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Educational policy analysis archives.
n Vol. 12, no. 19 (May 04, 2004).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c May 04, 2004
Predicting higher education graduation rates from institutional characteristics and resource allocation / Florence A. Hamrick, John H. Schuh [and] Mack C. Shelley, II.
Arizona State University.
University of South Florida.
t Education Policy Analysis Archives (EPAA)
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1 of 23 A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright is retained by the first or sole author, who grants right of first publication to the EDUCATION POLICY ANALYSIS ARCHIVES EPAA is a project of the Education Policy Studies Laboratory. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education Volume 12 Number 19May 4, 2004ISSN 1068-2341Predicting Higher Education Graduation Rates from Institutional Characteristics and Resource Allocati on Florence A. Hamrick Iowa State University John H. Schuh Iowa State University Mack C. Shelley, II Iowa State UniversityCitation: Hamrick, F. A., Schuh, J. H., & Shelley, M. C. (2004, May 4). Predicting higher education graduation rates from institutional characteristics and resource allocation. Education Policy Analysis Archives, 12 19. Retrieved [Date] from http://epaa.asu.edu/epa a/v12n19/.AbstractThis study incorporated institutional characteristi cs (e.g., Carnegie type, selectivity) and resource allocations (e.g., instructional expenditures, student affairs expenditures) into a statistical model to predict undergraduate graduation rates. Instruct ional expenditures, library expenditures, and a number of institutional classification variables were significant predictor s of graduation rates. Based on these results, recommendations as w ell as warranted cautions are included about allocating ac ademic financial resources to optimize graduation rates
2 of 23 Introduction and Conceptual FrameworkThe deployment of financial resources for instituti ons of higher education is a crucial aspect of institutional management. Balderston (199 5, p. 6), for example, concluded, Â“Efforts toward more effective use of resources and a fine instinct for the inevitable trade-offs will be important and will even tend to dominate the institutional scene. Therefore, increasing weight is now given to explic it decisions about the allocation of resources.Â” How institutions spend their financial resources tends to reflect their priorities, although Hansen and Stampen (1996, p. 2 95) point out that Â“overall expenditure data are not particularly helpful in un derstanding the impact of changes on the quality of higher education,Â” since such exp enditure totals include expenditures not related to instructional activities, and includ e public service, research, and auxiliary enterprises.The conceptual framework guiding this study is to l ink institutional planning with the successful retention of undergraduate students to g raduation, through implementing a careful fiscal strategy. While a fiscal strategy, b y definition, is to establish Â“the basis upon which allocations are to be madeÂ” (Brinkman & Morgan, 1997, p. 291), fiscal strategies often are not integrated into the instit utional planning processes that specify desired goals and outcomes (Peterson, 1999) Retention and graduation rates are central indicators of success for institutions of higher education, and a variety of negative consequences for undergraduate students ar e related to attrition (Tinto, 1987). However, it not clear whether, and, if so, h ow, institutional resource allocation decisions are linked to student graduation rates. O ur approach to the study of this important problem in higher education policy is to develop a statistical model that explores resource allocation decisions as predictor s of student graduation rates, together with other measures of institutional type and selected institutional traits that are suggested in the research literature and are av ailable in a national database. If the efficacy of such a model can be demonstrated em pirically, leaders of higher education institutions may be able to make more str ategic resource allocation decisions in pursuit of the goal of improved gradua tion rates. The results of this study are intended to help promote data-driven approaches to strategic resource allocation by institutions of higher education.Support for students is perceived in some quarters as an essential ingredient of program quality (Haworth & Conrad, 1997). They (199 7, p. 143) concluded: Â“Throughout our study, support for students repeate dly surfaced as an important feature of high-quality programs.Â” Yet, during the most recent 15-year period when higher education expenditures have been tracked by the National Center for Education Statistics (2000), the percentage of budg et dedicated by four-year public institutions to instruction and libraries, principa l consumers of the budget on many campuses, has declined.Cost escalation is of considerable concern to the h igher education community. Rising tuition charges is a particular concern. Clotfelter (1996, p. 1) concluded, Â“Tuition charges rose sharply as well, making the rate of in flation in private college tuition even worse than the much-heralded run-up in medical costs.Â” One of the ways institutions have addressed revenue problems has be en to try to improve retention of students. Indeed, how institutions of higher educat ion deploy their resources to influence students may be an important thrust of re search in the future. Pascarella
3 of 23 and Terenzini (1998, p. 158), for example, noted th at Â“future research on the impact of college will not be able to avoid coming to term s with issues of cost effectiveness. Examination of benefits in relation to costs will b e particularly important for college impact research designed to inform policy.Â”Various strategies can be used to improve retention rates (e.g., Astin, 1997; Elkins, Braxton, & James, 2000; McLaughlin, Brozovsky, & Mc Laughlin, 1998; Murtaugh, Burns, & Schuster, 1999). In a classic report, Tint o (1987) pointed out that as students are more likely to be integrated socially and academically in their institutions, the more likely it is that they would be retained. Berger and Braxton (1998, p. 116) studied students at a private institution and concl uded that Â“organizational attributes play an important role not only as a source of soci al integration, but in the first year persistence process in general at this institution. Â” Murtaugh, Burns, and Schuster (1999) analyzed retention at Oregon State Universit y, and identified several steps that the university could take to improve retention, inc luding pointing out that out-of-state students were at greater risk than are in-state stu dents. In the two reports identified above, students were studied at single institutions and recommendations, consistent with TintoÂ’s model, wer e made to improve the institutionÂ’s retention rates. While such studies c an be particularly useful to the institutions studied, and may have applicability at other institutions, large-scale studies of multiple institutions that focus on how institutional resource allocations influence graduation rates are rare. This study was intended to fill that void; more specifically, it was undertaken to determine how in stitutional resource allocations influence graduation rates at over 400 public fouryear institutions of higher education.Data and MethodsThis study explored the extent to which institution al characteristics and decisions about institutional resources could be used to pred ict undergraduate graduation ratesÂ—a common indicator of undergraduate student s uccess. Our analysis is based on variables derived primarily from the Integrated Postsecondary Education Data System (IPEDS) data, obtained from the National Cen ter for Education Statistics (NCES). Institutions of higher education are requir ed by law to participate in IPEDS annual surveys conducted by NCES (National Center f or Education Statistics, 1998). Copies of these surveys are available at the follow ing World Wide Web site: www.nces.ed.gov/ipeds. In addition, institutions ma y choose to participate in annual surveys conducted by publications such as U.S. News & World Report magazine; the results from those surveys are published at the Web site www.usnews.com. Variables from the IPEDS plus a measure of admissions selecti vity from U.S. News & World Report were utilized in the multiple regression statistic al model. Most of the data for this study came from the IPEDS relational data base, including enrollment information, financial information, and graduation rates. The Survey Year that was chosen for enrollment and financial inform ation was 1998, since that Survey Year bridged the available years for graduation rat es and selectivity rates. Graduation rates were drawn from 1997, the most current data a vailable at the time the study was conducted. The IPEDS data set served as the pri mary source of cases and data for this study for a number of reasons. First, inst itutions of higher education receiving Title IV funding are required by law to participate in annual surveys such as IPEDS
4 of 23 that are conducted by NCES. Second, all but one of the variables of interest was contained in the IPEDS data. Third, IPEDS data were easily accessible to the researchers via the World Wide Web.All 513 accredited public institutions that grant a t least a baccalaureate degree were selected for this study, but a number of institutio ns containing missing data on key variables were eliminated from this study, as were several other observations with Â“outlierÂ” values on one or more variables that were markedly different from the data for the remaining observations and that threatened the assumption of normality. As a consequence, the final sample size comprised n = 444 public institutions with complete data on all variables of interest. Variabl es selected from the IPEDS relational database included enrollment information financial information, and graduation rates. Graduation rates for 1997 (the mo st recent available) were used. All other institution-level data were from 1998 (the mo st recent available at the time of the study). Additionally, 1998 most closely matched the year for which institutional selectivity data were available (1999). The selecti vity rates of undergraduate admissions were drawn from the annual data publishe d by U.S. News & World Report The variables employed in the study are described i n detail in the following section. We have endeavored to use least squares statistical models to provide a comprehensive look at the factors that help in unde rstanding the effects that higher education expenditure patterns and other institutio nal characteristics have on student success, measured as undergraduate student graduati on rates. The variables employed in this analysis include: undergraduate gr aduation rate (the dependent variable); Carnegie classification; U.S. region; de gree of urbanization; presence of a medical, dental, veterinary, or related program; se lectivity; institutional financial aid; and number of dollars allocated to each of the foll owing categories of expenditures: student affairs, instruction, library, physical pla nt, institutional support, academic support minus library, and total education and gene ral (E & G).1We have chosen to predict graduation rates from a c ombination of institutional characteristics, some of which, such as expenditure patterns, are more or less within the discretionary control of institutional leadersh ip, and some of which, such as region or historically black college or university (HBCU) status, are beyond reasonable control by institutional decisionmakers. Arguably, other characteristics, such as Carnegie classification (the data were collected un der the pre-1999 revised Carnegie rating system), to some degree may be influenced th rough the decisions taken by the senior administrators of higher education instituti ons, but remain relatively immutable without major commitments of effort and resources. Prior to data analysis, institutional allocation categories (e.g., instructional expendit ures) were transformed into Â“per studentÂ” dollar equivalents by dividing each expend iture category by the institutionÂ’s headcount enrollment. Due to its non-normal distrib ution, the institutional financial aid per headcount variable was transformed into a low-t o-high quintile ordered categorical variable.We believe this mix of institutional traits provide s a set of perspectives that promise to facilitate the understanding of what makes for a mo re successful academic institution, measured in terms of one of its ultimate productsi ts graduates. In these results there is information that may be of great use to th ose who plan and administer the process of higher education. Which is better, for t he goal of improving graduation
5 of 23 rates: increase spending by $100 per student headco unt for library expenditures, or increase spending by $10 per student headcount on s tudent affairs? Does the presence of a medical school improve an institution Â’s undergraduate graduation rate? How much does the degree of selectivity of admissio ns decisions influence the rate at which students successfully complete their studies? How are these, and other, considerations related to each other, and what trad eoffs among these alternatives are important to know about? These are among the questi ons that are addressed in the results discussed below.ResultsFull-Model Multiple RegressionIn a full-model multiple regression, graduation rat e (GRAD) was predicted by a combination of categorical and continuous predictor s. The categorical predictors include: Carnegie classification (CARNEGIE), region (REGION), the presence (coded 1) or absence (coded 0) of a medical, dental, veter inary, or other similar school (MEDICAL), whether the institution is (coded 1) or is not (coded 0) an historically black college or university (HBCU), and quintiles o f institutional financial assistance (IFA5). The model also incorporates interactions be tween MEDICAL and URBAN and between REGION and MEDICAL. The continuous predicto rs include degree of urbanization (URBAN), selectivity of admissions (SE LECT), expenditures on student affairs per student headcount (SAFEXP), instruction al expenditures per student headcount (INSTEXP), library expenditures per stude nt headcount (LIBEXP), expenditures on physical plant per student headcoun t (PPLEXP), institutional support per student (INSTIEXP), total education and general expenditures per headcount (EGEXP), and academic support minus library expendi tures per student headcount (NOTLIB).In subsequent tables, the predictive validity of mo dels overall is evaluated by: the value of the coefficient of determination (R2), which measures the proportion of total variation in the dependent variable associated with or Â“explained by,Â” variation in the complete set of predictor variables; adjusted R2, an index of the proportion of dependent variable variance explained relative to t he mean squared error and the number of degrees of freedom for model and error, w hich may assume a negative value for ill-fit models; and the F-value formed fr om the ratio of estimated model variance to estimated error variance, where a large r F-ratio implies a Â“strongerÂ” model, and its associated p-value. The validity of the separate predictor variables included in each model is ascertained from: a parti al F statistic and its associated p-value; eta-squared, (eta)2, which is the proportion of the total variability in the dependent variable accounted for by that independen t variable; and by observed power, or the probability of correctly determining that there is a real effect attributable to that model component. Larger values of eta-squar ed indicate stronger model predictors, but often are modest (less than .10). L arger values of power (maximum of one) indicate a greater likelihood of that particul ar predictor having a genuine effect on the dependent variable.The assumption of equal error variances is satisfie d, as measured by LeveneÂ’s test ( F = .966, df1 = 175, df2 = 268, p = .596). The model provides a reasonably strong fi t to
6 of 23 the data, as measured by the coefficient of determi nation ( R2 = .588) and the adjusted coefficient of determination (adjusted R2 = .550). As shown in Table 1, the following full-model effects were significant: CARN EGIE ( F = 5.765, p < .001, (eta)2 = .090, power = .999), MEDICAL ( F = 5.793, p = .017, (eta)2 = .014, power = .670), HBCU ( F = 18.663, p < .001, (eta)2 = .044, power = .991), IFA5 ( F = 4.588, p = .001, (eta)2 = .043, power = .945), URBAN ( F = 22.266, p < .001, (eta)2 = .052, power = .997), SELECT ( F = 15.911, p < .001, (eta)2 = .038, power = .978), INSTEXP ( F = 5.867, p = .016, (eta)2 = .014, power = .676), LIBEXP ( F = 26.523, p < .001, (eta)2 = .061, power = .999), NOTLIB ( F = 4.844, p = .028, (eta)2 = .012, power = .593), and the MEDICAL*URBAN interaction ( F = 4.905, p = .027, (eta)2 = .012, power = .593). The assumption of normality in the dependent variab le also is satisfied following deletion of outliers identified by model residuals.Table 1 Full Model Multiple Regression Tests of Between-Sub jects Effects for Predicting Graduation RatesSourceType III Sum of Squares df MeanSquare Observed F p > F (eta)2Power Corrected Model64384.510371740.12215.662<.001.5881. 000 Intercept3133.17113133.17128.200<.001.0651.000CARNEGIE4483.7567640.5375.765<.001.090.999REGION614.927787.847.791.595.013.342MEDICAL643.5891643.5895.793.017.014.670HBCU2073.61812073.61818.663<.001.044.991IFA52039.1594509.7904.588.001.043.945URBAN2473.89712473.89722.266<.001.052.997SELECT1767.81611767.81615.911<.001.038.978SAFEXP12.268112.268.110.740<.001.063INSTEXP651.8861651.8865.867.016.014.676LIBEXP2946.91512946.91526.523<.001.061.999PPLEXP286.7851286.7852.581.109.006.361INSTIEXP11.977111.977.108.743<.001.062EGEXP89.049189.049.801.371.002.145NOTLIB538.1481538.1484.844.028.012.593
7 of 23 MEDICAL*URBAN544.9851544.9854.905.027.012.599REGION*MEDICAL1217.5967173.9421.566.144.026.653Error45109.257406111.107Total889697.070444Corrected Total109493.767443 The adjusted, or estimated, marginal means for inst itutions at different levels of the Carnegie classification scale (CARNEGIE) are presen ted in Table 2. These results adjust for region, presence of a medical or related component, whether the institution is an HBCU, institutional student financial support urbanization, selectivity, and the indicated measures of expenditures, as well as the interactions of MEDICAL*URBAN and REGION*MEDICAL. There is a general, and nearly monotonic, decline in mean graduation rates as Carnegie classification varies from Research I (the most prestigious by that measure of external research fu nding acquired) to BachelorÂ’s II, although there is little difference between Researc h II and Doctoral I mean graduation rates and little difference in the mean graduation rates for Doctoral II, MasterÂ’s I, and MasterÂ’s II institutions.Table 2 Estimated Marginal Mean Graduation Rates by Carnegi e Classification (CARNEGIE) from Full Model Multiple Regression95% Confidence Interval CarnegieClassification Mean StandardError LowerBound UpperBound Research I49.2272.93043.46754.987Research II42.0052.70536.68647.323Doctoral I41.9482.80236.44047.455Doctoral II35.6832.51730.73440.632MasterÂ’s I36.7511.88633.04340.458MasterÂ’s II36.0393.13329.88042.199BachelorÂ’s I32.7595.08322.76642.752BachelorÂ’s II26.8122.70821.48832.136 Note: The marginal means reported here are evaluate d at the means of the following covariates that appeared in the model: URBAN (degre e of urbanization) = 3.45, SELECT (selectivity = percentage of admissions appl ications accepted/applications received) = 75.187, SAFEXP (student affairs expendi tures per student headcount) = 728.2527, INSTEXP (instructional expenditures per s tudent headcount) = 4282.9243, LIBEXP (library expenditures per student headcount) = 360.4734, PPLEXP (physical plant expenditures per student headcount) = 925.997 7, INSTIEXP (institutional support per student headcount = 1156.2228, EGEXP (e ducational and general
8 of 23 expenditures per student headcount) = 11556.6124, N OTLIB (academic support minus library expenses, per student headcount) = 70 7.3338. Pairwise multiple comparisons (using FisherÂ’s Least Significant Difference method (Howell, 2002) confirm that the estimated mean grad uation rate for the most prestigious institutions, measured by the Carnegie classification (Research I), is significantly greater than the estimated mean gradu ation rates for the institutions in each of the other Carnegie classifications. Similar ly, the multiple comparison results demonstrate that the estimated mean graduation rate for the least prestigious Carnegie classification institutions (BachelorÂ’s II ) is significantly lower than the estimated mean graduation rates for all other categ ories of institutions other than those at the BachelorÂ’s I level. Other pairwise dif ferences in estimated mean graduation rates are found for Carnegie classificat ion as expected from the rankings of the group means.Estimated marginal mean graduation rates by region are shown in Table 3. Although there is no significant effect of regional variatio n in the model, it is noteworthy that estimated mean graduation rates are highest in New England (44.086%) and the Mid-East (41.005%) and lowest in the Plains (34.679 %) and Rockies (35.021%). Pairwise multiple comparisons of regions show no si gnificant differences, consistent with the finding of no overall effect of region in the full regression model including interactions. (Note 2)Table 3 Estimated Marginal Mean Graduation Rates by Region (REGION) from Full-Model Multiple Regression95% Confidence Interval Region of U.S.Mean StandardError LowerBound UpperBound New England44.0865.90432/47955.693Mid-East41.0053.02335.06246.949Great Lakes36.6262.25132.20141.052Plains34.6792.53129.70339.656Southeast36.8531.82033.27540.431Rockies35.0214.30326.56243.481Southwest37.5603.22731.21643.904West Coast35.3922.89429.70441.080 Note: The marginal means reported here are evaluate d at the means of the following covariates that appeared in the model: URBAN (degre e of urbanization) = 3.45, SELECT (selectivity = percentage of admissions appl ications accepted/applications received) = 75.187, SAFEXP (student affairs expendi tures per student headcount) = 728.2527, INSTEXP (instructional expenditures per s tudent headcount) = 4282.9243, LIBEXP (library expenditures per student headcount) = 360.4734, PPLEXP (physical
9 of 23 plant expenditures per student headcount) = 925.997 7, INSTIEXP (institutional support per student headcount = 1156.2228, EGEXP (e ducational and general expenditures per student headcount) = 11556.6124, a nd NOTLIB (academic support minus library expenses, per student headcount) = 70 7.3338. In the full model including interactions, instituti ons with a medical, dental, veterinary, or similar component had a significantly lower esti mated mean graduation rate (37.065%) than did institutions without such a comp onent (38.241%). HBCUs had an estimated mean graduation rate of 32.877%, signific antly less than the 42.429% result for non-HBCUs. The statistically significant differences in estimated mean graduation rates among quintiles of institutional f inancial assistance (IFA) range from 42.740% for the top quintile (level 5) to 33.665% f or level 2, with intermediate values for level 1 (38.223%), level 4 (37.636%), and level 3 (36.001%). The significant interaction between MEDICAL and REGION is amplified by the range in estimated mean graduation rates from a low of just 31.708% fo r institutions with medical schools or similar components in the Plains to a high of 47 .374% for New England institutions with medical schools or similar components.Independent Bivariate Regression ResultsThe results reported above are based on the full mu ltiple regression model. Determining how the independent variables employed in the full model play out on their own is important, because the chief consequen ce of including a large number of independent variables in a prediction model is to e nhance the likelihood that the effect of each predictor may be masked (either enha nced or attenuated) by intercorrelations with other predictors. By examini ng the individual effects of each predictor within the overall analysis we can look f or inconsistencies that might confound interpretations based on the full model.IFA5 has a significant individual effect on graduat ion rates ( F = 3.288, p = .011), although the proportion of variance explained is mo dest ( R2 = .029). Mean graduation rates are 43.103% for level 1, 37.740% for level 2, 39.900% for level 3, 44.097% for level 4, and 44.769% for level 5.The independent effect of REGION is significant ( F = 6.247, p < .001, R2 = .091). Mean graduation rates were 44.369% for New England, 48.626% in the Mid-East, 42.887% in the Great Lakes, 40.074% for the Plains, 40.056% in the Southeast, 36.618% for the Rockies, 32.134% for the Southwest, and 47.712% on the West Coast.Urbanization (URBAN) alone does not have any indepe ndent relationship with graduation rates ( F = 0.035, p = .808, R2 < .001). Separately, MEDICAL is a significant independent pr edictor of graduation rates ( F = 52.459, p < .001, (eta)2 = .106), although not a particularly good predicto r ( R2 = .106). Institutions without a medical, dental, veterinary, or similar component had a markedly lower mean graduation rate (39.878%), compared to i nstitutions with such a component (54.736%).By itself, the fact that an institution is an HBCU has a statistically significant effect on
10 of 23 graduation rates ( F = 18.231, p < .001), although the effect size is relatively mo dest ( R2 = .040). The mean graduation rate for students at a non-HBCU (42.848%) is over 10 percentage points greater than the corresponding result for students attending HBCUs (31.397%). This comparison is confounded by t he fact that HBCUs are not found at all Carnegie levels for the institutions s tudied in this analysis, so we also compared only those HBCU and non-HBCU institutions that share the same Carnegie rating, to provide a fairer and more nuanced apprec iation of the role played by HBCUs in higher education. This refined analysis ag ain demonstrates a significant difference in mean graduation rates between HBCU an d non-HBCU institutions at comparable Carnegie levels ( F = 9.101, p = .003). However, the magnitude of this effect ( R2 = .027) is less than for comparing HBCUs against a ll non-HBCU institutions, and is substantially less than the ef fect size for other elements of the model. There is about a 7-percentage-point advantag e in mean graduation rates for students not attending an HBCU (38.440%, compared t o 31.397% for students attending an HBCU).Institutional selectivity in undergraduate admissio ns (SELECT) is significantly related to graduation rates ( F = 43.825, p < .001, R2 = .090). However, the less than overwhelming effect of admissions selectivity on gr aduation success is shown by the finding that admitting one percentage point more of those who apply for undergraduate admission results, on average, in a d ecline of .295 percentage point in graduation rates. Presumably, greater selectivity i s associated with institutions having more rigorous standards that students find difficul t to negotiate; similarly, the incremental students admitted under less restrictiv e criteria are likely to be more marginal academically and thus less likely to gradu ate. By itself, Carnegie classification level (CARNEGIE) has a very pronounced ( F = 24.905, p < .001, R2 = .286) effect on graduation rates. Not considerin g the effects of any other variables employed in the full regression model, the institutions at each Carnegie classification have the mean graduation ra tes shown in Table 4. Comparing unadjusted (Table 4) and adjusted means (Table 2) s hows the sensitivity of our estimates to the specification of the model and to the effects of the other predictor variables. The effect of the other predictors in th e model is evident for the institutions at higher Carnegie classification levels. For examp le, the unadjusted mean graduation rate for Research I institutions (60.247 %) is much lower (49.227%) after adjusting for the other circumstances measured in o ur model, and so is the unadjusted marginal mean graduation rate (52.567%) much higher than the adjusted (32.759%) mean for BachelorÂ’s I institutions. Simil arly, the unadjusted rate of 52.465% for Research II institutions is lowered to 42.005% by controlling for the other predictors. Less dramatic reductions occur in adjus ted, compared to unadjusted, graduation rates for Doctoral I (from 47.115% to 41 .948%), Doctoral II (from 38.997% to 35.683%), MasterÂ’s I (from 38.348% to 36.751%), MasterÂ’s II (from 39.241% to 36.039%), and BachelorÂ’s II (from 32.384% to 26.812 %) institutions. The differentially higher actual (unadjusted) compared to adjusted gra duation rates are most evident for relatively more prestigious institutions (that is, Carnegie Research I and Research II classifications), moderated greatly for Doctoral I, Doctoral II, and MasterÂ’s I classifications, dramatically higher for BachelorÂ’s I institutions, and again moderated for BachelorÂ’s II institutions.Table 4
11 of 23 Mean Graduation Rates by Carnegie Classification (C ARNEGIE), Unadjusted for Other PredictorsCarnegieClassification Mean Standard Deviation Number ofInstitutions Research I60.24714.24955Research II52.46512.24126Doctoral I47.11515.88127Doctoral II38.99713.32335MasterÂ’s I38.34812.995220MasterÂ’s II39.24114.39517BachelorÂ’s I52.56712.1216BachelorÂ’s II32.38413.16858Total41.91915.721444 INSTEXP, instructional expenditures, have a pronoun ced effect on graduation rates ( F = 207.616, p < .000), and substantial explanatory power ( R2 = .320, adjusted R2 = .318). An increase of 10% in mean instructional exp enditures (i.e., an additional $428.29) per student headcount, on average, leads t o an increase of 1.99 percentage points in graduation rates, assuming a linear relat ionship. Expenditures on physical plant per student headcount (PPLEXP) also are signi ficantly related to graduation rates ( F = 58.778, p < .001), but this variable independently contribut es modestly to explained variance in graduation rates ( R2 = .117). On average, an increase of 10% in mean per student headcount spending on physical plant (an additional $92.60) Â“buysÂ” 1.07 percentage points of higher graduation rates. INSTIEXP, institutional support, similarly has a significant ( F = 38.437, p < .001), but not very potent ( R2 = .080) independent impact on graduation rates. An in crease of 10% in mean institutional support per student headcount ($115.6 2) results in an increase, on average, of 0.83 percentage points in graduation ra tes. The level of student affairs expenditures (SAFEXP) is a significant independent predictor of graduation rate ( F = 29.828, p < .001), with rather modest explanatory power ( R2 = .063). On average, each additional 10% per student headcount spent on student affairs ($72.83) results in an increase in graduation rates of about 0.89 pe rcentage points. Library expenditures (LIBEXP) provide a very robust and sta tistically significant explanation of graduation rates ( F = 230.422, p < .001, R2 = .343). Every 10% per student headcount increase in library expenditures ($36.05) results, on average, in an additional 1.77 percentage points of graduation rat es. Total education and general expenditures (EGEXP) has a potent independent impac t on graduation rates ( F = 186.535, p < .001, R2 = .297). On average, an additional 10% in mean EGE XP ($115.66) is associated with an extra 0.16 percenta ge point in graduation rates. Finally, NOTLIB, academic support minus library exp enditures per student headcount, is a reasonably good independent predictor of gradu ation rates ( F = 115.490, p <
12 of 23 .001, R2 = .207). Higher values of NOTLIB are significantly more likely than lower values of NOTLIB to result in higher graduation rat es. On average, an extra $100 of spending on non-library academic support expenditur es per student is associated with a 0.98 percentage point increase in graduation rates. Based on these results, the best Â“payoffsÂ” in highe r graduation rates from strategically targeted institutional budgetary enhancements would seem to come from increasing per student expenditures for instruction (+1.99 per centage points), followed closely by library (+1.77) and more distantly by physical plan t (+1.07) and nonlibrary academic (+0.98). In a lower tier of impact are student affa irs (+0.89) and institutional support (+0.83). Lagging far behind is education and genera l (+.16). However, these findings do not control for the simultaneous effects of chan ges in each expenditure category (and the often high correlation of any one budget c ategory with another, leading to collinearity among the budgetary predictors and att enuated partial regression coefficients) together with other effects that are captured in the full model. In the full model, for the same benchmark 10% per student headc ount increase in any one expenditure category, the net effects of greater sp ending on physical plant (-0.28) and education and general (-0.36) actually are negative and the greatest Â“payoffÂ” is attributable to enhanced expenditures on library (+ 0.92) and instruction (+0.80), with only modest contributions from increased nonlibrary academic (+0.27) expenditures and very minimal improvements from heightened spend ing for institutional support (+0.05) and student affairs (+0.05).Hierarchical ModelsA further check on the validity of our results is p rovided by analyzing the patterns of relationships between the predictor variables and g raduation rates in hierarchical stages of model building. Stage 1 estimates graduat ion rates from three institutional Â“demographicÂ” variables (REGION, HBCU, and URBAN) t hat are historically-determined traits beyond the control o f current higher education decisionmakers. For Stage 2, to these three predict ors are added institutional characteristics that are more likely to be controll ed by longer-range actions taken by the institutionÂ’s decisionmakers (CARNEGIE, MEDICAL IFA5, and SELECT) with the interactions of MEDICAL with URBAN and of REGION wi th MEDICAL. Finally, Stage 3 adds the set of expenditure variables that more p roximally are under the control of institutional leaders as they set annual budget and policy priorities: SAFEXP, INSTEXP, LIBEXP, PPLEXP, INSTIEXP, EGEXP, and NOTLI B. The Stage 3 results are the same as those for the full multiple regress ion model shown in Table 1. Table 5 summarizes the fit of each stage of the model, sh owing the partial F statistic and accompanying p-value ( p > F ) testing the significance of each predictor, (eta)2, and the power of each parameter estimate, as well as ov erall model F statistics, p -values, R2, and adjusted R2.Table 5 Summary of Hierarchical Multiple Regression Model R esults
13 of 23
14 of 23 The results in Table 5 provide evidence of the pred ictive validity of each stage, or set, of predictors. The three Stage 1 institutional demo graphic variables collectively are significant predictors, accounting overall for 13.3 % of the variation in graduation rates, and both REGION and HBCU are significant individual ly. The Stage 2 combination of institutional traits with the Stage 1 predictors ar e significant collectively, accounting for a combined 52.3% of the variation in graduation rat es, with REGION, HBCU, URBAN, CARNEGIE, IFA5, and SELECT significant individually A partial F-test demonstrates that the added institutional characteristic predict ors contribute significantly ( F = 9.29, p < .01) to explaining graduation rates beyond what is accounted for by the Stage 1 variables. Adding the financial variables in Stage 3 to the previous sets of predictors results in greater explanatory power ( R2 = .588), which is a significant improvement over both the Stage 1 (partial F = 8.14; p < .01) and the Stage 2 (partial F = 8.09, p < .01) sets of predictors. That is to say, the instit utional financial information makes a major contribution to our understanding of what dri ves graduation rates beyond what we know from institutional demographics and other i nstitutional characteristics. Table 5 also shows that the institutional characteristics variables added in Stage 2 are by themselves (without interactions, which cannot be e stimated separately here because they require the URBAN and REGION variables in Stag e 1) significant predictors of graduation rates ( F = 21.493, p < .001), as are the financial variables added in S tage 3 ( F = 38.702, p < .001). In addition, the explanatory power of the financial variables alone ( R2 = .383, adj R2 = .373) roughly equals that of the institutional c haracteristics variables alone ( R2 = .394, adj R2 = .376). Each of these additional sets of predicto rs considerably outweighs the explanatory power of the institutional demographics from Stage 1 ( R2 = .133, adj R2 = .115).LimitationsSeveral limitations to this study must be acknowled ged. First, the study was framed with reference to public accountability for resourc es and student success. Consequently, data were analyzed from public higher education institutions only. While this decision allowed us to examine character istic patterns of these institutions more closely by focusing the analysis and interpret ation, the important private sector of higher education in the United States nonetheles s was omitted from this analysis. The conclusions and recommendations therefore are a pplicable only to public colleges and universities. It is unclear whether or how these findings would apply to private institutions of higher education.Second, although this study focuses on student succ ess in terms of graduation rates, it is important to note that this study reveals lit tle about the qualities of student-level experiences (Tinto, 1998) that also certainly influ ence graduation rates. Numerous other considerations, such as the nature of educati onal environments, the quality of student/instructor interactions, and studentsÂ’ use of available resources, reveal the more subtle finer points of successful educational experiences. This study addresses these issues only obliquely, through its focus on t he deployment and allocation of institutional financial resources that enable provi sion and/or enhancement of the educational experience.Third, institutional expenditure categories were co mpared across institutions, and expenditure categories were aggregated broadly in t he original IPEDS data set.
15 of 23 Although the outcome variable here is undergraduate graduation rates, it was not possible to distinguish amounts expended on graduat e programs and graduate students from those related to undergraduate progra ms and undergraduate students. Incorporating Carnegie classification into the anal ysis represented a partial control for this lacuna since the Carnegie classification syste m is based partly on the existence and scope of graduate programs, but the internal al locations of institutions for undergraduate and graduate purposes were not availa ble. A related limitation is the inability to disaggrega te financial aid data into separate expenditures on undergraduate and graduate/professi onal education using the IPEDS database. Consequently, it is impossible to determi ne the extent to which financial aid is awarded to undergraduate or graduate students. P resumably, institutional financial aid awarded to undergraduate students could be Â“mer it-based,Â” meaning that it is used to encourage enrollment by rewarding talent an d therefore is seen as a way of positively connecting students to their college or university (see Astin, 1993). Graduate student aid could include fee remissions o r other forms of aid that presumably have different purposes, but it is not p ossible to disaggregate the IPEDS financial aid data in this manner.Finally, cross-sectional data from one year (1997-9 8) were used in this analysis. This study thus provides a snapshot of a single yearÂ’s a llocations and expenditures across a large number of institutions. Although revenues a nd allocations generally remain constant with the exceptions of incremental adjustm entsa common form of budgeting (Dickmeyer, 1996; Woodard & von Destinon, 2000)a longitudinal design would be needed to account for multiple-year trends or changes in expenditure patterns, to test the long-range applicability and stability of this model over a period greater than one year.Discussion, Conclusions, and ImplicationsAs is clear from the findings above, not all catego ries of variables affect graduation rates equally. The institutional demographic variab les contributing to a prediction of higher graduation rates were: higher status within the Carnegie classification system; the presence of a medical, dental, or veterinary pr ogram; a more urbanized location; and a lower percentage of applicants admitted. The MEDICAL and URBAN variables combined to produce an interactive effect on gradua tion rates. However, many of these variables represent characteristics or condit ions over which institutions have little to no control.Characteristics such as regional location are more or less fixed features of an institution. Mission (e.g., inclusion of a medical, dental, or veterinary program; admissions selectivity) and Carnegie classification represent characteristics that could be affected (and likely have been affected, as many of these institutions have Â“climbedÂ” the Carnegie Â“ladderÂ”) through institutio nal and political processes. However, these characteristics are not highly or re adily malleable. Additional variables in the model, however, represent decision points that are more readily subject to policy discussions and institutional dec isionmaking, and may represent promising levers for institutional decisionmakers o r external policymakers. There are important differences among public institutions at different Carnegie levels (Winston, Carbone, & Lewis, 1998, pp. 21-22) in their ability to accommodate the recent trend of privatizing public sector education through the withdrawal of public support in the
16 of 23 face of growing enrollments: The strongest schools were apparently able both to discourage enrollments, husbanding their subsidy resources, an d to raise net tuitions, increasing their share of costs borne by their stud entsÂ’ tuition income. The poorest schools were protected, in contrast, by a p ublic policy that maintained their subsidies, allowing them to get by with modest sticker price increases that they used largely to increase financial aid. Relative prices changed to make the poorer schoolsÂ—the Two-y ear Colleges prominent among themÂ—a lot better bargain. The midd ling schoolsÂ—the public Comprehensive Universities and Liberal Arts CollegesÂ—were caught, absorbing large increases in enrollments wi th large reductions in subsidy resources so that their efforts to shift co sts to their students werenÂ’t enough to prevent large reductions in educa tional quality. For example, the provision of institutional financi al aid was a statistically significant component of the model and modestly affected gradua tion rates. However, the relationship was not linear since higher graduation rates were associated with the lowest and with the two highest quintile measures o f financial aid (a marginal mean graduation rate of 38.026% for the firstÂ—that is, l owestÂ—quintile, with per student headcount support of $54 or less; and 37.354% for t he fourth, and 42.521% for the fifthÂ—highestÂ—quintile, or a range of over $378 per student headcount). Institutions that can do so may wish to consider investing addit ional institutional monies in student financial support, but modest amounts of st udent financial support for institutional dollars are not associated with highe r graduation rates. Of the institutional expenditure categories include d in the model, instructional, library, and academic support minus library expenditures wer e significantly related to graduation rates in the full model. These variables also had the greatest independent effects on graduation, and each explained between 2 1% and 34% of the variance in graduation rates when analyzed as sole predictors. The robustness of these variables singly as well as in the broader model supports the importance of funding instruction and academic support budgets. It is important to no te, however, that the ultimate nature of the expenditures and any separate impacts remai n unclear. For example, the largest proportion of instructiona l expenditures clearly is salaries and benefits for instructional personnel. Due to th e aggregate nature of the data, it is not possible to comment on relationships between va rious levels of instructional personnel and graduation rates. Furthermore, such a n analysis would have to be planned carefully to incorporate the levels of cour ses and students typically taught by, say, full professors versus adjunct instructors.As another example, library resource allocations ma y be expended disproportionately on digital technology and information retrieval sys tems rather than on periodical subscriptions and book purchases. In such cases, it is not possible to separate the effects of traditional library resources on graduat ion rates from the effects of advanced technological resources that libraries on many campuses increasingly house. Nonetheless, higher library allocations and instructional expenditures are associated strongly with higher student graduation rates. As mentioned in the discussion of independent effects above, expenditur es on student affairs is a significant independent predictor of graduation rat es, but its effects are negligible when analyzed as one variable within the context of the full model.
17 of 23 One issue that arose in the course of our data anal ysis is related to the higher graduation rates among undergraduates at institutio ns representing higher Carnegie classification levels. It is somewhat puzzling that undergraduate students succeeded at higher rates at research-oriented institutions t han at colleges and universities with prevailing emphases on undergraduate education, as indicated by institutional mission and espoused purpose. Graduation rate is no t the sole outcome indicator of studentsÂ’ success; stopping in and out to take cour sework that satisfies individual studentsÂ’ needs also constitutes a successful educa tional experience for many undergraduates. Additionally, however, among input characteristics, more selective admissions is associated with higher Carnegie ratin gs, suggesting that academically better-prepared students are more likely to attend research, rather than baccalaureate, institutions. It also may be the cas e that research-oriented institutions are better positioned financially to offer resource -rich environments that foster higherÂ—or at least more timelyÂ—graduation rates.Recommendations for Further ResearchIn addition to contributing empirical findings, thi s study provides a framework for institutional planners and representatives of state systems of higher education for incorporating questions of resource allocation into strategic thinking about undergraduate persistence to degree attainment. Ins titutional planners, as well as various campus units, can use these findings to sup port their cases for dedicated or increased funding. For example, an institutionÂ’s de clining rank on a national survey of libraries may be seen mostly as an unfortunate cond ition, but evidence of a predictive relationship between library allocations and underg raduate graduation rates can help connect the need for increased library funding with an institution-wide goal of student retention. When significant new monies are not like ly to be realized from state appropriations, this study also can provide guidanc e for fund-raising priorities and targeted capital campaigns. Conversely, however, th e results from this model also may provide guidance for strategic budget reduction s, as institutional planners will be better able to determine the implications for gradu ation rates of selective allocation reductions.Institutional planners wishing to implement insight s from this research are not likely to have infusions of new monies with which to do so. I t may be decided instead to load dollars disproportionately into strategically defin ed categories, but this represents a balancing of resource allocations among several cat egories; gains and losses affect other categories as allocations are shifted and red istributed. It is not clear how shifts and reallocations in some categories will affect st udent graduation rates, nor whether there perhaps is a marginal or threshold proportion of funding that, if not realized or exceeded, is necessary for budgetary categories unr elated to graduation rates. Further research can pursue these questions and pro vide more targeted guidance to institutional planners and to policy and budget ana lysts. In general, better information for planners will make them more likely to attain b enchmarks through thinking strategically about obtaining and spending funds. I n this context, it is appropriate to consider that, based on analysis of IPEDS data, eco nomic disparities among institutions and their students are increasing (Win ston, 2000). Finally, this study can provide useful guidance for interpreting academic work to various publics, such as legislative bodies or medi a representatives. It can be unclear
18 of 23 whether or how an institutionÂ’s financial decisions are related to desirable outcomes such as graduation rates. This study can assist, by demonstrating connections between institutionsÂ’ accountability for their stew ardship of public resources and the larger good that is served by strategic allocation of resources to support the goal of student graduation and other aspects of the institu tionÂ’s mission. Institutional decisionmakers may be better able to decide where t o make budget cuts and to make more finely-tuned determinations of the tradeoffs a nd other consequences of such budgetary reallocations across areas of university activity (e.g., Kissler, 1997). Further research can focus on examining private col leges and universities or incorporating additional variables that will enable reasonable comparisons between public and private higher education institutions. A dditional research also will be necessary to see whether the revised Carnegie class ification system is similarly useful, in conjunction with other variables, in exa mining student graduation rates. Longitudinal research also is warranted to test thi s analysis across time. This study combined data from two sources to analyze one yearÂ’ s worth of data, but it remains unclear how patterns of resource allocation decisio ns spanning a number of years may affect graduation rates or provide additional i nsights into how such decisions relate to student graduation. Finally, HBCU status and admissions selectivity warrant much more study. Each of these variables presented much more complexity than was expected initially, and the role that each plays in graduation rates is accounted for only partly in this model in conjunction with the o ther variables that were selected. Additional variables that were not available for th e data set employed in this analysis may be useful in future research. Disaggregating in stitutional expenditure and financial aid data into separate undergraduate and graduate components would be extremely useful for predicting undergraduate gradu ation rates. Also, it remains to be seen what differences in ability to predict graduat ion rates will emerge from any further revisions in the Carnegie classification sy stem. Furthermore, within the current Carnegie classification system, it would be informa tive to include private institutions, to assess whether these findings are unique to publ ic institutions. We have no direct measures of socioeconomic status at the institution al level, although future research may find it productive to employ measures of studen t eligibility for financial aid such as percentage of students eligible for Pell grants. In addition, a measure of the extent to which a campus is residential would be informati ve, particularly regarding the allocation of institutional costs for on-campus stu dent support. Future research may be guided, too, by the reality that many of the significant predictors in this analysis involved variables that were not directly controllable by institutional administrators. Institutional locatio n and type are not changed easily, if at all, and selectivity is difficult to change in the short run particularly in public institutions owing to legal requirements to admit a wide range o f in-state high school graduates. Nonetheless, our results suggest that controllable variables such as student financial aid, instructional expenditures, library expenditur es, and nonlibrary academic support expenditures exert major influence over graduation rate outcomes. An elaboration of these controllable aspects of institutional realiti es, perhaps fortified by exemplary case studies, would provide valuable additional per spectives on what institutional officers and public decisionmakers can do to influe nce the rate at which students successfully complete their undergraduate studies.Notes
19 of 23 The authors wish to thank Professors Don Hossler an d George Kuh for their thoughtful reviews of an earlier draft of this manu script. 1. The categories listed preceding E&G are included within the E&G total, but E&G also contains other categories of expenditures (suc h as auxiliary enterprises) that were not included in this analysis. Thus, including E&G in our model does not produce exact collinearities with its constituent v ariables that are included in the same model.2. It is important to note that, although the effec t of REGION is not significant in this full model, there are significant differences in es timated mean graduation rates attributable to REGION ( F = 5.134, p < .001, (eta)2 = .080, power = .998) when the two interactions are removed from the full model. C learly, the effect of REGION in the full model containing interactions is attenuated in particular by the interaction with MEDICAL. In the alternative non-interaction model, URBAN is significant ( F = 25.782, p < .001, (eta)2 = .059, power = .999). There are no other major ch anges between the interaction model results shown in Table 1 and the alternative model without interactions ( R2 = .571, adjusted R2 = .541; see Table 5). For the model lacking interaction effects, the estimated marginal means a re also highest for the Mid-East (43.632%) and New England (38.898%), followed by th e Great Lakes (36.995%), Southeast (36.057%), West Coast (35.400%), Plains ( 35.232%), Rockies (32.790%), and Southwest (30.530%).ReferencesAstin, A. W. (1997). How Â“goodÂ” is your institution Â’s retention rate? Research in Higher Education, 38 (6), 647-658. Balderston, F. E. (1995). Managing todayÂ’s university (2nd ed.). San Francisco, CA: Jossey-Bass.Berger, J. B., & Braxton, J. M. (1998). Revising Ti ntoÂ’s interactionist theory of student departure through theory elaboration: Examining the roles of organizational attributes in the persistence process. Research in Higher Education, 39, 103-119. Brinkman, P. T., & Morgan, A. W. (1997). Changing f iscal strategies for planning. In M. W. Peterson, D. D. Dill, L. A. Mets, & Associate s, Planning and management for a changing environment (pp. 288-306). San Francisco, CA: Jossey-Bass. Clotfelter, C. T. (1996). Buying the best: Cost escalation in elite higher education Princeton, NJ: Princeton University Press.Dickmeyer, (1996). Budgeting. In D.W. Breneman, L. L. Leslie, & R. E. Anderson (Eds.), ASHE reader on finance in higher education (pp. 539-561). Needham Heights, MA: Simon & Shuster.Elkins, S. A., Braxton, J. M., & James, G.W. (2000) TintoÂ’s separation stage and its influence on first-semester college student persist ence. Research in Higher Education, 41 (2), 251-268.
20 of 23 Hansen, W. L., & Stampen, J. O. (1996). The financi al squeeze on higher education institutions and students: Balancing quality and ac cess in financing higher education. In D. W. Breneman, L. L. Leslie, and R. E. Anderson (Eds.), ASHE reader on finance in higher education (pp. 291-302). Needham Heights, MA: Simon & Schust er. Haworth, J. G., & Conrad, C. F. (1997). Emblems of quality in higher education. Needham Heights, MA: Allyn & Bacon.Howell, D. C. (2002). Statistical methods for psychology (5th ed.). Pacific Grove, CA: Duxbury.Kissler, G. R. (1997). Who decides which budgets to cut? Journal of Higher Education, 68 (4), 427-459. McLaughlin, G. W., Brozovsky, P. V., & McLaughlin, J. S. (1998). Changing perspectives on student retention: A role for insti tutional research. Research in Higher Education, 39 (1), 1-17. Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999 ). Predicting the retention of university students. Research in Higher Education, 40, 355-371. National Center for Education Statistics. (2000). Digest of education statistics, 1999 Washington, DC: United States Department of Educati on. National Center for Education Statistics. (1998). Integrated postsecondary education data system finance survey FY 1998 Washington, DC: United States Department of Education.Pascarella, E. T., & Terenzini, P. T. (1998). Study ing college students in the 21st century: Meeting new challenges. The Review of Higher Education, 21, 151-165. Peterson, M. W. (1999). Analyzing alternative appro aches to planning. In M. W. Peterson (Ed.), Planning and institutional research (pp. 11-49). Needham Heights, MA: Pearson.Tinto, V. (1987). Leavingcollege Chicago, IL: University of Chicago Press. Tinto, V. (1998). Colleges as communities: Taking r esearch on student persistence seriously. Review of Higher Education, 21, 167-177. Woodard, Jr., D. B., & von Destinon, M. (2000). Bud geting and financial management. In M. J. Barr, M. K. Desler, & Associat es, The handbook of student affairs administration (2nd ed.) (pp. 327-346). San Francisco: Jossey-Bass. Winston, G. (2000). Economic stratification and hierarchy among U.S. colleges an d universities. Williamstown, MA: Williams Project on the Economic s of Higher Education. DP-58. [On-line. Available: http://www.w illiams.edu/wpehe/.] Winston, G. C., Carbone, J. C., & Lewis, E. G. (199 8, March). WhatÂ’s been happening to higher education? Facts, trends, and d ata 1986-7 to 1994-95. Williamstown, MA: Williams Project on the Economics of Higher Education. DP-47.
21 of 23 [On-line. Available: http://www.williams.edu/wpehe/ .]About the AuthorsFlorence A. Hamrick Department of Educational Leadership and Policy St udies N232 Lagomarcino HallIowa State UniversityEmail: email@example.comJohn H. SchuhDepartment of Educational Leadership and Policy Stu dies N243 Lagomarcino HallIowa State UniversityEmail: firstname.lastname@example.orgMack C. Shelley, IIDepartment of Statistics and Department of Educatio nal Leadership and Policy Studies323 Snedecor HallIowa State UniversityEmail: email@example.com The World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu Editor: Gene V Glass, Arizona State UniversityProduction Assistant: Chris Murrell, Arizona State University General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, firstname.lastname@example.org or reach him at College of Education, Arizona State Un iversity, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: email@example.com .EPAA Editorial Board Michael W. Apple University of Wisconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling-Hammond Stanford University Sherman Dorn University of South Florida Mark E. Fetler California Commission on TeacherCredentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Thomas F. Green Syracuse University Aimee Howley Ohio University
22 of 23 Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute ofTechnology Patricia Fey Jarvis Seattle, Washington Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Los Angeles Michele Moses Arizona State University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven University of Auckland Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of IllinoisÂ—UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British ColumbiaEPAA Spanish and Portuguese Language Editorial BoardAssociate Editors for Spanish & Portuguese Gustavo E. Fischman Arizona State Universityfischman@asu.eduPablo Gentili Laboratrio de Polticas Pblicas Universidade do Estado do Rio de Janeiro firstname.lastname@example.orgFounding Associate Editor for Spanish Language (199 8-2003) Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.email@example.com Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx
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