• TABLE OF CONTENTS
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 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 Advertising
 Introduction
 Review of related literature
 Development of the model
 Conclusions, implications...
 Bibliography
 Biographical sketch














Group Title: predictive model for the repayment of student loans in community colleges /
Title: A predictive model for the repayment of student loans in community colleges /
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Title: A predictive model for the repayment of student loans in community colleges /
Physical Description: vii, 91 leaves : ; 1983.
Language: English
Creator: Schmidt, James A., 1950-
Publication Date: 1983
Copyright Date: 1983
 Subjects
Subject: Student loans -- Florida   ( lcsh )
Default (Finance) -- Florida   ( lcsh )
Community colleges -- Administration -- Florida   ( lcsh )
Educational Administration and Supervision thesis Ph. D
Dissertations, Academic -- Educational Administration and Supervision -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (Ph. D.)--University of Florida, 1983.
Bibliography: Bibliography: leaves 86-89.
General Note: Typescript.
General Note: Vita.
Statement of Responsibility: by James A. Schmidt.
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Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000450204
oclc - 11437236
notis - ACL1872

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Table of Contents
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
    Advertising
        Page vi
        Page vii
    Introduction
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
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        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
    Review of related literature
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
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        Page 35
        Page 36
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        Page 48
        Page 49
        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
    Development of the model
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
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        Page 77
        Page 78
    Conclusions, implications and recommendations
        Page 79
        Page 80
        Page 81
        Page 82
        Page 83
        Page 84
        Page 85
    Bibliography
        Page 86
        Page 87
        Page 88
        Page 89
    Biographical sketch
        Page 90
        Page 91
        Page 92
        Page 93
Full Text












A PREDICTIVE MODEL FOR THE
REPAYMENT OF STUDENT LOANS
IN COMMUNITY COLLEGES








BY


JAMES A. SCHMIDT


A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY



UNIVERSITY OF FLORIDA


1983














ACKNOWLEDGMENTS


The writer wishes to acknowledge the assistance of the

many persons who took an active interest in the preparation

of this study.

The guidance and assistance provided throughout the last

two years by Dr. James L. Wattenbarger, chairman of the

writer's supervisory committee, are deeply appreciated. In

addition, sincere thanks are also extended to the faculty of

the Department of Educational Administration and Supervision

and in particular to the members of the committee, Dr. John

M. Nickens and Dr. Harold C. Riker.

For their assistance in collecting the data the writer

is indebted to the Florida Student Financial Aid Commission.

The technical assistance provided by Mr. Kenneth Goehle and

Mrs. Betty Davis are also gratefully appreciated.

Finally, the writer wishes to acknowledge the support

provided by his family. For their encouragement, assist-

ance and emphasis on the value of an education, the writer

is deeply indebted to his parents, Dr. and Mrs. Paul H.

Schmidt. To his wife, Linda, and his sons, Daniel and

Timothy, the writer wishes to express his deepest gratitude

for their love, patience and understanding during the prepa-

ration of this study.
















TABLE OF CONTENTS


Page

ACKNOWLEDGMENTS . . . . . .. . . . ii

LIST OF TABLES . . . . . . . . . . v

ABSTRACT . . . . . . . . . vi

CHAPTER

I. INTRODUCTION . . . . . . . . 1

The Role of Financial Aid . . . . . 2
The Growth and Importance of Loans . . .. .10
The Problem of This Study . . . . .. 19
Justification for the Study . . . . .. 20
Delimitations & Limitations . . . . .. 21
Research Design and Procedures . . . .. 22

II. REVIEW OF RELATED LITERATURE . . . .. 26

Problems Inherent in Student Loan
Programs . . . . . . . . . 31
Institutional Responses to
Student Loan Defaults . . . . . .. 38
Professional Organization Action
Regarding Student Loan Defaults . . .. 40
Governmental Action Regarding
Student Loan Defaults . . . . . . 42
Responses to Student Loan Default
Problem by Education Spokesmen . . .. 44
Antecedent Studies Relating to Student
Loans and Demographic Characteristics . 48
Summary . . . . . . . . .. . 53

III. DEVELOPMENT OF THE MODEL . . . . .. 55

Analysis of Variables . . . . . .. 60
Development of the Model . . . . .. 69
Results . . . . . . . . . . 73
Summary . . . . . . . . . . 77

















Page

IV. CONCLUSIONS, IMPLICATIONS AND
RECOMMENDATIONS . . . . . . .. 79

Conclusions . . . . . . . . .. 79
Implications . . . . . . . .. 80
Recommendations . . . . . . . .. 83

BIBLIOGRAPHY . . . . . . . . . .. . 86

BIOGRAPHICAL SKETCH . . . . . . . .. 90
















LIST OF TABLES


Table


1. Philanthropical Support for Higher
Education . . . . . . .

2. Overview of Major Variables Selected
by Previous Researchers . . . .

3. Chi-Square Analysis of Relationship
Between Age and Loan Payback . .

4. Chi-Square Analysis of Relationship
Between Marital Status and Loan
Payback . . . . . . . .

5. Chi-Square Analysis of Relationship
Between Sex and Loan Payback . .

6. Chi-Square Analysis of Relationship
Between College Standing and
Loan Payback . . . . . .

7. Chi-Square Analysis of Relationship
Between Grade Point Average and
Loan Payback . . . . . .

8. Chi-Square Analysis of Relationship
Between Loan Total and Loan
Payback . . . . . . . .

9. Interaction Effects Between the
Variables . . . . . . .


. . 63


. . 63



. . 64



. . 65



. . 66


. . 68


Page










Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfilment of
the Requirements for the Degree of Doctor of Philosophy


A PREDICTIVE MODEL FOR THE
REPAYMENT OF STUDENT LOANS
IN COMMUNITY COLLEGES

By

James A. Schmidt

August, 1983

Chairman: James L. Wattenbarger
Major Department: Educational Administration and Supervision

The problem of this study was to determine the relation-

ship between selected variables which characterize community

college students and student loan defaults and to develop a

model using these variables to predict student loan payback.

Given the current economic crisis and the increasing reliance

on the student loan programs to help students meet educational

expenses, a study of the importance of selected student demo-

graphic characteristics and their relationships to the student

loan default problem is of great importance to the future

support of the student loan programs.

The literature provided a theoretical basis for this

study including appropriate variables for study as predictors

of student default. These variables included size of loan

total, marital status, sex, grade point average, college

standing, and age.

The data presented in this study were supplied by the

Florida Student Financial Aid Commission, Tallahassee,










Florida, and represented a statewide sample of 76 community

college students who have participated in the Guaranteed

Student Loan program.

Of the six variables selected, only the size of the loan

total and marital status distinguished significantly those

who repaid their student loans from those who did not. In

addition to these variables, sex, grade point average,

college standing, and age were useful in developing a pre-

diction model. Although the model did not provide an in-

fallible formula for predicting those students who are most

likely to repay their student loans, the model predicted

group membership (defaulter of non-defaulter) for 70% of the

sample cases. These findings underscore Pattillo and

Wiant's conclusion that items reflecting financial rather

than biographical data appear to be better predictors of

loan delinquency.

Therefore, it appears that the inclusion of additional

discriminating variables and a more detailed study design

may be necessary in order to improve the identification of

students who are likely to repay their student loans.















CHAPTER I

INTRODUCTION



From modest beginnings over three hundred years ago the

role and impact of financial assistance to students in

American colleges have changed dramatically. Like the gift

presented by Lady Anne Mowlsen of London to the deserving

Harvard student, most early student financial aid awards

were granted with money given to colleges by private individ-

uals to aid worthy, needy students. Therefore, the original

purpose of providing student financial aid was to expand

educational opportunities to those students whose incomes

presented a barrier to higher education.

In fact, only during the period of time immediately

following World War II was substantial aid awarded on the

basis of merit alone. From the late 1940s through the early

1960s the Serviceman's Readjustment Act of 1944 (GI Bill)

provided substantial amounts of money to returning service-

men for educational expenses regardless of individual finan-

cial need. Many colleges and universities found that the

large amounts of scholarship funds, previously used to sup-

port needy students, were no longer needed and those insti-

tutions began to use the funds to attract and reward students











with academic or other special talents, with little or no

regard for financial need.

Currently, the essential criterion of the major federal

aid programs (Pell Grant, College Work Study, National

Direct/Guaranteed Student Loan Programs) is the students'

need for funds. Accordingly, the purpose of the principal

financial aid programs of today is remarkably similar to the

original intention of the early student aid programs. The

return to a need-based student aid system has caused a great

deal of consternation to financial aid administrators. Today

it becomes increasingly difficult to reward students with

merit-based aid when the available resources do not ade-

quately take care of the needy students. In order to encour-

age both needy and worthy students, a balance between need-

based and merit-based aid is critical to the development of

an impartial and equitable student financial aid delivery

system. However, most student financial aid administrators

apparently feel that the current, primarily need-based,

delivery system provides the best use of available resources

for it attempts to extend the opportunity for higher education

to as many worthy students as possible.


The Role of Financial Aid


Since 1972 there has been considerable discussion con-

cerning the role of financial aid in American higher educa-

tion. In nearly every deliberation much attention has been










devoted to the related issues of access and tuition charges

because they are so intertwined with the role of financial

aid.

As a nation, the United States has demonstrated its

commitment to citizen access to postsecondary educational

opportunities by instituting programs of assistance to needy

students, establishing support programs for colleges and

universities (i.e., Land Grant College Program) and develop-

ing a strong system of public colleges and universities.

According to Ostar (1978), 80% of all students attend-

ing higher educational institutions today are in programs

at public institutions where the state partially subsidizes

the institutional costs of the student. As a part of the

public higher educational system, the public community

college is an important factor in providing higher educa-

tional opportunities to American students. The impact of

the community college system on the issue of access is high-

lighted in the state of Florida where more than 95% of the

state population live within commuting distance of a commun-

ity college program.

Although the costs of attending a community college

program may be lower because of public subsidies, the indi-

viduals attending the community college seem to have the

greatest need for financial assistance. A 1972 American

College Testing (ACT) Program Study indicated that the

financial needs of community college students sharply










exceeded those of the majority of students enrolled in other

colleges and universities. The ACT study estimated that

financially disadvantaged students constituted 25% of the

total student population at community colleges and cited the

following factors as contributing to this high percentage:

(1) 70% of the students depend on the
automobile to reach campus,

(2) the higher age distribution (average
age of student is 29) indicates the
average student has assumed adult
responsibilities, and

(3) 66% of the students come from families
with (1974) incomes below $10,000.
(Blocker, 1974, p. 126)

Therefore, it would also appear that the retention of

low tuition policies is critical to maintain access to

higher educational opportunities for a large number of stu-

dents in the community college system. In fact, a Univer-

sity of Wisconsin study highlights the impact tuition

policies have on the issue of access (Ostar, 1978). This

study determined that for every 1% reduction in total cost

of attending a University of Wisconsin Center participating

in the study there was a corresponding 1.33% increase in en-

rollment. The study also attributed the increased enroll-

ment to new students who were not enrolled in the University

of Wisconsin system and concluded that many of these new

students would not have been able to attend were it not for

the lower tuition policy (Ostar, 1978).










Despite the establishment of the relationship between

low tuition and access by this and other studies, much de-

bate regarding federal aid centers on the strategy of pro-

viding individual student aid rather than institutional aid.

It is feared that the reliance on the student-based aid

delivery system may cause additional pressure on state govern-

ments and lead both public and private institutions to raise

tuition fees (Ostar, 1978). Although the underlying assump-

tion of raising tuition fees is that the truly needy student

will be taken care of, a University of Tennessee study in-

dicated that a $100 increase in tuition would yield only a

$26 increase in an individual student's Pell Grant Award

(Ostar, 1978). Truthfully, every tuition increase falls

hardest on the $13,000 $25,000 income family and yet this

student is eligible for little federal aid in the form of

grants or College Work Study (Ostar, 1978). In addition,

Ostar maintains that an overemphasis on student-based aid

leads to a growing number of charges involving fraud, waste,

and abuse and leads to ever-tightening regulations and in-

creased numbers of bureaucrats, investigators and bill

collectors.

The previous discussion regarding access would indicate

that the best approach regarding federal assistance may be

the increasing of federal support to higher educational in-

stitutions enabling colleges and universities to retain

lower tuition charges. However, because of economic and











political factors the financial aid delivery system is

oriented toward individual student awards. Given this under-

standing two major questions must be addressed: what aid is

available to students and how effective is the delivery

system?


Student Aid Resources

Essentially student financial aid is available in the

form of grants, work-study opportunities and loans. A grant

is gift money that is offered to those most needy in order

to prevent over-indebtedness through borrowing. Work-study

is a self-help program which provides meaningful, college-

related job experiences and earnings enabling the student to

earn money and meet current expenses from hi-weekly wages.

Loans constitute a program of borrowing whereby students

must repay the loan with interest from future earnings after

leaving college. The federal programs representing these

categories of aid include Pell (BEOG) and Supplemental Educa-

tional Opportunity Grants, College Work-Study, and National

Direct and Guaranteed Student Loan Programs.

Most of the federal programs have not seen substantial

increases in federal money in recent years. However, the

Pell Grant Program (BEOG) which was traditionally limited to

low income students has been expanded to assist families

from middle income levels. In addition, the amount of money

expended and the numbers of students served through the











Guaranteed Student Loan Program (GSL) have risen dramati-

cally.

During the 1982-83 fiscal year modest cuts were made

in the financing of the campus-based programs (SEOG, CWSP

and NDSL). Although support for the Pell Grant Program was

retained at previous levels, eligibility and dollar delivery

regulations were instituted to reduce fraud, abuse and

errors. The combination of reduced support for some pro-

grams and increased regulation of the Pell Grant Program

created a dilemma for student financial aid administrators

who could stretch local and institutional funds only so far.

At the same time, colleges and universities are exper-

iencing higher costs because of increases in major dollar

operating items (i.e., salaries, energy, supplies) which

seem to be severely affected by an inflationary economy.

This increase in costs translates into higher tuition

charges. Since the financial aid application requires the

student's resources to be balanced with the expected ex-

penses, many students are experiencing higher need figures

as the expected expenses rise. Without corresponding in-

creases in student aid to offset the rise in expenses, a

"need gap" crisis occurs for the student. Although existing

support for assistance programs is generally at the highest

level ever recorded, the increasing cost factor is rising

faster than increased federal support and therefore the

number of "need gap" victims continues to grow.











The Student Aid Delivery System

Most of the blame for the "need gap" crisis must be put

on increasing costs and expenses since the majority of the

financial aid researchers conclude that the student aid

delivery system appears to be quite effective (Deitch, 1978).

The financial aid delivery system in the United States

is the mechanism used to provide individual students with the

resources to attend postsecondary schools. Application for

the Federal Pell Grant Program may be made separately on a

free federal application; or, for a nominal charge, an in-

depth assessment of need may be presented along with the

eligibility report for the Pell Grant Program. Although

there has been some recent discussion of the effectiveness

and equity of the need-analysis system, previous studies

indicate the system is in pretty good shape. According to

a study presented at the Annual Meeting of the American

Association for Higher Education in March of 1979 the

following conclusions were presented:

(1) The need based system of financial
aid has gone far in removing a mean-
ingful element of price competition
from the buying and selling of higher
education,

(2) the structure of the aid system (mix
of parental contribution, need-based
grant and self help) is meeting the
public's perception of how post-
secondary education should be financed,

(3) the major goal of providing access is
quite clear, and correspondingly the
programs have been successfully reach-
ing toward the goal (i.e., the number
of minorities has increased signifi-
cantly),











(4) federal, state and institutional
funds seem sufficient to insure
that any qualified student could
afford to attend most of the
nation's institutions, and

(5) the problem is not with the dif-
ferent kinds of aid available but
for more efficient and equitable
distribution of the funds that are
available. ("Premises of Federal,"
1979)

According to Kenneth Dietch (1978), the current system of

student financial aid is a thoroughly developed, pervasive

and well-functioning system of price discrimination. How-

ever, he cites several problems which are likely to arise

with the current system including

(1) competitive pricing (by institutions),

(2) lack of sensitivity to student costs,

(3) loss of financial privacy of families
of applicants,

(4) efforts to depart from the need-based
system to attract (non-needy, out-
standing) students, and

(5) need for fairness in the treatment of
independent students. (Dietch, 1978)

Regardless of the potential problems with the financial

aid delivery system, its current status seems to be that it

is well respected among professionals and researchers.

Financial aid administrators and federal program personnel

have struggled to solve the problems of "need-gap" by work-

ing together to tighten requirements and enforce regula-

tions through extensive validation procedures.











The Growth and Importance of Loans


As other political priorities become apparent in Wash-

ington and free student aid programs (grants) are retained at

previous funding levels, most aid administrators have increas-

ingly used the available loan programs to meet student aid

needs. In fact, loan programs have been increased substan-

tially while most of the other aid programs have been limited

or decreased in size. Therefore, the importance of an

equitable loan system has far greater implications today than

it did previously as more and more students use the device to

solve the "need gap" crisis.


General Definition and Philosophy
of Student Loan Programs

As indicated previously, loans are awards of money

granted with the prior requirement that they subsequently

be repaid, in whole or in part, with or without interest.

According to Davis and Van Dusen (1978), loans are the most

attractive form of student aid to most policy-makers because

they must be repaid and therefore are an investment rather

than a direct expenditure of funds. Recent discussion of

national student aid policies indicates that those in

decision-making roles believe loan programs to be very

effective. Therefore, the great increase in the use of the

current loan programs and the addition of new loan programs

are quite understandable.










The underlying philosophical premise of a loan program

is that the person who derives the benefit from an education

should pay for its costs, without denying low income stu-

dents access to postsecondary education due to lack of

current resources (Davis & Van Dusen, 1978). Not only has

such a philosophy been utilized to develop loan programs

but it has also led to arguments concerning how much of the

cost of an education should be reflected in the tuition

charge. For example, in 1973 the Committee for Economic

Development recommended that tuition costs should be raised

to 50% of the total educational cost within five years at

colleges and universities and within 10 years at community

colleges. Subsequently the issue was studied in detail by

the Carnegie Commission. It suggested that tuition should

be raised to 30% of the educational cost within 10 years at

colleges and universities, but the Carnegie Commission

exempted community colleges due to the historical concept

of two years of free access to higher education. Many

states have cited the Carnegie Commission report in intro-

ducing data supporting the raising of fees at low-cost

public institutions.

However, Howard Bowen (cited in Balderston, 1970) effec-

tively argues that an investment in higher education is

really an investment in the society. It is an investment

in the nation's human capital leading to increased indi-

vidual productivity and societal benefits (i.e., a more

informed electorate, increased national productivity, lower











unemployment rates, a populace more adaptable to technology,

increased social consciousness). Mr. Bowen concludes that

typical students already bear 75 to 80% of the cost of their

education in the form of tuition, books and foregone earn-

ings. Many studies conclude that the heavy reliance on loan

financing has the effect of shifting the eventual burden of

payment for the cost of higher education toward students and

their future income (Balderston, 1970). According to Mr.

Bowen, the increased availability of loans is not an appro-

priate solution to the need gap crisis since students al-

ready bear such a large portion of the cost of their educa-

tion. Mr. Bowen argues that since society receives signifi-

cant benefits from educating students, it should bear an

increasing portion of the cost of educating them.

Those who support the argument that students should

bear the cost of their education also favor the increasing

use of loans to help students finance the increasing costs.

However, the desirability of increasing the burden of cost

on students by raising fees and then assisting the needy

students through loan programs is an issue that will remain

unresolved as both pro and con advocates have many arguments

of merit. However, the current federal administration

favors the use of loans to help needy students bear the in-

creasing costs of higher education and therefore it has

instituted new loan programs to help relieve the "need gap"

crisis.











Types of Loan Programs

Among the original loan programs are the National

Direct Student Loan (NDSL) Program (formerly the National

Defense Student Loan Program) and the Guaranteed Student

Loan (GSL) Program. The NDSL is a low-cost deferred repay-

ment loan administered by the educational institution and

funded 90% with federal monies and 10% with institutional

monies. Under the NDSL Program there are no security or

endorsement requirements and its distinctive feature is that

in addition to a deferment of three years for public service,

the student may arrange for cancellation of the loan based

on military service in an area of hostility, full-time

teaching service in selected schools (for five years) or

full-time staff service in a head start program (for seven

years).

The Guaranteed Student Loan Program is available in

nearly all states where a state level guarantee agency has

been developed. In the remaining states students may apply

for a Federally Insured Student Loan (FISL). Administra-

tively, private parties and non-federal agencies loan their

own capital to students in both programs. In the GSL pro-

gram there is a central monitoring agency. While the

federal government insures the loan and helps pay the in-

terest, the agency acts as intermediary, overseer and

collections processor. However, in the FISL program the










lenders rely directly on the U. S. Department of Education

for an interest supplement, insurance against default and a

collection mechanism. Repayment regulations for both the

GSL and FISL programs are similar to the NDSL; however,

deferments of up to three years are only available for ser-

vice in the armed forces, Action or the Peace Corps, and

there are no provisions for cancellation.


The Increasing Use of Loans

The growth of both of these programs is indicative of the

current emphasis on the issuance of loans to solve the student

aid crisis. In 1977, Ernest L. Boyer (cited in Hauptman, 1978),

then Commissioner of Education, estimated that between 1965 and

1977 six million students borrowed $11.1 billion. Commissioner

Boyer stated that in 1975-76 alone $2.6 billion were loaned

to students and that the national increase rate in the num-

ber of loans processed was averaging 5% each year. Mr.

Boyer concluded that thousands of students have been served

by the loan programs including many students who otherwise

may have been denied a postsecondary education. In addition,

Sanford (1980), in his research of the impact of loans on

graduate education, found that graduates with loans are

slightly more likely to attend graduate or professional

school than those without loans. Therefore, it appears that

there is substantial value in supplying loans beyond merely

providing access to undergraduate degree programs. The most











recent data indicate a total of 2.75 million loans were pro-

cessed through the GSL system alone during the 1981-82

academic year representing an investment of $6.1 billion

(The Chronicle of Higher Education, 1983).

Accordingly, the success of the federal loan programs

seems to be the impetus for the development of new programs.

For example, the Federal Government announced the creation

of the PLUS/ALAS Loan program in 1980. This program is

similar in administrative details to the other federal loan

programs. The major difference between these programs and

the original loan programs appears to be the lack of defer-

ment and cancellation provisions and higher interest rates.

It also should be noted that, in addition to the federal

loan programs, many institutions, states and private founda-

tions provide loan funds to students. Many of these programs

pre-date the federal efforts, but the importance and reliance

on loan programs are a result of the widespread application

of the loan principles effected through the federal programs.

Of the federal loan programs the GSL/FISL program is the

most widespread. The reason is that the NDSL is institution-

ally administered and many institutions choose not to parti-

cipate in the program. Other institutions have been re-

stricted from participating in the NDSL program due to their

high student default rates. Therefore, due to the universal-

ity of the GSL/FISL program, much of the data and research

presented in the remainder of this study will focus on the

GSL/FISL program.











Program Problems and the Default Rate

The federal loan programs have several disadvantages.

According to Tate (1979), abuses in loan programs occur at

each of the three stages through which any student loan

passes: (1) eligibility; (2) disbursement; and (3) collec-

tion. Both institutions and students are responsible for

abuses apparent in the loan programs of today. Institu-

tional abuses range from mismanagement to halfhearted

collection efforts and student abuses include outright fraud

in supplying eligibility data to shrewd bankruptcy declara-

tions in order to discharge outstanding debts. The most

obvious abuse regarding the loan system is the default or

lack of repayment by student borrowers.

By statute a default is defined as an overdue loan pay-

ment of 120 days (20 U.S.C. & 1080 (c), 1976). However, the

Department of Education does not recognize a default until

the lender's obligation is suspended and the subrogation by

the Federal Court takes effect.

Regardless of the procedural issue concerning the

commencement of the default status, the American public is

constantly reminded of the thousands of students who have

participated in the federal loan programs and who have de-

cided not to repay their loan obligations. Headlines in

papers consistently highlight the default rate and recent

efforts to initiate novel collection methods such as im-

pounding automobiles have received national television

attention.











The "default rate" is computed as a ratio of the cumu-

lative amount of delinquent funds to total receivables and

can be measured by the following formula:


cumulative claims paid to lenders

all loans having entered repayment status.

Current researchers estimate the overall default rate to be

around 12%; however, the rate is slightly lower in the GSL

program because the financial institutions exercise more

selective lending practices and the guarantee agencies ex-

ercise more administrative vigilance.

There is much debate concerning the reliability of data

regarding the default rate. Arthur Hauptman (1978) argues

that loan default rates are not consistent due to several

factors:

(1) the measurement standards changed
and the data prior to 1973 are not
consistent with the data collected
after 1974;

(2) the default rate is conceptually
flawed since it (a) uses only the
federal expenditures in the numer-
ator (federal expenditures are only
80% of the costs) and (b) includes
death and disability defaults; and

(3) the default rate is cumulative;
therefore, data for each year's
statistics are not sorted out
(not everyone defaults in the
first year of repayment). (Hauptman,
1978, p. 160)

Hauptman concludes that a single measure of default is in-

adequate to explain levels and trends in the behavior of











students who default because default activity differs by the

type of guarantee (FISL or GSL), the type of educational

institution the borrower attends, the type of lender and

the demographic characteristics of the borrowers.

In addition to the internal problem concerning the re-

liability of the default rate statistics, there is much con-

cern about the use of the default rate as a performance

indicator regarding the success of the student loan programs.

As cited by Hauptman, an Office of Education Report (1979)

illustrates the three major weaknesses of this argument:

(1) too many variables beyond the con-
trol of the Guarantee Agency have a
significant impact on default rates
(i.e., depression/recession, high
unemployment rates, etc.);

(2) loan default rates may only reflect
the availability of loans (higher
rate may be due to significantly
more loans arranged); and

(3) cumulative default rates are his-
torical and may not reflect current
management philosophies. (Hauptman,
1978, p. 162)

In effect, two conclusions can be reached based on the

previous discussion:

(1) the tendency to default may have
increased over the last several
years but it may not be as high
as it has been reported, and

(2) a large number of variables which
are external to the guarantee
agency and the financial lender
affect the default rate.











The Problem of This Study


Regardless of the elusiveness of concrete reliable

statistics, the public perceives the default rate as too

high. The federal government's determination to stem the

default rate must include research on the socio-economic

characteristics of student borrowers and defaulters in

addition to data concerning administrative procedures.

Accordingly this study has attempted to provide data con-

cerning some of the variables that may be related to the

student default rate.

In addition, these variables may be useful in formulat-

ing a prediction model to determine who will repay their

student loans. Such a model would be of valuable assistance

to the student aid administrator in reducing the default

rate because it would distinguish the characteristics of

defaultees and non-defaultees. The aid administrator could

then award and approve loans for only those students with

the greatest likelihood of repayment. Since loan money may

not be as readily available in the future as it is today, a

model that could provide assistance in selecting the "best"

student loan risk may be critical in maintaining the integ-

rity of the loan programs and in replenishing the source of

funds for future loans.











Statement of the Problem

The problem of this study was to determine the relative

influence of selected variables which characterize community

college students who are most likely to repay student loans.


Questions

Specifically, a primary question of this study was to

determine the usefulness of the model derived from the data

presented in this study to predict those students likely to

repay student loans from those students who are not likely

to repay student loans.

Therefore, the following supplemental question was

addressed: will the selected variables be useful in discrim-

inating between students who will repay student loans and

students who will not repay student loans?


Justification for the Study


As indicated previously, throughout the last few years

concern regarding student loan default rates has risen tre-

mendously. The issue has become the source of critical edu-

cational fiscal management decisions and delicate political

debates. During the period from September, 1982, through

February, 1983, The Chronicle of Higher Education reported

on 17 articles relating to recent developments concerning

student loans. Thus, the selection of this issue is











justified on its standing as a practical problem facing

higher education administrators today. Discussion relative

to some of the many student variables contributing to the

default rate is imperative. This study analyzes six vari-

ables (age, marital status, sex, college standing, grade

point average and loan total) identified in the previous

research of Dyl and McGann (1977), Myers and Siera (1980),

Emmert (1978) and other researchers for the impact these

variables have on the student default rate.


Delimitations and Limitations


Since there are several procedural issues impacting the

results of this study, the following delimitations and limi-

tations are applicable.


Delimitations

(1) This study was confined to students
who have received a loan through a
program administered by a Florida
public community college.

(2) Only data made available to the
researcher by the Student Financial
Assistance Commission of the State
of Florida were utilized in this study.

(3) A sample approximating 31% of the
total population was selected by a
systematic random process for both
categories of students identified
in the analysis (i.e., students who
repay student loans and students
who do not repay student loans).

(4) The analysis of data was confined to
the discriminant analysis procedure
utilized by the computer











techniques of the Statistical
Analysis System (SAS), which pro-
duced a set of equations from
which a predictor model could be
evolved.


Limitations

(1) This study was limited to those
variables identified in the re-
search design, specifically:
college standing, age, marital
status, sex, grade point average,
and loan total.


Research Design and Procedures


This study was designed as an ex post facto research

study in which several continuous and discrete independent

variables obtained from data collected on loan application

forms were observed. From these variables, the dependent

variable of loan repayment was predicted.

According to Ary, Jacobs and Razavich (1979), ex post

facto research can supply much information of value in edu-

cational decision making even though its design differs sig-

nificantly from pure experimental research. In an ex post

facto research design an experimenter can study two groups

that are different in some respect in order to discover the

reasons for the difference. Such is the case with this

study. The design format as suggested by Ary et al. is as

follows:













Group Independent Variable(s) Dependent Variable


E (X) (variable) Y1 (Repaid Loan)

C Y 2 (Did Not Repay
Loan)

The supposed effect of treatment occurs in the experimental

group. Therefore, according to Ary et al. (1979), the re-

searcher attempts to relate the dependent variable Y1 to a

previously occurring independent variable, a nonmanipulable

variable indicated by (X) which occurred in the experimental

group but not in the control group.

The purpose of utilizing the ex post facto research

design was to illustrate the significant data to be util-

ized in a model that could be useful in predicting the re-

payment rate probability of students requesting loans for

educational use at community colleges. Such a model would

be helpful to financial aid personnel in determining what

student characteristics are representative of those students

who have repaid their student loans.


Sample

The data for this study were selected randomly from

the total population of students in each of the following

two categories:

(1) students who are repyaing their
student loans and/or are not











identified as defaulting on
their student loan commitment,
and

(2) students who are not repaying
their student loans and have
been identified as defaulting
on their student loan commit-
ment.

These categories were determined based on the data

collection and storage procedures established by the Florida

Student Financial Assistance Commission (FSFAC) in Talla-

hassee, Florida. The FSFAC is the central storage/retrieval

center regarding GSL repayment data for all institutions in

Florida. The sample size (76) represents approximately 31%

of the total population in each category involving loans made

to community college students in Florida.


Data Collection

Data on each student included in this study were

supplied by the FSFAC to the researcher in June, 1982.

During this session files were randomly selected by FSFAC

personnel for review by the researcher.

The appropriate data were then extracted from the files

for those students who were identified as community college

students. Since there are two separate files maintained by

the FSFAC (i.e., those who have not defaulted, those who

have defaulted) the researcher obtained a sample for each

category representing 31% of the total population.











The sample size (38 in each category) meets the re-

quired minimum (30) sample size suggested by Ary et al.

(1979) and the 31% sample size exceeds the 10 to 20% sample

size suggested by Art et al. for descriptive research.

The following variables were extracted from the data

files based on their availability and their identification

in previous research studies by Dyl and McGann (1977), Myers

and Siera (1980), and Emmert (1978):

(1) Age,

(2) Marital Status,

(3) Sex,

(4) College Standing,

(5) Grade Point Average, and

(6) Loan Total.

A full discussion of the selection of these variables and

the results of previous studies utilizing these variables

appears at the beginning of Chapter III.















CHAPTER II

REVIEW OF RELATED LITERATURE


The financial pressures exerted on colleges and univer-

sities have had a direct effect on the expansion of loan pro-

grams. As a result the loan programs are experiencing some

unique problems. An analysis of the responses to the prob-

lems surfacing in the student loan programs and a review of

the research regarding student loan default and student

demographics provide an overview of the issues surrounding

the problem presented in this study.


Fiscal Pressures and the Demand
For Expanded Student Loan Programs


Higher education is a social institution that depends

on tuition and service charges, philanthropy, and govern-

mental support in order to balance its budget. The heavy

reliance on these sources of support has created a dilemma

concerning the funding of higher education during weak eco-

nomic times. The current fiscal crisis can be readily con-

firmed by reviewing the funding sources and relative support

for higher education since 1950.

In terms of tuition and service charges, there has been

a percentage decline in these fees as a source of income











from 1950 to 1974 (Change Magazine, 1976). This decline

has been attributed to the larger percentage of students

attending public colleges in 1974 (76%) compared to 1950

(50%) and the corresponding lower tuition charges at these

public institutions (Change Magazine, 1976). In addition,

colleges and universities have found themselves in compe-

tition with local facilities for the student dollar regarding

auxiliary charges including food, housing and books. There-

fore, there has been a decline in income from this revenue

source as well.

Although all service charges such as room and board

accounted for approximately 40% of the total income in

colleges and universities in 1974, Kelly (1983) estimated

that tuition charges in 1977 accounted for only 11% of the

total income for colleges and universities. As a result of

present trends on college campuses, Kelly (1983) estimates

that this percentage will increase to 20% by 1988. Based

on this estimate it appears that students will be forced

to bear an increased percentage of the cost of their educa-

tion in the form of increased tuition charges. Correspond-

ingly there is likely to be additional pressure on the

student aid delivery system to compensate for this in-

creased cost. However, many colleges and universities are

fearful of an increased dependency on student fees. These

colleges and universities are justifiably concerned that

the resultant increased fees will add a new pressure to











the university budget--declining enrollments and the loss

of dollar income from student charges based on underenroll-

ment.

In terms of philanthropical support, higher education

has been the beneficiary of increased dollar support from

1950 ($200 million) until 1974 ($2.4 billion) (Change Maga-

zine, 1976). As indicated in Table 1, due to the tremen-

dous rise in the cost of higher education, this increased

dollar support represents an actual percentage decline from

1950 (8%) to 1974 (5%) in terms of total income for colleges

and universities (Change Magazine, 1976).


Table 1
Philanthropical Support
for Higher Education



Year Total Dollar Percent of Income for
Support Colleges & Universities


1950 $200 million 8%

1974 $2.4 billion 5%



Since the dollar increase is not consistent with the needed

percentage increase for institutional philanthropical sup-

port, more of the cost of higher education will have to be

absorbed elsewhere.

In the recent past, the government (federal, state,

and local) has provided this increased support for higher











education. In 1974 it was estimated that the government pro-

vided 54% of the total income to colleges and universities

(Change Magazine, 1976). This increase in total income

represents additional support for both private and public

institutions as it includes research monies, building

assistance, and student aid dollars. However today, govern-

ments whose support figures so largely in the financing of

higher education are concentrating on other major concerns

and priorities. Therefore, there has been a decline in the

rate of income growth to higher education from the govern-

ments. For example, the federal government is under con-

trary pressures to hold down expenditures in student aid

(which has risen 600% from 1967 to 1977) on the one hand and

to respond to the continuing demands of middle income par-

ents for selective relief (i.e., additional aid, tuition tax

credits) on the other. In addition, many concerns outside of

higher education are uppermost in the minds of our legisla-

tors. For example, state legislators are highly conscious

of the "Proposition Thirteen" sentiments in their districts.

Therefore, the new depression in higher education is

generally not based upon declining support, but it is due in-

stead to a decrease in the rate of increase of support for

higher education. Nowhere is this more evident than in the

dollar support for student aid during the last two fiscal

years. Colleges and universities have received essentially

the same amount of support in total dollars from the











government, but as the rate of increase has dropped, increas-

ed pressure to raise fees and start fund raising activities

has surfaced. Because the slowdown has not been uniform,

some states and individual institutions have had to contend

with declining income.

As a solution to the pressure added by increased fees,

the Carnegie Council on Policy Studies in Higher Education

(1979) has recommended expanding explicit self-help compon-

ents (work-study and loan opportunities) of the student aid

delivery system and revising the loan system to make loans

more easily attained and widely available with less risk of

default (i.e., longer payback terms, loan consolidation

procedures).

It appears that the Reagan administration has seriously

considered the Carnegie recommendations since they have pro-

posed a 60% increase in College Work-Study funds, a "self-

help" requirement for Pell Grant eligibility and changes in

the Needs Test for the Guaranteed Student Loan Program for

fiscal 1984. In addition, Mr. Reagan has proposed continuing

dollar support for the federal grant programs at the same

level as the past two fiscal years. However, under his pro-

posal, eligibility qualifications for individual students have

been substantially altered. Therefore, it is safe to assume

that as fees increase, endowment expansion decreases and fed-

eral grant money remains the same, an increasing number of

students will seek to participate in the student loan program.











A potential problem arising from the decision to rely

on the student loan programs for the increased dollar support

to higher education is that the private lenders simply may

not have funds they want to lend to students (as other loans

may be more financially advantageous) or there may not be

enough loan money to meet recognized need. Further, the

loan programs in effect today have many problems. Therefore,

before considering an expanding role for student loans in

the financing of higher education, it appears the government

should consider the weaknesses of the current loan system.


Problems Inherent in Student Loan Programs


A closer examination of the student loan default prob-

lem reveals several contributing factors. Therefore, nearly

all researchers agree that the student should shoulder only

a part of the blame for the escalating default ratio.

Foremost among the factors to consider is the history

of incompetent management of loan programs. For years nei-

ther the lender nor the government was making any noticeable

effort to collect many of the outstanding loans (Jenkins,

1978). In addition, Jenkins noted that the Office of Educa-

tion could not afford to install a computerized collection

system until 1978 (Jenkins, 1978). Although governmental

investments in the area of administrative strategies have

resulted in much improvement in collection procedures,

the previous administrative procedures account for a










substantial part of the default problem attributed to the

historically impacted default rate (Jenkins, 1978).

A second factor to consider is the nature of student

loans. Kendis (1978) states that even though student loans

are not the same as consumer loans, the lack of rules con-

cerning asset and income analysis, borrowing limits and

collection procedures are "diametrically opposed" to method-

ologies long since proved successful in consumer credit

(Kendis, 1978). Although Kendis feels that the rules should

be somewhat different from the rules utilized in consumer

credit situations, he states that the result is an "attitude

problem" surrounding student loans. This "attitude problem"

affects not only students but lenders and legislators as well.

Students borrowing as a part of their investment in their

own human capital are forced into a decision process to attend

or not to attend a college or university, wherein the negative

consequences of increasing costs, and debt, are minimized by

the ability to borrow funds. Lenders continue to lend greater

volumes of funds leading to a greater median debt loan (with

increasing financial concern) in the interest of assuring access:

and individual collegiate choice. And finally, the Government

in its effort to assume capital availability guarantees loans

by merely increasing existing programs while disregarding the

long-term costs and the social consequences of the program.

Any one of the conditions highlighted previously would have










significant impact on the student loan problem; however, in

combination there is no doubt that the "attitude problem"

suggested by Klendis is a very real factor affecting student

loan defaults.

A third major factor affecting the student loan problem

is the mechanism for repayment. Balderston (1970) notes in

an analysis of incomes between those students choosing to go

to college and those choosing not to go to college that the

former will not "catch up to" the latter in real dollars

realized until very late in the working life of the individ-

ual primarily due to the foregone earnings (lack of income

during college years) of the college student (Balderston,

1970). In fact, in some instances the college-going student

may never catch up to the non-college student whether the

dollars are discounted for inflation or not. Therefore,

any student researching the wisdom of the investment of a

college education from a purely financial perspective may

decide it is not worthwhile to attend college based on the

market payoff (Balderston, 1970).

Beyond the decision the student must make regarding the

wisdom of attending college, if a loan is utilized, then the

repayment mechanism that currently operates which seeks to

concentrate the repayment in the early years of one's work-

ing life (10 years) may have some deterrent effect upon

college attendance. If loan financing is heavily relied

upon, the effect may be substantial. Balterston's











conclusion is that short repayment periods lead to fewer

students opting to attend college if loans must be utilized

because they impose too heavy a burden of cash outflow on

the student. He also notes the effect is stronger on stu-

dents who do not finish programs or finish programs with

less optimistic forecasts of projected future incomes (i.e.,

humanities, home economics) because these students do not

have the advantage of cashing in on a completed or market-

able degree and the loan payment constitutes a higher per-

centage of their total income than it does for students with

higher incomes.

The solution proposed by Balderston is to make long

term loans and to tie the repayment mechanism into the in-

come tax system or set up a similar system which reduces

administrative costs (Balderston, 1970). Although there is

much debate as to the wisdom of Balderston's proposal,

especially as it relates to the use of the income tax system,

there appears to be little doubt that the repayment system

for current loans is a component of the overall problem

regarding loan defaults.

Many other factors are cited in the literature as con-

tributing to the student loan problem. For example, Jenkins

notes that there is such a "smorgasbord" of student assist-

ance programs that it is possible for one student to be

eligible for eight different loan programs, sponsored by

eight different banks or lending institutions to pay eight











different terms with eight different sets of conditions for

forgiveness and eight different groups to deal with (Jenkins,

1978). Some consolidation appears to be necessary.

Another factor involves the apparent lack of sensi-

tivity to issues of student loans from student financial aid

administrators. For example, the student aid packaging pro-

cess usually relegates the discussion of the terms of a

student loan to the exit interview. Certainly this factor

is within the control of the student aid offices. Hope-

fully, these offices will incorporate the suggestions made

in the National Association of College and University Busi-

ness Officers (NACUBO) manual in order to manage better the

loan programs.

Finally, a legal process has been cited as a major

factor contributing to the student loan problem. The avail-

ability of a bankruptcy action to discharge a student loan

debt has become increasingly apparent to many student borrow-

ers. The use of a bankruptcy action, its effect on the

system of student aid and the governmental response to its

use are important considerations in student loan research

because they demonstrate an area where judicial and legis-

lative cooperation has assisted in reducing the number of

the student loan bankruptcies and encouraging more students

to repay their student loans.











Bankruptcy Actions

Bankruptcy is the mechanism by which insolvent debtors

may be released from the obligation to repay their debts.

The proceedings are handled by a federal bankruptcy court

and conducted under laws enacted by Congress. In the mid-

1970s the problem of student loan bankruptcies received sig-

nificant attention from Congress, colleges and universities,

professional organizations and the courts and governmental

agencies. This attention was not unwarranted as the problem

became quite prominent. During the five fiscal years from

1966 through 1970 only 348 bankruptcies were discharged on

NDSL and GSL loans totaling $400,000. However, in the fis-

cal year 1975 alone, a total of 4,559 bankruptcies were dis-

charged on loans totaling $6,800,000 (Leonard, 1980).

The effect of the tremendous rise in the use of bank-

ruptcy actions to discharge student loan debts resulted in

a great deal of negative publicity for all student aid pro-

grams. Prior to the disclosures regarding the use of bank-

ruptcy actions, support for all federal aid programs was

exceedingly strong and resulted in the passage of the BEOG

(Pell Grant) Program. After considerable press exposure

(i.e., New York Times) public resentment led to a hearing by

the House Subcommittee on Postsecondary Education in October,

1975. The resentment was specifically aimed at a very few of

the 12 to 15% defaulting student borrowers, since very few











students default through bankruptcy. Most defaulting stu-

dents choose not to provide forwarding addresses and thus

are "lost." According to some researchers, the small number

of students abusing the bankruptcy process solely to escape

federal educational debts is not a serious threat to the

loan programs. However, the anger surfacing at the October

committee hearing appeared to be generated by the apparent

misuse of a constitutionally sanctioned remedy to escape a

moral obligation (Tate, 1979).

Consequently, in 1976, Congress passed the Education

Amendments of 1976 which changed the procedures utilized to

discharge student loan debts by setting out specific restric-

tions. The primary restriction was a mandatory five-year

delay from the commencement of the repayment period in the

filing of a bankruptcy action. Although a subprovision

allows for actions based on undue hardship, the purpose of

the amendment is to enforce a moratorium on all but the most

necessary bankruptcy discharges until sufficient time has

passed for a loan holder to either pay off the debt or have

enough at stake to make bankruptcy a genuine last resort

effort (Tate, 1979). This provision has since been incor-

porated into the Bankruptcy Reform Act of 1978.

The Congressional action highlighted above indicates

that student loan problems can be adequately addressed and

resolved. However, some writers feel that the most potent

weapon against bankruptcy abuse and student loan defaults











may rest in the college registrar's office. Therefore,

actions taken by institutions as well as professional organi-

zations, the government and professional educators are worth

researching as possible weapons in combatting the high stu-

dent loan default rate.


Institutional Responses
to Student Loan Defaults


Nearly every college has a statement in its catalog in-

dicating that the college reserves the right to withhold

grades, degrees, statements of honorable dismissal or a

transcript of credits until satisfactory settlement of col-

lege fees and other financial obligations has been made.

This strategy has worked well for many years at several in-

stitutions and is probably partly responsible for keeping

the default rate within its current level.

The appropriateness and legality of utilizing this

strategy have been questioned in several court cases; how-

ever, the courts have not provided a definitive response to

guide colleges and universities especially if the student

has discharged the debt by bankruptcy action. It appears

that in situations where the debt was discharged prior to

October 1, 1979, the court will require state institutions

to release the transcript (Handsome v. Rutgers University,

445 F. Supp. 1362). However, private institutions may have

the authority to withhold transcripts of students whose

debts were discharged prior to October 1, 1979 (Girardier v.












Webster College, 563 F. 2d 1267). Due to the change in the

bankruptcy code, students are not now likely to have their

educational debts discharged, but if they do, it seems that

the college cannot take any action (including withholding

transcripts) to collect the debt.

The Handsome and Girardier cases point out that even if

the debt to the college has been discharged by a bankruptcy

decree there are other legal obstacles to the practice of

withholding transcripts. A future court may decree that the

student has sufficient property interest in the transcript

to require the college to provide adequate notice prior to

withholding the transcript. Or a court may determine that

there exists between the student and the college a contract

and that the college must demonstrate that the contract was

entered into with adequate notice regarding the withholding

of the transcript. In either case, additional responsibility

may be placed upon the colleges utilizing this strategy to

compel students to pay their educational debts.

In addition, courts have indicated that an unpaid debt

must be one that is sufficiently related to the student's

acquiring an education to justify retention of the transcript.

This decision presents several new issues. For example, is

a student's unpaid bookstore bill or a student parking fine

"sufficiently related" to permit the college to withhold

the student's transcript?











A final consideration regarding the withholding of

transcripts rests with the Family Educational Rights and

Privacy Act (Public Law 93-380). This act requires an edu-

cational institution to allow the inspection of a transcript.

Although this act does not require a college to release an

official transcript, a court may rule in the future that

the institution must release an unofficial copy of a tran-

script.

Obviously the strategy of withholding transcripts has

been viable throughout the years, but educational institu-

tions should consider the legal ramifications of their

policies in light of the recent court decisions and the

questions presented in the court dicta.


Professional Organization Action
Regarding Student Loan Defaults


Beyond the strategies utilized by colleges and univer-

sities to encourage student borrowers to repay their educa-

tional loans, a few professions have begun policing their

members regarding loan defaults. The most notable example

is the case of the Florida Board of Bar Examiners re G.W.L.

(364 So 2d 454, Fla. 1978). In this case the Florida

Supreme Court refused admission to the state bar to the

petitioner, who had discharged his student loans in bank-

ruptcy, on the premise that the petitioner's financial










status and apparent motivation at the time of filing re-

flected a sufficient lack of the proper moral character and

requisite values to practice law in the State of Florida

(Martin, 1980). Since the court never questioned the act

of filing for bankruptcy but rather based its decision on

the student's conduct and the apparent breach of the

Florida Character Standards for bar admission (which were

established to protect the public interest), the decision

did not conflict with federal bankruptcy law regarding un-

lawful penalties imposed on bankruptcy petitioners.

This court decision was the first to link discharge in

bankruptcy to moral turpitude and it has been cited in cases

in other states with similar provisions for professional li-

censing. The purpose of the standards as stated by the

Minnesota Supreme Court is not to punish the attorney but to

guard the administration of justice and to protect the gen-

eral public (in re Peterson, 274 N.W. 2d 922, 925, Minn.

1979). Therefore, the legal profession has set a precedent

by refusing bar admission to those who have utilized the

student loan system and irresponsibly neglect their debt

obligation. Since this strategy appears to be useful for

professional student bankruptcy actions and defaults, other

professional organizations, for example the AMA or ADA,

should institute and enforce similar provisions for licen-

sing. This action is particularly relevant since the











pursuit of higher education is valued so greatly by the Amer-

ican public in whose interest the professional serves and

any interference with the financial aid system supporting

the program of higher education in America should not be

treated lightly.


Governmental Action Regarding
Student Loan Defaults


Many governmental organizations and agencies have demon-

strated concern for the student loan default problem and have

initiated reports and actions to help remedy the problem.

In addition, the U. S. Congress has been involved with legis-

lation designed to curtail the default rate among federal

employees and in providing a forum in which to discuss

possible solutions for the loan default problem.

The General Accounting Office (GAO) has published sev-

eral reports concerning the student loan system. Some of the

suggestions listed in its 1977 report included the following:

(1) require financial statements of
all debtors,

(2) refer all defaults immediately
to legal action,

(3) set up guidelines for compromise
settlements,

(4) institute a payroll deduction
system for all federal employees,
and

(5) require any contractor for collec-
tion service to document unsuccess-
ful efforts. (U.S. General Account-
ing Office, 1977)










Although these suggestions seem realistic, they have met re-

sistance from several sources. For example, in 1979 the

U. S. Department of Health, Education and Welfare (now the

U. S. Department of Education) argued successfully that only

it should negotiate settlements of compromise. In a subse-

quent (1981) report the United States Comptroller General in-

dicated that many schools visited by the GAO made inadequate

efforts to collect defaulted loans, that the schools needed

to adopt a tougher attitude toward collecting defaulted

loans and that the Department of Education should take

stronger actions against those institutions that failed to

do so. Consequently, in April of 1982, the Department of

Education notified colleges with a poor record of loan

collection that they would not receive any new federal loan

money appropriated for the 1982-83 year. Although these

actions referred specifically to the NDSL program, they

sufficiently demonstrate the interest in Washington regard-

ing the student loan problem.

During the Oversight Hearing before the Subcommittee

on Postsecondary Education of the Committee on Education and

Labor (House of Representatives) the following methods were

suggested as appropriate to cut down the GSL default rate:

(1) discharging federal employees
who default,

(2) extending deferments to one year
for those who are unemployed,











(3) permitting lower rates of
repayment for those with low
incomes, and

(4) prohibiting defaulters from
obtaining other federal loans.
(U. S. Congress, 1977)

In response to item number one above, Education Secre-

tary Terrence Bell and Senator Charles Percy (R-Ill.) spon-

sored legislation in 1982 enabling the government to garnishee

the wages of federal employees who have defaulted on their

student loans. Recently, the Department of Education sent

letters to federal employees notifying them of the Debt

Collection Act of 1982 and indicating they must make

arrangements for the repayment of their loan by February 1,

1983,or risk the garnishment of their wages. Therefore, it

is apparent that both the Executive and Legislative branches

of the U. S. Government have also taken actions to assist

in the solution of the loan problem.


Responses to the Student Loan
Default Problem by Education Spokesmen


Professional educators have also addressed the problem

of student loans and are often quoted in the literature of

higher education. For example, Robert Hartman identifies

several factors that impinge on the effectiveness of the

current student loan system including the variety of loan

programs and regulations, the use of bankruptcy actions to

discharge debts and the differences among students and











their respective needs. He recommends several restrictions

to the current loan program. Among his suggestions are:

(1) the development of a single
loan instrument,

(2) curtailing the use of bank-
ruptcy actions (at least for
the first 10 years rather
than the five years provided
in the Education Amendments
of 1976), and

(3) a provision allowing different
loan amounts for students of
different needs, and extended
payment and variable repayment
options. (Hartman, 1978, p. 96)

Hartman argues that his suggestions would increase the cost

effectiveness of the loan program (items one and two), in-

crease the manageability of loan repayment procedures (items

one and three), widen student accessibility (items one and

three), and reduce defaults and delinquency (items one and

three). Essentially he is suggesting that the loans be

conformed to fit the student, that the loan programs be

supported identically by each of the fifty states and that

the performance requirements and benefits of the loan pro-

grams be identical regardless of the program utilized.

Hartman's suggested actions seem to be consistent with the

goals of the student aid delivery system and show great in-

sight concerning the many elements constituting the student

loan problem.

Several other plans have been suggested by professional

educators regarding the student loan program. A panel of











advisors during the Kennedy Administration developed the idea

of an Educational Opportunity Bank. The proposal would have

allowed students to borrow money to cover their expenses at

whichever college they decided to attend. The loan would be

paid back over the next 30 to 40 years and an interest rate

of 1% of the students' gross income would be applied. Al-

though this program would have allowed students to pay their

own college expenses, Duke University and Yale University en-

countered several problems when instituting similar programs

including administrative and collection difficulties.

D. Bruce Johnstone, in his proposal of the National

Student Loan Bank, recommends that one new major govern-

mental agency should assume the responsibility for all stu-

dent loans (Johnstone, 1978). In addition, Johnstone sug-

gests that the colleges and universities (rather than the

lending institutions) should originate all loans and that

the repayment of interest should be tax deductible necessi-

tating a tie-in to the Internal Revenue Service. This

radical plan appears to be quite practical; however, it

tends to come into conflict with many existing offices,

agencies and banks as it would drastically change and/or

perhaps eliminate their participation in the student loan

system. Therefore, support for this proposal has never

materialized.

Most attention today regarding changes in the student

loan programs has focused on John Silber's Tuition Advance











Fund (TAF) proposal. The proposal incorporates several key

factors including

(1) loans would only be negotiated
for successful students (after
their freshman year),

(2) individual student TAF accounts
would have to be established
with the respective college
business office,

(3) involvement of the Internal
Revenue Service would be re-
quired in the transaction and
repayment procedures, and

(4) indefinite repayment periods
would be instituted since the
repayment provisions require
students to repay their loan
at the rate of 2% of their
annual gross income each year
until they have paid back 150%
of their loan. (Silber, 1978, p. 7)

To its credit the TAF proposal provides some novel

ideas such as treating loan payments as tax obligations and

pooling the income of married couples with loans for the

purpose of calculating their taxable income. However, the

plan has come under negative scrutiny as many have argued

that the proposal assumes that the burden of paying for an

education should rest with the student. Beyond the previous

arguments concerning who should bear the cost of an educa-

tion, many professionals fear this approach may lead par-

ents, private donors and the taxpayer to abandon their

support for higher education which would force an even

greater burden on the student.










In addition, although research studies show that a long

repayment period provides the best opportunity for a reason-

able return incentive for investing in higher education,

many bankers indicate that the TAF could never be self-

sustaining based on the proposed 2% interest rate over an

estimated 30 year repayment period. Other arguments against

the TAF include the feeling that any plan that provides for

the paying of higher education through long term student

debts will result in limiting equal access and opportunity

for higher education. Further, the tuition and enrollment

controls suggested to distribute TAF funds to institutions

would cause widespread disruptions in the higher education

system. For example, the expanded eligibility requirements

for private college and university students would enable them

to be eligible for a larger proportionate share of the

available money. Regardless of one's point of view, the TAF

represents a proposal which has provided healthy discussions

of the student loan system and the options available to

solve the problems.


Antecedent Studies Relating to
Student Loans and Demographic Characteristics


It is apparent that all the participants in the student

aid delivery system are concerned about the student default

rate on educational loans. The changes implemented thus far

in the student loan system have been instrumental in improv-

ing administrative procedures. For example, new and better











resources for collecting loans and monitoring repayment pro-

gress have been devised. A second approach to solving the

student loan problem should consist of researchers studying

the demographic characteristics of student borrowers in

order to distinguish those students who are likely to repay

student loans from those students who are not likely to repay

student loans. Thus far only a few researchers have explored

the characteristics of student borrowers in the last five

years.

In 1977, Dyl and McGann applied a multivariate discrim-

inant analysis to the problem of identifying defaulting and

non-defaulting student borrowers at the University of Wyoming.

They analyzed 33 different variables including class standing,

college attended, grade point average, amount of the loan,

academic major, marital status, living quarters, total amount

of university loans, and size of the monthly payments. Four

of the significant discriminators displayed direct positive

relationships with actual loan repayment behavior. The stu-

dents who were more likely to repay their student loans in-

cluded those students who

(1) had higher grade point averages,

(2) were married,

(3) were engineering majors, and

(4) chose high monthly payments.

Dyl and McGann also discovered three factors which were nega-

tively associated with repayment of student loans including











the (large) size of the loan, the residence of the student

in an apartment and the total amount of indebtedness to the

university (including previous loans). As cited by Davis and

Van Dusen (1978), this study, in concert with the earlier

studies of Pattillo and Wiant (1972) and Spencer (1974), was

instrumental in identifying for other researchers the factors

most worthy of analysis and providing a format in which to

research the characteristics of student borrowers.

Hauptman, in his 1978 study for the College Scholarship

Service, explored many of the same characteristics for stu-

dents participating in the GSL/FISL programs. One of the

most interesting findings of his study was that the follow-

ing groups of students had a higher percentage of defaults

than would be expected:

(1) students with low family incomes,

(2) female students,

(3) married students,

(4) black students, and

(5) junior college students.

Hauptman discovered that students attending public schools

had a lower default rate than those attending either private

or proprietary schools and that college and university

students had a lower default rate than junior college stu-

dents or vocational students. Although the results of this

study did not duplicate the results presented by Dyl and











McGann (1977), it is equally important because it researched

a variety of institutions and concentrated its data on one

loan program.

Also in 1978, Emmert studied the characteristics of

students defaulting on their NDSL loans and concluded that

the demographic make-up of student populations has an equal

or greater impact upon default rates than do internal admin-

istrative factors. He suggested that schools with higher

default rates are not necessarily administering their pro-

grams any less effectively. Instead he argued that they may

have students from substantially different backgrounds that

account for the different default rate. Emmert also cited

all the previous researchers in synthesizing the crucial

variables selected in his study. Among the variables

selected were:

(1) age,

(2) marital status,

(3) sex,

(4) college standing,

(5) grade point average,

(6) loan total, and

(7) employment status.

Like the Hauptman study (1978), the differing results Emmert

discovered seem to suggest that student demographic character-

istics should be weighted in order to establish variable de-

fault rates for the different postsecondary institutions.











In 1979 the Office of Education completed its first

study of student borrower characteristics. Although the

data collection was restricted to eight proprietary insti-

tutions, the study singled out several variables that may

impact the default rate. Essentially this study underscores

the necessity of establishing statistical validity by re-

searching individual types of institutions independently

since the results vary for the separate types of institutions

studied. Therefore, because of the unique demographic char-

acteristics of the proprietary student, this study concluded

that the higher default rate among proprietary institutions

discovered in the Hauptman study is understandable.

Finally, a study completed by Myers and Siera in 1980

concentrated on data available from the New Mexico Student

Loan Program for the New Mexico State University. Using the

Statistical Package for the Social Sciences (SPSS) Discrim-

inant Program, Myers and Siera attempted to develop a pre-

diction model to discern defaulters from non-defaulters.

The results indicated that the variables selected did not

lead to an accurate prediction of the likelihood of a stu-

dent defaulting on a loan. What prediction was possible

was not substantially different from what one might expect

to accomplish based on chance alone. Although the study did

not present an acceptable prediction model, the work of

Myers and Siera was instrumental in pointing out the need












to explore and refine the application of predictive models

to the student loan problem.


Summary


All of the personnel, institutions, organizations and

governmental agencies involved with student loan programs

have provided input and taken selective action to reduce

the student loan default rate. A majority of the strate-

gies implemented thus far have been concerned with the ad-

ministrative or judicial (bankruptcy) aspects of the pro-

blem. In order to deal effectively with the problem of de-

faults the studies cited demonstrate the necessity of iden-

tifying the characteristics of defaultees and non-default-

ees.

Several of the studies presented emphasized the cru-

cial demographic characteristics worth researching. Other

studies demonstrate the necessity of restricting the popu-

lation researched due to the inherent differences among the

students choosing to attend the different institutions of

higher education. Therefore, this study has restricted its

data base to community college students in the state of

Florida and researched the available data that corresponded

to the variables highlighted in the previous studies.

The student loan problem is not the creation of a

single causality but rather it is the result of the







54



attitudes, procedures, economics and actions of all of the

participants (students, lenders, aid administrators) in the

program. Certainly, additional study needs to be performed

regarding the administrative problems within the student

loan program. However, research regarding the students

who partake in the program has long been relegated to a

subsidiary status. It appears crucial that additional re-

search needs to be conducted to identify those variables

which are effective in predicting the likelihood of a stu-

dent default, because the student is indeed the major

factor contributing to the student loan default problem.















CHAPTER III

DEVELOPMENT OF THE MODEL


The research reviewed in Chapter II provided an over-

view of the demographic variables to consider in the forma-

tion of a prediction model for student loan repayment.

Names of the major contributors to the literature and the

primary variables they studied are shown in Table 2. As

presented in Chapter II many of these studies provided rele-

vant findings concerning relationships between demographic

characteristics of student borrowers and loan default. These

relationships will be reviewed as a basis for developing the

prediction model.

In one of the first studies researching demographics

and student loans, Pattillo and Wiant (1972) as cited by Davis

and Van Dusen (1978), discovered that students who borrowed

late in their academic years, who had previous loans, or who

came from large families were more likely to be delinquent

in their student loan payments (Davis & Van Dusen, 1978). In

addition, Pattillo and Wiant concluded that items reflecting

financial rather than biographical data appeared to be better

predictors of loan delinquency (Davis & Van Dusen, 1978).

Spencer completed a study of the relationship between

demographics and student loan default in 1974. In this study,








Table 2
Overview of Major Variables
Selected by Previous Researchers

(1972) (1974) (1977) (1978) (1978) (1979) (1980)
Pattillo Dyl & Office of Myers
& Wiant Spencer McGann Hauptman Emmert Education & Siera Total


Age X X X X X 5
Marital Status X X X X X X 6
Sex X X X X X X 6
College Standing X X X X X 5
Grade Point Average X X X 3
Loan Total X X X X X 5
Family Income X X X 3
Student Aid Received X X 2
Cost of Attendance X 1
College Major X X 2
Type of College X 1
Ethnic Background X X X 3
Employment Status X X 2
Academic Load X 1
Degree Earned X 1
Type of Residence X X 2
Dependents (Number) X X 2
Age of Automobile X X 2
Family Size X 1
Telephone X X 2


en











according to Emmert (1978), Spencer described the worst

possible student loan risk as an unmarried, unemployed male

student, in his last semester, with an old car, without a

phone, and with a large outstanding loan debt (Emmert, 1978).

The results that Dyl and McGann obtained in 1977 indi-

cated that the following factors related positively to the

repayment of student loans: college major (engineering),

marital status (married), grade point average (high), and

the size of loan payments (low). Dyl and McGann (1977) also

found the following three factors to be negatively associated

with repayment: residence type (apartment), total university

indebtedness, and loan indebtedness (high loan totals). In

this study Dyl and McGann also presented the results of a

discriminant analysis of their data, and they were able to

correctly classify 84% of the cases in their study. However,

as pointed out by Myers and Siera (1980), Dyl and McGann did

not test their model with any new cases. Therefore, the

reliability of the model presented by Dyl and McGann is, as

yet, untested.

Essentially these three studies presented the basis

upon which most of the research in this area has concen-

trated. These researchers have not only provided insight con-

cerning the appropriate variables to research but they also

have suggested the appropriate statistical designs for fu-

ture studies. However, the conclusion originally presented

by Pattillo and Wiant (1972) regarding the inappropriate use










of biographical data for predictive purposes has not been

heeded. Many researchers have continued to search for stu-

dent demographic characteristics which would distinguish

student loan defaulters from non-defaulters.

For example, in 1978 Hauptman studied five demographic

variables and reported that the worst risk student loan par-

ticipant was a married, Black, female student from a low

family income who attended a proprietary vocational school.

In another study presented in 1978, Emmert summarized the

previous studies reported in the literature. In this

article Emmert reported the variables that have been studied

and the conclusions that have been presented concerning the

effect these variables have had on the student loan default

problem. Emmert also noted the different default rates

among postsecondary institutions and suggested that the demo-

graphic make-up of a given student population may place an

institution in a high risk category for student loan defaults.

In conclusion, Emmert suggested that the demographic factors

should be weighted in order to establish variable default

rates for differing types of institutions.

A 1979 study prepared by the Office of Education also

reported student demographic data in relation to the problem

of student loan defaults. Although the study primarily was

designed to research the administrative procedures utilized

by selected proprietary institutions, it did show that many










but not all of the same variables selected by other research-

ers were important variables concerning the student loan

default problem in proprietary institutions. The findings

presented in this study demonstrate the necessity of study-

ing individually the different types of postsecondary insti-

tutions.

The most recent study in this area of financial aid re-

search was carried out in 1980 by Myers and Siera. Through

t-test analysis, Myers and Siera found the following vari-

ables to be highly associated with the repayment or non-

repayment of student loans: college standing, loan total,

amount of loan requested, grade point average, and degree

completion. Myers and Siera also attempted to replicate

the study design presented by Dyl and McGann (1977); and, al-

though they were able to formulate a promising prediction

model, when this model was tested using new cases only 42.5%

of the new cases were correctly predicted. The validity

problem experienced by Myers and Siera in their prediction

model illustrates the difficulty researchers have encountered

in their search for an accurate, useful prediction model for

student loan defaults.

It is apparent from the above discussion that although

the conclusions of the previous studies differ considerably,

there does appear to be some consensus regarding the impor-

tance of researching student demographics and which demo-

graphics should be studied. In selecting which variables











to use in this study, the researcher selected the variables

which had been most frequently used by previous researchers

(see Table 2). Although ethnic status and family income

were studied by several researchers, these variables were

not included in this study because of the difficulty in col-

lecting and verifying these variables. The remaining six

variables were available in the data bank of the Florida

Student Financial Aid Commission in Tallahassee, Florida.

As a result, the following six variables were selected for

the prediction model presented in this study:

(1) age,

(2) marital status,

(3) sex,

(4) college standing,

(5) grade point average, and

(6) loan total.


Analysis of Variables


As indicated in Chapter I, this ex post facto study was

designed to determine the usefulness of a model to distin-

guish those students likely to repay student loans from those

students who are not likely to repay student loans. Prior to

developing any model, there was one major issue to address:

were the selected variables useful in discriminating between

students who will repay student loans and students who will

not repay student loans?











In order to determine the usefulness of the variables

in discriminating those students who will repay student loans

from those students who will not, data representing each of

the six variables were collected for both defaulting and non-

defaulting students in the sample. Since three of the vari-

ables were nominal in nature, the t-test statistic utilized

by previous researchers to determine significant differences

between defaulters and non-defaulters was not applicable to

the data. Therefore, a chi-square analysis was performed on

the data collected for each of the variables in this study

to determine if the differences between the proportions of

subjects that fell into the two different categories (de-

faulters and non-defaulters) were significant.

Since this study represents exploratory research and

any model developed from the data will need to be validated

with new data, the researcher selected a .10 significance

level. Essentially the chi-square analysis determines if

the difference between the expected and observed frequencies

for each of the classifications within each variable is

greater than the difference one would expect to find based

on chance alone. The results of the chi-square analyses

showing the relationship of each of the six variables to

student loan default are presented in Table 3 through

Table 9.











Table 3
Chi-Square Analysis of the
Relationship Between Age and Student Loan Default


Age Defaulters Non-Defaulters


18-20 Number 8 10
% of total frequency 10.53 13.16

21-24 Number 10 9
% of total frequency 13.16 11.84

25-27 Number 8 6
% of total frequency 10.53 7.89

Over 27 Number 12 13
% of total frequency 15.79 17.11



Chi-Square = 0.6010 DF = 3 Probability = .8960



As shown in Table 3, the chi-square analysis of the re-

lationship between age and student loan default indicates

that age was not significant at the .10 level. The expected

frequency for each cell based on chance alone would be 12.5%

and, as shown, the actual frequency percentage for the cells

ranged from 7.9% to 17.1%.

The chi-square analysis of the relationship between

marital status and student loan default is shown in Table 4.

The results of the chi-square analysis for marital status

indicate a statistical significance at the .10 level. The

expected frequency for each cell based on chance alone would

be 25% and, as shown by the table, the actual frequency per-

centage for the cells ranged from 14.5% to 35.5%. Therefore,










Table 4
Chi-Square Analysis of the Relationship
Between Marital Status and Student Loan Default


Status Defaulters Non-Defaulters


Married Number 18 11
% of total frequency 23.68 14.47

Single Number 20 27
% of total frequency 26.32 35.53


Chi-Square = 2.732 DF = 1 Probability = 0.0983



the use of the variable marital status in a subsequent pre-

diction model was appropriate.

As shown in Table 5, the chi-square analysis of the re-

lationship between sex and student loan default indicates

that sex was not significant at the .10 level.


Table 5
Chi-Square Analysis of the Relationship
Between Sex and Student Loan Default


Sex Defaulters Non-Defaulters


Male Number 18 13
% of total frequency 23.68 17.11

Female Number 20 25
% of total frequency 26.32 32.89


Chi-Square = 1.362 DF = 1 Probability = 0.2432










The expected frequency for each cell based on chance alone

would be 25% and, as shown, the actual frequency percentage

ranged from 17.1% to 32.9%.

The chi-square analysis of the relationship between

college standing and student loan default is shown in

Table 6. The data collected did not indicate college stand-

ing to be significantly related to defaulters and non-

defaulters at the .10 level. The expected frequency for each


Table 6
Chi-Square Analysis of the Relationship Between
College Standing and Student Loan Default


College Standing Defaulters Non-Defaulters


Freshman Number 21 17
% of total frequency 27.63 22.37

Sophomore Number 17 21
% of total frequency 22.37 27.63



Chi-Square = 0.8420 DF = 1 Probability = 0.3588



cell based on chance alone would be 25% and, as shown, the

actual frequency percentage ranged from 22.4% to 27.6%.

Table 7 shows the chi-square analysis of the relation-

ship between grade point average and student loan default.

The results of the chi-square analysis indicated that grade

point average was not significant at the .10 level. The











Table 7
Chi-Square Analysis of the Relationship Between
Grade Point Average and Student Loan Default


Grade Point Average Defaulters Non-Defaulters


1.50-1.99 Number 2 0
% of total frequency 2.63 0.00

2.00-2.49 Number 18 13
% of total frequency 23.68 17.11

2.50-2.99 Number 8 12
% of total frequency 10.53 15.79

3.00-3.49 Number 8 8
% of total frequency 10.53 10.53

3.50-4.00 Number 2 5
% of total frequency 2.63 6.58




expected frequency percentage for each cell would be 10%

based on chance alone; however, the actual frequency percent-

age for the cells ranged from 0.00% to 23.68%.

The chi-square results of the relationship between loan

total and student loan default are presented in Table 8. The

loan total variable was significant at the .10 level. The

expected frequency for each cell based on chance alone would

be 8.3% and, as shown, the actual frequency percentage for

the cells ranged from 0.0% to 18.42%. Therefore, the size

of the loan total was a significant variable to include in

any prediction formula designed to ascertain the likelihood

of a student loan default.











Table 8
Chi-Square Analysis of the Relationship Between
Loan Total and Student Loan Default


Size of Loan Defaulters Non-Defaulters



0-$1,000 Number 13 6
% of total frequency 17.11 7.89

$1,001-$2,000 Number 11 6
% of total frequency 14.47 7.89

$2,001-$3,000 Number 13 14
% of total frequency 17.11 18.42

$3,001-$4,000 Number 1 5
% of total frequency 1.32 6.58

$4,001-$5,000 Number 0 5
% of total frequency 0.00 6.58

$5,001-$6,000 Number 0 2
% of total frequency 0.00 2.63



Chi-Square = 15.7530 DF = 5 Probability = 0.0173




The results of the chi-square tests showed that only two

of the variables were significant at the .10 level (marital

status and loan total). The differences found between the

categories (student loan defaulters and non-defaulters) for

both of these variables cannot be attributed to chance alone.

The remaining four variables (sex, grade point average, age

and college standing) were not statistically significant

according to the chi-square results of this study, but they

may demonstrate some differentiation between defaulters and











non-defaulters on student loans based on the results of

previous researchers.

The results of the chi-square analyses indicated that

only two of the six variables studied most frequently by pre-

vious researchers were significant according to the data

collected in this study. The discovery that the only finan-

cial type variable (loan total) was also one of the two vari-

ables found to be significant may underscore the importance

of Pattillo and Wiant's (1972) conclusion that items reflect-

ing financial rather than biographical data appear to be

better predictors of loan delinquency. This finding may

indicate that since students with high loan needs have good

loan payback rates, the students with the highest needs are

the best loan risk for any one of a number of reasons (i.e.,

they may value money more due to its scarcity, they may have

learned to manage money more frugally or they may have

learned how to budget more effectively). The strength of the

relationship of this one variable to student loan payback

indicates that other financial type data should be studied.

Before developing a prediction model based on the re-

sults of the chi-square analyses, the researcher explored

the possibility of interaction effects between the six vari-

ables. These results are presented in Table 9. It can be

observed that no interaction between the variables related

significantly to payback.











Table 9
Interaction Effects Between the Variables


Variables F Pr)F Significant (co =.10)


Loan by Sex 1.45 .2320 No

Loan by Grade Point 0.01 .9185 No

Loan by Marital Status 0.10 .7573 No

Sex by Marital Status 0.05 .8250 No

Sex by Grade Point Aver. 0.47 .4956 No

Grade Point by Marital
Status 2.14 .1483 No




Therefore, in developing the prediction model, the researcher

utilized only the six original variables selected from the

literature review.

Although four of the selected variables were not signi-

ficant according to the chi-square results of this study,

they were included in the formation of the prediction model

because the literature was inconclusive regarding which vari-

ables should be used in a prediction model. It should be

noted that the unique results of the chi-square analyses in

this study may be due to the unique population. Since the

previous studies did not examine the relationship between

selected variables and student loan default for community

college students, the difference in results may be due to the











specific characteristics of the community college population.

Since the results presented in the literature and the results

found for the community college students in this study were

not corroborative regarding the significance of the vari-

ables, all six selected variables were utilized in the pre-

diction model.


Development of the Model


In order to try to predict categorical membership based

on the most discriminating variables, a multiple discriminant

analysis technique was employed. Essentially this statisti-

cal procedure "weights" the predictor variables to yield

maximum discrimination between the two groups (i.e., in this

study it discriminates defaulters from non-defaulters) (Hays,

1981). In addition, the nominal variables of marital status,

college standing and sex were used to define dummy variables

in order to introduce variance into the prediction equation.

Three notable formulae generated by the multiple discrim-

inant analysis function of the Statistical Analysis System

(SAS) program were useful in distinguishing student loan

defaulters. Therefore, three prediction models were devel-

oped.

The first formula included all six variables which were

entered into the data file in the following (arbitrary)

order: age, marital status, sex, college standing, grade











point average, and loan total. The results of this multiple

discriminant analysis indicate that the six selected vari-

ables correctly predicted group membership for approximately

68% of the sample cases. Considering that the prior proba-

bility (based on chance alone) would be a 50% correct pre-

diction, the six selected variables appear to be successful

in improving the accuracy of predicting group membership.

In order to improve the chances of making more accurate

predictions, a second discriminant analysis procedure was

performed. This time the multiple discriminant analysis

enabled the researcher to catalogue the individual effect of

each selected variable on the accuracy of the categorical

prediction. Based on the probability and significance

levels of the chi-square analysis the most differentiating

variables were first included in the prediction model. When

the first variable, loan total, was entered into the pre-

diction formula, the chances of making accurate predictions

improved notably. Instead of the 50% prior probability of

making accurate categorical predictions the accuracy rate

improved to 66%. The only other significant variable

(marital status) was entered into the model next. The in-

clusion of this variable increased the probability of

correctly categorizing the defaulting and non-defaulting

student loan recipient from 66% to 71%. Therefore, this

second formula (generated by the stepwise multiple











discriminant technique) included only the two variables

demonstrating the acceptable degree of significance based

on the chi-square results ( oP = .10) and it provided the

best accuracy for categorical prediction (71%).

When the next two variables, sex and grade point aver-

age, were included in the model, the percentage of correct

categorical classifications essentially remained the same.

Although the variables would appear to be useful in a pre-

diction formula as indicated in the literature review, their

inclusion was not helpful in formulating a more accurate

prediction model. When the variable sex was included in the

model, the accuracy rating remained at 71%; however, when

the variable grade point average was included, the number

of correct classifications decreased by one and reduced the

accuracy rating to 70%. Such a result seems to be incon-

sistent with statistical theory. After studying the values

for each of the sample cases generated by the prediction

formula, the researcher believes that the decreasing

accuracy may be due to the mathematical rounding procedure

within the Statistical Analysis System program. As a result,

one sample case that defaulted on a student loan was classi-

fied by the prediction model as a non-defaulter on the total

probability difference of .0033. Therefore, the apparent

decreasing accuracy of the prediction model may be attributed

to the mathematical precision of the computer program rather

than the reflection of a conflict with statistical theory.











The third formula was generated at the conclusion of

the stepwise multiple discriminant technique and it included

all six selected variables. The addition of the remaining

two variables, college standing and age, did not improve the

accuracy rating for the prediction model. Therefore, the

formula, including all six selected variables, generated by

the stepwise technique was able to predict correct categori-

cal membership for 70% of the sample cases.

Although both the first and third formulae included the

six selected variables, the disparity in the predictive

accuracy of the formulae is accounted for in the statistical

technique employed for the derivation of the formulae.

Since the stepwise multiple discriminant analysis technique

enters the variables in the order of probability, the im-

proved accuracy of the third formula (70%) is a result of

the mathematical process of weighting the variables as they

are entered into the prediction formula. Because the order

of inclusion in the formula is different and the mathemati-

cal weighting is improved, the accuracy of the formula is

improved.

Based on the results of the discriminant analysis, it

would appear that the best predictive formula for determin-

ing correct classification of defaulting and non-defaulting

community college students would include only data regarding

the students' loan totals and marital status. Adding the











variables grade point average, sex, college standing and

age, essentially maintained the effectiveness of the pre-

diction formula. As evidenced, the stepwise technique em-

ployed to produce the second and third formulae improved the

accuracy for categorical classification over the regular

multiple discriminant analysis technique utilized for the

first formula.



Results


Based on chance alone, one would expect to predict

correctly the proper placement of students into the two

categories (defaulting and non-defaulting) 50% of the time.

The prediction formula generated by the inclusion of data

concerning loan total and marital status increases the like-

lihood of making an accurate prediction from 50% to 71%.

Therefore, it would appear that these data are relevant in

predicting whether a student is likely to default on a

student loan. The additional data that were collected for

the other four variables (sex, grade point average, college

standing and age) also appear to be relevant in producing a

prediction model since the prediction formula derived from

all six variables produced a 70% accuracy rating concerning

categorical prediction and this rating is substantially

higher than the 50% probability based on chance alone.










However, all three of the prediction formulae reflect

an error factor of nearly 30% indicating that roughly one-

third of the cases were classified in error. In addition,

the testing of the prediction formula utilizing new cases is

not possible since the formula is derived from a stratified

sample (which was necessary in order to perform the chi-

square tests to determine variable usefulness). Therefore,

the usefulness of the results of this study is limited even

though the prediction formula demonstrates moderate success

in differentiating defaultees from non-defaultees.

Previous researchers also produced prediction models

with limitations. For example, although Dyl and McGann

(1977) reported significant success with their prediction

model, Myers and Siera (1980) indicated that Dyl and McGann

did not validate their results by applying the formula to

cases other than those from which the formula was derived.

In addition, the results of the study performed by Myers

and Siera seem to indicate that although they presented a

useful model, when they attempted to validate their model

by predicting new, non-sample cases, the model was not

reliable or useful.

The results of this study show that the six variables

which were selected based upon the results of the studies

presented by previous researchers were not reliable predic-

tors of payback for community college students. Therefore,

a dependable prediction model cannot be formulated for











community college students based on these variables. Perhaps

the importance of this study is that the results coincide

with findings reported by Hauptman (1978), Emmert (1978) and

Pattillo and Wiant (1972).

Hauptman first suggested that research must be indepen-

dently conducted on the different types of postsecondary in-

stitutions. Underscoring this perception, Emmert concluded

that institutional default rates may not be comparable if

the schools' respective populations are from substantially

different backgrounds. It may be significant that the two

studies previously successful in determining reliable demo-

graphic variables and formulating prediction models (Dyl and

McGann, 1977, and Myers and Siera, 1980) were based on four-

year public univeristy (University of Wyoming and New Mexico

State University respectively) samples.

Previous discussion regarding the importance of finan-

cial aid for community college students demonstrated the

meaningful differences between the typical community college

student and the typical four-year university student. The

disappointing accuracy of this model, specifically the in-

correct classification of 30% of the sample, may be attrib-

uted to the demographic differences between the sample in

this study and the samples used in the previous studies.

For example, the results of the previous research reported

in the literature indicate that the demographic data repre-

sented by the six selected variables discriminate student










loan defaulters and non-defaulters for four-year college or

university students. However, as indicated by the chi-square

results in this study, these same variables do not entirely

discriminate student loan defaulters from non-defaulters for

community college students. Therefore, the variables,

selected based on the success of the previous studies con-

ducted with a university student sample, are apparently not

the best variables to use when attempting to predict

community college student loan defaulters.

The results of this study also appear to support

Pattillo and Wiant's (1972) conclusion that financial rather

than biographical data are better predictors of loan delin-

quency. One of the two variables that met the .10 statistical

significance level in the chi-square analyses of this study

was the loan total. It was also the only variable that was

financially related since all of the other (five) variables in

this study represented purely biographical demographic charac-

teristics. Considering that the only financially related vari-

able was also statistically significant, perhaps more finan-

cially related variables would yield a more accurate prediction

model. Therefore, the results of this study may lead to the

conclusion that, as suggested by Pattillo and Wiant, more

financial data need to be collected and analyzed in order to

make any reliable prediction of loan delinquency. This

conclusion may be also applicable to the results presented

by Dyl and McGann (1977) and Myers and Siera (1980).










Although they have been able to develop successfully a pre-

diction model, neither Dyl and McGann nor Myers and Siera

has been able to validate successfully their models by pre-

dicting accurately new cases, cases not presented in the

sample that derived the prediction model. Perhaps they need

to collect more financial data regarding their students in

order to formulate an accurate prediction model that can be

validated by new cases.


Summary


The prediction formula derived from the data available

through the Florida Student Financial Aid Commission for

Florida community college students indicates that it is

possible to make a useful discrimination regarding who will

and who will not repay their student loans for approximately

70% of the sample cases. This prediction formula was

derived through a (stepwise) multiple discriminant analysis

computer program utilizing the six selected variables high-

lighted in previous research studies as useful discriminators

between defaulting and non-defaulting students. A second

(stepwise) multiple discriminant analysis utilizing only the

two variables demonstrating statistical significance

(c = .10), loan total and marital status, yielded a pre-

diction formula with a categorical prediction accuracy

rating of 71%. A third formula utilizing all six selected

variables entered into the formula in random order










(non-stepwise) generated a model that predicted correct

categorical membership for 68% of the sample. Regardless of

the formula utilized, approximately one-third of the sample

cases were not predicted accurately. Although none of the

prediction models are accurate for one-third of the cases,

each prediction model is capable of predicting nearly 70%

of the cases correctly. Therefore, the models are of some

value in addressing the questions of this study. Future

models may be more useful in predicting those students

likely to repay student loans from those students who are

not likely to repay student loans since the models derived

from the data in this study have limitations concerning

their application as discussed in Chapter IV.

The limited success of these prediction models appears

to be consistent with the findings presented by the previous

researchers in the field. Since the difficulty in validat-

ing even successful prediction models appears to be wide-

spread, as confirmed by the results of previous researchers,

it may be that the variables utilized thus far are not the

most discriminating variables regarding student loan de-

faults. Perhaps future research should focus on financial

type variables in order to provide relevant, statistically

acceptable prediction models to assist student financial aid

administrators in predicting student loan defaulters.















CHAPTER IV

CONCLUSIONS, IMPLICATIONS AND
RECOMMENDATIONS


Previous studies researching the relationship between

student demographics and loan repayment recorded similar

results to this study. However, the results of this study

demonstrate several unique implications for future re-

searchers.


Conclusions


Since the models presented in this study improve the

chances of making an accurate prediction concerning the

likelihood of default on student loans from 50% (based on

chance alone) to nearly 70% (based on the models), the

models are successful in discriminating student loan de-

faulters from non-defaulters. Therefore, the questions of

this study presented in Chapter I must be answered in the

affirmative.

Even though some of the six selected variables were not

statistically significant in this study, the inclusion of

all six variables in the prediction models was warranted

because of the inconclusive results regarding the importance

of the individual variables as demonstrated by previous

university researchers. Inclusion of all six variables was










also appropriate since this study was the first to utilize a

community college sample, and the results of the chi-square

analyses in this study may indicate that the variables

distinguishing community college student loan defaulters may

be different from the variables distinguishing university

student loan defaulters.

Despite the differing results of the chi-square analyses

for the individual variables, the models formulated utilizing

all six selected variables were accurate in predicting

default or non-default for nearly 70% of the sample cases.

Therefore, the models presented in this study are useful in

predicting student loan defaulters. The conclusions of this

study suggest several implications of importance for future

studies.


Implications


The most notable implication of this study is that

there are differences between community college students and

university students regarding the significant variables dis-

tinguishing student loan defaulters from non-defaulters.

This study shows that, although previous researchers identi-

fied the six selected variables and determined that they were

significant in distinguishing defaulting university students,

only two of the six variables were significant in distin-

guishing defaulting community college students. Therefore,

this study underscores the importance of performing separate










study designs yielding specific results for the different

kinds of institutions utilizing the Guaranteed Student Loan

system.

The results of the chi-square analyses also appear to

indicate that the data currently being collected for commun-

ity college students do not adequately distinguish defaulting

from non-defaulting student borrowers. As suggested pre-

viously, it appears that more financial data should be

collected and analyzed if one is to achieve a more accurate

prediction model for community college student loan de-

faults. Although there are inherent differences between the

administrative mechanisms employed for the student loan

system and those utilized by commercial lending institutions,

information found useful in determining eligibility for

commercial loans may be useful in distinguishing student

loan defaulters from non-defaulters. Therefore, a second

implication based on the results of this study is that future

research should provide more focus on the financial data of

community college students in order to improve the chances

of formulating a better prediction model.


Limitations of the Model

The results of this study reveal two major limitations

of importance for future researchers.

First, the models presented in this study do not

accurately categorize nearly one-third of the sample cases.











As indicated, an inaccurate prediction regarding the proba-

bility of default or repayment was made for nearly one-third

of the students in the sample. This imperfection results in

the limited application of the prediction models. Previous

researchers were unable to formulate a prediction model and

validate their models with any great success; similarly, this

study did not find a sufficiently accurate prediction model

for determining who will default on their student loan.

Second, since the models were not effective in making

an accurate prediction for all cases in the sample, it

appears that additional (especially financially related)

variables may be of value in future research studies. This

study was restricted to the use of data highlighted as sig-

nificant in the literature and available through the Florida

Student Financial Aid Commission (FSFAC) in Tallahassee,

Florida. The results of this study and the previous studies

seem to indicate that some variables which were not avail-

able through the FSFAC data files may be of critical impor-

tance in formulating a more accurate prediction model. For

example, the employment status and type of job held at the

time of loan application or the family's financial data in-

cluding loan repayment history may be reliable variables to

consider when predicting student loan defaults. In addition,

data concerning ethnic background, college major, and ad-

justed gross income were found to be of value in prediction

formulae presented by university researchers. Although the










data are not currently available in the data system provided

through FSFAC, perhaps these variables are also important in

the formation of a prediction model concerning the probabil-

ity of default for community college students. Any further

study regarding a prediction model for determining the prob-

ability of default should also research the value of includ-

ing these variables. Based on the difficulty of collecting

data concerning these variables, the inclusion of these

variables would alter considerably the methodology section

of any similar research study. However, the variables do

appear to be of value in examining critically the student

loan default problem and presenting a model that improves

predictive accuracy. Therefore, the resources employed to

collect the data would appear to be prudently utilized.


Recommendations


The use of any of these predictive models for deter-

mining the likelihood of students defaulting on their stu-

dent loans is limited. Therefore, the foremost recommenda-

tion derived from the data and the literature search is the

need to encourage further research. The importance of this

study is that the data indicate it is possible to formulate

a model which will accurately predict 70% of the students

within the sample. It appears realistic to assume that

further research, especially the financially related

research suggested in the previous discussion, may result










in a more inclusive and accurate prediction model which would

be useful in determining the probability of default. Such

research would need to be more inclusive and therefore the

collection of the new data would demand much more time and

financial resources. However, the possibility of finding an

appropriate collection of variables and the usefulness of

the resultant prediction formula would indicate that student

loan default prediction is an appropriate area for further

study.

In the event that a subsequent study does provide a re-

liable prediction model, it should be tested and validated

by the researcher using new cases derived from a different

sample. In addition, before the model is implemented on any

individual college campus, it should be tested and validated

by the financial aid officer for applicability on his/her

campus. Only when these validations are performed can there

be confidence in the predictive capability of the model, for

the results of this study and the research presented in the

literature clearly indicate that the models and appropriate

variables vary from institution to institution. This varia-

tion is dependent upon the type of institution being re-

searched. Another reason for establishing institutional

validation procedures is to determine if significant

differences occur among institutions of the same type. It

is possible that a single, reliable prediction model that is

derived from data representing a state-wide population for a







85



selected type of institution may have no validity or useful-

ness on a single campus.

Because of the increased emphasis on student loan pro-

grams as a major component of the student financial aid

delivery system, further research concerning student demo-

graphics and the probability of default is of utmost impor-

tance. Such importance is underscored by the discussions

presented in the literature search and the successful

results of this study.











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BIOGRAPHICAL SKETCH


James A. Schmidt was born December 11, 1950, at Pitts-

burg, Pennsylvania. He attended local schools in St.

Petersburg, Florida, and graduated from Dixie M. Hollins

High School in 1968. He entered the Florida State Univer-

sity in 1968 and received the Bachelor of Science degree in

criminology in 1972 and the Master of Science degree in

education (student personnel/higher education) in 1973. He

served Southeastern Community College (N.C.) as Director of

Admissions and Student Activities and Researcher from 1973

until mid-1976. In the fall of 1976, he entered Stetson

University College of Law in St. Petersburg, Florida. The

following year, 1977, he accepted a counseling position with

Edison Community College in Fort Myers, Florida. During the

1981-82 academic year, he attended the University of Florida

while on leave from Edison Community College in order to

complete residency and course requirements for the Doctor

of Philosophy degree in educational administration (higher

education). Upon returning to Edison Community College in

July, 1982, he assumed his present position as Director of

Financial Aid.

James A. Schmidt is married to the former Linda Dale

Thompson of Callahan, Florida. They have two sons, Daniel

and Timothy. He is a member of Beta Theta Pi, Kappa Delta






91



Pi, Phi Delta Kappa, several professional and service organ-

izations and has been selected as an Outstanding Young Man

of America for 1983.










I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.




James L. Wattenbarge Chairman
professor of Educational
Administration and Supervision

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.




/John M. Nickens
Professor of Educational
Administration and Supervision

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.




Harold C. Riker
Professor of Counselor Education

This dissertation was submitted to the Graduate Faculty of
the Department of Educational Administration and Supervisi'!
in the College of Education and to the Graduate Council, and
was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.

August, 1983


Dean for Graduate Studies and
Research




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