Proprietary Schools: Poorer Student Outcomes at Schools That Rely More on
Federal Student Aid (Letter Report, 06/13/97, GAO/HEHS-97-103).
Pursuant to a congressional request, GAO reviewed the relationship
between school performance and reliance on title IV funds in the
proprietary school sector.
GAO noted that: (1) proprietary schools that relied more heavily on
title IV funds tended to have poorer student outcomes; (2) GAO's
analysis showed that, on average, the higher a school's reliance on
title IV, the lower its students' completion and placement rates, and
the higher its students' default rates; (3) although reliance on title
IV was a significant factor in explaining completion and default rates,
it was not significant in explaining placement rates; (4) requiring
proprietary schools to obtain a higher percentage of their revenues from
non-title-IV sources could save millions in default claims; (5) based on
GAO's analysis, however, achieving this result would require a
substantial increase to the current 15-percent threshold; (6) this is
because, in relative terms, large differences in schools' 85-15 measures
are associated with small differences in outcomes; (7) for example,
raising the threshold to 45 percent could improve the average default
rate of schools currently relying the most on title IV funds to the
level of those that rely the least, 3 percentage points lower, for an
estimated annual savings of $11 million; and (8) however, a standard
this high might cause schools to make changes, such as admitting fewer
low-income students, that might compromise student access to
postsecondary education.
--------------------------- Indexing Terms -----------------------------
REPORTNUM: HEHS-97-103
TITLE: Proprietary Schools: Poorer Student Outcomes at Schools
That Rely More on Federal Student Aid
DATE: 06/13/97
SUBJECT: Proprietary schools
Student financial aid
Student loans
Vocational education
Educational grants
Loan defaults
Education program evaluation
**************************************************************************
* This file contains an ASCII representation of the text of a GAO *
* report. Delineations within the text indicating chapter titles, *
* headings, and bullets are preserved. Major divisions and subdivisions *
* of the text, such as Chapters, Sections, and Appendixes, are *
* identified by double and single lines. The numbers on the right end *
* of these lines indicate the position of each of the subsections in the *
* document outline. These numbers do NOT correspond with the page *
* numbers of the printed product. *
* *
* No attempt has been made to display graphic images, although figure *
* captions are reproduced. Tables are included, but may not resemble *
* those in the printed version. *
* *
* A printed copy of this report may be obtained from the GAO Document *
* Distribution Facility by calling (202) 512-6000, by faxing your *
* request to (301) 258-4066, or by writing to P.O. Box 6015, *
* Gaithersburg, MD 20884-6015. We are unable to accept electronic orders *
* for printed documents at this time. *
**************************************************************************
Cover
================================================================ COVER
Report to the Chairman, Subcommittee on Human Resources, Committee on
Government Reform and Oversight, House of Representatives
June 1997
PROPRIETARY SCHOOLS - POORER
STUDENT OUTCOMES AT SCHOOLS THAT
RELY MORE ON FEDERAL STUDENT AID
GAO/HEHS-97-103
Proprietary Schools and Student Aid
(104851)
Abbreviations
=============================================================== ABBREV
ABHES - Accrediting Bureau of Health Education Schools
ACCET - Accrediting Council for Continuing Education & Training
ACCSCT - Accrediting Commission of Career Schools and Colleges of
Technology
ACICS - Accrediting Council for Independent Colleges and Schools
EFC - expected family contribution
GED - general equivalency diploma
HEA - Higher Education Act of 1965, as amended
NACCAS - National Accrediting Commission of Cosmetology Arts &
Sciences
Letter
=============================================================== LETTER
B-276560
June 13, 1997
The Honorable Christopher Shays
Chairman, Subcommittee on Human Resources
Committee on Government Reform and Oversight
House of Representatives
Dear Mr. Chairman:
Under title IV of the Higher Education Act of 1965, as amended (HEA),
the federal government annually spends billions of dollars on various
grant and loan programs to assist students seeking postsecondary
education and training.\1
In the late 1980s and early 1990s, high student loan default rates
attracted increased congressional attention. This attention focused
in part on proprietary schools--private, for-profit institutions
primarily offering vocational training--because their default rates
were higher than those for nonprofit postsecondary institutions. For
example, in fiscal year 1994, the average student loan cohort default
rate\2 at proprietary schools was 21 percent, compared with 14 and 7
percent at 2- and 4-year nonprofit colleges, respectively. Each
percentage point of proprietary schools' average default rate costs
the government about $5 million annually.\3
In response to problems in the proprietary sector, the Congress, in
1992, added a provision to the HEA requiring that proprietary
institutions obtain at least 15 percent of their revenues from
sources other than title IV student financial aid programs; schools
failing to meet the 15-percent threshold lose their title IV
eligibility. The rationale behind this provision, known as the
"85-15 rule," is that schools providing a quality educational product
should be able to attract a reasonable percentage of their revenues
from sources other than title IV. Supporters of the provision said
it was intended to "weed out" the "bad" proprietary schools.
Given continued concerns about proprietary school performance, you
asked us to explore the relationship between school performance and
reliance on title IV funds in the proprietary school sector. To meet
this objective, we performed a variety of statistical analyses using
data from over 900 proprietary institutions that participated in
title IV during 1994 and 1995 to determine whether or not a greater
reliance on title IV is associated with poorer school performance
measures.\4 We sent a questionnaire to these schools to ascertain the
percentage of each school's total revenues received from title IV, a
percentage we refer to as the "85-15 measure." We classified schools
as high reliance, medium reliance, or low reliance on the basis of
the relative value of their 85-15 measures.
As indicators of school performance, we used data on three measures
of student outcomes: (1) program completion, (2) training-related
placement, and (3) student loan default rates. The Department of
Education uses each of these outcomes to some extent as quality
measures for gatekeeping--the process of ensuring that students
receiving title IV funds attend only schools that provide quality
education and training programs. Completion rates generally
represent the percentage of students starting an education or
training program who complete the program within a designated time
period. Placement rates generally represent the percentage of
students completing a program who are placed in jobs related to their
field of training.\5
We conducted our work from May 1996 to April 1997 in accordance with
generally accepted government auditing standards. We checked all
data for internal consistency, called accrediting agencies and
schools in some cases to obtain corrected data, and excluded schools
from the analysis in cases where inconsistent data could not be
corrected. For a complete discussion of scope and methodological
issues, definitions of completion and placement rates for each
agency's schools, and limitations of our study, see appendix I.
--------------------
\1 Student financial aid programs authorized under title IV include
Pell grants, Federal Family Education Loans (FFEL), Federal Direct
Student Loans (FDSL), Perkins loans, and Supplemental Educational
Opportunity Grants.
\2 The cohort default rate is measured as the percentage of students
entering repayment on FFEL and FDSL loans in a fiscal year who
default on their loan in that or the succeeding year. We refer to
this as the "default rate."
\3 This figure is based on 1992 data, the most recent available.
\4 These schools were accredited by five national accrediting
agencies that together accredit a large majority of the proprietary
schools eligible for title IV programs. Accrediting agencies are
nongovernmental, voluntary associations that review educational
institutions and their professional programs to ensure a consistent
level of performance, integrity, and quality. The five accrediting
agencies were (1) the Accrediting Bureau of Health Education Schools
(ABHES), which accredits schools training students for jobs in the
health professions, such as medical assistants and lab technicians;
(2) the Accrediting Council for Continuing Education & Training
(ACCET), which accredits schools that train students in a wide
variety of fields including computer technology and paralegal and
secretarial services; (3) the Accrediting Commission of Career
Schools and Colleges of Technology (ACCSCT), which accredits schools
that teach paralegal, computer, and electrical technology skills,
among many others; (4) the Accrediting Council for Independent
Colleges and Schools (ACICS), which accredits schools training
students for primarily business-related occupations, such as
secretaries and bookkeepers; and (5) the National Accrediting
Commission of Cosmetology Arts & Sciences (NACCAS), which accredits
schools that train in the cosmetology profession, such as barbers,
hair stylists, and manicurists.
\5 Because each agency reported completion and placement data
differently, our completion and placement rate measures were not
defined consistently, requiring us to test the relationship between
the 85-15 measure and these measures separately by agency. Because
default rates have a standard definition, we tested the relationship
between the 85-15 measure and the default rate by aggregating data
from all five agencies.
RESULTS IN BRIEF
------------------------------------------------------------ Letter :1
Proprietary schools that relied more heavily on title IV funds tended
to have poorer student outcomes. Our analysis showed that, on
average, the higher a school's reliance on title IV, the lower its
students' completion and placement rates, and the higher its
students' default rates. Although reliance on title IV was a
significant factor in explaining completion and default rates, it was
not significant in explaining placement rates.
Requiring proprietary schools to obtain a higher percentage of their
revenues from non-title-IV sources could save millions in default
claims. Based on our analysis, however, achieving this result would
require a substantial increase to the current 15-percent threshold.
This is because, in relative terms, large differences in schools'
85-15 measures are associated with small differences in outcomes.
For example, raising the threshold to 45 percent could improve the
average default rate of schools currently relying the most on title
IV funds to the level of those that rely the least--3 percentage
points lower--for an estimated annual savings of $11 million.
However, a standard this high might cause schools to make changes,
such as admitting fewer low-income students, that might compromise
student access to postsecondary education.
BACKGROUND
------------------------------------------------------------ Letter :2
Since 1972, when proprietary school students became eligible for the
full range of title IV grant and loan programs, proprietary schools'
students have consistently accounted for a disproportionate share of
defaults. For example, in fiscal year 1991, proprietary school
students held 35 percent of loans entering repayment but accounted
for 71 percent of those who defaulted in fiscal years 1991 and 1992.
Default claims associated with these proprietary school students'
loans totaled $140 million.
In response to high default rates, the Congress enacted several
legislative requirements proprietary schools must meet for title IV
eligibility. One such measure, the 85-15 rule, became part of the
HEA in 1992. This rule requires each school to calculate a
percentage: The title IV dollars its students receive is the
numerator, and total revenues from its educational programs make up
the denominator. This percentage cannot exceed 85 percent; an
independent accountant must certify to Education that this
calculation is correct.\6 The 85-15 rule is similar to one applicable
to veterans' benefits.\7
Considerable controversy arose over Education's implementing
regulations that defined "revenues" for the 85-15 calculation and
required that schools base their first year's calculations on the
fiscal year prior to the regulations' publication. Under Education's
regulatory definition, schools cannot include revenues from certain
contracts--for example, to train a group of workers for an employer
if the course does not meet title IV eligibility criteria--in the
denominator. Critics warned that using prior-year data could force
many proprietary schools, even those with good student outcomes, to
close because it would not provide them ample opportunity to comply
with the new rule. In response, the Congress delayed the effective
date of the final 85-15 regulations 1 year, until July 1, 1995.
Even some lawmakers who supported this delay generally agreed that
the basic intent of the 85-15 rule was good and that the concept
behind the rule made sense. A few members of the Congress, however,
suggested the 85-15 rule needed more study, such as examining the
nature of the relationship between revenue sources and school
performance.
Some observers believe a threshold higher than the current 15 percent
would be more effective. Others favor basing regulations on
performance measures, such as those already employed as gatekeeping
tools. For example, default rates already play a major role in
governing program participation: Schools with default rates
exceeding 25 percent for 3 successive years can lose eligibility for
student loan programs, and schools with rates exceeding 40 percent in
a single year can lose eligibility for all title IV aid. In
addition, students in short-term programs\8 cannot receive title IV
aid unless these programs have completion and placement rates of at
least 70 percent.
--------------------
\6 As of July 1, 1997, proprietary schools no longer need this
attestation but instead must disclose, in their annual audited
financial statements, the percentage of their revenues derived from
title IV funds.
\7 Veterans' benefits may not be used to pay for postsecondary
education instruction when more than 85 percent of program
participants have all or part of their education benefits paid for by
the educational institution or the Department of Veterans Affairs.
As initially proposed, the 85-15 rule would have focused on the
percentage of students receiving aid, similar to the veterans'
benefits rule; as ultimately passed, the 85-15 rule focuses instead
on the percentage of school revenues coming from title IV programs.
\8 Short-term programs are defined as those with fewer than 600 clock
hours of instruction. A 60-week program where students meet for 10
hours a week, and a 15-week program where students meet for 40 hours
a week, are both 600 clock hour programs. Students cannot receive
title IV aid for a program with fewer than 300 clock hours.
SIGNIFICANT RELATIONSHIP
BETWEEN RELIANCE ON TITLE IV
AND PERFORMANCE MEASURES
------------------------------------------------------------ Letter :3
Schools that relied more heavily on title IV funds generally had
poorer student outcomes. High-reliance schools had lower completion
and placement rates and higher default rates than low-reliance
schools. Regression analysis substantiated the significance\9 of the
relationship with completion and default rates but not with placement
rates.
Completion rates for schools that relied heavily on title IV funds
were lower than for schools that relied on title IV to a lesser
extent (see fig. 1). For schools accredited by four of the five
accrediting agencies, high-reliance schools had an average completion
rate more than 10 percentage points lower than low-reliance schools.
Across the board, high-reliance schools had the lowest completion
rates. For the four accrediting agencies' schools, we found
significant correlations between reliance on title IV and completion
rates; regression analysis confirmed the relationship's significance.
Figure 1: Schools With High
Reliance on Title IV Funds Had
Lowest Completion Rates
(See figure in printed
edition.)
Note: Definitions of completion rate and low-, medium-, and
high-reliance schools vary by agency.
Generally, placement rates for schools that relied heavily on title
IV funds were slightly lower than low-reliance schools (see fig. 2).
Correlations between placement rates and the 85-15 measure were
negative and significant for schools from three agencies; for schools
from the other two agencies, the correlations were not significant.
However, our regression analysis showed that reliance on title IV
funds was not a significant factor in explaining placement rates.
While correlation analysis examines the relationship of two variables
in the absence of information about other influential factors,
regression analysis illuminates how other factors exert their own
influence on the outcome; accounting for these factors, the
relationship was no longer significant.
Figure 2: Schools With High
Reliance on Title IV Funds Had
Lowest Placement Rates
(See figure in printed
edition.)
Note: Definitions of placement rate and low-, medium-, and
high-reliance schools vary by agency.
Default rates at schools with high reliance on title IV were higher,
on average, than those at schools with medium or low reliance.
Schools in the highest one-third of the distribution of the 85-15
measure had an average default rate 4 percentage points higher than
schools in the lowest one-third (see fig. 3).\10 We found a
significant relationship between default rates and schools' reliance
on title IV funds using both correlation and regression analyses.
Figure 3: Schools With High
Reliance on Title IV Funds Had
Highest Default Rates
(See figure in printed
edition.)
For more detailed results, including sample sizes, break points for
85-15 measure categories and correlation results for each agency,
regression results, and results of sensitivity analyses, see appendix
II.
--------------------
\9 "Significance" refers to statistical significance at the 5-percent
confidence level. This significance means that we can be 95 percent
certain that a measured association is not due to chance or random
variation.
\10 This pattern generally held for each agency separately. Default
rates were lowest in the low-reliance group for four of the five
agencies and were highest in the high-reliance group for three of the
five agencies.
A SIGNIFICANTLY HIGHER 85-15
THRESHOLD WOULD LIKELY REDUCE
DEFAULTS BUT MIGHT IMPAIR
STUDENT ACCESS
------------------------------------------------------------ Letter :4
Increasing the 85-15 rule's 15-percent threshold--requiring a higher
percentage of total revenues from non-title-IV sources--could save
millions of dollars annually by reducing default claims. However,
because, in relative terms, large differences in schools' 85-15
measures are associated with small differences in outcomes, it would
take a substantial increase to attain the outcomes demonstrated by
schools that rely the least on title IV. Furthermore, impacts on
students' access to postsecondary education would depend on how
schools react.
A far more stringent standard would be required to materially improve
the effectiveness of the 85-15 rule. Each percentage point
difference in a school's level of reliance on title IV funds is
associated with about a 0.27 percentage point difference in its
completion rate and about a 0.11 percentage point difference in the
default rate. A significantly higher threshold could save millions
in default claims.
For illustrative purposes, consider the results achieved by
redefining the 85-15 rule to include only schools classified in our
sample as low-reliance, or tripling the 15-percent threshold to 45
percent. Take a school that receives 80 percent of its revenues from
title IV and has a completion rate of 70 percent. Compare this
school to another one identical in all respects to the first, except
it receives only 50 percent of its revenues from title IV. Our
analysis suggests the second school would have a 78-percent
completion rate--8 percentage points higher than the first.
Similarly, if the school with the higher reliance on title IV has a
default rate of 20 percent, the school with less reliance would be
expected to have a 17-percent default rate--3 percentage points
lower. If high- and medium-reliance schools' default rates decreased
to the low-reliance school level--that is, if the results illustrated
by this example could be achieved across the proprietary school
sector--resulting annual default claims savings could be about $11
million.
However, the effect of raising the 15-percent threshold on students'
access to postsecondary education would depend on how the affected
schools would react to such a change. Two somewhat extreme
assumptions illustrate how savings could be achieved without
affecting access. One such assumption underlies our savings
estimate: all high- and medium-reliance schools in our sample would,
among other things, successfully reduce their reliance on title IV
and remain eligible for the program, for example by enhancing the
quality of their programs and thereby attracting other revenue
sources, without changing the characteristics of their student
bodies.\11 Similar savings would be predicted under a different, also
extreme, assumption: All high- and medium-reliance schools become
ineligible to participate in title IV, but all their students
transfer to other title-IV-eligible proprietary schools.\12
On the other hand, meeting a higher standard may cause schools to
change their behavior in ways that compromise student access. For
example, as a means of reducing revenues from title IV,
higher-reliance schools might admit fewer low-income financial aid
recipients. Also, if some schools fail to meet the new standard and
close, remaining title IV-eligible schools might not have the
capacity to absorb all their students, forcing some students out of
higher education altogether.
--------------------
\11 A further assumption is that other characteristics of each school
and its students do not change.
\12 This example also assumes that the remaining schools have the
capacity to absorb these students and the students take on the lower
default rates of the new schools to which they transfer.
CONCLUSIONS
------------------------------------------------------------ Letter :5
Our results generally support the notion underlying the 85-15
rule--that greater reliance on federal financial aid funds by
proprietary schools is associated with poorer student outcomes.
Overall, the descriptive statistics, the number of significant
correlation results, and the regression analysis confirming the
correlations for two of the three performance measures indicate
students attending proprietary schools that rely heavily on federal
student aid as a revenue source fare worse--in terms of completion
and default rates--than students at proprietary schools that rely
less on student aid.
A more stringent standard than the current 85-15 rule could save
millions of dollars but also might have unintended consequences.
Because a small change to the 15-percent threshold would not
materially improve school outcomes, such as lower default rates, a
rather large change would be necessary. However, a significantly
higher threshold could adversely affect student access because
schools may be limited in their ability to reduce reliance on title
IV funds without displacing some low-income students.
AGENCY COMMENTS
------------------------------------------------------------ Letter :6
We provided a draft copy of this report to Education for review. We
discussed the draft with Education officials, who generally agreed
with our findings and conclusions, and we incorporated technical
corrections they suggested.
---------------------------------------------------------- Letter :6.1
We are sending copies of this report to the Secretary of Education,
members of relevant congressional committees, and other interested
parties. Copies will also be made available to others on request.
This report was prepared under the direction of Wayne B. Upshaw,
Assistant Director. If you or your staff have any questions
concerning this report, please call me at (202) 512-7014 or James W.
Spaulding, Senior Evaluator, at (202) 512-7035. Tim Silva and Dianne
Murphy Blank also contributed to the design and implementation of
this study.
Sincerely yours,
Cornelia M. Blanchette
Associate Director, Education
and Employment Issues
OBJECTIVE, SCOPE, AND METHODOLOGY
=========================================================== Appendix I
Our study was designed to explore the relationship between reliance
on title IV funds and school performance in the proprietary school
sector. To meet this objective, we performed a variety of
statistical analyses on data from a substantial number of the
proprietary schools that participated in the Higher Education Act of
1965's title IV programs during 1994 and 1995.
SCOPE
--------------------------------------------------------- Appendix I:1
The 85-15 rule requires that proprietary schools obtain at least 15
percent of their revenues from sources outside of title IV funding.
The rule applies only to proprietary schools--for-profit institutions
that provide postsecondary education and training programs in a wide
variety of fields, many for 2 years or less but some for 4 years.
Our analysis treated individual proprietary schools as the unit of
analysis. We used school data from 1994 and 1995.
We obtained our data on proprietary schools from five nationally
recognized accrediting agencies: the Accrediting Bureau of Health
Education Schools (ABHES); the Accrediting Council for Continuing
Education & Training (ACCET); the Accrediting Commission of Career
Schools and Colleges of Technology (ACCSCT); the Accrediting Council
for Independent Colleges and Schools (ACICS); and the National
Accrediting Commission of Cosmetology Arts & Sciences (NACCAS).
Together, these five agencies accredit a large majority of all
proprietary schools that participate in title IV programs. Each
agency requires member schools to submit annual reports that provide
information on various aspects of school operations. For example,
schools typically report the number of students who (1) matriculated
in their programs, (2) completed programs, and (3) were placed in
training-related jobs.
All the schools in our study met two criteria. First, each school
had a title IV institution code number assigned by the Department of
Education, signifying the school's eligibility for title IV programs.
Second, each school was a main campus, not a branch campus or
additional location.\13 Regulations require the 85-15 calculation to
be performed at the institutional level, which includes one main
campus and all of its branch campuses and additional locations.\14
--------------------
\13 The terms "branch campus" and "additional location" are often
used interchangeably. They refer to school operations that are under
the administrative control of a main campus but are located
elsewhere. For example, a main campus in Los Angeles might have
branch campuses in San Diego and Phoenix. All federal financial aid
for students attending branch campuses is administered through the
institution's main campus.
\14 We used one additional criterion in selecting NACCAS schools for
this study. While other accrediting agencies collect student-outcome
data from each campus individually, NACCAS collects student-outcome
data by program, across all campuses under the same ownership. Thus,
if an owner filed one annual report to NACCAS covering two main
campuses, both of which offered the same course, it was impossible to
determine separately the placement rate for students taking the
course at each of the two schools. We included in our study only
those main campuses whose annual report contained data for a single
main campus. As a result of this necessary step, our analysis of
NACCAS data does not include some schools that are part of
multicampus chains; that is, schools that share the same name and are
owned or operated by the same individual(s) or corporation. Of 997
records in the database NACCAS provided us, we identified 314 cases
in which annual reports combined data from two or more main campuses.
We cannot determine whether our results would have been different if
such schools had been included in our analysis.
DATA COLLECTION
--------------------------------------------------------- Appendix I:2
Because Education does not yet require schools to disclose the
results of 85-15 calculations\15 in their certified financial
statements, we conducted a confidential mail survey of schools from
the five accrediting agencies. Our questionnaire asked school
officials to report the results of their institution's 85-15
calculation for the first fiscal year that ended after June 30,
1995.\16 It also asked them to identify all other affiliated
campuses--such as branch campuses or additional locations--whose
revenue data were included in the institution's 85-15 calculation.
This information enabled us to (1) eliminate from our analyses any
schools that performed the 85-15 calculation using revenue data from
more than one main campus and (2) make sure we included information
on school performance and characteristics from all the additional
campuses that the institution included in its 85-15 calculation.
Thus, we would not be comparing the results of an 85-15 calculation
from a main campus and its branch campuses with student outcome data
from the main campus alone.\17 We use the term "school" hereafter to
refer to a respondent, or a main campus plus any associated branch
campuses. The accrediting agencies helped us identify schools for
our survey and assisted in following up on survey responses.
We sent questionnaires to 1,624 schools, with an initial mailing in
October 1996 and follow-up mailings in December 1996 and January
1997. Of the 1,624 schools we surveyed, 81 were ineligible for our
study, yielding an "adjusted" population of 1,543. We categorized
schools as ineligible if (1) they had closed, (2) they were actually
nonprofit institutions, or (3) they were not currently participating
in title IV programs. We received responses from 1,181 of the 1,543
schools in our adjusted sample, a 77-percent response rate. The
response pattern for schools from each accrediting agency is shown in
table I.1.
Table I.1
Response to GAO's Survey of Proprietary
Schools, by Accrediting Agency
ABHES ACCET ACCSCT ACICS NACCAS Total
----------------------------- -------- -------- -------- -------- -------- --------
Number of schools surveyed 42 93 503 341 645 1,624
Number of schools determined 2 3 40 15 21 81
ineligible
Adjusted size of population 40 90 463 326 624 1,543
Number of questionnaires 34 70 358 253 466 1,181
returned
Response rate 85.0% 77.8% 77.3% 77.6% 74.7% 76.5%
-----------------------------------------------------------------------------------------
For each accrediting agency, we compared respondents with
nonrespondents using data on school size and student outcomes from
the agency's annual report database. For schools accredited by four
of the five agencies, including the three agencies accrediting the
largest number of schools, schools that responded were slightly
larger, on average, than nonrespondents. Because there were no
systematic differences in completion and placement rates, however, we
concluded that our respondents did not differ substantially from
nonrespondents. Therefore, because we surveyed the population of
schools that met our selection criteria in each accrediting agency,
we assumed that the information provided by our respondents gives a
representative picture of all proprietary schools participating in
title IV programs accredited by the five agencies.\18
The number of schools accredited by each agency included in most of
our statistical analyses, however, was somewhat lower than the number
of usable returns listed in table I.1, because some respondents did
not answer particular items in the questionnaire or gave nonvalid
responses. For example, if respondents indicated they did not know
the result of their 85-15 calculation, we excluded them from our main
analyses. Similarly, if school officials indicated they did the
85-15 calculation using revenue data from more than one main campus,
we ruled it a nonvalid response.\19
--------------------
\15 The 85-15 calculation produces a percentage. The numerator is
"Title IV, HEA program funds the institution used to satisfy tuition,
fees, and other institutional charges to students." The denominator
is "the sum of revenues generated by the institution from: Tuition,
fees, and other institutional charges for students enrolled in
eligible programs . . .; and activities conducted by the
institution, to the extent not included in tuition, fees, and other
institutional charges, that are necessary for the education or
training of its students who are enrolled in those eligible
programs." See 34 C.F.R. Sec. 600.5(d)(1). New rules going into
effect July 1, 1997, require proprietary institutions to disclose
this percentage as a footnote to their financial statement audits.
\16 The 85-15 regulation became effective on July 1, 1995.
\17 Schools are required to calculate their 85-15 measure by
combining main and branch campus revenue data.
\18 We are less confident of this conclusion with NACCAS member
schools because, as described earlier, in selecting schools for our
study, we excluded those who filed a single annual report for more
than one main campus.
\19 In addition, we excluded schools from our analyses if they
reported that their 85-15 calculation included revenue data from a
branch campus or additional location that we could identify as not
affiliated with the main campus. For NACCAS schools, we also ruled a
school's response invalid if it indicated that the 85-15 calculation
did not include revenue data from a branch campus or additional
location that was included in its annual report. We could not,
however, take these same steps for our analyses using default rates,
because we could not identify the branch campuses or additional
locations included in an institution's default rate. While we did
exclude invalid 85-15 results, we could not be certain whether valid
85-15 results were based on data from the same set of campuses that
contributed to the default rate.
DATA ANALYSIS
--------------------------------------------------------- Appendix I:3
Our completion and placement rate calculations for schools varied by
accrediting agency because of variations in the data the agencies
collected. We performed separate but similar analyses on schools by
agency. We used descriptive statistics and correlation analysis to
explore the relationship between school performance indicators and
reliance on title IV funds for schools from all five agencies. For
ACCSCT and ACICS schools, we also used regression analysis.
COMPLETION AND PLACEMENT
RATE CALCULATIONS
------------------------------------------------------- Appendix I:3.1
For schools accredited by ACCSCT, the completion rate was the number
of students that graduated from a program within a specified time
divided by the number that started, adjusted for transfers in and out
of the school. The completion rate for schools accredited by ACCET
and NACCAS was the number completing a program within a specified
time divided by the number scheduled to complete in that year. For
schools accredited by ABHES and ACICS, the completion rate was the
number of students who graduated (or completed) in the program year
divided by the number of students that left the school through
graduation (or completion), dismissal, or withdrawal. Because
neither of the latter two agencies had cohort-based data, and because
the schools often had programs lasting longer than 1 year, we could
not simply divide the number of graduates by the number of students
starting the program that year.
The placement rate was some measure of the number of graduating or
completing students placed in jobs divided by the number that
graduated or completed that year. For schools accredited by ABHES
and ACICS, the numerator was the number of students placed in the
field of training or a related field; for schools accredited by
ACCET, the numerator was the number placed in training-related
employment. For schools accredited by ACCSCT, the numerator was the
number of graduates who were employed in the field of training. For
schools accredited by NACCAS, the numerator was the number who had
found jobs.
DESCRIPTIVE STATISTICS
------------------------------------------------------- Appendix I:3.2
We initially examined the relationship between title IV reliance and
school performance using simple descriptive statistics. Within each
accrediting agency, we sorted schools from low to high based on the
extent to which they relied on title IV funds as a revenue source.
We divided the schools into three roughly equal groups--categorized
as low-reliance, medium-reliance, and high-reliance schools--and
computed the mean value of the three outcome variables for schools in
each category. This approach yielded descriptive statistics for
schools with low, medium, and high reliance on title IV.
CORRELATION ANALYSIS
------------------------------------------------------- Appendix I:3.3
We used correlation analysis to determine the direction and strength
of association between reliance on title IV and each outcome
variable. We examined whether this relationship was in the direction
predicted by the theory underlying the 85-15 rule--that is, as
reliance on financial aid revenues increases, outcomes worsen. The
statistic measuring correlation, the correlation coefficient, may
vary between -1 and 1. Direction of association refers to whether
the values of two variables tend to move in the same direction (a
positive correlation) or in opposite directions (a negative
correlation). For example, if higher levels of reliance on title IV
funds is generally associated with higher student loan default rates,
we would say that the two variables are positively correlated.
Strength of association refers to how tightly the scores on one
variable are distributed, on average, given particular values on the
other variable. When this range is wide, the correlation is weak;
when it is narrow, the correlation is strong. The farther the
correlation coefficient is from 0 (zero), the stronger the
association. Thus, a correlation coefficient for two variables of
0.78 indicates a stronger association than if the same variables had
a correlation coefficient of 0.13, and a correlation coefficient of
-0.78 is stronger than one of -0.13. However, a correlation
coefficient of 0.78 for two variables cannot be compared to one of
0.13 for two other variables.
To guard against the possibility that our findings were due to
chance, we tested for statistical significance at the 5-percent
level, a standard practice in this type of research. Thus, we report
a correlation as statistically significant only if the probability of
getting that result by chance is less than 5 in 100. We used a
one-tailed significance test, because the legislation presumes that
high values of the 85-15 measure are associated with unfavorable
outcomes, that is, low completion and placement rates and high
default rates.
Finally, it is important to note that correlation does not indicate
causality; that is, just because two variables are correlated does
not mean that one "causes" the other. When correlation analysis
shows two variables are related, a third, unmeasured variable may
really explain the observed relationship. In the prior example, the
level of poverty among a school's students might "cause" both
reliance on title IV funds and student loan default rates to be high.
REGRESSION ANALYSIS
------------------------------------------------------- Appendix I:3.4
Regression analysis is a method for exploring how a dependent
variable is affected by a number of independent variables. We
performed several regressions to isolate the unique influence of one
particular independent variable (extent of reliance on title IV
funds) on a series of dependent variables (completion rate, placement
rate, and default rate) while holding constant the influence of
various other independent variables. As with our correlation
analyses, we used tests of statistical significance to determine the
likelihood that our regression analysis results were due to chance.
We accounted for
the number of students at the school;
the percentages of students who were female; were black; were
Hispanic; were under age 25; were age 45 or older; were admitted
under the ability-to-benefit provision, that is, with no high
school diploma or general equivalency diploma (GED); were
admitted with a GED; were admitted with some prior postsecondary
education; received Pell grants; received Stafford loans; had an
expected family contribution (EFC) of zero, that is, were not
required to contribute from their own resources toward the cost
of education;\20 and attended part time;
the ratio of students to faculty;
the faculty turnover rate;
the number of years--since its founding or 1972, whichever is
later--that the school operated before participating in title IV
programs;
the number of years the education director and the placement
director have held their positions;
the average years of tenure for all instructors;
weighted average program length, in weeks;
weighted average cost of tuition and fees, in thousands of dollars;
weighted average starting salary for school graduates, in thousands
of dollars;\21
the unemployment rate of the area where the school is located; and
the percentage of gross tuition income spent on new equipment and
teaching aids.
Our regression model specified a particular relationship between the
three outcome variables and the independent variables. Our model was
recursive--completion rates (and the full set of independent
variables) were modeled to influence placement rates, and completion
and placement rates (and the full set of independent variables) were
modeled to influence default rates. We believe knowing a school's
completion rate helps predict its placement rate and knowing both
completion and placement rates helps predict its default rate. For
example, a school with low completion and low placement rates might
be expected to have a high default rate, because many of its students
would either leave without completing their education or complete but
not find a job. Both types of students might be at higher risk than
average of defaulting, thus the school's default rate could be higher
than average.
We performed our baseline regression analysis on schools accredited
by ACCSCT. ACCSCT was the only agency that had data on the requisite
independent variables. ACICS had data on some but not all of the
independent variables. We also performed regressions on the ACICS
data to try to determine whether the results obtained from the ACCSCT
data could be replicated with a different data set. We then
performed new regressions on the ACCSCT data, using independent
variables available for ACICS, and compared the results. In these
regressions, we accounted for
the number of students at the school;
the percentages of students who were female; were minority;\22 were
admitted under the ability-to-benefit provision, that is, with
no high school diploma or GED; were admitted with some prior
postsecondary schooling; had an EFC of zero; and attended part
time;\23 and
the ratio of students to faculty.
Detailed results of our analyses appear in appendix II.
--------------------
\20 The EFC is determined by a formula that accounts for family
income and assets and is used in awarding financial aid.
\21 Data on program length, tuition and fees, and starting salary of
graduates were provided for each of a school's programs. We weighted
the figure for each program by the number of students in that program
to determine an average for the school.
\22 For schools accredited by ACCSCT, this variable includes only
black and Hispanic students.
\23 For schools accredited by ACICS, this variable covered students
enrolled in less than a full program, which may be different from
students who were part time.
LIMITATIONS ANALYSIS
--------------------------------------------------------- Appendix I:4
Our study could not fully assess the impact of the 85-15 rule because
of certain data limitations. For example, we could not measure
qualitative factors involved in schools' vocational training
processes. Accrediting agencies' data typically pertain to easily
measurable inputs, such as student or faculty characteristics, or
outcomes, such as completion and placement rates. We could not
directly assess the quality of instruction or schools' equipment, to
give just two examples of key aspects of the training process that
may influence outcomes like program completion or training-related
placement rates.
Also, our findings cannot be generalized to all proprietary schools
participating in title IV. The schools that we included in our
study, though they make up a large proportion of title-IV-eligible
proprietary schools, are not necessarily representative of all such
schools in the nation. In addition, as noted previously, not all
schools that responded to our survey knew the value of their 85-15
measure or computed it correctly. We did not verify schools'
computations.
Finally, variables in our analyses came from different time periods.
Our measure of school reliance on title IV funds--the 85-15
measure--pertains to each school's first fiscal year ending after
June 30, 1995, which for many schools covered the period of January
1, 1995, to December 31, 1995. Thus, our key independent variable
typically represents a time period slightly later than, though
usually overlapping with, the period that was the basis for most of
our dependent and other independent variables, which came from
accrediting agency annual report data and whose time periods differed
by agency. At the time of our study, the most recently available
student loan default data were for 1994, reflecting the percentage of
loans in default among each school's borrowers who entered repayment
in fiscal year 1994. Such students would have attended school at
least 1 year prior to the time period for which annual report data
were collected and the fiscal year for which officials did the 85-15
calculation. These students' experiences at a given school thus do
not necessarily represent the experiences of students who were
enrolled during the time period for which accrediting agencies
collected annual report data.
However, we do not believe the mismatching time periods raise
significant questions about the results of our analyses using default
rates as the dependent variable. A researcher who previously
analyzed the relationship between default rates and various school
and student characteristics among ACCSCT schools reported that using
default rates and annual report data for matching time periods
yielded results "virtually identical to those obtained with the
time-lagged data."\24
--------------------
\24 Morgan V. Lewis, "Analysis of Annual Report Data for School
Years 1990 to 1993," study prepared for ACCSCT, Center on Education
and Training for Employment (Columbus, Oh.: The Ohio State
University, Nov. 1994), p. 25.
DETAILED RESULTS OF DESCRIPTIVE,
CORRELATION, AND REGRESSION
ANALYSES
========================================================== Appendix II
This appendix presents technical detail and results of our analyses
of the relationship between reliance on title IV funds and school
performance. It includes sample sizes, standard deviations or
standard errors, and significance levels for many of our results, as
well as sensitivity tests for some of the assumptions we made in
conducting our analyses.
DEFINITIONS OF LOW, MEDIUM, AND
HIGH RELIANCE
-------------------------------------------------------- Appendix II:1
We ranked the schools accredited by each agency by their 85-15
measure and grouped them into three categories, which we refer to as
low-reliance, medium-reliance, and high-reliance schools. For each
agency, each category contained roughly one-third of the schools.
Table II.1 shows the break points for each agency and the number of
schools falling into each category.
Table II.1
Categories of Low, Medium, and High
Reliance on Title IV Funds, by
Accrediting Agency
Category of 85-15
measure ABHES ACCET ACCSCT ACICS NACCAS
------------------- ------------ ------------ ------------ ------------ ------------
Low
-----------------------------------------------------------------------------------------
Range of measure 23%-65% 4%-58% 1%-59% 12%-64% 2%-40%
Number of schools 10 18 110 73 138
in category
Medium
-----------------------------------------------------------------------------------------
Range of measure 67%-77% 61%-76% 60%-75% 65%-77% 41%-61%
Number of schools 10 19 107 85 138
in category
High
-----------------------------------------------------------------------------------------
Range of measure 78%-85% 77%-84% 76%-85% 78%-85% 62%-85%
Number of schools 10 17 114 71 135
in category
-----------------------------------------------------------------------------------------
Some of the analyses, however, used fewer schools than shown in table
II.1 because some schools had missing data for a particular outcome.
COMPLETION RATES
-------------------------------------------------------- Appendix II:2
Schools with high reliance on title IV, on average, had lower
completion rates than schools with low or medium reliance. The
differences between the high one-third and low one-third of schools
ranged from 12 to 18 percentage points for schools accredited by four
of the five agencies. Schools from the fifth agency, NACCAS, showed
virtually no difference in completion rates across the three
categories. Table II.2 shows means and standard deviations, as well
as sample sizes, for completion rates for schools in low, medium, and
high title IV reliance categories.
Table II.2
Average Program Completion Rate at
Schools With Low, Medium, and High
Reliance on Title IV Funds, by
Accrediting Agency
(Numbers in percent)
Standard Standard Standard Standard Standard
85-15 deviatio deviatio deviatio deviatio deviatio
category Mean n Mean n Mean n Mean n Mean n
--------- ---- -------- ---- -------- ---- -------- ---- -------- ---- --------
Low 60 24 75 11 75 12 58 20 68 14
Medium 50 16 69 24 66 16 50 17 69 14
High 43 19 57 17 61 15 46 18 65 14
-----------------------------------------------------------------------------------------
The correlation coefficients between completion rates and reliance on
title IV were negative for schools from all five agencies. The
coefficients were significantly different from zero\25 for four of
the five. Table II.3 shows correlation coefficients, standard
errors, and sample sizes for these analyses.
Table II.3
Correlation Coefficients Between
Completion Rates and Title IV Reliance
ABHES ACCET ACCSCT ACICS NACCAS
------------------------------ ------ ------ ------ ------ ------
Correlation coefficient - - - - -0.07
0.36\a 0.29\a 0.41\a 0.23\a
P-value 0.03 0.02 0.00 0.00 0.07
Number of cases 30 54 262 229 411
----------------------------------------------------------------------
\a Significant at 5-percent level.
Regression analysis on schools accredited by ACCSCT confirmed the
statistically significant negative relationship between completion
rates and title IV reliance (see table II.4). Even accounting for
other factors, the 85-15 measure--our measure of title IV
reliance--was statistically significant. The coefficient indicated
that for each 10-percentage-point increase in title IV reliance,
completion rates were 2.7 percentage points lower. The regression
showed that five other factors were statistically significant: the
number of students at the school, the percentage of students who
received Pell grants, the faculty turnover rate, the average length
of the school's program, and the average starting salary of a
school's graduates. In addition, the constant term, which we
included in each regression rather than forcing the regression line's
intercept to equal zero, was significant.
Table II.4
Regression Results for Completion Rates
Using ACCSCT Data
Standard
Variable Coefficient error
------------------------------ ------------ ------------
85-15 measure -0.2747\a 0.0698
Number of students -0.01468\a 0.00438
Percentages of students who
----------------------------------------------------------
Were female 0.01547 0.0341
Were black -0.08514 0.0572
Were Hispanic 0.01018 0.0721
Were under age 25 0.00004117 0.0558
Were age 45 or older 0.06918 0.241
Did not have a high school -0.03939 0.118
diploma or GED
Had a GED 0.2112 0.144
Had some prior postsecondary 0.006901 0.0537
education
Received Pell grants -0.1508\a 0.0568
Received Stafford loans 0.04981 0.0466
Had an expected family 0.03271 0.0501
contribution of zero
Attended part time -0.1184 0.0735
Student-faculty ratio 0.1244 0.115
Faculty turnover rate -0.1321\a 0.0590
Years school operated before -0.08259 0.229
participating in title IV
Years of experience of 0.02447 0.138
education director
Years of experience of 0.02926 0.173
placement director
Average years of tenure of all -0.5154 0.342
instructors
Average program length -0.2390\a 0.0680
Average tuition and fees 0.5622 0.301
Average starting salary of 0.1709\a 0.0532
graduates
Unemployment rate in school's 0.3690 0.283
local area
Percentage of revenues spent 0.2633 0.194
on new equipment
Constant 93.26\a 6.71
----------------------------------------------------------
Note: Sample size was 187.
\a Significant at 5-percent level.
We also performed regressions of completion rates on the 85-15
measure and a limited set of independent variables for schools from
ACICS. The results were similar--the coefficient on the 85-15
measure was negative and significant. When we replicated this
regression using the ACCSCT data--that is, regressed completion rates
on the same set of independent variables in ACCSCT data that we used
for ACICS--the results were again consistent. Table II.5 shows the
results for both regressions.
Table II.5
Regression Results for Completion Rates
Using Limited ACICS and ACCSCT Data
Standard Standard
Variable Coefficient error Coefficient error
--------------------------------- ------------ ------------ ------------ ------------
85-15 measure -0.3361\a 0.0571 -0.3055\a 0.0951
Number of students -0.01743\a 0.00396 -0.004033\a 0.00200
Percentages of students who
-----------------------------------------------------------------------------------------
Were female -0.005707 0.0338 0.02571 0.0734
Were minority -0.06444 5.05 -0.01911 0.0640
Did not have a high school 0.07422 0.115 -0.1573 0.224
diploma or GED
Had some prior postsecondary 0.04783 0.0511 -0.1479\a 0.0733
education
Had an expected family 0.01855 0.0492 0.06821 0.0768
contribution of zero
Attended part time -0.09079 0.0704 -0.3562 0.187
Student-faculty ratio 0.1328 0.117 0.1110 0.142
Constant 90.93\a 3.86 75.53\a 8.98
-----------------------------------------------------------------------------------------
Note: Sample sizes were 195 for ACCSCT and 160 for ACICS.
\a Significant at 5-percent level.
--------------------
\25 We used one-tailed significance tests for our correlation results
throughout this report because the 85-15 rule presumes that high
values of the 85-15 variable are associated with bad outcomes, that
is, low completion and placement rates and high default rates. We
conducted significance tests based on this presumption.
PLACEMENT RATES
-------------------------------------------------------- Appendix II:3
Schools with high reliance on title IV had slightly lower placement
rates than schools with low or medium reliance, but the differences
were much smaller than for completion rates. The differences between
the high one-third and low one-third of schools were only 3 to 8
percentage points for schools from four of the five agencies.
Schools from the other agency, ACICS, showed no difference in
placement rates. Table II.6 shows means and standard deviations, as
well as sample sizes, for placement rates for schools in low, medium,
and high title IV reliance categories.
Table II.6
Average Placement Rate at Schools With
Low, Medium, and High Reliance on Title
IV Funds, by Accrediting Agency
(Numbers in percent)
Standard Standard Standard Standard Standard
85-15 deviatio deviatio deviatio deviatio deviatio
category Mean n Mean n Mean n Mean n Mean n
--------- ---- -------- ---- -------- ---- -------- ---- -------- ---- --------
Low 77 9 74 16 79 15 71 15 84 15
Medium 75 18 68 18 75 13 71 12 87 13
High 74 8 66 14 74 13 71 13 79 17
-----------------------------------------------------------------------------------------
As with the descriptive statistics, the correlation analysis showed a
weaker relationship between title IV reliance and placement rates
than it did for completion rates. Only three of the five correlation
coefficients were significant and negative; the other two were
insignificant. Table II.7 details the results.
Table II.7
Correlation Coefficients Between
Placement Rates and Title IV Reliance
ABHES ACCET ACCSCT ACICS NACCAS
------------------------------ ------ ------ ------ ------ ------
Correlation coefficient -0.01 - - 0.01 -
0.26\a 0.14\a 0.13\a
P-value 0.49 0.03 0.01 0.43 0.00
Number of cases 29 54 262 229 411
----------------------------------------------------------------------
\a Significant at 5-percent level.
Regression analysis showed that the relationship between placement
rates and title IV reliance was not statistically significant when
accounting for other factors that could affect placement rates (see
table II.8). The only factors that were significant besides the
constant term were the number of students, the student-faculty ratio,
and the unemployment rate in the school's local area.
Table II.8
Regression Results for Placement Rates
Using ACCSCT Data
Standard
Variable Coefficient error
------------------------------ ------------ ------------
85-15 measure -0.02117 0.0753
Completion rate 0.01967 0.0812
Number of students -0.01173\a 0.00467
Percentages of students who
----------------------------------------------------------
Were female 0.01573 0.0351
Were black 0.02753 0.0594
Were Hispanic -0.02689 0.0743
Were under age 25 -0.005373 0.0575
Were age 45 or older 0.4787 0.248
Did not have a high school -0.07588 0.122
diploma or GED
Had a GED 0.2700 0.150
Had some prior postsecondary -0.04097 0.0554
education
Received Pell grants 0.04122 0.0598
Received Stafford loans 0.05588 0.0482
Had an expected family -0.02750 0.0517
contribution of zero
Attended part time -0.06594 0.0764
Student-faculty ratio 0.3472\a 0.119
Faculty turnover rate -0.06935 0.0617
Years school operated before 0.2769 0.236
participating in title IV
Years of experience of 0.02647 0.143
education director
Years of experience of 0.2177 0.178
placement director
Average years of tenure of all 0.1943 0.355
instructors
Average program length 0.03623 0.0727
Average tuition and fees -0.07255 0.313
Average starting salary of 0.001333 0.0565
graduates
Unemployment rate in school's -0.9791\a 0.293
local area
Percentage of revenues spent 0.2916 0.201
on new equipment
Constant 67.01\a 10.3
----------------------------------------------------------
Note: Sample size was 187.
\a Significant at 5-percent level.
Placement rate regressions using the more limited set of independent
variables from ACICS also showed that the coefficient on the 85-15
measure was not significant. Furthermore, regressions on the same
set of variables for ACCSCT confirmed that reliance on title IV did
not significantly affect placement rates (see table II.9).
Table II.9
Regression Results for Placement Rates
Using Limited ACICS and ACCSCT Data
Standard Standard
Variable Coefficient error Coefficient error
--------------------------------- ------------ ------------ ------------ ------------
85-15 measure -0.07563 0.0612 0.09922 0.0722
Completion rate 0.005950 0.0724 0.04107 0.0599
Number of students -0.01515\a 0.00410 -0.001468 0.00149
Percentages of students who
-----------------------------------------------------------------------------------------
Were female 0.01745 0.0333 0.1007 0.0539
Were minority -0.05108 0.0499 -0.08013 0.0470
Did not have a high school -0.1292 0.113 -0.1803 0.165
diploma or GED
Had some prior postsecondary -0.06564 0.0504 -0.0853 0.0546
education
Had an expected family -0.02387 0.0485 -0.04739 0.0565
contribution of zero
Attended part time -0.009300 0.0696 -0.09208 0.139
Student-faculty ratio 0.4260\a 0.116 0.04753 0.104
Constant 81.43\a 7.60 61.20\a 8.00
-----------------------------------------------------------------------------------------
Notes: Sample sizes were 195 for ACCSCT and 160 for ACICS.
\a Significant at 5-percent level.
DEFAULT RATES
-------------------------------------------------------- Appendix II:4
Schools with high reliance on title IV had higher default rates than
schools with low or medium reliance for three of the five agencies.
The differences between the high one-third and low one-third of
schools were only 6 to 7 percentage points for these agencies, but
these differences are large relative to the values of the default
rates. For example, high-reliance schools from NACCAS had default
rates of 22 percent, about half again as high as the 15-percent rate
for low-reliance schools. Table II.10 shows means and standard
deviations, as well as sample sizes, for default rates for schools in
low, medium, and high title IV reliance categories.
Table II.10
Average Default Rate at Schools With
Low, Medium, and High Reliance on Title
IV Funds, by Accrediting Agency
(Numbers in percent)
Standard Standard Standard Standard Standard
85-15 deviatio deviatio deviatio deviaito deviatio
category Mean n Mean n Mean n Mean n Mean n
--------- ---- -------- ---- -------- ---- -------- ---- -------- ---- --------
Low 13 10 18 13 15 11 14 9 15 12
Medium 19 7 15 10 18 10 16 8 20 17
High 16 6 16 12 22 13 20 9 22 18
-----------------------------------------------------------------------------------------
The correlation between default rates and reliance on title IV was
positive for four agencies; for three of these agencies it was
statistically significant (see table II.11).
Table II.11
Correlation Coefficients Between Default
Rates and Title IV Reliance
ABHES ACCET ACCSCT ACICS NACCAS
------------------------------ ------ ------ ------ ------ ------
Correlation coefficient 0.07 -0.19 0.21\a 0.18\a 0.19\a
P-value 0.37 0.11 0.00 0.01 0.00
Number of cases 25 43 230 203 352
----------------------------------------------------------------------
\a Significant at 5-percent level.
Our regression analysis confirmed that schools with high reliance on
title IV had high default rates. The coefficient on the 85-15
measure was positive and significant; it indicated that a
10-percentage-point increase in reliance on title IV was associated
with a 1.1-percentage-point increase in the default rate. Besides
the 85-15 measure, other factors associated with higher default rates
include the percentage of students who were black or age 45 or older,
and a high student-faculty ratio. Three factors negatively affected
default rates: a high placement rate and a high percentage of
students who were women or received Stafford loans.
Table II.12
Regression Results for Default Rates
Using ACCSCT Data
Standard
Variable Coefficient error
------------------------------ ------------ ------------
85-15 measure 0.1088\a 0.0539
Completion rate 0.03715 0.0581
Placement rate -0.1296\a 0.0565
Number of students -.0005475 0.00341
Percentages of students who
----------------------------------------------------------
Were female -0.06055\a 0.0252
Were black 0.2216\a 0.0425
Were Hispanic 0.02875 0.0532
Were under age 25 -0.02427 0.0411
Were age 45 or older 0.3665\a 0.179
Did not have a high school 0.02615 0.0870
diploma or GED
Had a GED 0.01321 0.108
Had some prior postsecondary -0.01811 0.0397
education
Received Pell grants 0.06405 0.0428
Received Stafford loans -0.09434\a 0.0346
Had an expected family 0.07023 0.0370
contribution of zero
Attended part time 0.004140 0.0547
Student-faculty ratio 0.2186\a 0.0875
Faculty turnover rate 0.02639 0.0443
Years school operated before -0.2848 0.170
participating in title IV
Years of experience of -0.01353 0.102
education director
Years of experience of -0.1146 0.128
placement director
Average years of tenure of all -0.2236 0.254
instructors
Average program length 0.01765 0.0520
Average tuition and fees -0.08231 0.224
Average starting salary of 0.003740 0.0404
graduates
Unemployment rate in school's -0.2229 0.217
local area
Percentage of revenues spent 0.2410 0.145
on new equipment
Constant 14.96 8.26
----------------------------------------------------------
Note: Sample size was 187.
\a Significant at 5-percent level.
Default rate regressions using the more limited set of independent
variables from ACICS showed the only result inconsistent with our
baseline analyses. In the limited default rate regressions, on both
ACCSCT and ACICS data, the coefficient on the 85-15 measure was not
significant (see table II.13).
Table II.13
Regression Results for Default Rates
Using Limited ACICS and ACCSCT Data
Standard Standard
Variable Coefficient error Coefficient error
--------------------------------- ------------ ------------ ------------ ------------
85-15 measure 0.07628 0.0436 0.05851 0.0412
Completion rate -0.001833 0.0513 -0.01933 0.0340
Placement rate -0.1023 0.0523 -0.03611 0.0464
Number of students -0.001682 0.00301 0.0007948 0.000845
Percentages of students who
-----------------------------------------------------------------------------------------
Were female -0.05948\a 0.0237 -0.04922 0.0309
Were minority 0.1532\a 0.0355 0.02449 0.0269
Did not have a high school 0.1386 0.0808 0.3696\a 0.0939
diploma or GED
Had some prior postsecondary 0.002262 0.0359 0.008830 0.0312
education
Had an expected family 0.08619\a 0.0344 0.07270\a 0.0321
contribution of zero
Attended part time 0.002495 0.0494 0.1203 0.0790
Student-faculty ratio 0.2075\a 0.0850 0.09847 0.0592
Constant 12.41 6.87 12.31\a 5.35
-----------------------------------------------------------------------------------------
Note: Sample sizes were 195 for ACCSCT and 160 for ACICS.
\a Significant at 5-percent level.
SENSITIVITY ANALYSIS
-------------------------------------------------------- Appendix II:5
In any quantitative analysis of this kind, the results may be
sensitive to the definition and measurement of the variables used.
If there is any uncertainty about how well the variables capture the
concept they are intended to represent, or about the accuracy of the
data, it is important to test to what extent the results are
sensitive to those factors. For example, variables we used could
have been defined and measured in more than one way. Therefore,
where possible, we conducted analyses to explore whether or how much
our results were sensitive to methodological decisions we made.
We tested sensitivity to three factors:
the definition of placement rates for each agency,
the time frames within which our data were defined, and
the types of programs included for each school.
DEFINITIONS OF PLACEMENT
RATES
------------------------------------------------------ Appendix II:5.1
Placement rate definitions varied by agency. Our general definition
was the number of graduates placed in their field of training, or a
related field, divided by the number of graduates. For schools
accredited by ABHES and ACICS, we knew both the number of graduates
placed in the field of training and the number of graduates placed in
a related field. For schools accredited by ACCET and ACCSCT, we knew
the number of graduates who went on for further education or were
otherwise unavailable for employment; furthermore, for ACCSCT
schools, we knew the number employed in the field of training who had
not actually graduated.
We tested variations on the placement rate definition for these
agencies. We computed a new placement rate for ABHES and ACICS
schools by deleting those placed in a related field from the
numerator, yielding a lower placement rate. We computed a new
placement rate for ACCET schools, excluding students unavailable for
employment from the denominator, yielding a higher rate. For ACCSCT
schools, we computed two new measures, one excluding those
unavailable for placement from the denominator and the other
including those employed in their field, but who did not graduate, in
the numerator, both yielding higher rates.
The results of the correlation analyses between these new measures
and the 85-15 measure were similar to those for our baseline
analyses. For each agency with an insignificant correlation
coefficient in our baseline analyses, the new coefficient remained
insignificant. For each agency with a significant correlation
coefficient, the new coefficient remained significant, with one
exception: for schools accredited by ACCET, the correlation
coefficient became insignificant when students ineligible for
placement were excluded.
TIME FRAMES FOR DATA
DEFINITIONS
------------------------------------------------------ Appendix II:5.2
We performed sensitivity analyses to explore the implications of
using data from differing time periods. To carry this out, we
analyzed only the subset of schools with 6 or more months of overlap
between the time periods for their annual report and their 85-15
calculation, for four of the five agencies,\26 and compared the
results to the analysis for all schools. Our sample sizes decreased
somewhat because, for some agencies, many schools had less than a
6-month overlap. However, the correlations that were significant in
our baseline analyses were always of the same sign, and nearly always
significant, in the sensitivity analyses.
--------------------
\26 Virtually none of the schools accredited by NACCAS had more than
a 6-month overlap.
TYPES OF PROGRAMS INCLUDED
FOR EACH SCHOOL
------------------------------------------------------ Appendix II:5.3
Schools calculate the 85-15 measure by incorporating only
title-IV-eligible programs. Students in programs that are shorter
than 300 clock hours cannot receive title IV aid for those programs.
Ideally, our data would always cover title-IV-eligible programs only,
to match the coverage of the 85-15 rule.
However, three of the accrediting agencies--ABHES, ACCSCT, and
ACICS--provided data on schools with either all data aggregated up to
the school level or program-level data that did not include the
number of hours per program for all relevant variables. Some of the
programs at those schools might have been shorter than 300 clock
hours; thus, students in those programs would not be eligible for
title IV aid. However, we could not exclude students in those short
programs from our analysis because we could not separate them from
the rest of the programs the schools offered.
For schools from agencies that provided data at the program level,
including length of program--ACCET and NACCAS--we performed two sets
of analyses. Our baseline analysis, the results of which we discuss
throughout this report, excluded programs shorter than 300 clock
hours. We tested sensitivity of the analysis to this exclusion, that
is, we performed all our analyses anew for these two agencies by
including all programs each school offered.
When we compared the results for eligible programs only with results
for all programs, for schools accredited by ACCET and NACCAS, we
found the results did not change substantially. We thus feel
confident that our results for schools accredited by ABHES, ACCSCT,
and ACICS would not change materially if we had the data to exclude
ineligible programs.
RELATED GAO PRODUCTS
High-Risk Series: Student Financial Aid (GAO/HR-97-11, Feb. 1997).
Department of Education: Status of Actions to Improve the Management
of Student Financial Aid (GAO/HEHS-96-143, July 12, 1996).
Higher Education: Ensuring Quality Education From Proprietary
Institutions (GAO/T-HEHS-96-158, June 6, 1996).
Defaulted Student Loans: Analysis of Defaulted Borrowers at Schools
Accredited by Seven Agencies (GAO/HRD-90-178FS, Sept. 12, 1990).
*** End of document. ***