United States General Accounting Office

GAO 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

GAO United States

General Accounting Office

Washington, D.C. 20548

Health, Education, and

Human Services Division

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 rate2

 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

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.

Page 1 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

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

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.

Page 2 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

and placement rates for each agency’s schools, and limitations of our

study, see appendix I.

Results in Brief 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 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

Page 3 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

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 programs8

 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.

Page 4 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

Significant

Relationship Between

Reliance on Title IV

and Performance

Measures

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 significance9

 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.

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.

Page 5 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

Figure 1: Schools With High Reliance on Title IV Funds Had Lowest Completion Rates

ABHES ACCET ACCSCT ACICS NACCAS

0

20

40

60

80

100

43

57

61

46

65

50

69 66

50

69

60

75 75

58

68

Accrediting Agency's Schools

Completion Rate (Percent)

High-Reliance Schools

Medium-Reliance Schools

Low-Reliance Schools

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.

Page 6 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

Figure 2: Schools With High Reliance on Title IV Funds Had Lowest Placement Rates

ABHES ACCET ACCSCT ACICS NACCAS

0

20

40

60

80

100

74

66

74 71

79 75

68

75

71

87

77

74

79

71

84

Accrediting Agency's Schools

Placement Rate (Percent)

High-Reliance Schools

Medium-Reliance Schools

Low-Reliance Schools

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.

10This 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.

Page 7 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

Figure 3: Schools With High Reliance

on Title IV Funds Had Highest Default

Rates

0

5

10

15

20

25

30

20

16

Default Rate (Percent)

19

High-Reliance Medium-Reliance Low-Reliance

Schools

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.

A Significantly Higher

85-15 Threshold

Would Likely Reduce

Defaults but Might

Impair Student Access

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

Page 8 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

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

11A further assumption is that other characteristics of each school and its students do not change.

12This 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.

Page 9 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

might not have the capacity to absorb all their students, forcing some

students out of higher education altogether.

Conclusions 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 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.

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.

Page 10 GAO/HEHS-97-103 Proprietary Schools and Student Aid
B-276560 

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

Page 11 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Contents

Letter 1

Appendix I 

Objective, Scope, and

Methodology

14

Scope 14

Data Collection 15

Data Analysis 17

Limitations Analysis 21

Appendix II 

Detailed Results of

Descriptive,

Correlation, and

Regression Analyses

23

Definitions of Low, Medium, and High Reliance 23

Completion Rates 23

Placement Rates 26

Default Rates 29

Sensitivity Analysis 32

Related GAO Products 36

Tables Table I.1: Response to GAO’s Survey of Proprietary Schools, by

Accrediting Agency

16

Table II.1: Categories of Low, Medium, and High Reliance on Title

IV Funds, by Accrediting Agency

23

Table II.2: Average Program Completion Rate at Schools With

Low, Medium, and High Reliance on Title IV Funds, by

Accrediting Agency

24

Table II.3: Correlation Coefficients Between Completion Rates

and Title IV Reliance

24

Table II.4: Regression Results for Completion Rates Using

ACCSCT Data

25

Table II.5: Regression Results for Completion Rates Using

Limited ACICS and ACCSCT Data

26

Table II.6: Average Placement Rate at Schools With Low,

Medium, and High Reliance on Title IV Funds, by Accrediting

Agency

27

Table II.7: Correlation Coefficients Between Placement Rates and

Title IV Reliance

27

Table II.8: Regression Results for Placement Rates Using

ACCSCT Data

28

Table II.9: Regression Results for Placement Rates Using Limited

ACICS and ACCSCT Data

29

Page 12 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Contents

Table II.10: Average Default Rate at Schools With Low, Medium,

and High Reliance on Title IV Funds, by Accrediting Agency

30

Table II.11: Correlation Coefficients Between Default Rates and

Title IV Reliance

30

Table II.12: Regression Results for Default Rates Using ACCSCT

Data

31

Table II.13: Regression Results for Default Rates Using Limited

ACICS and ACCSCT Data

32

Figures Figure 1: Schools With High Reliance on Title IV Funds Had

Lowest Completion Rates

6

Figure 2: Schools With High Reliance on Title IV Funds Had

Lowest Placement Rates

7

Figure 3: Schools With High Reliance on Title IV Funds Had

Highest Default Rates

8

Abbreviations

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

Page 13 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix I 

Objective, Scope, and Methodology

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 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

13The 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.

Page 14 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix I 

Objective, Scope, and Methodology

institutional level, which includes one main campus and all of its branch

campuses and additional locations.14

Data Collection Because Education does not yet require schools to disclose the results of

85-15 calculations15 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.

14We 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.

15The 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.

16The 85-15 regulation became effective on July 1, 1995.

17Schools are required to calculate their 85-15 measure by combining main and branch campus revenue

data.

Page 15 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix I 

Objective, Scope, and Methodology

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 ineligible 2 3 40 15 21 81

Adjusted size of population 40 90 463 326 624 1,543

Number of questionnaires returned 34 70 358 253 466 1,181

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

18We 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.

Page 16 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix I 

Objective, Scope, and Methodology

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

Data Analysis 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

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.

19In 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.

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Appendix I 

Objective, Scope, and Methodology

Descriptive Statistics 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 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.

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Objective, Scope, and Methodology

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 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;

20The EFC is determined by a formula that accounts for family income and assets and is used in

awarding financial aid.

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Objective, Scope, and Methodology

• 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.

21Data 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.

22For schools accredited by ACCSCT, this variable includes only black and Hispanic students.

23For 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.

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Objective, Scope, and Methodology

Detailed results of our analyses appear in appendix II.

Limitations Analysis 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

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Objective, Scope, and Methodology

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

24Morgan 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.

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Appendix II 

Detailed Results of Descriptive, Correlation,

and Regression Analyses

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

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 in category 10 18 110 73 138

Medium

Range of measure 67%-77% 61%-76% 60%-75% 65%-77% 41%-61%

Number of schools in category 10 19 107 85 138

High

Range of measure 78%-85% 77%-84% 76%-85% 78%-85% 62%-85%

Number of schools in category 10 17 114 71 135

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 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.

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.2: Average Program Completion Rate at Schools With Low, Medium, and High Reliance on Title IV Funds, by

Accrediting Agency

ABHES

(30 schools)

ACCET

(54 schools)

ACCSCT

(262 schools)

ACICS

(229 schools)

NACCAS

(411 schools)

Numbers in percent

85-15 category Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation

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 zero25 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 –0.36a –0.29a –0.41a –0.23a –0.07

P-value 0.03 0.02 0.00 0.00 0.07

Number of cases 30 54 262 229 411

aSignificant 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.

25We 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.

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Appendix II 

Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.4: Regression Results for

Completion Rates Using ACCSCT Data Variable Coefficient Standard error

85-15 measure –0.2747a 0.0698

Number of students –0.01468a 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 diploma or GED –0.03939 0.118

Had a GED 0.2112 0.144

Had some prior postsecondary education 0.006901 0.0537

Received Pell grants –0.1508a 0.0568

Received Stafford loans 0.04981 0.0466

Had an expected family contribution of zero 0.03271 0.0501

Attended part time –0.1184 0.0735

Student-faculty ratio 0.1244 0.115

Faculty turnover rate –0.1321a 0.0590

Years school operated before participating in

title IV –0.08259 0.229

Years of experience of education director 0.02447 0.138

Years of experience of placement director 0.02926 0.173

Average years of tenure of all instructors –0.5154 0.342

Average program length –0.2390a 0.0680

Average tuition and fees 0.5622 0.301

Average starting salary of graduates 0.1709a 0.0532

Unemployment rate in school’s local area 0.3690 0.283

Percentage of revenues spent on new

equipment 0.2633 0.194

Constant 93.26a 6.71

Note: Sample size was 187.

aSignificant 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

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Appendix II 

Detailed Results of Descriptive, Correlation,

and Regression Analyses

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

ACCSCT data ACICS data

Variable Coefficient Standard error Coefficient Standard error

85-15 measure –0.3361a 0.0571 –0.3055a 0.0951

Number of students –0.01743a 0.00396 –0.004033a 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 diploma or GED 0.07422 0.115 –0.1573 0.224

Had some prior postsecondary education 0.04783 0.0511 –0.1479a 0.0733

Had an expected family contribution of zero 0.01855 0.0492 0.06821 0.0768

Attended part time –0.09079 0.0704 –0.3562 0.187

Student-faculty ratio 0.1328 0.117 0.1110 0.142

Constant 90.93a 3.86 75.53a 8.98

Note: Sample sizes were 195 for ACCSCT and 160 for ACICS.

aSignificant at 5-percent level.

Placement Rates 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.

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.6: Average Placement Rate at Schools With Low, Medium, and High Reliance on Title IV Funds, by Accrediting

Agency

ABHES

(29 schools)

ACCET

(54 schools)

ACCSCT

(262 schools)

ACICS

(229 schools)

NACCAS

(411 schools)

Numbers in percent

85-15 category Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation

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 –0.26a –0.14a 0.01 –0.13a

P-value 0.49 0.03 0.01 0.43 0.00

Number of cases 29 54 262 229 411

aSignificant 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.

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.8: Regression Results for

Placement Rates Using ACCSCT Data Variable Coefficient Standard error

85-15 measure –0.02117 0.0753

Completion rate 0.01967 0.0812

Number of students –0.01173a 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 diploma or GED –0.07588 0.122

Had a GED 0.2700 0.150

Had some prior postsecondary education –0.04097 0.0554

Received Pell grants 0.04122 0.0598

Received Stafford loans 0.05588 0.0482

Had an expected family contribution of zero –0.02750 0.0517

Attended part time –0.06594 0.0764

Student-faculty ratio 0.3472a 0.119

Faculty turnover rate –0.06935 0.0617

Years school operated before participating in

title IV 0.2769 0.236

Years of experience of education director 0.02647 0.143

Years of experience of placement director 0.2177 0.178

Average years of tenure of all instructors 0.1943 0.355

Average program length 0.03623 0.0727

Average tuition and fees –0.07255 0.313

Average starting salary of graduates 0.001333 0.0565

Unemployment rate in school’s local area –0.9791a 0.293

Percentage of revenues spent on new

equipment 0.2916 0.201

Constant 67.01a 10.3

Note: Sample size was 187.

aSignificant 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

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

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

ACCSCT data ACICS data

Variable Coefficient Standard error Coefficient Standard 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 –0.01515a 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 diploma or GED –0.1292 0.113 –0.1803 0.165

Had some prior postsecondary education –0.06564 0.0504 –0.0853 0.0546

Had an expected family contribution of zero –0.02387 0.0485 –0.04739 0.0565

Attended part time –0.009300 0.0696 - 0.09208 0.139

Student-faculty ratio 0.4260a 0.116 0.04753 0.104

Constant 81.43a 7.60 61.20a 8.00

Notes: Sample sizes were 195 for ACCSCT and 160 for ACICS.

aSignificant at 5-percent level.

Default Rates 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.

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.10: Average Default Rate at Schools With Low, Medium, and High Reliance on Title IV Funds, by Accrediting

Agency

ABHES

(25 schools)

ACCET

(43 schools)

ACCSCT

(230 schools)

ACICS

(203 schools)

NACCAS

(352 schools)

Numbers in percent

85-15 category Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviaiton Mean

Standard

deviation

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 0.21a 0.18a 0.19a

P-value 0.37 0.11 0.00 0.01 0.00

Number of cases 25 43 230 203 352

aSignificant 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.

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

Table II.12: Regression Results for

Default Rates Using ACCSCT Data Variable Coefficient Standard error

85-15 measure 0.1088a 0.0539

Completion rate 0.03715 0.0581

Placement rate –0.1296a 0.0565

Number of students –.0005475 0.00341

Percentages of students who

Were female –0.06055a 0.0252

Were black 0.2216a 0.0425

Were Hispanic 0.02875 0.0532

Were under age 25 –0.02427 0.0411

Were age 45 or older 0.3665a 0.179

Did not have a high school diploma or GED 0.02615 0.0870

Had a GED 0.01321 0.108

Had some prior postsecondary education –0.01811 0.0397

Received Pell grants 0.06405 0.0428

Received Stafford loans –0.09434a 0.0346

Had an expected family contribution of zero 0.07023 0.0370

Attended part time 0.004140 0.0547

Student-faculty ratio 0.2186a 0.0875

Faculty turnover rate 0.02639 0.0443

Years school operated before participating in

title IV –0.2848 0.170

Years of experience of education director –0.01353 0.102

Years of experience of placement director –0.1146 0.128

Average years of tenure of all instructors –0.2236 0.254

Average program length 0.01765 0.0520

Average tuition and fees –0.08231 0.224

Average starting salary of graduates 0.003740 0.0404

Unemployment rate in school’s local area –0.2229 0.217

Percentage of revenues spent on new

equipment 0.2410 0.145

Constant 14.96 8.26

Note: Sample size was 187.

aSignificant 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

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Detailed Results of Descriptive, Correlation,

and Regression Analyses

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

ACCSCT data ACICS data

Variable Coefficient Standard error Coefficient Standard 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 –0.05948a 0.0237 –0.04922 0.0309

Were minority 0.1532a 0.0355 0.02449 0.0269

Did not have a high school diploma or GED 0.1386 0.0808 0.3696a 0.0939

Had some prior postsecondary education 0.002262 0.0359 0.008830 0.0312

Had an expected family contribution of zero 0.08619a 0.0344 0.07270a 0.0321

Attended part time 0.002495 0.0494 0.1203 0.0790

Student-faculty ratio 0.2075a 0.0850 0.09847 0.0592

Constant 12.41 6.87 12.31a 5.35

Note: Sample sizes were 195 for ACCSCT and 160 for ACICS.

aSignificant at 5-percent level.

Sensitivity Analysis 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.

Page 32 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix II 

Detailed Results of Descriptive, Correlation,

and Regression Analyses

Definitions of Placement

Rates

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

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.

26Virtually none of the schools accredited by NACCAS had more than a 6-month overlap.

Page 33 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Appendix II 

Detailed Results of Descriptive, Correlation,

and Regression Analyses

Types of Programs

Included for Each School

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.

Page 34 GAO/HEHS-97-103 Proprietary Schools and Student Aid
Page 35 GAO/HEHS-97-103 Proprietary Schools and Student Aid
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).

(104851) Page 36 GAO/HEHS-97-103 Proprietary Schools and Student Aid
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