Graduation Rates: Do Students' Academic Program Choices Make A

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AUTHOR Kroc, Rick; Howard, Rich; Hull, Pat; Woodard, Doug TITLE Graduation Rates: Do Students' Academic Program Choices Make a Difference? PUB DATE 1997-05-00 NOTE 27p.; Paper presented at the Annual Forum of the Association for Institutional Research (37th, Orlando, FL, May, 1997). PUB TYPE Reports Research (143)-- Speeches/Meeting Papers (150) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS College Freshmen; *Course Selection (Students); Decision Making; Diversity (Institutional); *Graduation; Higher Education; Land Grant Universities; *Majors (Students); Prediction; Predictor Variables; Research Universities; State Universities; *Student Characteristics; *Time to Degree IDENTIFIERS Scholastic Assessment Tests

ABSTRACT This study looked at the relationship between the programs students chose upon college entry, the programs from which they graduated, and the time taken to graduate. Individual student data on more than 204,000 freshmen entering 38 public, land grant, and Research I universities in 1988 and 1990 were collected. Descriptive statistics were used to summarize student entry characteristics such as college admission test scores and high school grade point averages. Logistic analysis was used to calculate and compare predicted and actual graduation rates based on student entry characteristics. Among findings were: graduation rates varied more by university than by program; time to completion varied by academic prograth; business and social sciences were the program areas which experienced the largest in-migration from other areas; students entering education programs had the lowest average Scholastic Assessment Test scores whereas engineering students had the highest; students initially undecided about their major were no less likely to graduate than other students and their graduation was not delayed; there was a correlation of about .28 between actual and predicted graduation rates; and there were significant differences among institutions between actual graduation rates and predicted graduation rates. Tables provide detailed findings for each institutions. (DB)

******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** GRADUATION RATES: DO STUDENTS' ACADEMIC PROGRAM CHOICES MAKE A DIFFERENCE?

Rick Kroc Rich Howard Pat Hull University of Arizona Montana State Research Consultant University

Doug Woodard University of Arizona

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Association for Institutional Research Forum Orlando 1997 U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION PERMISSION TO REPRODUCEAND CENTER (ERIC) DISSEMINATE THIS MATERIALHAS fWThis document has been reproduced as BEEN GRANTED BY received from the person or organization originating it. Rick Kroc Minor changes have been made to improve reproduction quality.

Points of view or opinions stated in this TO THE EDUCATIONAL RESOURCES document do not necessarily represent INFORMATION CENTER (ERIC) official OERI position or policy. GRADUATION RATES: DO STUDENTS' ACADEMIC PROGRAM CHOICES MAKE A DIFFERENCE?

Introduction

In this paper, the results of the second phase of a multi-phase study which attempts to look at the graduation rates of first-time, full-time freshmen at public land-grant and research universities are discussed. At the Boston Forum in 1995, the authors of this paper presented the results of the first phase of the study. In fact, two papers were presented that year about the study. In one, the methodology used to collect the data was outlined. In the second (Kroc, Woodard, Howard and Hull, 1995), the results of Phase I were presented and discussed. As background for this study, the two papers presented in 1995 will be reviewed, since they form the foundation for this paper.

An important component of the study was the methodology used to create the data base. Specifically, all public land-grant and research universities were invited to participate in the study. Each institution was asked to send unit record files of their entering 1988 freshman class. Using IPEDS definitions for first- time, full-time freshmen, records were created for each student, which included high school GPA, SAT or ACT scores, gender, ethnicity, class rank, residency, four and five year graduation and persistence status. The files were built according to a format defined by the authors. These files were then sent to the authors' university using either FTP or e-mail as the method of transfer. The files from each of the participating institutions were then merged to form a file with over 160,000 records from some fifty-three institutions. A program was written that edited the files as they came into the server at the authors' institution. At no time in the building of this large data base did the authors have to clean or modify any of the data by hand, cutting down significantly data cleanup procedures usually necessary before analysis can begin. The successful transfer of large data bases and the subsequent building of a very large data base using technology found on virtually all campuses has implications for data exchanges among institutions and reporting in general.

The initial research drew on the work of Astin (1993) in which the predictability of graduation rates was examined in relation to students' entry characteristics. In the first paper, the authors replicated Astin's work, specifically for land-grant research universities. Student characteristics were regressed on graduation rates to produce a predicted graduation rate for each of the institutions in the study, which was then compared to the actual rate. In this analysis, the data base was composed of some 130,000 student records from 44 universities. Graduation and persistence rates (four and five year) were estimated, using high school GPA, SAT or ACT scores, gender, and ethnicity.

BEST COPY AVAILABLE 3 Comparison of the results of Astin's predictionequation and the equation derived from the analysis described aboverevealed that whereas Astin obtained the strongest correlation with fouryear graduation rates (R=.34), our best results were obtained using fiveyear rates (R=.32). The Astin equation over predicted four year graduation rates for 93% of theuniversities in the sample. However, prediction of fouryear graduation and persistence rates were essentially equivalent for both equations. These resultsraise questions about the use of Astin's equation in predictinggraduation rates for land-grant, research universities.

A second analysis compared the efficiency oflogistic regression equations and linear regression (the methodology used in theabove analyses). In contrast to other reported findings (Dey and Astin, 1993), ineach instance, logistic regression performed better than linear regression.Residual analysis showed a better fit, particularly at the extremes. Sixty-eight percent ofthe universities had a closer fit between their actual and predictedrates using a logistic regression model.This analysis indicated that, although it addsto the complexity to the analysis and the interpretation, logisticregression may be better than linear regression for predicting graduationrates.

A third analysis was conducted to examine theimpact of university level variables on the ability to predict graduationrates. Some twenty-two variables were identified for inclusion in the study. Because of the numberof variables and their disparate nature, factor analysiswas used to simplify the data. Using an orthogonal rotation, six primary factors were identified fromthe twenty-two variables. Adding factor scores from this analysisto the student background variables improved the prediction of graduationrates somewhat. The overall logistic regression equation correctly predicted70.2% of the students' graduation status, with the correlation betweenpredicted and actual rates of graduation being .35. Fifty-eight percent of theuniversity graduation rateswere more accurately predicted using both student and institutionalvariables than with only student variables.

Methodology

In this part of our study, Phase II,we were interested in gathering data from Land Grant, Research I and AAU universitiesupdated with a more recent cohort (1990) and including information about theprogram students chose upon entry and the program from which they graduated.CIP codes, which all universities use for federal IPEDS reporting, were gathered from each participating university. This database enabledus to update our previous findings and to extend our research by analyzingprogram level information. We answered a series of questions about graduation rates bothat the university and program levels, as detailed in the results section below.

2 4 Two approaches were used. First, descriptive statistics were used to tabulate and summarize the data from a variety of perspectives. Understanding the data in a simple manner is both important in its own right and essential before proceeding to more sophisticated analyses. To simplify the data, ACT scores were converted to the SAT scale using ETS concordance tables (SAT scores are on the old scale, not recentered). Also, we converted high school class ranks to high school grade point averages for those universities that had only ranks by using a concordance table we developed from data in this study using a method developed by Chisholm (1993).

Second, based on student entry characteristics, we calculated and compared predicted and actual graduation rates using logistic regression. This extension of Astin's work (Astin, 1993; Dey and Astin, 1994) provided performance indicators by calculating predicted graduation rates against which actual rates were compared. The independent variables were high school grade point average, SAT score, sex, ethnicity and domicile; the dependent variable was whether the student had graduated after five years. This analysis was done both at the program and university levels. We plan to use the results of this analysis to identify universities with much higher than predicted rates in particular academic disciplines for future qualitative study.

Results Descriptive Statistics

What were the summary statistics for the study?

Individual student data was collected on more than 204,000 freshmen entering 38 public, land grant, Research I universities in 1988 and 1990. Table 1 provides a list of the participating universities. As shown in Table 2 (sorted by graduation rate), the five year graduation rate for all of the students was 54.8% with an additional 9.9% still enrolled but not yet graduated. The mean SAT score was 1029; mean high school grade point average was 3.28; 49.4% were female; 14.9% were minority; and in-state residents made up 76.9% of the freshmen.

Did graduation rates vary by academic program and by university?

Yes, much more by university than by program. Table 3 displays program level data showing that five year graduation rates vary from 58.3% for freshmen who entered social sciences and interdisciplinary programs to 48.2% for those who entered health related professions. Table 2 shows a much larger variation among universities, from 25.7% for university #8 to 77.1% for #38. To illustrate

3 5 the variation of graduation rates withina specific set of programs, Table 4 details data in the sciences and math, showinga variation from a low of 29.2% for #8 to 85.5% for #38. It is also noteworthy thatwithin sciences and math, women had a 61.2% five year graduation rate, while therate for males was only 54.1%.

Did time to completion vary by academic program?

Yes. Table 3 shows that engineering studentstake longest--16.3% are still enrolled after five years--whereas businessstudents finish fastest--only 7.3% remain enrolled after five years. Aconsequence of this is the underestimation of graduation rates for engineering students whenlooking out five or even six years.

What were the student migration patternsacross programs?

Of the 112,000 graduates in the database,26.0% graduated in business fields and 22.5% in the social sciences (see Table 5).These were also the programs that experienced the largest in-migration fromother areas (each gained about 15% from other programs or undecided students).Engineering had the least amount of "swirling", while liberal arts and social scienceshad the most. As we defined it: swirling = [(number of out-migrants)+ (number of in-migrants)] / (number who graduated from thesame program as they entered).

Since the study gathered six digit CIP codes for93,447 graduates, we were also able to assess the number of students whochanged majors between entry and graduation. Almost three ofevery four entering freshmen (72%) who initially chose a major--undecided studentswere excluded--changed to a different major before graduating.

Is there an interaction betweenprogram and university: do some universities do relatively better at graduating students insome disciplines?

No, the university rankingson five year graduation rate were highly correlated across programs--a university with a high graduation rate inone program was likely to have a high rate in otherprograms (r >.95 for seven out of the nine comparisons).

Did academic preparation varyamong programs and universities?

Yes, students entering educationprograms had the lowest average SAT score at 949, whereas engineering studentswere highest at 1103. High school grade point averages had the same pattern. Toput this into context, the average student entering education would have beenat the 16th percentile in engineering and the 22nd percentile in sciencesand math. Table 3 details these results.

4 Preparation varied considerably among universities, as well, ranging from an average SAT of 933 at university #8 to 1108 at #38 (see Table 2). The average freshman at #8 would have been at the 12th percentile at university #38. Looking at a specific area, the average entering sciences and math students at #8 would have been at only the 7th percentile at university # 38. Our data, then, indicate considerable variation in academic preparation across both programs and universities.

How did other student characteristics vary among programs?

As shown in Table 3, females were most likely to enter education (76%) and least likely to enter engineering fields (18%). Minority students were most likely to enter sciences and math (22.9%); least likely to enter education (7.2%). Underrepresented minorities, which we define as African American, Hispanic and Native American students, were most likely to enter health related professions (13.1%) and least likely to enter education (7.2%). Asian Americans were most likely to enter sciences and math. These findings are consistent with other national data.

Were undecided students at risk?

No, in fact undecided students, who were about 26% of the study population, perform slightly better than students who choose a major upon entry--they graduated at a slightly higher rate (56.9% compared with 54.8%), and their graduation did not appear to be delayed (a five year graduation and persistence rate of 66.5% compared with 64.7% for "decided" student4 Also, undecided students had very similar entry characteristics, including high school preparation, to other students.

This finding is significant given the mythology about undecided students. Clearly undecided students are not poorly prepared upon entry, do not drop out at higher rates, and do not take longer to graduate.

Predicting Graduation Rates

As discussed earlier in this paper, in our previous study of graduation rates (Kroc, Woodard, Howard and Hull; 1995), Astin's (1993) model was used to account for the influence of student background characteristics on graduation rates.In this study we extended the logistic regression model used in Phase I of our research by including an additional, more recent, cohort(1990) with the original 1988 freshman cohort, and by adding program level data (six digit CIP codes) that could be used to predict students' graduation rates within individual programs or program areas.

57 How well could university graduationrates be predicted using entry characteristics?

Using logistic regression, we obtained about66% concordance-- students' were correctly classified as having graduatedor not 66% of the time, where 50% would be chance level. This translates intoa correlation of about .28 between actual and predicted graduation rates.

What does this mean? As an example, lookat university #1 in Table 2.If you knew nothing about this university,your best guess about its graduation rate would be the mean of the sample, 54.8%, whichwas much lower than this university's actual rate of 68.2%. This is the kind of logicnaive readers might use when looking at graduation rate data from the upcoming StudentRight-to- Know reports or, currently, when perusing USNews and World Report or the grad rate data in the NCAA Report. Usingentry characteristics, however, the predicted rate for university #1was 64.0%, closer to its actual rate--and a better representation of what its actual graduation rate shouldbe.

The best predictor was high school grade pointaverage, followed by SAT, sex, ethnicity and domicile, respectively. In Table 6,to illustrate some of these relationships in tabular form, we have aggregatedthe universities into either a high, medium or low group basedon their graduation rates. Differences in SAT and high school grade point averagesamong these groups are most evident.

Clearly, the results shown in Table 2 show thatentry variables can help us understand some of the large variations in graduationrates among universities. Examining the differences between actual andpredicted rates, then, can help us sharpen our thinking and refine our questions about whyuniversity graduation rates vary.

Were there differences in the predictability ofgraduation rates at the program level?

No, we found little variation in either thestrength of the relationship or the coefficients for the independent variablesamong the regressions produced for each program. The equationswere essentially interchangeable. Given the degree of "swirling" among undergraduates, thisis probably not a surprising finding. Table 7 displays the beta weights andthe concordance values for each program.

Did individual universities excel insome programs. but not in others?

Again, generally, no. The correlation coefficientswere about .95 when comparing the differences between actual andpredicted graduation rates for universities across programs (see Table 8). Auniversity with better than predicted graduation rates in oneprogram was likely to have better rates for its

66 other programs. The exception in these data was health related professions, but much smaller numbers of students in this area may have been the primary reason for the lower correlation.

Are there differences in actual graduation rates among universities with similar predicted graduation rates?

One of the purposes of this study was to identify, at the program level, universities that graduated more students than might be expected from their entry characteristics. These universities would then be studied more thoroughly using qualitative, case study, methods in Phase III of our project.In particular, we would be looking for the influences of university "environmental" characteristics on graduation rates.

Table 9, sorted by predicted graduation rate, shows results for sciences and math programs. Examining pairs of universities with similar predicted rates, some interesting differences are evident. For example, universities #16 and #17 both had high predicted rates but differed by almost 16 percentage points in their actual graduation rates for science and math students. University #34 and #38 showed a similar pattern. University #15 and #27 were also interesting--#27 had a lower predicted rate, but higher actual rate than did #15.

Implications

These results suggest that there may be value in further studies of the particular conditions at individual universities that may influence graduation rates. Accounting for entry characteristics as best we could with this methodology, there appeared to be variance in graduation rates that was unexplained. Moreover, we found intriguing differences within specific programs among universities that appeared to have similar entering students.

Some of the differences and unexplained variance in graduation rates could be attributed to limitations in the methodology, specification error in particular. The variables used to account for "input" differences among universities do not perfectly measure what they purport to measure, and do not represent all of the dimensions of student difference. Socio-economic status, for example, is probably not adequately measured in this study. Nonetheless, we believe that this methodology does move us considerably closer to understanding differences in graduation rates that are caused by university culture and environment.

How do we answer the question posed in the title of this paper: "Graduation rates: Do students' program choices make a difference?" Our answer is a definitive yes--and no. We found that student characteristics varied

C7 BEST COPY AVAILABLE considerably among programs, with nearlya standard deviation of difference between the low and high programs. But, program-levelgraduation rates for individual universities were almost entirely predictablefrom the university's overall graduation rate--universities with the highesttotal graduation rates also tended to have the highest rates in their individualprograms. And when we accounted for entry characteristics using logisticregression, universitieswith, better than predicted overall graduation rates alsotended to have better rates for their individual programs.

Comparing one university with another, then, the variationsamong programs that occur at the graduate level are not evidentat the undergraduate level. This is probably not surprising given the impact of lowerdivision course work, when students are often taking a common core ofcourses; the fact that nearly three out of four students change majors; and the large numberof students who are undecided when they matriculate. Overall admission criteriaand the overall university environment appear to be more important thanthe selection of a program among undergraduates.

Finally, we believe that the findings of this study bear directlyon this year's AIR Forum program theme, performance indicators. For public,Research I, Land Grant and MU universities, the data in this studycan provide valuable benchmark and comparative information. Aswe face increasing pressures to improve--and report on--undergraduate education, thisinformation may be useful.

References

Astin, A. (1993). How good is your institution's retentionrate? Los Angeles: Higher Education Research Institute.

Chisholm, M. P. (1993). An evaluation ofa statewide admission standards policy. Association for Institutional Research Annual Forum,Atlanta.

Dey, E. L. and Astin, A. (1993). Statistical alternativesto studying college retention: A comparative analysis of logit, probit and linearregression. Research in Higher Education, 34, 569-582.

Kroc, R. J., Woodard, D. B., Howard, R., and Hull, P. S. (1995)Predicting graduation rates: A study of public Land Grant, ResearchI, and AAU universities. Association for Institutional Research AnnualForum, Boston.

8 Table 1

National Graduation Rate Study Participating Universities

Arizona State U. Auburn U. Clemson U. Iowa State U. Louisiana State U. Mississippi State U. Montana State N. Carolina State U. North Dakota State U. Oklahoma State U. Penn State U. Rutgers U. South Dakota State U. SUNY at Buffalo Texas A&M U. U. of Arkansas U. of Tennessee U. C. Irvine U. C. Santa Barbara U. of Arizona U. of Colorado-Boulder U. of Connecticut U. of Georgia U. of Idaho U. of Iowa U. of Kansas U. of Maine U. of Massachusetts U. of Minnesota U. of Missouri U. of Montana U. of New Hampshire U. of New Mexico U. of Oregon U. of Vermont U. of Washington U. of Wisconsin-Madison Virginia Tech U.

11 Table 2 Proaram at Entry: ALL Number of National Graduation Rate Study - Combined Data From 1988 and 1990 Freshmen Cohorts institution ID 8 FreshmenEntering 3,521 Mean SAT933 H.S. GPA Mean 3.11 Female 52.3 % Minority 37.9 % In-State 80.3 % Actual Rate25.7 5-Year Graduation Rate Predicted 45.8Rate PredictedActual - -20.1 GraduationPersistence & 5-Year 48.4 271529 7 4,7192,6162,3141,602 943937936977 3.143.173.042.92 47.644.043.852.9 10.0 5.13.74.0 82.772.975.180.4 35.135.031.829.9 50.151.449.347.8 -15.0-16.4-17.5-17.9 46.252.449.144.2 322818 3 7,0916,7201,8932,332 973954943935 3.053.142.903.08 48.444.354.041.7 14.017.614.1 1.6 47.774.493.459.4 38.338.037.836.5 49.849.745.750.7 -11.5-11.7-14.2-7.9 53.652.155.150.2 3117 49 12,0589,1215,5364,730 10921041981953 3.563.173.323.23 45.650.547.0 17.918.710.311.2 95.461.874.690.2 47.544.541.538.6 61.851.857.851.7 -14.3-16.3-13.1-7.3 60.257.159.649.9 241312 2 4,8117,7977,0443,426 1008995970975 3.283.223.113.17 56.352.748.044.6 11.97.37.63.1 73.286.186.471.5 53.352.648.848.7 56.054.250.553.5 -2.7-1.6-1.7-4.8 60.358.660.856.3 21261914 - 6,9396,2715,5654,849 1052102210181057 3.353.233.123.17 38.053.947.242.2 14.320.5 7.37.1 84.769.158.096.4 58.557.055.453.9 57.155.552.553.2 2.90.81.41.5 71.264.367.562.5 37353016 7,4927,1596,7986,746 1041106710561037 3.213.403.503.10 52.446.848.256.2 29.016.99.67.8 80.556.485.387.6 61.060.859.859.1 55.159.761.252.1 -1.45.91.17.0 67.067.677.165.8 222310 5 10,6195,3816,1185,086 1047103410791046 3.403.383.523.32 53.344.752.052.9 31.013.09.5 90.066.891.279.0 65.565.564.463.6 59.457.961.457.3 6.13.07.66.3 70.174.472.170.5 33 16 10,5283,7024,993 107110231088 3.323.663.20 52.555.051.5 54.46.47.7 42.798.761.8 71.268.265.9 59.564.057.0 11.84.28.9 74.874.475.4 Total 383411 204,609 6,6724,1088,252 i 4 1029110811021117 3.283.473.393.40 49.447.443.356.8 14.911.912.41.8 76.971.772.958.6 54.877.171.971.7 54.862.260.261.8 14.911.70.09.9 64.780.576.174.7 13 National Graduation Rate Study - Combined Data From Table 3 1988 and 1990 Freshmen Cohorts Number of Summary by Program at Entry % Under- % Graduated % Graduated 5 -Year Rates Total Total Business, Management & Public Admin. Program Entry FreshmenEntering33,280 Mean1004SAT H.S. GPA Mean3.23 Female 49.1 % Minority 13.8 % represented' Minority 10.2 Programin Same 38.7 Programin Other 17.7 Graduation 56.4Rate GraduationPersistence & 63.7 Education 5,788 949 3.18 76.2 8.1 7.2 34.8 18.4 53.2 61.467.7 Health-RelatedEngineeringProfessions 21,6996,487 1103987 3.313.45 69.017.6 16.616.0 13.110.4 33.120.2 28.018.3 48.251.4 59.3 Liberal Arts, HumanitiesSciences& General and Studies Math 13,34617,522 1073998 3.483.17 49.859.6 22.911.1 11.58.4 31.612.4 37.226.1 49.657.7 66.859.1 Social Sciences andOther DisciplinesInterdisciplinary 26,51017,900 10011031 3.353.19 49.357.9 20.48.9 11.26.6 30.632.9 27.717.2 58.350.1 67.961.1 Undecided or Unknown MajorTotal 193,48950,957 10291033 3.283.24 49.449.6 14.914.5 10.09.9 30.7 - 24.156.9 54.856.9 64.766.5 3.4 Standard deviations:Underrepresented High school minorities grade Include point average African Amercian, = 0.51; Hispanic and Native SAT = 168 Amercian. 15 roaram at Entry Sciences and Math National Graduation Rate Study - Combined Data From 1988 and 1990 Freshmen Cohorts Table 4 Institution ID8 NumberFreshmenEntering of Mean SAT H.S. GPA Mean Female % Minority % In-State % Actual Rate 5-Year Graduation Rate Predicted Rate PredictedActual - GraduationPersistence & 5-Year 32 37 226263274 71 10091038994961 3.303.203.19 56.645.235.258.0 18.116.044.92.8 75.759.781.783.9 35.033.129.629.2 49.948.948.647.1 -15.0-15.9-19.1-17.9 46.052.546.551.5 2715289 197290153259 1060109810001020 3.383.463.423.12 48.237.246.452.9 15.212.114.36.5 83.874.575.892.7 42.140.735.935.5 53.957.855.047.8 -11.8-17.1-19.0-12.3 50.859.356.251.0 311729 2 1,008 537218111 114610521017994 3.703.323.273.28 50.345.647.247.7 20.419.6 2.85.4 94.163.369.976.6 46.943.643.142.3 63.652.953.453.5 -16.7-10.3-11.2-9.3 58.258.150.558.6 24121826 242116122120 1081106510131100 3.423.323.273.39 50.849.145.156.7 11.68.62.55.8 73.184.566.459.2 54.551.749.2 57.453.253.156.6 -2.9-1.5-3.9-7.4 59.162.958.261.7 193013 4 627313334 9 1077109411201028 3.353.223.533.49 43.749.246.733.3 14.19.70.09.0 100.079.993.692.5 59.556.656.055.6 54.050.959.256.3 -3.3-0.85.55.7 69.266.163.577.8 3537105 559534470866 1063110510791075 3.403.493.343.55 42.644.247.250.6 17.933.014.611.3 84.361.876.887.8 61.961.659.859.6 55.058.955.259.8 -0.22.74.6 68.265.570.3 16 61 274187 11291136 3.423.66 47.442.8 20.99.5 81.479.7 63.562.6 58.464.5 -1.95.16.9 78.881.868.9 231122 1,3451,544343838 1094109510871012 3.553.413.543.75 54.856.255.153.1 30.513.767.98.8 71.473.089.898.8 70.670.069.564.6 60.057.263.065.3 10.612.8-0.76.5 76.473.374.672.5 Total 383334 i6 13,346 368422106 1073115710991141 3.483.583.443.53 49.831.143.559.2 22.93.88.21.7 80.236.447.962.3 57.785.872.070.9 17 57.762.258.360.9 23.713.70.09.9 66.887.774.774.6 National Graduation Rate Study - Combined Data From 1988 and 1990 Freshmen Percent of 5-Year Graduates by Entry and Exit Program Table 5 Cohorts Business, Management & GraduationProgram at 11 BPA .5% 0.2% ED 1.2%ENG 0.2% HP LA&HUProgram1.7% at Entry 0.5%S&M 2.0%SS&I Other0.8% Undecided 8.0% 26.0%Total Public Admin. (BPA) Education (ED) 0.4% 1.8% 0.1% 0.1% - 0.4% 0.2% 0.3% 0.3% 1.3% 4.9% Engineering (ENG) 0.2% 0.0% 1r 6.4% 0.0% 0.1% 0.2% 0.6% 0.1% 3.3% 10.9% Health-Liberal Related Arts, Professions Humanities & (HRP) 0.9%0.1% 0.1% 0.2%0.1% 0.1%1.2% 1.9%0.2% 0.3%0.2% 1.3%0.3% 0.3%0.1% 2.7%0.8% 7.9%2.9% SciencesGeneral and Studies Math (LA&HU) (S&M) Social Sciences and 2.2%0.3% 0.3%0.1% 0.8%0.6% 0.4%0.5% o 2.1%0.4% 3.8%1.2% 7.2%1 .1% 0.8%0.2% 7.6%2.3% 22.5%9.3% Other DisciplinesInterdisciplinary (Other) (SS&I) Total 16.7%1.1% 2.7%0.3% 10.0%0.6% 2.8%0.2% 7.7%1.0% 6.9%0.5% 13.8%1.0% 8.0%5.3% 31.4%5.5% 100.0%15.5% = Graduated in same program area as entered. 'EST COPY AVAILABLE C's, 't . Table 6 National Graduation Rate Study - Combined Data From 1988 and 199035 InstitutionsFreshmen CohortsGrouped by High, Medium and Low 5-Year Graduation Rates NumberFreshmenEntering of MeanSAT H.S. GPA Mean Female % Minority % represented* % Under-Minority In-State % Graduation 5-Year Rate GraduationPersistence & 5-Year 77.1%Graduation (Institutions Rate 64.4 = 9) - HIGH - 5-Year 60,373 1073 3.40 50.4 18.8 9.3 74.5 68.6 74.5 63.6% (InstitutionsGraduation = Rate14)MEDIUM 47.5 - 5-Year - 92,041 1036 3.29 48.8 12.9 8.3 80.6 55.4 65.0 44.5% Graduation(Institutions Rate = 12) 25.7 - LOW - 5-Year 52,195 964 3.11 49.1 13.8 10.8 73.2 37.7 52.9 TOTAL 204,609 1029 3.28 49.4 14.9 9.9 76.9 54.8 64.7 2C Underrepresented minorities include African Amercian, Hispanic and Native Amercian. 21 Table 7 Logistic Regression on 5-Year Graduation - National Graduation Rate Study - Combined Data From 1988 Variables in Logistic Regression - Standardized Estimate Standardized Estimates and Percent Concordant by Program and 1990 Freshmen Cohorts Business, Management & EntryPublic Program Admin. 0.12SAT H.S. GPA 0.28 Female0.05 African Am. -0.07 Asian Am. -0.01 Hispanic -0.05 Native Am. -0.05 -0.03White Resident 0.02 Concordant 67.2% Education 0.15 0.26 0.060.14 -0.04-0.12 -0.030.00 -0.11-0.09 -0.08-0.07 -0.06-0.01 0.020.01 67.5%68.7% Health Related ProfessionsEngineering 0.140.12 0.250.26 0.04 -0.05 -0.07 -0.09 -0.12 -0.05 0.03 66.6% Liberal Arts, Humanities & General Studies 0.13 0.26 0.10 -0.05 -0.02 -0.02-0.11 -0.06-0.04 -0.12-0.01 0.010.03 66.2%67.3% SocialSciences Sciences and and MathInterdisciplinary 0.060.08 0.280.26 0.06 -0.07-0.11 -0.06-0.07 -0.07 -0.06 -0.06 0.03 65.4% Undecided or Unknown Other Disciplines 0.08 0.24 0.08 -0.03 -0.04 -0.08-0.09 -0.06 -0.06-0.01 -0.020.04 66.4%65.2% MajorTotal 0.120.10 0.230.25 0.080.08 -0.08 -0.04-0.04 -0.08 -0.06 -0.06 0.02 23 66.2% National Graduation Rate Study - CombinedDifference DataBetween From Actual 1988 and Predicted1990 Freshmen 5-Year Cohorts Graduation Rate (minimum N=50) Table 8 Institution 38ID Business& PublicAdmin.16.4% Education 16.7% Engineering 17.1% Professions RelatedHealth -- Humanities LiberalArts16.2% & Sciences 23.7%Mathand interdiscip.Sciences & Social10.8% Disciplines 18.5%Other or UnknownUndecided 12.1%Major TOTAL14.9% 3433116 10.1%10.9%8.6% -- 15.7%21.3%11.6%2.4% 11.7%14.0%5.8% -- 24.4%12.1%19.6% -- 13.7%14.0%14.4% -- 12.8%13.7%5.1%9.9% 12.5%6.7% -- 11.2%12.3%15.1%9.1% 7.4%7.7%9.3% -- 11.7%11.8%8.9%9.9% 223023 5 3.3%5.2%8.1%9.0% 14.2%4.8% ---- 6.8%3.3%6.7% -- 8.5%1.7%1.4% -- 15.0%9.2%5.7%3.5% -10.6% 6.5%6.9%5.7% 9.6%8.5%0.5%8.9% 14.9%7.8%3.7%5.1% 3.5%7.5%3.6% -- 6.1%6.3%7.0%7.6% 37 1 3.8%6.7% 9.6% -- 7.2%9.8% 18.8% -- . 7.8%7.4% -0.7%4.6% 4.9%5.8% 7.2%8.3% -- 4.2%5.9% 261910 i 1.0%9.0%0.2% 16.3%8.5% -- 15.0%6.0%7.1% 2.6% -- 11.5%9.3%8.5% -7.4%-0.2%5.5% 7.7%0.4%3.1% 3.9%2.3%1.7% -4.4%-0.7%2.0% 2.9%3.0%1.4% 12131635 2.3%0.0%5.0% -- -4.9%-4.2% -- -0.7%-0.1%1.6% -- -0.8%1.2% -- -8.3%3.8% ---- -1.5%-3.3%-1.9%2.7% -5.9%-4.5%-3.2%0.9% -5.1%-1.6%-5.6%-1.5% -3.6%-1.7% ---- -1.7%-1.6%-1.4%1.1% 2831242 -4.4%-6.6%-5.2%0.8% -10.7%-4.8%2.8% -- -8.6%-7.4%2.4% -- 25.7%-9.9%-5.1% -- -3.6%-4.3%-2.6%1.9% -12.3%-10.3%-9.3%-2.9% -3.0%-4.5%-5.5%0.2% -10.7%-5.2%-2.8%-8.2% -11.7%-13.2%-7.1% -- -7.9%-7.3%-4.8%-2.7% 183243 -11.6%-12.5%-4.0%-8.2% -11.2%-2.6%-8.0% -- .-14.8%-12.9%-11.4%-16.4% -6.7%-1.7% -- -19.4%-10.0%-12.8% -- -15.0%-.15.9%-3.9% -- -14.5%-6.5%-9.0% -- -14.6%-14.2%-3.3%-7.9% -12.7%-12.3% --- -14.2%-13.1%-11.7%-11.5% 271517 9 -15.4%-15.6%-12.7%-14.9% -14.0%-14.9%-10.5%-9.4% -15.8%-11.9%-10.3%-6.7% -12.6%-15.0%12.1% -- -14.4%-19.1%-12.1%-7.4% -19.0%-17.1%-11.8%-16.7% -17.2%-15.5%-21.5%-8.6% -13.5%-16.4%-14.5%-11.4% -17.4%-23.4% -- -16.4%-16.3%-15.0%-14.3% 297 -13.7%-18.9% -17.1%-21.3% -10.0% -- -21.9% -3.1% -15.7%-16.1% -19.1%-11.2% , -16.9%-15.4% -15.2%-15.4% -21.8% -- -17.9%-17.5% ofCorrelation Program to Total 8 -18.9% 0.97 - -15.4% 0.91 -17.1% 0.93 -14.2% 0.70 -20.8% 0.95 -17.9% 0.93 -23.8% 0.94 -15.7% 0.94 -21.7% 0.97 -20.1% National Graduation Rate Study - Combined Data From 1988 and 1990 Table 9 Freshmen Cohorts Proaram at Entry:Institution Sciences and Math ID NumberFreshmen ofEntering MeanSAT H.S. GPA Mean Female % Minority % In-State % Actual Rate 5-Year Graduation Rate Predicted Rate PredictedActual - GraduationPersistence & 5-Year 3228 87 26325927471 100910381020961 3.203.193.123.30 45.235.252.958.0 44.916.014.32.8 59.781.792.783.9 33.129.635.529.2 48.948.647.847.1 -15.9-19.1-12.3-17.9 52.546.551.051.5 183130 3 537313226122 101310521094994 3.273.323.223.30 45.145.649.256.6 19.614.118.12.5 66.463.393.675.7 49.243.656.635.0 50.949.953.152.9 -15.0-3.9-9.35.7 66.146.058.258.1 272912 2 218197111116 106010171065994 3.273.383.283.32 48.247.249.147.7 15.25.42.88.6 83.869.984.576.6 42.142.343.151.7 53.953.553.453.2 -11.8-10.3-11.2-1.5 50.858.650.562.9 371915 5 470627559153 1079106310771000 3.343.403.423.35 47.243.742.646.4 11.317.96.59.7 76.884.379.975.8 59.861.935.959.5 55.255.054.0 -19.04.66.95.5 65.568.956.269.2 241126 4 242838120 9 1081109511001028 3.423.533.413.39 50.833.356.256.7 11.613.75.80.0 100.073.159.273.0 54.549.255.670.0 57.457.256.656.3 12.8-2.9-7.4-0.8 59.173.361.777,8 3533 69 534368274290 1105112910991098 3.493.423.443.46 44.247.443.537.2 14.612.19.58.2 36.461.881.474.5 61.663.572.040.7 58.958.458.357.8 -17.113.72.75.1 68.259.378.874.7 34231013 422343866334 1141109410751120 3.533.553.49 59.254.850.646.7 33.08.89.01.7 92.547.971.487.8 70.970.659.656.0 60.960.059.859.2 10.6-0.2-3.39.9 63.574.676.470.3 16223817 1,0081,345 187106 1136108711571146 3.663.703.543.58 42.850.355.131.1 20.920.430.53.8 79.794.189.862.3 62.646.969.585.8 64.563.062.263.6 -16.723.7-1.96.5. 87.781.858.274.6 Total 1 13,3461,544 10121073 3.483.75 49.853.1 BEST COPY AVAILABLE 22.967.9 80.298.8 57.764.6 65.357.7 -0.70.0 66.872.5 27 U.S. Department of Education Office of Educational Research and Improvement (OERI) National Library of Education (NLE) Educational Resources Information Center (ERIC) REPRODUCTION RELEASE (Specific Document) I. 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