Identifying Factors Influencing Engineering Student Graduation: A Longitudinal and Cross-Institutional Study
Pre-existing factors are quantitatively evaluated as to their impact on engineering student success. This study uses a database of all engineering students at nine institutions from 1987 through 2002 (a total of 87,167 engineering students) and focuses on graduation in any of the engineering disciplines. We report graduation rate as a function of years since matriculation, and determine the typical time-to-graduation. A multiple logistic regression model is fitted to each institution’s data to explore the relationship between graduation and demographic and academic characteristics. A pooled model is fitted to six institutions where a complete data set was available. High school GPA, gender, ethnicity, quantitative SAT scores, verbal SAT scores, and citizenship had significant impact on graduation. While HSGPA, SATQ were significant for all models tested, the significance of other predictors varied among institutions. These studies add to the existing body of research about factors affecting the success of engineering students.
Keywords: retention, graduation, longitudinal study, logistic regression
Improving our understanding of factors that influence retention should be useful in suggesting approaches to improving student success in engineering, and will also aid the counseling and advising of students seeking an engineering degree. Much research has focused on identifying predictors of success in college and in engineering. Astin’s study of 36,581 students indicated that the students’ academic record in high school was the best single indicator of how well they would do in college . He also indicated that there was a clear positive relationship between students’ performance on tests of academic ability (e.g., SAT) and performance in college. Astin also listed gender as useful in predicting college freshman GPA. Seymour and Hewitt  reported that the students leaving engineering were academically no different than those that remained, noting that students left for reasons relating to perceptions of the institutional culture and career aspects.
Perceptions and attitudes of engineering students have been examined in the literature. Besterfield-Sacre, Moreno, Shuman and Atman developed the Pittsburgh Freshman Engineering Attitude Survey (PFEAS) . Administering the survey at the beginning of the first semester and at either the end of the first semester or the end of the first academic year, a gender effect for responses on the pre-survey is observed. Female engineering students begin their engineering programs with lower confidence in background knowledge about engineering, their abilities to succeed in engineering, and their perceptions of how engineers contribute to society than their male counterparts. Those same female students indicated they were more comfortable with their study habits than did the male students. Differences for minority students were reported for African American vs. majority students, Hispanic vs. majority and Asian Pacific vs. majority students.
In a preceding study, Besterfield-Sacre, Atman and Shuman focused on student perceptions among three groups: students who left the freshman engineering program in good academic standing, students who left the freshman engineering program in poor academic standing, and students who stayed in engineering . They found that those who left in good academic standing had significantly different attitudes about engineering and themselves than those in the other comparison groups. Specifically, students who left in good standing began their undergraduate program liking engineering less and had a lower appreciation of the engineering profession than other students.
Zhang and RiCharde examined 462 freshmen that matriculated in the fall of 1997 . Roughly 32 percent of these students were engineering majors. They tested several cognitive, affective, and psychomotor variables to determine which ones were significant predictors of college persistence. Their logistic regression identified self-efficacy and physical fitness as positive predictors of freshman retention, while judgment and empathy were negatively associated with persistence. They reported three reasons for freshman attrition: inability to handle stress, mismatch between personal expectations and college reality, and lack of personal commitment to a college education.
Levin and Wyckoff gathered data on 1043 entering freshmen m the College of Engineering at Pennsylvania State University . They developed three models to predict sophomore persistence and success at the pre-enrollment stage, freshman year, and sophomore year. Eleven intellective and nine non-intellective variables were measured. For the pre-enrollment model, the variables best predicting success were high school GPA, algebra score, gender, non-science points, chemistry score, and reason for choosing engineering. The freshman year model identified the best predictors of retention as grades in Physics I, Calculus I and Chemistry I. In the sophomore year model the best predictors of retention were grades in Calculus II, Physics II and Physics I. They noted that predictors of retention were dependent on the students’ point of progress through the first two years of an engineering program.
Moller-Wong and Eide tracked 1151 freshman and transfer students in the College of Engineering at Iowa State University from 1990 to 1995 . They examined demographic and pre-entrance academic data, and used logistic regression models to determine the factors most important to student retention. Among academic factors, they found transfer credit, high school rank, ACT math score, number of semesters of high school physics, and number of semesters of high school social science correlated positively with retention, while the ACT composite score, number of semesters of high school English, and number of semesters of high school art correlated negatively with retention. They also found significant correlation between marital, residency and citizenship status with student attrition.
Other studies indicate the freshman year is critical. Lebold and Ward indicated the best predictors of engineering persistence were the first and second semester college grades and cumulative GPA . They also reported that students’ self-perceptions of math, science and problem-solving abilities were strong predictors of engineering persistence. In a study examining the effect of specific first-year courses taken by engineering students, Budny, LeBold and Bejdov also found a strong correlation between first-semester GPA and graduation rate .
In our study, approximately fifteen years of data for students of nine universities were used to evaluate the influence of preexisting demographic and academic factors on graduation. Many studies have examined retention and graduation of engineering students for only one or two years. This snapshot approach, while immediately informative, does not offer the power of examining predictors over time. The cross-institutional nature allows us to compare the results across the universities to find their generalizability. The longitudinal nature of our data allows study of changes over time. Multiple logistical regression techniques allow us to examine the effect of each predictor while controlling for the other variables. Through these statistical methods, we are able to compare the effects of the prefactors on graduation in engineering.
II. DATA COLLECTION
This study uses the Southeastern University and College Coalition for Engineering Education (SUCCEED) longitudinal database (LDB) to identify pre-college entrance demographic and academic factors that predict engineering students’ graduation. The LDB contains data from eight colleges of engineering involving nine universities: Clemson University, Florida A&M University, Florida State University, Georgia Institute of Technology, North Carolina A&T State University, North Carolina State University, University of Florida, University of North Carolina at Charlotte and Virginia Polytechnic Institute and State University. To protect the rights of human subjects, each university is assigned a letter that is only known by the researchers involved in the study. Throughout the paper, we examine the effects of predictors on graduation, including only those students who have had at least six years to have their graduation recorded in the dataset.
Specifically, we only include students matriculated in an engineering field between 1987 and summer 1996. This excludes students who have transferred to or from another institution, or have matriculated in another field but later entered engineering. The appropriateness of using a six-year cutoff, which is established practice, can be seen in Figure 1, where the percentage of matriculated students who have graduated by a particular year is shown.
Since the study includes data through 2002, the eight-year graduation rate includes data through the 1994 cohort, the seven-year graduation rate includes data through the 1995 cohort, and so on, whereas the three-year graduation rate would include data through the 1999 cohort. The graduation rate in engineering plateaus after six years, leveling off between 45 percent and 55 percent. Variation in the final graduation rate fluctuates within this range, but the fluctuations did not follow any consistent trend when viewed chronologically. The four-year graduation rate is strikingly small (15 percent) in contrast with the six-year graduation rate. The five-year graduation rate is still a poor estimate of the final value. Figure 1 supports the use of six-year cutoff as the most appropriate for studying graduation rates.
We study the dependence of graduation on six predictors (independent variables): ethnicity (ETHNIC), gender (GENDER), high school grade point average (HSGPA), SAT math score (SATQ), SAT verbal score (SATV), and citizenship status (CITIZEN). HSGPA, SATQ, and SATV are continuous numerical variables, while ETHNIC, GENDER, and CITIZEN are categorical variables having several levels. Specifically, ETHNIC has six levels: Black (Black), Asian (Asian), Hispanic (Hisp), Native American (NatAm), White (White) and other (Other). These six levels do not imply any scale or ranking. They are independent possible values for the ETHNIC variable. GENDER has two levels: female (Female) and male (Male). CITIZEN is divided among three levels: U.S. citizen (Citizen), U.S. resident but not citizen (ResAlien) and foreign (NRAlien).
The SAT scores have been recentered according to the College Board conversion charts , which adjust scores before 1996 to allow the uniform use of SAT scores throughout the analysis. The HSGPA scores required care, since advanced placement and other programs grade on a scale beyond [0.0, 4.0], Most institutions convert non-standard HSGPA to a 4.0 scale (on which adjustments can generate HSGPAs over 4.0). To ensure that HSGPA’s on alternate scales did not skew results, we excluded all students with HSGPA > 5.0. These students accounted for less than 5 percent of the total engineering population, so that their exclusion is unlikely to affect any of our conclusions.
Pair-wise deletion is used wherever there is missing data. In essence, any student who has a missing value on any of the predictors is excluded from the study. For most institutions, this exclusion has minimal impact on the analysis. However, a serious missing value issue involves three universities in particular. The SUCCEED LDB does not contain high school GPA information for two of the universities, and the analyses on these two universities are done without the high school GPA predictor. In addition, one of the universities does not have SAT math, SAT verbal and high school GPA, and the analyses on that university are done with only GENDER, ETHNIC and CITIZEN as predictors.
III. STATISTICAL METHODS
We are interested in the statistical relationship between the predictors and graduation in engineering. To this end, we have modeled graduation versus predictors along multiple logistic regression curves, which are suitable for analyzing data where the predictors take on continuous and/or discrete ranges of values, but the dependent variable is discrete. From these logistic analyses we are able to infer the probability of graduation as a function of each predictor, as well as the statistical significance of the results. We present an overview of the modeling procedure in this subsection, and refer to Neter et al.  for further mathematical details.
Type III analyses of effects provide the magnitude of each predictor’s effect by controlling the other predictors. In other words, the Type III effect can “strip off” the effect of other predictors and focus on the predictor under investigation. The Wald Chi-squared statistics on the predictors’ effects are reported along with a p-value. The Chi-squared test of independence, proposed by Karl Pearson in 1900, is one of the common approaches to investigating statistical dependence . It tests the null hypothesis that graduation is independent of the predictor. A large Chi-squared statistic (which corresponds to a smaller p-value) provides evidence that the null hypothesis is false. Generally a p-value smaller than 0.05 is required to reject the null hypothesis.
The Stepwise Selection Procedure is used to select predictors that effectively predict graduation. At each step, the Stepwise Selection Procedure selects the variable that has the strongest effect among the variables that have not entered the model. This process is repeated until no effects meet the 0.05 significance level for entry into the model. The Chi-squared statistics of variables that are selected by the Stepwise Selection Procedure are boldfaced in Table 1. The numbers of engineering students included in the model were, in descending order: 14084, 10080, 8829, 6396, 3043, 2023, 1182, 806, 622. We cannot associate these numbers with the university labels, because of confidentiality agreements.
The β parameters (slopes) are estimated using Maximum Likelihood Estimates. With these estimated values of slopes, we can obtain the estimates of the Odds Ratio . The estimated Odds Ratios are reported in SAS output and are based on maximum likelihood estimates as well. To understand the meaning of the Odds Ratio, consider the graduation analyses. For a continuous variable, an Odds Ratio provides the increase in the relative probability of graduation with one unit increase in the predictor. For example, for university A, the Odds Ratio estimate of graduation due to HSGPA is 3.657. This says that a given engineering student is (3.657)(0.50) = 1.8 times more likely to graduate in engineering as another engineering student whose high school GPA is 0.50 point lower. In this way, the magnitude of the Odds Ratio depends on the range of the predictor-since SATQ scores have a much greater range (typically 500-800 for SATQ in engineering) than HSGPA (typically 2.0-5.0 for engineering), the increase in Odds Ratio per point of difference in SATQ score will be small, even though SATQ may be a strong predictor. When the predictor is a categorical variable, the Odds Ratio is the ratio of probability of graduation between two levels on the categorical variable. For example, for university E, the Odds Ratio estimate for GENDER (female vs. male) is 1.507. It tells us that a female engineering student at university E is 1.507 times as likely to graduate in engineering as a male engineering student at that university. A 95 percent Wald Confidence Interval (CI) is provided for every Odds Ratio estimate. If the Wald CI does not contain 1.0, then the probability of graduation is significantly different (to the 95 percent confidence level) for the levels compared. If the Wald CI does contain 1.0, then the probability of graduation is not significantly different. The Odds Ratio analyses and 95 percent Wald CIs are reported in Tables 2A-2B.
How well does the formulated multiple logistic regression model, as a whole, account for the dependent variables’ behavior? This question is investigated by a likelihood ratio test for global null hypothesis, which is true if graduation likelihood does not depend on any of the six independent variables (β = 0). The test yields a likelihood ratio chi-square statistic for each individual university’s model. The likelihood ratio chi-square statistics, which are the analogs of the F-statistics in a linear regression model, along with the p-value are reported in Table 3. The fact that all the p-values for the likelihood ratio are χ^sup 2^ much smaller than 0.01 provides strong evidence against the global null hypothesis, indicating that the independent variables collectively predict graduation and retention at the 0.01 significance level.
The predictive efficacy of the model is examined by looking at the coefficient of determination, generalized R-square. The generalized R-square represents the amount of variance in the dependent variable explained by the independent variables. (We have used the maximum rescaled R-square, which normalizes the measure for models with binary dependent variables.) For example, an R-square of 0.2366 for university B’s graduation model says that 23.66 percent of the variance in graduation likelihood is accounted for by the independent variables in the model: HSGPA, SATQ and SATV. The R-squares are also included in Table 3. Some of these are low due to the large number of factors that affect student success, demonstrating that, while these factors contribute significantly to student success, that predicting student behaviors is more complex.
In addition to the multiple logistic regression study based on individual institution’s data, we subsequently conducted a number of follow-up analyses based on the questions raised by that study. We calculated a correlation matrix for the six predictors to examine the interdependence of the variables, which is an intuitive approach to identify multicolinearity, a problem that occurs when the independent variables are highly correlated; we re-fitted the models with second-order interaction terms between the independent variables to check the necessity to include the interactions; and we developed a single pooled model that included data from all schools having all predictors available (institutions B, C, D, E, F and G). We used a series of five dummy variables to represent the different institutions to take advantage of the large data set and compare the results from those obtained from the unpooled analyses.
IV. ANALYSIS AND RESULTS
Chi-squared test statistics on the effects of the variables are reported in Table 1 along with the p-values. For university A, HSGPA, SATQ and SATV are not included in the model due to missing values. However, GENDER and ETHNIC predict graduation with p-values generally less than 0.04, which means the probability that GENDER and ETHNIC do not predict graduation is less than 4 percent. For university B, HSGPA and SATQ predict graduation. For university C, besides HSGPA and SATQ, SATV and ETHNIC are found to be effective predictors of graduation. For university D, HGSPA, SATQ, SATV, ETHNIC and CITIZEN are found to be significant to graduation. For university E, all variables except SATV and CITIZEN are found to predict graduation. For universities F and G, all predictors are found significant to graduation. HSGPA is excluded from study in university H, and among the five remaining variables, SATQ and SATV are predictive. Finally, for university I, HSGPA was excluded from study, but SATQ, SATV and ETHNIC are predictors of graduation. While all variables are found to be significant predictors of graduation for certain institutions, HSGPA and SATQ are found to be predictors of graduation across all universities in which this data was available.
The effects of the predictors are further quantified by an estimate of the slope in the multiple logistic regression model. Odds Ratios are transformations of the β parameter estimates, which provide an intuitive view of how much students’ odds of graduation differ due to differences in the predictors. Tables 2A-2B show Odds Ratio estimates and the Wald 95 percent confidence intervals for the Odds Ratio for the significant predictors of Table 1, with blank cells indicating no statistical significance.
For all universities (except A, H and I, where HSGPA was not included), a marked Odds Ratio is associated with HSGPA, ranging from 2.12 to 4.47. This indicates that a one-point increase in high school GPA increases likelihood of graduation by a factor of 2.12 to 4.47. GENDER was significant to graduation (although not consistently positively or negatively) for universities A, E, F and G, where the Odds Ratios for female vs. male were 0.87, 1.51, 0.84 and 0.55, respectively. This means that a female’s likelihood of graduation in university E is 3/2 that of a male’s, while in universities A, F and G, a female’s likelihood of graduation is below that of a male’s, to 0.87, 0.84 and 0.55, respectively. For all universities that included SATQ, the Odds Ratios varied from 1.003 to 1.008, suggesting that math SAT scores correlate positively with graduation. Specifically, a ten-point increase in math SAT score results in a 3 (0.003*10) to 8 percent increase in likelihood of graduation. Interestingly, the Odds Ratios for SATV for all but one university tested, varied from 0.997 to 0.999, indicating that, controlling for other predictors, verbal SAT scores correlate negatively with graduation. It should be noted that for three of these Wald confidence intervals, the upper bound was 1.0, indicating that the negative correlation with graduation was significant to slightly less than a 95 percent confidence level. Ethnicity played a role in graduation in seven of the universities, but with the ethnic group having the highest likelihood of graduation strongly dependent on the institution. Finally, citizenship was a significant predictor for universities D and F. In university D, the Odds Ratio of NRAlien vs. ResAlien was 1.827, while in university F, the Odds Ratio of Citizen versus NRAlien was 1.975.
One of the key questions raised from the results is, is there multicolinearity in the model? That is, are the independent variables highly correlated? The correlation matrices for the six predictors indicate that for most schools, the predictors are not highly correlated. The correlation between SATQ and SATV is the most notable, ranging from -0.22 to -.52 for the nine schools. Future research may combine these two variables into one variable “SAT”, but the current study’s purpose is to investigate the effects of SAT math and SAT verbal separately. The next largest correlations are: the correlation between Female and SAT math (0.21); the correlation between SAT math and HSGPA ranges from -0.16 to -0.28; the correlation between Female and HSGPA ranges from -0.14 and -0.28; the correlation between SAT verbal and HSGPA ranges from -0.12 to -0.25. The remaining correlations of any two predictors have an absolute value below 0.10. Therefore, almost all the correlations among the independent variables are considered low correlations. In the absence of strong dependency among the predictors, it seems that the modeling approach is appropriate.
Another question is, will including the interaction terms relating the predictors improve the proposed model? To answer this question, we fitted a “full” model which included the second order interactions between each of the predictors and compared its performance with the original “reduced” model without the interactions. The full model with interactions did not yield higher predictive efficacy than the original model, as the generalized R-square remained virtually the same. Moreover, the model with interactions did not provide better fit to the data than the original model, as indicated by the results of the likelihood ratio difference test. Therefore, we rejected the model with second order interactions and concluded that the original model with only the main effects fit the data adequately. Hence, third order interactions were not investigated. The variance that cannot be accounted for is therefore assumed to be due to many other factors, including post-college-entrance factors that may have significant influence on graduation but are not included in the models in this study as the purpose of this study was to only investigate pre-college-entrance factors that may have an impact on graduation.
A third question is, will the insignificant predictors become significant once we pool the data for schools that have data available for all six predictors? In order to answer this question, data from schools B, C, D, E, F and G were pooled and a series of five dummy variables were used to represent the different institutions. The noteworthy results from this effort are fourfold. First, the generalized R-square is 0.126, which says that approximately 12.6 percent of the variance in graduation is explained by the predictors in the model. second, the significant variables are INSTITUTION, HSGPA, SATQ, SATV, CITIZEN, and ETHNIC, all with p-value of less than 0.0003, GENDER is the only variable that is not significant with the pooled data, which suggests that with other variables in the model holding constant, male and female engineering students’ graduation likelihood, is the same. Third, the correlations between the predictors with the pooled data are: -0.39 between SATQ and SATV, 0.18 between Female and SATQ, -0.18 between between Female and HSGPA, -0.14 between SATV and HSGPA, and -0.11 between SATQ and HSGPA Fourth, the odd ratios for SATV is 0.998, which says that with other variables being the same, students with SAT verbal score ten points higher is 2 percent less likely to graduate in engineering than students whose SAT verbal score is 10 points lower.
The negative correlation between graduation and SAT verbal may simply be a result of the fact that students with higher verbal skills are more likely to change majors from engineering and switch to other fields where verbal skills are more valued and more critical to success. In light of the importance ascribed to SATV and verbal skills in engineering, this will be investigated further in another study. An interaction effect with variables absent from the model is suspected, possibly due to difficulties in student pathways that are more prevalent among students who enter college with AP English credit. It is also possible that students with the highest verbal scores were more likely to be suited to a variety of other professions.
We found that graduation in engineering for students who enter in an engineering discipline depends significantly upon several factors. High school GPA and math SAT scores were positively correlated with graduation rates for all universities for which this data was available. Interestingly, verbal SAT scores correlated negatively with odds of graduation-the consistency of this outcome for seven out of eight universities makes this observation even more notable. This is particularly interesting in light of the emphasis that industry places on the communications skills of the students they hire. While gender, ethnicity and citizenship also showed significant effects, these were not consistently positive or negative. In three universities, the graduation rate for males was higher than that for females, while in one university the graduation rate was higher for females. Ethnicity was significant in seven universities. Finally, in two of the universities, citizenship significantly affected graduation. While HSGPA, SATQ were significant for all models tested, this was not the case for gender, SATV, ethnicity, and citizenship. While the lack of significance of ethnicity in a pooled model is likely explained by the presence of two HBCUs among the institutions studied, we are intrigued by the lack of significance of GENDER in predicting graduation. The lack of significance of GENDER does not imply that women who matriculate in engineering graduate in engineering at the same rate as men who matriculate in engineering. GENDER lacks significance only if the other variables are held constant, which means that the known difference in the graduation rates of females and males can be accounted for using the other variables (such as SATQ). Still, it is intriguing that, for such a large sample size, that there is no difference in graduation rates that can be significantly attributed to GENDER, and we will investigate this further.
The magnitudes of the coefficients of determination indicate that the factors studied account for a small, though meaningful and statistically significant, fraction of the variation in student graduation. The release of these results to the engineering education community is hoped to spark a discussion of our methods and variables of choice, since the low coefficients of determination indicate that there are other pre-existing variables that might be included in the predictive model. In trying to predict student success, there is certainly an upper limit on how much ofthat variation can be predicted from pre-existing factors-even if we could quantify them all. The choices students make after matriculation affect student success significantly. With the data available in the database already, it will, however, be possible to expand the predictive model to include such factors as first semester GPA, which other researchers have found to be a valuable predictor. In the longer term, we are also in the process of expanding the scope of the SUCCEED LDB to create the Multiple-Institution Database For Investigating Engineering Longitudinal Development (MIDFIELD) to develop a longitudinal database including course-level data for all undergraduate courses across several institutions , which would allow us to study more detailed models of student success for a wide range of engineering programs, including studying the effect of the pathways students choose. The MIDFIELD database will particularly permit us to study more carefully the relationship of SATV and success in engineering. As the model improves, we believe that a series of recommendations will emerge that will guide programs for at-risk students as well as academic policies for the larger population of students.
REFERENCES Astin, A.W., What Matters in College?: Four Critical Years Revisited, Jossey-Bass Publishers, San Francisco, 1993, especially Chapter 11. Seymour, E., and N.M. Hewitt, Talking About Leaving: Why Undergraduates Leave the Sciences, Westview Press, 2000. Besterfield-Sacre, M., M. Moreno, L. Shuman, and C. Atman, “Gender and Ethnicity Differences in Freshmen Engineering Student Attitudes: A Cross-Institutional Study,” Journal of Engineering Education, Vol. 90, No. 4, October, 2001, pp. 477-489. Besterfield-Sacre, M., C. Atman, and L. Shuman, “Characteristics of Freshman Engineering Students: Models for Determining Student Attrition in Engineering,” Journal of Engineering Education, Vol. 86, No. 2, April 1997, pp. 139-149. Zhang, Z., and R.S. RiCharde, “Prediction and Analysis of Freshman Retention,” AIR 1998 Annual Forum Paper, Minneapolis, MN, 1998. Levin, J., and J. Wyckoff. “Identification of Student Characteristics that Predict Persistence and Success in an Engineering College at the End of the Sophomore Year: Informing the Practice of Academic Advising,” Division of Undergraduate Studies Report No. 1990.1, Pennsylvania State University. Moller-Wong, C., and A. Eide, “An Engineering Student Retention Study,” Journal of Engineering Education, Vol. 86, No. 1, January 1997, pp. 7-15. LeBold, W.K., and S.K. Ward, “Engineering Retention: National and Institutional Perspectives,” Proceedings, 1988 American Society for Engineering Education Conference, pp. 843-851. Budny, D., W. LeBold, and G. Bjedov, “Assessment of the Impact of Freshman Engineering Courses,” Journal of Engineering Education, Vol. 87, No. 4, October 1998, pp. 405-411. The College Board, . Online as of April, 2004. Neter, J., and M.H. Kutner, C.J. Nachtscheim, and W. Wasserman, Applied Linear Statistical Models, 4th edition, McGraw-Hill Companies, Inc., 1996. Agresti, A., An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., New York, 1996. Ohland, M.W., G. Zhang, B. Thorndyke, and T.J. Anderson, “The Creation of the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD),” Proceedings, 2004 American Society for Engineering Education Conference, Salt Lake City, Utah, June 2004.
University of Florida
TIMOTHY J. ANDERSON
University of Florida
MATTHEW W. OHLAND
BRIAN R. THORNDYKE
Guili Zhang is a Ph.D. candidate in Educational Research and Statistics, Department of Educational Psychology, University of Florida. She received a B.A. in British and American Language and Literature at Shandong University, China, and a M.Ed. in English Education at Georgia Southern University. She has published extensively and has won numerous awards at the national and regional level in the area of educational research in China. She teaches Measurement and Assessment in Education at the University of Florida. Her research interests involve applied quantitative research designs, categorical data analysis, and structural equation modeling.
Address: 150 New Engineering Building, University of Florida, Gainesville, FL 32611; telephone: 352-392-0857; fax: 352-392-9673; e-mail: email@example.com.
Tim Anderson is Chairman and Professor in the Department of Chemical Engineering, University of Florida. He received a Ph.D. at the University of California-Berkeley in 1979. His research interests include electronic materials processing, thermochemistry and phase diagrams, chemical vapor deposition, bulk crystal growth and advanced composite materials.
Address: 300 Weil Hall, Box 116550, University of Florida, Gainesville, FL 32611-6550; telephone: 352-392-0946; fax: 352-392-9673; e-mail: firstname.lastname@example.org.
Matthew Ohland is Assistant Professor in General Engineering at Clemson University. He received his Ph.D. in Civil Engineering with a minor in Education from the University of Florida, and served as the Assistant Director of the SUCCEED Coalition until 2000. His research is in freshman programs and educational assessment, and he is the President of Tau Beta Pi, the engineering honor society until 2006.
Address: 104 Holtzendorff Hall, Clemson, SC 29634-0902; telephone: 864-656-2542; fax: 864-656-1327; e-mail: ohland@ clemson.edu.
Brian R. Thorndyke is a Postdoctoral Fellow in Radiation Oncology at Stanford University. He received his Ph.D. in Chemical Physics from the University of Florida. He also received a M.S. in Computer Science from the University of Florida, and a M.Sc. in High Energy Physics at the University of Montreal. His current research focuses on the development of computational methods for diagnostic medical imaging and radiation therapy treatment planning.
Address: 875 Blake Wilbur Drive, Stanford, California, 94305-5847; telephone: 650-724-7620; e-mail: email@example.com.
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