Table 6. Enrollment and Completion in Degree and Certificate Programs in Selected Florida Community Colleges, 1997-98*
|
|
Enrollment
|
Enrollment
|
Completed
|
Completion
|
Completion
|
|
|
|
Distribution
|
|
Distribution
|
Rate
|
|
|
|
|
|
|
|
A. Indian River
|
|
|
|
|
|
1
|
Associate of Arts
|
4,627
|
35.5%
|
45
|
5.0%
|
1.0%
|
2
|
Building Trades/Manufacturing
|
357
|
2.7%
|
9
|
1.0%
|
2.5%
|
3
|
Public Services
|
1,223
|
9.4%
|
4
|
0.4%
|
0.3%
|
4
|
Agriculture
|
148
|
1.1%
|
1
|
0.1%
|
0.7%
|
5
|
Technology/Engineering
|
424
|
3.3%
|
8
|
0.9%
|
1.9%
|
6
|
Nonvocational/Leisure
|
217
|
1.7%
|
6
|
0.7%
|
2.8%
|
7
|
Other Field-of-Study
|
6,180
|
47.4%
|
825
|
92.0%
|
13.3%
|
8
|
All
|
13,028
|
100.0%
|
897
|
100.0%
|
6.9%
|
|
|
|
|
|
|
|
B. Seminole
|
|
|
|
|
|
1
|
Associates in Art
|
5,608
|
47.3%
|
106
|
55.5%
|
1.9%
|
2
|
Building Trades/Manufacturing
|
603
|
5.1%
|
19
|
9.9%
|
3.2%
|
3
|
Public Services
|
998
|
8.4%
|
13
|
6.8%
|
1.3%
|
4
|
Agriculture
|
218
|
1.8%
|
1
|
0.5%
|
0.5%
|
5
|
Technology/Engineering
|
1,116
|
9.4%
|
8
|
4.2%
|
0.7%
|
6
|
Nonvocational/Leisure
|
22
|
0.2%
|
1
|
0.5%
|
4.5%
|
7
|
Other Field-of-Study
|
3,499
|
29.5%
|
44
|
23.0%
|
1.3%
|
8
|
All
|
11,846
|
100.0%
|
191
|
100.0%
|
1.6%
|
|
|
|
|
|
|
|
C. Valencia
|
|
|
|
|
|
1
|
Associates in Art
|
23,795
|
70.8%
|
480
|
75.4%
|
2.0%
|
2
|
Building Trades/Manufacturing
|
22
|
0.1%
|
-
|
0.0%
|
0.0%
|
3
|
Public Services
|
1,251
|
3.7%
|
13
|
2.0%
|
1.0%
|
4
|
Agriculture
|
135
|
0.4%
|
1
|
0.2%
|
0.7%
|
5
|
Technology/Engineering
|
1,362
|
4.1%
|
22
|
3.5%
|
1.6%
|
6
|
Nonvocational/Leisure
|
|
|
|
|
|
7
|
Other Field-of-Study
|
7,166
|
21.3%
|
122
|
19.2%
|
1.7%
|
8
|
All
|
33,596
|
100.0%
|
637
|
100.0%
|
1.9%
|
|
|
|
|
|
|
|
D. All Florida Community Colleges
|
|
|
|
|
1
|
Associates in Art
|
233,827
|
58.2%
|
5,662
|
55.4%
|
2.4%
|
2
|
Building Trades/Manufacturing
|
7,365
|
1.8%
|
229
|
2.2%
|
3.1%
|
3
|
Public Services
|
18,443
|
4.6%
|
188
|
1.8%
|
1.0%
|
4
|
Agriculture
|
1,884
|
0.5%
|
17
|
0.2%
|
0.9%
|
5
|
Technology/Engineering
|
11,427
|
2.8%
|
126
|
1.2%
|
1.1%
|
6
|
Non vocational/Leisure
|
1,934
|
0.5%
|
79
|
0.8%
|
4.1%
|
7
|
Other Field-of-Study
|
128,666
|
32.0%
|
3,933
|
38.5%
|
3.1%
|
8
|
All
|
403,546
|
100.0%
|
10,217
|
100.0%
|
2.5%
|
* Enrollment includes full-time and part-time degree/award seeking students in academic year 1997-1998. Completers and graduates are enrolled students who completed a degree or diploma during academic year 1997-1998.
Overall, the Florida data show substantial promise for providing objective evidence concerning the differences in the focus of the Florida community colleges. However, the information would have been considerably more useful had the categorizations used to designate fields-of-study enabled them to create a finer breakdown, and if the wage data covered all enrollees leaving community colleges, not just those completing programs.
6.0 Summary
Westat was able to assemble a substantial amount of useful tabular data bearing on the extent to which community colleges focus on various missions and different fields-of-study. We found that individual-level data on field-of-study linked to wage record files could be of substantial use in assessing the value of career-oriented programs and in measuring the amount of various types of education different colleges provide. Having the data in-hand is of great value because it provides the flexibility needed to try out different ways of specifying tabulations to get around the plethora of problems that invariably develop before being able to design tables that are of the greatest use. This flexibility is particularly important in states such as Florida that use idiosyncratic systems of defining fields-of-study and course subject matter. Importantly, having the data in-hand greatly speeds up the entire analytic process as it rarely is possible to specify the precise tabulations that best meet one’s needs in advance. However, we found that it was extraordinarily time consuming and difficult to obtain individual level data on field-of-study linked to wage record files.
On the other hand, it is clear that published data describing the characteristics of local employers and residents as well as many college characteristics are easily obtained and readily manipulated into highly useful tables that describe the environments in which community colleges operate. Westat feels that this information is especially useful in delineating metropolitan areas such as Dallas and San Diego where there is both a high demand for community college training and a readiness among employers and civic groups to adequately fund career-oriented programs, as well as metropolitan areas such as the Quad-Cities, Springfield-Holyoke, and rural areas where demand is considerably lower, and financing much harder to obtain.
Finally, even had Westat been able to assemble and analyze all the data currently available at colleges and state agencies there still would have been substantial uncertainty about the full extent to which different colleges are labor-market responsive. The central issue stems from most college data systems being geared to counting enrollment in for-credit courses to meet state and local reimbursement requirements and satisfy federal reporting requirements. As a result, it often is difficult to determine how much customized training a college is performing and also how much basic education and other potentially career-enhancing education are taking place in noncredit programs. Perhaps when IPEDS data reporting requirements are redefined, attention should be given to improving the collection of data on career-oriented programs and services. This would require improving distinctions between transfer programs and career-oriented programs among for-credit programs, as well as being more inclusive and improving descriptions of noncredit programs.
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