic year the pre verification amount ( M =1,898.38) and post ver ification amount ( M =1,818.37) resulted in a net change of 4.21% (Table 4 20). Similar to the results of the pre and post EFC data above, these results suggest, that overall there was little change in the amount of financial aid the student was eligible to receive on completion of the verification process. Research Hypothesis Two The research problem guiding this hypothesis examined the impact that being selected for verification has on college student access by comparing enrollment patterns of students selected versus not selected for verification T o ensure reliability of the data two academic years were analyzed 2011 12 and 201213. The population consisted of students submitting the FASFA 61

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prior to September 1st of the academic year. T otal population for the 201112 academic year ( N =17,991) and the 2012 13 academic year ( N =17,409) w ere divided into two groups, students selected for verification ( N =7,239) and ( N =6,720), students not selected for verification ( N =10,752) and ( N =10,689) for the respective years (Table 4 5) The enrollment patterns of each group were compared. S tudents selected for verification for the 201112 and 201213 years 4,352 (6 0.1%) and 3,522 (52.4%) enrolled for f all for the respective years S tudents not selected for verificatio n, 6,839 (6 3.6%) and 6,447 (60.3%) enrolled for f all respectively (Table 4 6) Without providing statistical analysis, the data suggests that being selected for verification does not significantly influence enrollment as students selected versus not select ed for verification enrolled at similar rates. T o further analyze the data to predict the effect of being selected for verification has on enrollment a logistic regression analysis was conducted using selected for verification as a predictor. The model re vealed that the overall effect of selection for verification is statistically significant. For 201112 and 201213, a test of the full model against a constant only model was statistically significant, indicating that the predictor as a set reliably distin guished between selected for verification and not selected for verification ( = 22.341, p< .000 with df=1) and ( =105.082, p< .000 with df=1) respectively (Table 47). Prediction success overall was 62.2% and 57.3% respectively for 201112 and 201213 (Table 4 8). The Wald criterion demonstrated that being selected for verification made a significant contribution to prediction (p=.000) for both the 201112 and 201213 years (Table 4 9) For 2011 12, EXP(B)value, .863, indicates when a student is select ed for verification, the student is 13.7% less likely to enroll for f all (Table 4 9) Consistent with 201112, the EXP(B) value, .725 for 2012 13, indicates when a student is selected for verification, the student is 27.5% less likely to enroll for f all (T able 4 9) 62

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Therefore, the null hypothesis that there was no significant difference in students enrolling due to being selected versus not being selected for verification was rejected. Although further research would need to be conducted to determine the ac tual cause for student not enrolling one possible explanation drawing upon red tape theory suggests that the federal verification requirement is overwhelming creat ing time delays resulting in frustration, causing students to not finish the financial aid p rocess. Research Hypothesis T hree The research problem guiding this hypothesis examined the effect that completing verification has on enrollment by comparing students who completed versus did not complete verification The same two academic years, 2011 12 and 201213 were used for data analysis The population consisted of students that submitted the FASFA prior to September 1st of the academic year. The t otal population of students selected for verification for the 201112 academic year ( N =7, 239) and the 201213 academic year ( N =6,720) w ere divided into two groups, students completed verification ( N = 4,456) and ( N =3,508), students not completing verification ( N = 2,783 ) and ( N =3,212) for the respective years (Table 4 13) The enrollment patterns of each group were compared. S tudents completing verification for 201112 and 201213 years 3,663 ( 82.2 %) and 2,860 (81.5%) enrolled in fa ll for the respective years S tudents not completing verification, 689 ( 24.8% ) and 662 (20.6%) enrolled in f all respectively (Tabl e 4 14) L ogistic regression analysis was conducted to predict the effect that completing verification has on enrollment using completion as a predictor. The model revealed that the overall effect of completing verification is statistically significant. F or 2011 12 and 201213 a test of the full model against a constant only model was statistically significant, indicating that the predictor as a set reliably distinguished between completing verification and not completing verification ( 2448.412 p< .000 with df=1) and ( = 2675.093, p< .000 with df=1) respectively 63

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(Table 415 ). Logistical regression analysis uses a p rediction model to determine the log odds that one variable has on another, for this analysis the variables were verification complete and enrollment. The prediction success that completing verification leads to enrollment overall was 79.5% and 80.5% respectively f or the 201112 and 201213 academic years (Table 4 16 ). T he Wald criterion also demonstrated that completing verification made a significant contribution to prediction (p=.000) for both the 201112 and 201213 years (Table 4 17) For 201112, an EXP(B)value 14.039 indicates that when a student completes verification, the student is 14 times more likely to enroll in f all (Table 4 17) Consistent with 201112 the EXP(B) value, 17.001 for 201213indicates that when a student completes verification, the stude nt is 17 times more likely to enroll in f all (Table 4 17) Therefore, the null hypothesis that there is no significant difference in completing versus not completing verification on enrollment was rejected. In addition, a logistic regression analysis was c onducted from the population of students selected for verification to determine the likelihood of completion using age, gender, race, socioeconomic status and month the FASFA was submitted as predictors. For the 201112 and 201213 years results were no t consistent as to the significance of the predictor variables except for age, socio economic status and the month the FASFA was submitted Although age and socioeconomic status overall as predictor s were statistically significant (p=.000) none of the r anges used to further define the variables were significant (Table 4 18) However, the Wald criterion demonstrated that the month the FASFA was submitted made a significant contribution to predi ction (p=.000) and EXP(B) value: 1.504 for January March and 1 .482 for April June for the 201112 year (Table 4 18) This indicates that when a student submits the FASFA during these months, the student is 1.5 and 1.4 times more likely to complete the verification process than if 64

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submitted after June. F or the 201213 year the data revealed similar patterns for prediction (p=.000) and EXP(B) value : 1.644 for January March and 1.566 for April June (Table 4 18). This indicates that when a student submits the FASFA during these months, the student is 1.6 and 1.5 times more likely to complete the verification process than if submitted after June 65

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Table 4 1. Total population descriptive statistics Age Fall 2011 Fall 2012 Total population Selected for verification Not selected for verification Total population Selected for verification Not selected for verification n % n % n % n % n % n % Mean Age 25.72 --26.31 --25.32 --25.57 --25.18 --25.81 --Under 18 952 5.3% 380 5.2% 572 5.3% 920 5.3% 398 5.9% 522 4.9% 18 19 4346 24.2% 1613 22.3% 2733 25.4% 4180 24.0% 1809 26.9% 2371 22.2% 20 21 2909 16.2% 1033 14.3% 1876 17.4% 2904 16.7% 1109 16.5% 1795 16.8% 22 24 2661 14.8% 1021 14.1% 1640 15.3% 2593 14.9% 918 13.7% 1675 15.7% 25 29 2582 14.4% 1130 15.6% 1452 13.5% 2604 15.0% 892 13.3% 1712 16.0% 30 34 1567 8.7% 729 10.1% 838 7.8% 1452 8.3% 544 8.1% 908 8.5% 35 39 1098 6.1% 525 7.3% 573 5.3% 1052 6.0% 423 6.3% 629 5.9% 40 49 1409 7.8% 636 8.8% 773 7.2% 1282 7.4% 495 7.4% 787 7.4% 50 64 434 2.4% 161 2.2% 273 2.5% 403 2.3% 123 1.8% 280 2.6% 65 & Over 11 0.1% 4 0.1% 7 0.1% 11 0.1% 5 0.1% 6 0.1% Unknown 22 0.1% 7 0.1% 15 0.1% 8 0.0% 4 0.1% 4 0.0% Gender Fall 2011 Fall 2012 Total population Selected for verification Not selected for verification Total population Selected for verification Not selected for verification n % n % n % n % n % n % Female 11444 63.6% 4608 63.7% 6836 63.6% 10982 63.1% 4254 63.3% 6728 62.9% Male 6382 35.5% 2561 35.4% 3821 35.5% 6274 36.0% 2403 35.8% 3871 36.2% Unknown 165 0.9% 70 1.0% 95 0.9% 153 0.9% 63 0.9% 90 0.8% 66

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Table 4 1. Continued Race/ ethnicity Fall 2011 Fall 2012 Total population Selected for verification Not selected for verification Total population Selected for verification Not selected for verification n % n % n % n % n % n % Hispanic or Latino 4459 24.8% 1766 24.4% 2693 25.0% 4571 26.3% 1955 29.1% 2616 24.5% American Indian or Alaskan Native 53 0.3% 21 0.3% 32 0.3% 54 0.3% 24 0.4% 30 0.3% Asian 258 1.4% 105 1.5% 153 1.4% 275 1.6% 96 1.4% 179 1.7% Black or African American 2773 15.4% 1258 17.4% 1515 14.1% 2668 15.3% 1150 17.1% 1518 14.2% Native Hawaiian or Pacific Islander 34 0.2% 18 0.2% 16 0.1% 34 0.2% 16 0.2% 18 0.2% White 9503 52.8% 3715 51.3% 5788 53.8% 8833 50.7% 3148 46.8% 5685 53.2% Two or More 115 0.6% 42 0.6% 73 0.7% 162 0.9% 51 0.8% 111 1.0% Unknown 796 4.4% 314 4.3% 482 4.5% 812 4.7% 280 4.2% 532 5.0% Month FASFA submitted Fall 2011 Fall 2012 Total population Selected for verification Not selected for verification Total population Selected for verification Not selected for verification n % n % n % n % n % n % January 1266 7.0% 496 6.9% 770 7.2% 1499 8.6% 721 10.7% 778 7.3% February 2210 12.3% 985 13.6% 1225 11.4% 2496 14.3% 1145 17.0% 1351 12.6% March 2877 16.0% 1192 16.5% 1685 15.7% 2681 15.4% 1080 16.1% 1601 15.0% April 2535 14.1% 1031 14.2% 1504 14.0% 2473 14.2% 877 13.1% 1596 14.9% May 2822 15.7% 1111 15.3% 1711 15.9% 2638 15.2% 943 14.0% 1695 15.9% June 2632 14.6% 1007 13.9% 1625 15.1% 2314 13.3% 822 12.2% 1492 14.0% July 2033 11.3% 803 11.1% 1230 11.4% 1919 11.0% 666 9.9% 1253 11.7% August 1616 9.0% 614 8.5% 1002 9.3% 1389 8.0% 466 6.9% 923 8.6% 67

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Table 41. Continued Adjusted gross income Fall 2011 Fall 2012 Total population Selected for verification Not selected for verification Total population Selected for verification Not selected for verification n % n % n % n % n % n % Less than $25,000 8723 48.5% 3477 48.0% 5246 48.8% 8659 49.7% 3543 52.7% 5116 47.9% $25,000 $49,999 2687 14.9% 1569 21.7% 1118 10.4% 2625 15.1% 1045 15.6% 1580 14.8% $50,000 $74,999 791 4.4% 483 6.7% 308 2.9% 742 4.3% 284 4.2% 458 4.3% Greater than $75,000 390 2.2% 62 0.9% 328 3.1% 375 2.2% 52 0.8% 323 3.0% Unknown 5400 30.0% 1648 22.8% 3752 34.9% 5008 28.8% 1796 26.7% 3212 30.0% 68

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Table 4 2. Selected for verification descriptive statistics Age Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % n % n % n % n % n % Mean Age 26.31 --26.83 --25.46 --25.18 --25.7 --24.62 --Under 18 380 5.2% 219 4.9% 161 5.8% 398 5.9% 217 6.2% 181 5.6% 18 19 1613 22.3% 977 21.9% 636 22.9% 1809 26.9% 966 27.5% 843 26.2% 20 21 1033 14.3% 580 13.0% 453 16.3% 1109 16.5% 491 14.0% 618 19.2% 22 24 1021 14.1% 585 13.1% 436 15.7% 918 13.7% 431 12.3% 487 15.2% 25 29 1130 15.6% 719 16.1% 411 14.8% 892 13.3% 466 13.3% 426 13.3% 30 34 729 10.1% 487 10.9% 242 8.7% 544 8.1% 310 8.8% 234 7.3% 35 39 525 7.3% 337 7.6% 188 6.8% 423 6.3% 263 7.5% 160 5.0% 40 49 636 8.8% 435 9.8% 201 7.2% 495 7.4% 286 8.2% 209 6.5% 50 64 161 2.2% 114 2.6% 47 1.7% 123 1.8% 75 2.1% 48 1.5% 65 & Over 4 0.1% 3 0.1% 1 0.0% 5 0.1% 3 0.1% 2 0.1% Unknown 7 0.1% 0 0.0% 7 0.3% 4 0.1% 0 0.0% 4 0.1% Gender Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % n % n % n % n % n % Female 4608 63.7% 2901 65.1% 1707 61.3% 4254 63.3% 2227 63.5% 2027 63.1% Male 2561 35.4% 1513 34.0% 1048 37.7% 2403 35.8% 1256 35.8% 1147 35.7% Unknown 70 1.0% 42 0.9% 28 1.0% 63 0.9% 25 0.7% 38 1.2% 69

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Table 4 2. Continued Race/ ethnicity Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % n % n % n % n % n % Hispanic or Latino 1766 24.4% 1144 25.7% 622 22.3% 1955 29.1% 1048 29.9% 907 28.2% American Indian or Alaskan Native 21 0.3% 13 0.3% 8 0.3% 24 0.4% 20 0.6% 4 0.1% Asian 105 1.5% 75 1.7% 30 1.1% 96 1.4% 47 1.3% 49 1.5% Black or African American 1258 17.4% 800 18.0% 458 16.5% 1150 17.1% 619 17.6% 531 16.5% Native Hawaiian or Pacific Islander 18 0.2% 10 0.2% 8 0.3% 16 0.2% 8 0.2% 8 0.2% White 3715 51.3% 2247 50.4% 1468 52.7% 3148 46.8% 1640 46.8% 1508 46.9% Two or More 42 0.6% 23 0.5% 19 0.7% 51 0.8% 25 0.7% 26 0.8% Unknown 314 4.3% 144 3.2% 170 6.1% 280 4.2% 101 2.9% 179 5.6% Month FASFA submitted Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % n % n % n % n % n % January 496 6.9% 291 6.5% 205 7.4% 721 10.7% 387 11.0% 334 10.4% February 985 13.6% 624 14.0% 361 13.0% 1145 17.0% 618 17.6% 527 16.4% March 1192 16.5% 786 17.6% 406 14.6% 1080 16.1% 608 17.3% 472 14.7% April 1031 14.2% 649 14.6% 382 13.7% 877 13.1% 488 13.9% 389 12.1% May 1111 15.3% 724 16.2% 387 13.9% 943 14.0% 495 14.1% 448 13.9% June 1007 13.9% 620 13.9% 387 13.9% 822 12.2% 432 12.3% 390 12.1% July 803 11.1% 442 9.9% 361 13.0% 666 9.9% 307 8.8% 359 11.2% August 614 8.5% 320 7.2% 294 10.6% 466 6.9% 173 4.9% 293 9.1% 70

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Table 4 2. Continued Adjusted gross income Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % n % n % n % n % n % Less than $25,000 3477 48.0% 2109 47.3% 1368 49.2% 3543 52.7% 1777 50.7% 1766 55.0% $25,000 $49,999 1569 21.7% 1024 23.0% 545 19.6% 1045 15.6% 616 17.6% 429 13.4% $50,000 $74,999 483 6.7% 330 7.4% 153 5.5% 284 4.2% 175 5.0% 109 3.4% Greater than $75,000 62 0.9% 30 0.7% 32 1.1% 52 0.8% 31 0.9% 21 0.7% Unknown 1648 22.8% 963 21.6% 685 24.6% 1796 26.7% 909 25.9% 887 27.6% 71

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Table 4 3. Completed verification: descriptive statistics Age Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % n % n % n % n % n % Mean Age 26.83 --26.7 --27.42 --25.7 --25.5 --26.61 --Under 18 219 4.9% 192 5.2% 27 3.4% 217 6.2% 197 6.9% 20 3.1% 18 19 977 21.9% 856 23.4% 121 15.3% 966 27.5% 835 29.2% 131 20.2% 20 21 580 13.0% 465 12.7% 115 14.5% 491 14.0% 394 13.8% 97 15.0% 22 24 585 13.1% 450 12.3% 135 17.0% 431 12.3% 331 11.6% 100 15.4% 25 29 719 16.1% 575 15.7% 144 18.2% 466 13.3% 357 12.5% 109 16.8% 30 34 487 10.9% 392 10.7% 95 12.0% 310 8.8% 238 8.3% 72 11.1% 35 39 337 7.6% 280 7.6% 57 7.2% 263 7.5% 211 7.4% 52 8.0% 40 49 435 9.8% 364 9.9% 71 9.0% 286 8.2% 234 8.2% 52 8.0% 50 64 114 2.6% 88 2.4% 26 3.3% 75 2.1% 61 2.1% 14 2.2% 65 & Over 3 0.1% 1 0.0% 2 0.3% 3 0.1% 2 0.1% 1 0.2% Unknown 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Gender Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % n % n % n % n % n % Female 2901 65.1% 2366 64.6% 535 67.5% 2227 63.5% 1791 62.6% 436 67.3% Male 1513 34.0% 1259 34.4% 254 32.0% 1256 35.8% 1049 36.7% 207 31.9% Unknown 42 0.9% 38 1.0% 4 0.5% 25 0.7% 20 0.7% 5 0.8% 72

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Table 4 3. Continued Race/ ethnicity Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % n % n % n % n % n % Hispanic or Latino 1144 25.7% 941 25.7% 203 25.6% 1048 29.9% 832 29.1% 216 33.3% American Indian or Alaskan Native 13 0.3% 9 0.2% 4 0.5% 20 0.6% 19 0.7% 1 0.2% Asian 75 1.7% 67 1.8% 8 1.0% 47 1.3% 39 1.4% 8 1.2% Black or African American 800 18.0% 613 16.7% 187 23.6% 619 17.6% 480 16.8% 139 21.5% Native Hawaiian or Pacific Islander 10 0.2% 8 0.2% 2 0.3% 8 0.2% 8 0.3% 0 0.0% White 2247 50.4% 1880 51.3% 367 46.3% 1640 46.8% 1380 48.3% 260 40.1% Two or More 23 0.5% 19 0.5% 4 0.5% 25 0.7% 19 0.7% 6 0.9% Unknown 144 3.2% 126 3.4% 18 2.3% 101 2.9% 83 2.9% 18 2.8% Month FASFA submitted Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % n % n % n % n % n % January 291 6.5% 242 6.6% 49 6.2% 387 11.0% 323 11.3% 64 9.9% February 624 14.0% 524 14.3% 100 12.6% 618 17.6% 525 18.4% 93 14.4% March 786 17.6% 673 18.4% 113 14.2% 608 17.3% 509 17.8% 99 15.3% April 649 14.6% 555 15.2% 94 11.9% 488 13.9% 405 14.2% 83 12.8% May 724 16.2% 607 16.6% 117 14.8% 495 14.1% 417 14.6% 78 12.0% June 620 13.9% 526 14.4% 94 11.9% 432 12.3% 333 11.6% 99 15.3% July 442 9.9% 317 8.7% 125 15.8% 307 8.8% 223 7.8% 84 13.0% August 320 7.2% 219 6.0% 101 12.7% 173 4.9% 125 4.4% 48 7.4% 73

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Table 4 3. Continued Adjusted gross income Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % n % n % n % n % n % Less than $25,000 2109 47.3% 1712 46.7% 397 50.1% 1777 50.7% 1430 50.0% 347 53.5% $25,000 $49,999 1024 23.0% 826 22.5% 198 25.0% 616 17.6% 470 16.4% 146 22.5% $50,000 $74,999 330 7.4% 267 7.3% 63 7.9% 175 5.0% 145 5.1% 30 4.6% Greater than $75,000 30 0.7% 26 0.7% 4 0.5% 31 0.9% 30 1.0% 1 0.2% Unknown 963 21.6% 832 22.7% 131 16.5% 909 25.9% 785 27.4% 124 19.1% 74

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Table 4 4. Did not complete verification: descriptive statistics Age Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % n % n % n % n % n % Mean Age 25.46 --23.2 --26.2 --24.62 --22.7 --25.11 --Under 18 161 5.8% 57 8.3% 104 5.0% 181 5.6% 50 7.6% 131 5.1% 18 19 636 22.9% 205 29.8% 431 20.6% 843 26.2% 244 36.9% 599 23.5% 20 21 453 16.3% 151 21.9% 302 14.4% 618 19.2% 147 22.2% 471 18.5% 22 24 436 15.7% 106 15.4% 330 15.8% 487 15.2% 78 11.8% 409 16.0% 25 29 411 14.8% 71 10.3% 340 16.2% 426 13.3% 53 8.0% 373 14.6% 30 34 242 8.7% 33 4.8% 209 10.0% 234 7.3% 31 4.7% 203 8.0% 35 39 188 6.8% 28 4.1% 160 7.6% 160 5.0% 20 3.0% 140 5.5% 40 49 201 7.2% 33 4.8% 168 8.0% 209 6.5% 32 4.8% 177 6.9% 50 64 47 1.7% 5 0.7% 42 2.0% 48 1.5% 7 1.1% 41 1.6% 65 & Over 1 0.0% 0 0.0% 1 0.0% 2 0.1% 0 0.0% 2 0.1% Unknown 7 0.3% 0 0.0% 7 0.3% 4 0.1% 0 0.0% 4 0.2% Gender Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % n % n % n % n % n % Female 1707 61.3% 409 59.4% 1298 62.0% 2027 63.1% 374 56.5% 1653 64.8% Male 1048 37.7% 272 39.5% 776 37.1% 1147 35.7% 279 42.1% 868 34.0% Unknown 28 1.0% 8 1.2% 20 1.0% 38 1.2% 9 1.4% 29 1.1% 75

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Table 4 4. Continued Race/ ethnicity Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % n % n % n % n % n % Hispanic or Latino 622 22.3% 159 23.1% 463 22.1% 907 28.2% 193 29.2% 714 28.0% American Indian or Alaskan Native 8 0.3% 0 0.0% 8 0.4% 4 0.1% 2 0.3% 2 0.1% Asian 30 1.1% 7 1.0% 23 1.1% 49 1.5% 19 2.9% 30 1.2% Black or African American 458 16.5% 96 13.9% 362 17.3% 531 16.5% 99 15.0% 432 16.9% Native Hawaiian or Pacific Islander 8 0.3% 2 0.3% 6 0.3% 8 0.2% 2 0.3% 6 0.2% White 1468 52.7% 396 57.5% 1072 51.2% 1508 46.9% 311 47.0% 1197 46.9% Two or More 19 0.7% 4 0.6% 15 0.7% 26 0.8% 7 1.1% 19 0.7% Unknown 170 6.1% 25 3.6% 145 6.9% 179 5.6% 29 4.4% 150 5.9% Month FASFA submitted Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % n % n % n % n % n % January 205 7.4% 26 3.8% 179 8.5% 334 10.4% 54 8.2% 280 11.0% February 361 13.0% 58 8.4% 303 14.5% 527 16.4% 68 10.3% 459 18.0% March 406 14.6% 80 11.6% 326 15.6% 472 14.7% 84 12.7% 388 15.2% April 382 13.7% 91 13.2% 291 13.9% 389 12.1% 72 10.9% 317 12.4% May 387 13.9% 86 12.5% 301 14.4% 448 13.9% 90 13.6% 358 14.0% June 387 13.9% 112 16.3% 275 13.1% 390 12.1% 96 14.5% 394 15.5% July 361 13.0% 124 18.0% 237 11.3% 359 11.2% 107 16.2% 252 9.9% August 294 10.6% 112 16.3% 182 8.7% 293 9.1% 91 13.7% 202 7.9% 76

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Table 4 4. Continued Adjusted gross income Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % n % n % n % n % n % Less than $25,000 1368 49.2% 315 45.7% 1053 50.3% 1766 55.0% 347 52.4% 1419 55.6% $25,000 $49,999 545 19.6% 105 15.2% 440 21.0% 429 13.4% 63 9.5% 366 14.4% $50,000 $74,999 153 5.5% 36 5.2% 117 5.6% 109 3.4% 19 2.9% 90 3.5% Greater than $75,000 32 1.1% 10 1.5% 22 1.1% 21 0.7% 5 0.8% 16 0.6% Unknown 685 24.6% 223 32.4% 462 22.1% 887 27.6% 228 34.4% 659 25.8% 77

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Table 4 5. Selected for Verification: frequency Year Selected for verification Frequency Percent Valid percent 2011 12 No 10752 59.8 59.8 Yes 7239 40.2 40.2 Total 17991 100.0 100.0 2012 13 No 10689 61.4 61.4 Yes 6720 38.6 38.6 Total 17409 100.0 100.0 Table 4 6. Selected for verification and enrolled: frequency Year Selected for verification Enrolled Frequency Percent Valid percent 2011 12 No No 3913 36.4 36.4 Yes 6839 63.6 63.6 Total 10752 100.0 100.0 Yes No 2887 39.9 39.9 Yes 4352 60.1 60.1 Total 7239 100 100 2012 13 No No 4242 39.7 39.7 Yes 6447 60.3 60.3 Total 10689 100.0 100.0 Yes No 3198 47.6 47.6 Yes 3522 52.4 52.4 Total 6720 100.0 100.0 78

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Table 4 7. Omnibus tests of model coefficients: logistic regression Year Chi square df Sig. 2011 12 Step 22.341 1 .000 Block 22.341 1 .000 Model 22.341 1 .000 2012 13 Step 105.082 1 .000 Block 105.082 1 .000 Model 105.082 1 .000 Dependent variable: enrolled fall. Single covariate: selected for verification Table 4 8. Classification table: logistic regression Predicted Enrolled in fall Percentage correct Year Observed No Yes 2011 12 Enrolled in fall No 0 6800 .0 Yes 0 11191 100.0 Overall percentage 62.2 2012 13 Enrolled in fall No 0 7440 .0 Yes 0 9969 100.0 Overall percentage 57.3 The cut value is .500. Predictors: enrolled fall 79

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Table 4 9. Variables in the equation: logistic regression Year B S.E. Wald df Sig. Exp(B) 2011 12 Selected for verification .148 .031 22.374 1 .000 .863 Constant .558 .020 775.901 1 .000 1.748 2012 13 Selected for verification .322 .031 105.049 1 .000 .725 Constant .419 .020 448.279 1 .000 1.52 Variable: selected for verification Table 4 10. Selected for verification completion percentages: descriptive statistics Age Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % % n % % Mean Age 26.31 ----25.18 ----Under 18 380 57.6% 42.4% 398 54.5% 45.5% 18 19 1613 60.6% 39.4% 1809 53.4% 46.6% 20 21 1033 56.1% 43.9% 1109 44.3% 55.7% 22 24 1021 57.3% 42.7% 918 46.9% 53.1% 25 29 1130 63.6% 36.4% 892 52.2% 47.8% 30 34 729 66.8% 33.2% 544 57.0% 43.0% 35 39 525 64.2% 35.8% 423 62.2% 37.8% 40 49 636 68.4% 31.6% 495 57.8% 42.2% 50 64 161 70.8% 29.2% 123 61.0% 39.0% 65 & Over 4 75.0% 25.0% 5 60.0% 40.0% Unknown 7 0.0% 100.0% 4 0.0% 100.0% 80

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Table 410. Continued Gender Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % % n % % Female 4608 63.0% 37.0% 4254 52.4% 47.6% Male 2561 59.1% 40.9% 2403 52.3% 47.7% Unknown 70 60.0% 40.0% 63 39.7% 60.3% Race/ ethnicity Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % % n % % Hispanic or Latino 1766 64.8% 35.2% 1955 53.6% 46.4% American Indian or Alaskan Native 21 61.9% 38.1% 24 83.3% 16.7% Asian 105 71.4% 28.6% 96 49.0% 51.0% Black or African American 1258 63.6% 36.4% 1150 53.8% 46.2% Native Hawaiian or Pacific Islander 18 55.6% 44.4% 16 50.0% 50.0% White 3715 60.5% 39.5% 3148 52.1% 47.9% Two or More 42 54.8% 45.2% 51 49.0% 51.0% Unknown 314 45.9% 54.1% 280 36.1% 63.9% 81

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Table 410. Continued FASFA submission month ranges Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % % n % % Jan Mar 2673 63.6% 36.4% 2946 54.8% 45.2% Apr June 3149 63.3% 36.7% 2642 53.6% 46.4% July Aug 1417 53.8% 46.2% 1132 42.4% 57.6% Adjusted gross income Fall 2011 Fall 2012 Selected for verification Completed verification Did not complete verification Selected for verification Completed verification Did not complete verification n % % n % % Less than $25,000 3477 60.7% 39.3% 3543 50.2% 49.8% $25,000 $49,999 1569 65.3% 34.7% 1045 58.9% 41.1% $50,000 $74,999 483 68.3% 31.7% 284 61.6% 38.4% Greater than $75,000 62 48.4% 51.6% 52 59.6% 40.4% Unknown 1648 58.4% 41.6% 1796 50.6% 49.4% 82

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Table 4 11. Completed verification enrolled percentages: descriptive statistics Age Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % % n % % Mean Age 26.83 ----25.7 ----Under 18 219 87.7% 12.3% 217 90.8% 9.2% 18 19 977 87.6% 12.4% 966 86.4% 13.6% 20 21 580 80.2% 19.8% 491 80.2% 19.8% 22 24 585 76.9% 23.1% 431 76.8% 23.2% 25 29 719 80.0% 20.0% 466 76.6% 23.4% 30 34 487 80.5% 19.5% 310 76.8% 23.2% 35 39 337 83.1% 16.9% 263 80.2% 19.8% 40 49 435 83.7% 16.3% 286 81.8% 18.2% 50 64 114 77.2% 22.8% 75 81.3% 18.7% 65 & Over 3 33.3% 66.7% 3 66.7% 33.3% Unknown 0 n/a n/a 0 n/a n/a Gender Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % % n % % Female 2901 81.6% 18.4% 2227 80.4% 19.6% Male 1513 83.2% 16.8% 1256 83.5% 16.5% Unknown 42 90.5% 9.5% 25 80.0% 20.0% 83

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Table 4 11. Continued Race/ ethnicity Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % % n % % Hispanic or Latino 1144 82.3% 17.7% 1048 79.4% 20.6% American Indian or Alaskan Native 13 69.2% 30.8% 20 95.0% 5.0% Asian 75 89.3% 10.7% 47 83.0% 17.0% Black or African American 800 76.6% 23.4% 619 77.5% 22.5% Native Hawaiian or Pacific Islander 10 80.0% 20.0% 8 100.0% 0.0% White 2247 83.7% 16.3% 1640 84.1% 15.9% Two or More 23 82.6% 17.4% 25 76.0% 24.0% Unknown 144 87.5% 12.5% 101 82.2% 17.8% FASFA completion month ranges Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % % n % % Jan Mar 1701 84.6% 15.4% 1613 84.1% 15.9% Apr June 1993 84.7% 15.3% 1415 81.6% 18.4% July Aug 762 70.3% 29.7% 480 72.5% 27.5% Adjusted gross income Fall 2011 Fall 2012 Completed verification Enrolled Did not enroll Completed verification Enrolled Did not enroll n % % n % % Less than $25,000 2109 81.2% 18.8% 1777 80.5% 19.5% $25,000 $49,999 1024 80.7% 19.3% 616 76.3% 23.7% $50,000 $74,999 330 80.9% 19.1% 175 82.9% 17.1% Greater than $75,000 30 86.7% 13.3% 31 96.8% 3.2% Unknown 963 86.4% 13.6% 909 86.4% 13.6% 84

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Table 4 12. Did not complete verification enrolled percentages: descriptive statistics Age Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % % n % % Mean Age 25.46 ----24.62 ----Under 18 161 35.4% 64.6% 181 27.6% 72.4% 18 19 636 32.2% 67.8% 843 28.9% 71.1% 20 21 453 33.3% 66.7% 618 23.8% 76.2% 22 24 436 24.3% 75.7% 487 16.0% 84.0% 25 29 411 17.3% 82.7% 426 12.4% 87.6% 30 34 242 13.6% 86.4% 234 13.2% 86.8% 35 39 188 14.9% 85.1% 160 12.5% 87.5% 40 49 201 16.4% 83.6% 209 15.3% 84.7% 50 64 47 10.6% 89.4% 48 14.6% 85.4% 65 & Over 1 0.0% 100.0% 2 0.0% 100.0% Unknown 7 0.0% 100.0% 4 0.0% 100.0% Gender Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % % n % % Female 1707 24.0% 76.0% 2027 18.5% 81.5% Male 1048 26.0% 74.0% 1147 24.3% 75.7% Unknown 28 28.6% 71.4% 38 23.7% 76.3% 85

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Table 4 12. Continued Race/ ethnicity Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % % n % % Hispanic or Latino 622 25.6% 74.4% 907 21.3% 78.7% American Indian or Alaskan Native 8 0.0% 100.0% 4 50.0% 50.0% Asian 30 23.3% 76.7% 49 38.8% 61.2% Black or African American 458 21.0% 79.0% 531 18.6% 81.4% Native Hawaiian or Pacific Islander 8 25.0% 75.0% 8 25.0% 75.0% White 1468 27.0% 73.0% 1508 20.6% 79.4% Two or More 19 21.1% 78.9% 26 26.9% 73.1% Unknown 170 14.7% 85.3% 179 16.2% 83.8% FASFA completion month ranges Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % % n % % Jan Mar 972 16.9% 83.1% 1333 15.5% 84.5% Apr June 1156 25.0% 75.0% 1227 21.0% 79.0% July Aug 655 36.0% 64.0% 652 30.4% 69.6% 86

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Table 4 12. Continued Adjusted gross income Fall 2011 Fall 2012 Did not complete verification Enrolled Did not enroll Did not complete verification Enrolled Did not enroll n % % n % % Less than $25,000 1368 23.0% 77.0% 1766 19.6% 80.4% $25,000 $49,999 545 19.3% 80.7% 429 14.7% 85.3% $50,000 $74,999 153 23.5% 76.5% 109 17.4% 82.6% Greater than $75,000 32 31.3% 68.8% 21 23.8% 76.2% Unknown 685 32.6% 67.4% 887 25.7% 74.3% 87

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Table 4 13. Students completing verification: frequency Year Completed Frequency Percent Valid percent 2011 12 No 2783 38.4 38.4 Yes 4456 61.6 61.6 Total 7239 100.0 100.0 2012 13 No 3212 47.8 47.8 Yes 3508 52.2 52.2 Total 6720 100.0 100.0 Table 4 14. Students completing verification and enrolled: frequency Year Completed Enrolled Frequency Percent Valid percent 2011 12 No No 2094 75.2 75.2 Yes 689 24.8 24.8 Total 2783 100.0 100.0 Yes No 793 17.8 17.8 Yes 3663 82.2 82.2 Total 4456 100 100 2012 13 No No 2550 79.4 79.4 Yes 662 20.6 20.6 Total 3212 100.0 100.0 Yes No 648 18.5 18.5 Yes 2860 81.5 81.5 Total 3508 100.0 100.0 Table 415. Omnibus tests of model coefficients: logistic regression Year Chi square df Sig. 2011 12 Step 2448.412 1 .000 Block 2448.412 1 .000 Model 2448.412 1 .000 2012 13 Step 2675.093 1 .000 Block 2675.093 1 .000 Model 2675.093 1 .000 Dependent variable: enrolled fall. Single covariate: verification complete. 88

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Table 4 16. Classification table: logistic regression Predicted Enrolled in fall Percentage correct Year Observed No Yes 2011 12 Enrolled in fall No 2094 793 72.5 Yes 689 3663 84.2 Overall percentage 79.5 2012 13 Enrolled in fall No 2550 648 79.7 Yes 662 2860 81.2 Overall percentage 80.5 The cut value is .500. Predictors: enrolled fall. 89

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Table 4 17. Variables in the equation: logistic regression Year B S.E. Wald df Sig. Exp(B) 2011 12 Verification complete 2.642 .059 2015.361 1 .000 14.039 Constant 1.112 .044 640.578 1 .000 0.329 2012 13 Verification complete 2.833 .062 2114.931 1 .000 17.001 Constant 1.349 .044 955.824 1 .000 0.26 Variable: verification complete 90

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Table 4 18. Moderating variables logistic regression variables in the equation Year B S.E. Wald df Sig. Exp(B) 2011 12 Age Range 54.2 9 .000 Age Range (1) .791 1.159 .465 1 .495 .453 Age Range (2) .669 1.156 .335 1 .563 .512 Age Range (3) .851 1.156 .542 1 .462 .427 Age Range (4) .805 1.156 .484 1 .487 .447 Age Range (5) .539 1.156 .218 1 .641 .583 Age Range (6) .399 1.157 .119 1 .730 .671 Age Range (7) .515 1.158 .198 1 .657 .598 Age Range (8) .327 1.158 .080 1 .778 .721 Age Range (9) .213 1.168 .033 1 .856 .809 Constant 1.099 1.155 .905 1 .341 3 2012 13 Age Range 71.328 9 .000 Age Range (1) .224 .918 .060 1 .807 .799 Age Range (2) .269 .914 .087 1 .768 .764 Age Range (3) .636 .915 .483 1 .487 .530 Age Range (4) .528 .915 .332 1 .564 .590 Age Range (5) .316 .915 .119 1 .730 .729 Age Range (6) .124 .917 .018 1 .892 .883 Age Range (7) .092 .918 .010 1 .921 1.096 Age Range (8) .092 .917 .010 1 .920 .912 Age Range (9) .041 .931 .002 1 .965 1.042 Constant .405 .913 0.197 1 .657 1.5 91

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Table 4 18. Continued Year B S.E. Wald df Sig. Exp(B) 2011 12 Adjusted Gross Income 22.216 3 .000 Adjusted Gross Income (1) .497 .256 3.761 1 .052 1.644 Adjusted Gross Income (2) .695 .260 7.172 1 .007 2.004 Adjusted Gross Income (3) .833 .272 9.363 1 .002 2.301 Constant .065 .254 0.064 1 .800 0.938 2012 13 Adjusted Gross Income 35.423 3 .000 Adjusted Gross Income (1) .383 .285 1.813 1 .178 .682 Adjusted Gross Income (2) .028 .290 .009 1 .924 .973 Adjusted Gross Income (3) .084 .308 .074 1 .785 1.088 Constant .389 .283 1.899 1 .168 1.476 Year B S.E. Wald df Sig. Exp(B) 2011 12 FASFA Month Range 44.818 2 .000 FASFA Month Range (1) .408 .067 37.415 1 .000 1.504 FASFA Month Range (2) .393 .065 36.790 1 .000 1.482 Constant .151 .053 8.064 1 .000 1.163 2012 13 FASFA Month Range 52.62 2 .000 FASFA Month Range (1) .497 .071 49.513 1 .000 1.644 FASFA Month Range (2) .449 .072 39.199 1 .000 1.566 Constant .306 .060 25.931 1 .000 0.736 92

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Table 4 19. Estimated family contribution: frequency of change Year Frequency Percent Valid percent Cumulative percent 2011 12 Valid 29 .7 .7 .7 Decreased 98 2.2 2.2 2.9 Increased 109 2.4 2.4 5.3 No Change 4220 94.7 94.7 100.0 Total 4456 100.0 2012 13 Valid 22 .6 .6 .6 Decreased 106 3.0 3.0 3.6 Increased 49 1.4 1.4 5.0 No Change 3331 95.0 95.0 100.0 Total 3508 100.0 100.0 93

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Table 4 20. Pre and post estimated family contribution: descriptive statistics Year N Minimum Maximum Mean Std. deviation 2011 12 Pre verification EFC 4433 0 40808 2147.32 3251.758 Post verification EFC 4450 0 40808 2130.93 3235.749 Valid N (listwise) 4427 2012 13 Pre verification EFC 3491 0 85374 1898.38 3932.294 Post verification EFC 3503 0 85374 1818.37 3815.461 Valid N (listwise) 3486 94

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Table 4 21. Pre and post estimated family contribution percent change Year Pre verification Post verification Percent change 2011 12 Fall 2011 2147.32 2130.93 0.76% 2012 13 Fall 2012 1898.38 1818.37 4.21% 95

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Figure 4 1. Mean age: aggregate descriptive statistics comparison Figure 4 2. Gender: aggregate descriptive statistics comparison Figure 4 3. Race/ethnicity: aggregate descriptive statistics comparison 24.6 24.8 25 25.2 25.4 25.6 25.8 26 26.2 26.4 Total Population Fall 2011 Selected for Verification Fall 2011 Total Population Fall 2012 Selected for Verification Fall 2012Mean Age Mean Age 0% 20% 40% 60% 80% Female Male UnknownGender Total Population Fall 2011 Selected for Verification Fall 2011 Total Population Fall 2012 Selected for Verification Fall 2012 0% 20% 40% 60% Hispanic or Latino American Indian or Alaskan Native Asian Black or African American Native Hawaiian or Pacific Islander White Two or More UnknownRace/Ethnicity Total Population Fall 2011 Selected for Verification Fall 2011 Total Population Fall 2012 Selected for Verification Fall 2012 96

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CHAPTER 5 CONCLUSION T he purpose of the study was to determine if the verification process influences college access, for students seeking federal financial aid. The lack of information regarding the effects of these federal requirements is regrettable, because it could be preventing many students from pursuing their education goals. These federal requirements ar e complicated and add to an already intimidating and time consuming process that may delay students from enrolling in higher education (The Institute of College Access and Success, 2010) The potential impact of the federal financial aid verification process on college student access has been examined via enrollment patterns in a literature review and through quantitative analysis. This chapter will conc l ude the study with a discussion of the results, suggestions for future research and implications to high er education. Discussion of Results The overall hypothesis guiding this study stated that college access was not influenced by the verification process. To determine if the verification process influenced college access two aspects of the process were examined. The first aspect focused on the change in the financial aid award amount as a result of the verification process to ascertain whether the verification process makes a difference in the determination of the amount a student is eligible to receive. T he second aspect pertained to student access by examining enrollment patterns for students who were selected for verification and for students who completed verification. This inquiry was examined through statistical analysis of the significance of associa tion between enrollment and the independent variables and the analysis of student demographic data over a 2year period. 97

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Veri fi cation and Award Amount Data were analyzed before and after the verification process to determine if there was a difference in the students EFC. Results of the data revealed that overall 95% of students experienced no change in the amount of financial aid they were eligible to r eceive. In addition the financial aid award amount was compared before versus after verification for each year. For the 2 years analyzed, the award amounts changed by less than 5%. Results of the study support the findings of the ICAS (2010) study that the Verification require s colleges and universities to verify, or confirm, the data reported by students and their parent(s) on the FAFSA. Information is verified by requesting additional documentation or a signed statement attesting to the accuracy of the da ta. Institutions may either perform the verification internally or use a third party provider. Both options require additional funds to administer and could exceed $100,000 depending on the size of the institution. For students, obtaining the additional documentation can be time consuming and it delays the determination of financial aid eligibility. For instance one aspect of verification requires the student or students parents to submit IRS tax transcripts to verify wages. The tax transcript could take 2 to 4 weeks to receive. Based on the results of the study, overall the data submitted by individuals who completed the verification process was accurate or changes were insignificant to affect the amount the student was eligible to receive. Enrollment and Verification Two hypotheses were developed, differing by independent variables to compare enrollment patterns. The first hypothesis uses selected for verification as the independent variable while the second hypothesis used completed verification as the i ndependent variable. Analysis of the results indicated that enrollment was significantly associated with the verification process for both independent variables. Although the results indicate a significant association with enrollment the association for ea ch hypothesis is interpreted differently. 98

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Under the second hypothesis using selected for verification as the independent variable it was observed that enrollment was negatively associated with being selected for verification meaning that students are less likely to enroll after being selected for verification. The analysis revealed that 13.7% of students were less likely to enroll in Fall for the 201112 year and 27.5% less likely to enroll in Fall for the 201213 versus students who were not selected for verification. Although the actual reasons for students not enrolling have not been identified and would be an area for future research drawing upon red tape theory suggests one possible reason is the verification process is time consuming overwhelming an d serves as a barrier for students receiving their financial aid. These findings are consistent with prior research such as the study conducted by the Institute of College Access and Success that concluded there is evidence that students who are selected for verification and who may otherwise be eligible to receive financial aid did not complete the process (The Institute for College Access and Success, 2010). Federal student aid in its conception was primarily about helping those who otherwise might not have access to higher education. U nfortunately the additional regulations such as the verification process may have created additional barriers that leave many students frustrated resulting in students not enrolling. These results suggest that this federal requirement while aimed at preventing fraud and abuse may at the same time discourage applicants who just need help paying for college (The Institute for College Access and Success, 2010). The last hypothesis used completed verification as the independent variable and it was observed that completing verification exhibited itself to be positively associated with enrollment and students are more likely to enroll upon completing the verification process. The data revealed that when a student completed verification, the student was fourteen times more likely to enroll for Fall for the 201112 year and seventeen times more likely to enroll for Fall for the 99

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201213 year. It can be inferred from this data that students who spend the time navigating through the requirements of the verification process are committed to enroll and will see the process through to the end. T he results are so significant that institutions should consider reviewing their internal policies and processes to develop strategies and pr ovide resources to assist students in complet ing the verification process to bolster enrollment For instance communicating and providing information earlier to students so that they are aware of the necessary requirements such as the verification process and are prepared prior to completing the FASFA. Two Year Trends The FASFAs submitted for two academic years (2011 12 and 201213) captured a 2 year trend in the relationship of enrollment to each independent variable. Results of the study revealed that over the 2 year period on average 39% (6,979 students) were selected for verification. Of those 44% (3,042 students) did not enroll in the upcoming fall semester. Results of the data also revealed that on average 43% of students selected for verification did not complete the process (approximately 3,000 students). Of those, 77% (2,322 students) did not enroll in the upcoming fall semester. However, over the same time period, on average 82% of students who completed the verification process enrolled in the upc oming fall semester (3,261 students). In addition, s tudents selected for verification who completed the process were further analyzed over a 2 year period to determine if additional factors (such as age, race, gender, socio economic status, and the month t he FASFA was submitted) were variables that impacted completion of the verification process. Over the 2 year period a ge, socioeconomic status and the month the FASFA was submitted were consistent factors in determining whether a student completed the ver ification process Although age and socioeconomic status overall as 100

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predictor s were statistically significant, the ranges used to further define the variables were not significant. This means a particular age range or income range could not be identified. Further analysis would be required to determine the actual age and income that would be statistically significant. The month the FASFA was submitted however, was determined to be a significant variable in prediction of enrollment. For both years data we re collected, student s who submit ted the FASFA before June were 1 .4 to 1.6 times more likely to complete the verification process than if submitted after June. T hese findings support prior research that communicating information about financial aid early is critical to student success. The study Cracking the Student Aid Code: Parent and Student Perspectives on Paying for College (2010) found that lack of information and understanding of college financing is a difficult barrier for many students and families. Through the College Boards research, it was determined that communicating financial aid information at the right time and in ways that can be easily understood is critical to removing barriers to attending college. A study by LaManque (2009) showed that students who complete the FAFSA early versus late are more likely to be successful in college. LaManque (2009) hypothesized that early filing of the FAFSA was related to the knowledge the student has about college. The implicati on is that the more knowledgeable students are about college, the financial aid application process, and the benefits of applying early; the more likely they are to complete their FAFSA early (LaManque, 2009). Verification and Red Tape Red Tape Theory was used as the f ramework, to examine the federal verification process as discussed in the literature review. Under Red Tape Theory, rules and regulations are created to ensure that government processes are accountable (Bozeman, 1993). According to the theory e lements of red tape include, meaningless paperwork, formalization, unnecessary rules, 101

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inefficiency, unjustifiable delays, and frustration. Applying red tape theory to the verification regulation, results of the study suggest that the ver i fication proces s has characteristics of red tape. Drawing upon red ta pe theory, t he ver i fication process may be charact e rized as ruleevolved red tape (a rule functional at one time that has since gone bad; Bozeman, 1993). Of the many reasons given to explain rule evolved red tape, the two most pertinent to the financial aid regulations are rule strain and rule accretion. In rule strain, the number of rules creates an extensive compliance burden. In rule accretion, rules build atop one another and, if inconsistent, the n et effect could be damaging (Bozeman, 1993). The premise for providing financial assistance is to remove financial barriers for those pursuing higher education. However the verif i cation process, through rule accretion appears to be creating additional barr iers for many students who would otherwise be eligible. While wellintended, this regul a tion may be doing more harm than good placing additional burdens in processing federal aid that may be discouraging students who need help rather than preventing fraud and abuse. Is the verification regulation red tape? Unfortunately, there is not a concrete answer to this question. As described in the theory, r ules and regulations are not inherently good or bad for everyone. A rule may be red tape for one and useful for another (Bozeman, 1993). Therefore red tape is dependent on the views and perceptions of the users. Kaufman said One mans red tape is anothers treasured procedural safeguard (Bozeman, 1993). Looking at regulations through this perspective raises th e question: when are extensive rules and procedures considered red tape and when are they justified and beneficial. Bozeman distinguishes between white tape as the good rules that provide a benefit despite delays and frustrations and red tape ; as the dysfunctional rules that fail to help or cause much mischief. Regardless of the answer to the 102

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question, (Is the verification regulation red tape?) the results of the study suggest that the verification process may be hindering college access and warrant s f urther research to determine the actual cause of students who may be eligible to receive financial assistance but do not finish the process and ultimately do not enroll. Directions for Future Research This study presents multiple options for future studie s. Specifically, the model developed in obtaining the data could be replicated at other higher educational institutions that vary in size, student population, and institut ional type, to further examine the association between the verification process and enrollment. As this study applied to quantitative analysis only, the study design may have masked other circumstances that could account for students not completing the verification process and not enrolling In order to adequately determine if the verific ation process was a contributing factor to a students deci sion to not enroll, a qualitative or mixed methods research study would contribute to the dialogue Addit i onal variables need to be accounted for such as personal factors incl uding marriage, pregna ncy, job or unforeseen medical issues. F uture studies could examine whether a students prior experiences or knowledge of financial aid is a contributing factor to compl e tion of verification Additionally, examining first year students versus returning st udents could determine differences explaining the results This study was also limited to enrollment at one inst i tu t ion and did not consider whether the student enrolled at another institut ion. A future study accounting for enrollment at another institutio n would also add to the results of this study. Another area for future research is a further examination of student demographic information. For instance this study only analyzed student demographic information as predictors of completion of the verificati on process. Student demographic information could be examined as a predictor for students who did not complete the verification process; or it could be examined 103

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as a predictor of enrollment for students who completed versus did not complete the ver i ficatio n process. This study focused on the influence of the verification process on the students financial aid award amount. However the cost to the institution of admini stering the program was not considered, nor did the study consider the potential lost tuit ion revenue for students who did not enroll. Therefore, another area for future research would be to determine the true cost to the institution of complying with the verification requirement. Another direction for meaningful research is analyzing the addi tional documentation required for veri fication, to ascertain which information is the most error prone and typically requires correction. Results from such a study could inform policy makers of the type of information that affects financial aid eligibilit y, thereby collecting meaningful documentation that supports the intended purpose of the verification process in preventing fraud and abuse of financial aid funds. Implications for Higher Education Institu t ional Level Educational institu t ions that offer fe deral financial aid must adhere to federal regulations in order to provide federal aid to their students. Results from this study indicate that students selected for verifi c ation are less likely to enroll. However, once they complete the process, their chances for enrollment are significantly higher. Unless federal policy changes regarding the verification process, one way to increase enrollment is to ensure that students sele c ted for verification complete the process. This would require institu t ions to provide additional resources in the financial aid department, focusing on assisting students in completing the verification process. The difficulty, however, is balancing the distribution of resources with declining state funds and the demand to keep tuit ion costs low. 104

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Higher Education Research Several research studies ( Perna, 2011) have focused on a variety of theoretical and methodological approaches to examine student s college choice and the variables that impact students decisions to enroll. This s tudy provides new knowledge that R ed T ape T heory may be an addit i onal framework that effect s college acces s and student choice. For example, drawing on Red Tape Theory and the results of this study one could conclude that the verification requirement meets the definition of redtape and significantly impacts college student access. Federal Level Results of this study suggest that the ver i fication process may not be accomplishing the goal of preventing fraud and abuse, but rather creating additional barrier s that hinder college access. The verification regulation while well intended, needs to be reviewed by policy makers to ensure that the additional information requested promote accountability for financ i al aid funds. As mentioned earlier, Justin Draeger said, At some point, we must stop and ask to what extent federal regulations and requirements are either hindering college access and success or increasing costs for students ( NASFAA, 2011). 105

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APPENDIX A HIGHER EDUCATION AND FINANCIAL AID LEGISLATIVE HISTORY 1862 Land Grant College Act (Morrill Act) 1944 Servicemans Readjustment Act (GI Bill) 1958 National Defense Education Act 1963 Health Professions Educational Assistance Act 1964 Nurse Training Act 1964 Economic Opportunity Act 1965 Higher Education Act (HEA) 1968 HEA Amendments 1972 HEA Amendments 1976 HEA Amendments 1978 Middle Income Student Assistance Act 1980 HEA Amendments 1981 Omnibus Reconciliation Act 1986 HEA Amendments 1991 Justice Department anitrust action against Overlap Group 1992 HE A Amendments 1993 National and Community Service Trust Act 1993 Omnibus Reconciliation Act 1996 HEA Amendments 1997 Taxpayer Relief Act 1998 HEA Amendments 2001 Economic Growth and Tax Relief Reconciliation Act 2006 Deficit Reduction Act 2007 College Cost Reduction Act 2008 Ensuring Continued Access to Student Loans (ECASLA) 2008 Higher Education Opportunity Act (HEA reauthorization) 2009 Student Aid and Fiscal Responsibility Act 2010 Health Care and Education Reconciliation Act 106

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APPENDIX B FINANCIAL AID PROCES FLOWCHART Figure B 1. Financial Aid Process Flowchart 107

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APPENDIX C ACCESSING FINANCIAL AID FUNDS FLOW CHART Figure C 1. Flowchart for accessing financial aid funds Student Completes FASFA providing student and parent data Federal processor receives FASFA from student Once processed student receives a Student Aid Report (SAR) Institution receives SAR and notification of student verification Student Selected for Verification Financial Aid sends lists of required documents Student must submit all required documentation to Financial Aid prior to processing After all documents have been submitted and are reviewed for accuracy financial aid will resubmit file to DOE Student receives award amount and must accept offer Funds are credited to student account and any remaining balance will be refunded N 108

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REFERENCES Baird K (2006). Access to College: The Role of Tuition, Financial Aid, Scholastic Preparation and College Supply in Public College Enrollments. NASFAA Journal of Student Financial Aid, 36 ( 3 ), 1 638. Bozeman, B. (1993). A Theory of Government Red Tape. Journal of Public Administration Research and Theory 3, 273303. Bozeman, B. & Feeney, M. ( 2012). Rules and Red Tape: A Prism for Public Administration Theory and Research. New York: M.E. Sharpe, Inc Brewer, G. & Walker, R. (2009). The Impact of Red T ape on Governmental Performance: An Empirical Analysis. The Journal of Public Administration Research and Theory 20, 233257. Chen R & DesJardins, S. (20 10). Investigating the Impact of Financial Aid on Student Dropout Risks: Racial and Ethnic Differen ces. Journal of Higher Education, 81 ( 2 ), 179208. College Board, Advocacy and Policy Center, (2010). Cracking the Student Aid Code: Parent and Student Perspectives on Paying for College. Retrieved from http://advocacy.collegeboard.org/sites/default/files/11b_3172_Cracking_Code_Update_ WEB_110112.pdf College Board, Advocacy and Policy Center, (2010). Education Pays 2010: The Benefits of Higher Education for Individuals and Society. Trends in Higher Education Series College Board, Advocacy and Policy Center, (2011). Trends in Student Aid 2011. Trends in Higher Education Series. Retrieved From: http://advocacy.collegeboard.org/sites/default/files /2011_Student_Aid_11b 3630_Final_Web.pdf DeSantis, N. (2012). Florida State College of Jacksonville to Pay $4.7Million for Improperly Awarded Student Aid. Chronicle of Higher Education. Retrieved from http://chronicle.com/blogs/ticker/florida state collegeto pay 47million for improperly awarded student aid/49126). Dooley D. (20 01). Social Research Methods: Fourth Edition. Upper Saddle River, New Jersey: Prentice Hall. Draeger, Justin (2011). Administrative Burden. Retrieved from http://www.nasfaa.org/research/News/Administrative_Burden.aspx Dye, T. (2010). Politics in America, Sixth Edition. Retrieved from http://wps.prenhall.com/hss_dye_politics_6/27/7114/1821356.cw/index.html 109

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BIOGRAPHICAL SKETCH Gina Baratta Doeble was born in Omaha, Nebraska. An only child, she grew up mostly in Phoenix, Arizona graduating from high school in 1991. She earned her Bachelor of Science in Accounting from Arizona State University (ASU) in 1996 and earned a M aster of S cience in Accounting and Taxation from Florida Gulf Coast University (FGCU) in 2003. Gina began working in higher education finance in 1999 and received her Doctor of Education in Higher Education Administration in 2014. Upon graduating in May 2003 wit h her masters Gina earned her Certified Public Accountant (CPA) licensure in the State of Florida. Ginas career in accounting has been primarily focused in government accounting, with the last 15 years dedicated to higher education finance and administra tion Gina began supervising the Financial Aid department 4 years ago. She has reorganized the department to increase service to students and has held focus groups with students to understand the financial aid process from their perspective. Gina is commit ted to improving the services provided to students to assist them through the financial aid process. Gina is also affiliated with several community activities, participates in numerous professional organizations and serves as a member of the Florida State Funding Formula Committee for state colleges, and is the current Chair of the States Council of Business Affairs for the Florida College System. Gina is passionate about her work and thrives on assisting students in pursuing their educational goals as we ll as mentoring staff to achieve their career aspirations. 113