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The exception was the effect of full-time faculty turnover in 1994. This year a change of just 1 point in full-time turnover was associated with an increase of over half a point in withdrawal rates. This estimate is so out of line with prior results that we think it is the result of a few schools with unusually high turnover and withdrawal rates.
Because most schools do not have a large number of instructors, the addition or departure of a few can bave a large impact upon the turnover rate. For all five years, the average number of full-time equivalent instructors has been about 15 and the turnover rate among both full- and part-time instructors has been about 20 percent. This means in an average school with 15 instructors, three were departing each year. The departure of two or three additional staff in a single year would have a marked impact on turnover rate. Two more departures would bring the rate up to 33 percent and three more, six total, would yield a rate of 40 percent. A few schools that had very high turnover and withdrawals in 1994 are the most likely cause of the very high net effect found for this year.
Characteristics Related to Training-Related Placement
One of the strongest conclusions emerging from the last decade of research on the effects of technical training is that higher earnings are obtained primarily by graduates who obtain employment in jobs that require the skills they learned in their programs. We have labeled the variable that measures the percent of graduates who obtain such jobs, Training-Related Placement (TRP). Over the five school years analyzed, three-fourths or more of the graduates available for employment found jobs in related fields. Unfortunately, TRP is the outcome with the fewest consistent relationships with school characteristics. The main two relationships that have been found are presented in Table 2.
Programs that are offered on the main campuses and at schools that have enrollments of 300 or less had higher rates of TRP than programs offered on branch campuses and at schools that had enrollments of 901 or more. It will be recalled that these characteristics were also associated with higher graduation rates. Neither of these characteristics, however, is significant for all five years.
Two other characteristics were also significant three or more of the five years: percentage of enrollment receiving Stafford loans and percent of part-time-students. Here again their effect was less than 1 percentage point change in TRP for a 10 point change in the measure of the characteristic so they were not included in the chart.
We suspect the reason why more school characteristics do not have significant relationships with TRP is that acquiring a skill is the key determinant of finding related employment. Once students have completed programs and have skills needed by employers, it is of little importance whether they acquired these skills in large or small schools, how long the programs lasted, or the prior education of their classmates. What employers are interested in is whether job seekers have the skills needed by their companies.
The high default rates on student loans have received continuing national attention, and private, for profit postsecondary institutions have been a, perhaps the, primary target of this attention. All four of the school characteristics that have been found to have significant relationships with default rates for three of the five years are shown in Figure 12.
Ability to benefit. Even though the percentage of ATB students has declined by half over the five school years, this characteristic continues to bave a sizeable effect on default rates. In the two most recent years, however, the effect estimates are considerably less than in the prior years. As indicated carlier, the declining effect of the percentage of ATB may be caused by a higher degree of selectivity in the ATB that are admitted.
We had speculated in a previous report that the relationship between ATB and default probably lies with personal characteristics that are associated with dropping out of high school. Students who drop out are indicating their unwillingness to adapt to a structured educational setting. They have the mental ability to succeed, as measured by the test that classified them as ATB. Often, however, they do not have the personal qualities that enable them to benefit from classroom instruction. If the Commission accredited schools and colleges are being more selective, as the declining enrollments suggest they are, they may be trying to admit ATBs with the personal, as well as the mental, qualities needed for success.
Withdrawal. We reported in an earlier section that default rates have an independent relationship with withdrawal, the reverse is also true. Even when other characteristics are held constant, schools with high withdrawal rates tend to have high default rates. It is reasonable that students who withdraw may note have acquired an employable skill and are thus inclined to default on their loans.
NOTE: Years without data entries on the trend lines indicate that the characteristics did not have
statistically significant net effects on the outcome in the missing years.
Figure 12. Net effect of selected school characteristics on default rates
Other characteristics. The two other characteristics shown in Figure 12 bave bad fairly consistent relationships with default: percentage of enrollment classified as GED and program length. GED enrollments bave been stable at 11 to 12 percent for the five years. In three of the years, this percentage has been significantly associated with default at the same level of effect.
The average length of programs offered at a Commission-accredited school has increased markedly since 1990, but the estimate of the net effect of length on default has remained fairly constant. The size of effect is not large. Forevery 10 week increase in program length, default rates decline between .4 and 1.2 percentage points. This relationship may be due to graduates of longer programs baving higher earnings and being in a better position to repay their loans. An alternative explanation is that those who enroll in longer (more expensive) programs tend to come from higher income families that are less likely to default.
Many school characteristics we bad thought likely to be associated with poorer school performance, such as legal action pending and complaints under review, were not statistically significant or were significant only one or two years. We think variables that reflect undesirable conditions are not consistently related to outcomes because few schools report such conditions. Typically, less than 10 percent of schools report legal action pending and less than 5 percent report complaints under review or changes in ownership. When a few schools that report such conditions have very high or very low outcome measures, these few schools can have a distorting effect on the analysis. Consequently, in our discussion we have emphasized those variables that have yielded consistent results for at least three of the five years.
In the next section, we compare outcomes based on two different data sources for the 1994 school year only. Since we have only one year of cohort data, we cannot know if the results for this year will prove as stable as those presented for the annual total data.
Comparison of Annual Total and Cohort Data,
1994 School Year
The results in this section have been anticipated in the discussion of the outcome measures based on the annual total data. The cohort data were collected for the first time for the 1994 school year. It will not be possible to provide trend lines for the cohort data until additional years have been collected. Consequently the charts in this section show comparisons of similar, but not identical, outcomes derived from the annual total and cohort data for the 1994 school year only.
We stressed identical in the previous sentence because it should not be expected that the annual total and cohort measures would be the same. The annual totals reflect only the 1994 school year. The cohort data include some students who enrolled in programs more than three years earlier.
Schools were instructed to provide information about students who during the 1994 school year bad had one and one-half times the scheduled lengths of their programs in which to complete them. This means that students who began two year programs during the first month of the 1992 school year (July 1991) or earlier would be included in the cohorts who completed their allowed time during the 1994 school year. Programs of six months or less, in contrast, could bave completed their allowed time entirely within the 1994 school year.
Given the much different time periods covered by the annual total and cohort data, the school outcome measures calculated for them are more similar than might have been expected. Figure 13 presents the comparisons.
The annual total data yielded lower figures than the cohort data for all three outcomes. In the cohort data, graduates plus withdrawals equal 100 percent. This is not true for the annual total data. In the annual total measures, the graduation rate is based on the number leaving school either through graduation or withdrawal. The withdrawal rate is based on the number enrolled. Since the bases for these two rates are not the same, they do not sum to 100.
Some of the differences between the annual total and cohort data are due to how those students who withdrew because they obtained related employment were counted. In the cohort measures, those who withdrew for related employment were included in both the percentage trained and the percentage placed in related employment. In the annual total measures, these withdrawals were not included in the graduation or TRP rates. The inclusion of those who withdrew for related employment contributes to the higher rates found for the cohort data.
The biggest difference between the annual total and cohort data concerns withdrawal rates. The explanation of this difference that appears most reasonable to us involves students who drop out between school years. They enroll one year but do not complete their programs. They inform their schools that they intend to return the next school year, so they are not counted as withdrawing during their first year. When they do not return for their second year, they are not counted as either enrolling or withdrawing in the annual total data for that year. The cohort data requires a more accurate tracking of such students and thus yields higher dropout rates.
Multiple Regression Comparisons
Figures 14 and 15 compare the multiple regression results obtained for the similar measures obtained from the annual total and cohort data for the 1994 school year. The figures show the school characteristics that were found to have statistically significant relationships in both sets of data.
With the cohort data, a less rigorous level of significance (p. = .10) was used than with the annual total data (p. = 01). The less rigorous level was used so that comparisons could be made with more characteristics. Additional years of data will be needed to determine if the characteristic found significant in the cohort data are as stable as those presented for the annual total data.