oversight

Proprietary Schools: Poorer Student Outcomes at Schools That Rely More on Federal Student Aid

Published by the Government Accountability Office on 1997-06-13.

Below is a raw (and likely hideous) rendition of the original report. (PDF)

                  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
      United States
GAO   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.



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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.



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                   B-276560




                   and placement rates for each agency’s schools, and limitations of our
                   study, see appendix I.


                   Proprietary schools that relied more heavily on title IV funds tended to
Results in Brief   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.


                   Since 1972, when proprietary school students became eligible for the full
Background         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




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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.


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                       B-276560




                       Schools that relied more heavily on title IV funds generally had poorer
Significant            student outcomes. High-reliance schools had lower completion and
Relationship Between   placement rates and higher default rates than low-reliance schools.
Reliance on Title IV   Regression analysis substantiated the significance9 of the relationship with
                       completion and default rates but not with placement rates.
and Performance
Measures               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.



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                                               B-276560




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

Completion Rate (Percent)

100



80                                        75                 75
                                     69                                                          69   68
                                                        66                                 65
                     60                         61
60                              57                                                 58
                50                                                          50
                                                                      46
         43
40



20



 0
              ABHES               ACCET           ACCSCT                   ACICS             NACCAS
              Accrediting Agency's Schools

      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.




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                                              B-276560




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

Placement Rate (Percent)
100

                                                                                              87
                                                                                                   84
                   77                                     79                            79
 80      74   75                                     75
                                         74   74
                                                                   71    71     71
                               66   68

 60



 40



 20



  0
            ABHES             ACCET                ACCSCT               ACICS             NACCAS
           Accrediting Agency's Schools

       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.




                                              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.



                                              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                                   Default Rate (Percent)

                                         30


                                         25


                                                      20
                                         20                         19

                                                                                     16
                                         15


                                         10


                                          5


                                          0
                                                 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.


                                        Increasing the 85-15 rule’s 15-percent threshold—requiring a higher
A Significantly Higher                  percentage of total revenues from non-title-IV sources—could save
85-15 Threshold                         millions of dollars annually by reducing default claims. However, because,
Would Likely Reduce                     in relative terms, large differences in schools’ 85-15 measures are
                                        associated with small differences in outcomes, it would take a substantial
Defaults but Might                      increase to attain the outcomes demonstrated by schools that rely the
Impair Student Access                   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
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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


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.



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.


                  Our results generally support the notion underlying the 85-15 rule—that
Conclusions       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.


                  We provided a draft copy of this report to Education for review. We
Agency Comments   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                                                                                           14
                        Scope                                                                        14
Objective, Scope, and   Data Collection                                                              15
Methodology             Data Analysis                                                                17
                        Limitations Analysis                                                         21

Appendix II                                                                                          23
                        Definitions of Low, Medium, and High Reliance                                23
Detailed Results of     Completion Rates                                                             23
Descriptive,            Placement Rates                                                              26
                        Default Rates                                                                29
Correlation, and        Sensitivity Analysis                                                         32
Regression Analyses
Related GAO Products                                                                                 36


Tables                  Table I.1: Response to GAO’s Survey of Proprietary Schools, by               16
                          Accrediting Agency
                        Table II.1: Categories of Low, Medium, and High Reliance on Title            23
                          IV Funds, by Accrediting Agency
                        Table II.2: Average Program Completion Rate at Schools With                  24
                          Low, Medium, and High Reliance on Title IV Funds, by
                          Accrediting Agency
                        Table II.3: Correlation Coefficients Between Completion Rates                24
                          and Title IV Reliance
                        Table II.4: Regression Results for Completion Rates Using                    25
                          ACCSCT Data
                        Table II.5: Regression Results for Completion Rates Using                    26
                          Limited ACICS and ACCSCT Data
                        Table II.6: Average Placement Rate at Schools With Low,                      27
                          Medium, and High Reliance on Title IV Funds, by Accrediting
                          Agency
                        Table II.7: Correlation Coefficients Between Placement Rates and             27
                          Title IV Reliance
                        Table II.8: Regression Results for Placement Rates Using                     28
                          ACCSCT Data
                        Table II.9: Regression Results for Placement Rates Using Limited             29
                          ACICS and ACCSCT Data




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




          Table II.10: Average Default Rate at Schools With Low, Medium,               30
            and High Reliance on Title IV Funds, by Accrediting Agency
          Table II.11: Correlation Coefficients Between Default Rates and              30
            Title IV Reliance
          Table II.12: Regression Results for Default Rates Using ACCSCT               31
            Data
          Table II.13: Regression Results for Default Rates Using Limited              32
            ACICS and ACCSCT Data

Figures   Figure 1: Schools With High Reliance on Title IV Funds Had                    6
            Lowest Completion Rates
          Figure 2: Schools With High Reliance on Title IV Funds Had                    7
            Lowest Placement Rates
          Figure 3: Schools With High Reliance on Title IV Funds Had                    8
            Highest Default Rates




          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.


              The 85-15 rule requires that proprietary schools obtain at least 15 percent
Scope         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




              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.



              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


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


                  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.
                  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.



                  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


                                          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.



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                           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


                           Our completion and placement rate calculations for schools varied by
Data Analysis              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   For schools accredited by ACCSCT, the completion rate was the number of
Rate Calculations          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.

                           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.



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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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                          Appendix I
                          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;




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



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    Appendix I
    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.


    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.



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                       Appendix I
                       Objective, Scope, and Methodology




                       Detailed results of our analyses appear in appendix II.



                       Our study could not fully assess the impact of the 85-15 rule because of
Limitations Analysis   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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Appendix I
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




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.



Page 22                                 GAO/HEHS-97-103 Proprietary Schools and Student Aid
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.


                                          We ranked the schools accredited by each agency by their 85-15 measure
Definitions of Low,                       and grouped them into three categories, which we refer to as low-reliance,
Medium, and High                          medium-reliance, and high-reliance schools. For each agency, each
Reliance                                  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.


                                          Schools with high reliance on title IV, on average, had lower completion
Completion Rates                          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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                                         Appendix II
                                         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
Numbers in percent
                       ABHES                ACCET                      ACCSCT                      ACICS                     NACCAS
                    (30 schools)         (54 schools)                (262 schools)              (229 schools)              (411 schools)
                           Standard                Standard                   Standard                   Standard                    Standard
85-15 category   Mean      deviation    Mean       deviation      Mean        deviation       Mean       deviation      Mean         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                                       ABHES         ACCET       ACCSCT            ACICS        NACCAS
Reliance                                 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
                                         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.


                                         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.



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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
                                                                                                        a
                                     Number of students                                         –0.01468             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.
                                     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




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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
                                                                                  a                                    a
Number of students                                                  –0.01743                0.00396           –0.004033             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.

                                              a
                                                Significant at 5-percent level.




                                              Schools with high reliance on title IV had slightly lower placement rates
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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                                          Appendix II
                                          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
Numbers in percent
                       ABHES                 ACCET                      ACCSCT                      ACICS                 NACCAS
                    (29 schools)          (54 schools)                (262 schools)              (229 schools)          (411 schools)
                           Standard                 Standard                   Standard                 Standard               Standard
85-15 category    Mean     deviation    Mean        deviation      Mean        deviation       Mean     deviation      Mean    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                                         ABHES         ACCET       ACCSCT          ACICS    NACCAS
Reliance                                  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
                                          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.




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                                     Appendix II
                                     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
                                                                                                           a
                                     Constant                                                      67.01                 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




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                                              Appendix II
                                              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
                                                                                  a                                       a
Constant                                                                81.43                   7.60              61.20                 8.00
                                              Notes: Sample sizes were 195 for ACCSCT and 160 for ACICS.
                                              a
                                                Significant at 5-percent level.




                                              Schools with high reliance on title IV had higher default rates than schools
Default Rates                                 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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                                              Appendix II
                                              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
Numbers in percent
                         ABHES                   ACCET                      ACCSCT                      ACICS                   NACCAS
                      (25 schools)            (43 schools)                (230 schools)              (203 schools)            (352 schools)
                              Standard                  Standard                   Standard                 Standard                    Standard
85-15 category     Mean       deviation      Mean       deviation      Mean        deviation       Mean     deviaiton        Mean       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                                               ABHES         ACCET       ACCSCT            ACICS      NACCAS
Reliance                                      Correlation coefficient              0.07           –0.19        0.21  a
                                                                                                                              0.18  a
                                                                                                                                            0.19a
                                              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.




                                              Page 30                                     GAO/HEHS-97-103 Proprietary Schools and Student Aid
                                      Appendix II
                                      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
                                                                                                            a
                                          Were age 45 or older                                     0.3665               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
                                                                                                            a
                                      Student-faculty ratio                                        0.2186              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.
                                      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




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                                              Appendix II
                                              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
                                                                                                                          a
  Did not have a high school diploma or GED                            0.1386                0.0808              0.3696              0.0939
  Had some prior postsecondary education                            0.002262                 0.0359            0.008830              0.0312
                                                                                  a                                       a
  Had an expected family contribution of zero                        0.08619                 0.0344             0.07270              0.0321
  Attended part time                                                0.002495                 0.0494              0.1203              0.0790
                                                                                  a
Student-faculty ratio                                                  0.2075                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.
                                              a
                                                Significant at 5-percent level.




                                              In any quantitative analysis of this kind, the results may be sensitive to the
Sensitivity Analysis                          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   Placement rate definitions varied by agency. Our general definition was
Rates                      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       We performed sensitivity analyses to explore the implications of using
Definitions                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.



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                           Appendix II
                           Detailed Results of Descriptive, Correlation,
                           and Regression Analyses




Types of Programs          Schools calculate the 85-15 measure by incorporating only title-IV-eligible
Included for Each School   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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