Accounting for Model Uncertainty in the Prediction of University Graduation Rates

Goenner, Cullen F.; Snaith, Sean M.

doi:10.1023/b:rihe.0000010045.13366.a6

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…In a related paper, Goenner and Snaith (2004) used Bayesian averaging methods to estimate a graduation rate model. They argued that using different model specifications can lead to contrary findings given the uncertainty about the true set of covariates that explain the phenomena of interest.…”

Section: Existing Research On Institutional Graduation Ratesmentioning

confidence: 99%

“…For instance, previous research has shown that college size is related negatively to their graduation rate (see Astin et al, 1996;Mortenson, 1997;Terenzini, 1991, 2005;Porter, 2000). Minority students also graduate at lower rates than white students, and women graduate at higher rates, so we included aggregated institutional-level proportions of these demographic characteristics (Goenner and Snaith, 2004;Scott et al, 2006).…”

Section: Dataset and Variablesmentioning

confidence: 99%

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IS STUDENT-RIGHT-TO-KNOW ALL YOU SHOULD KNOW? An Analysis of Community College Graduation Rates

et al. 2006

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Over the last decade, policymakers, educators, and researchers have increasingly sought to understand community college policies and practices that promote students' success. This effort has been partly driven by an increased emphasis on outcome accountability, but it has also promoted a productive discussion about improving institutional performance. The research reported here has two related goals. One goal is to work towards strengthening the ability to assess and compare institutional performance. We thus have developed a model that can be used to adjust simple graduation rates for institutional characteristics, such as student composition, college resources, size, and location, all of which might influence those rates. Our long-term goal is to understand how to improve student outcomes, so the paper also uses the model to measure the effect of those institutional characteristics on graduation rates. We use data from the Integrated Postsecondary Education Data System (IPEDS) surveys, applying a weighted least-squares procedure for grouped data to estimate an institutional-completion rates model. This analysis confirms several hypotheses about institutional determinants of graduation rates at community colleges. Our results indicate a consistent negative relationship between enrollment size and completion. Additionally, colleges with high shares of minority students, part-time students, and women have lower graduation rates. A final significant finding among institutional characteristics is that greater instructional expenditures are related to a greater likelihood of graduation. The method developed here can be used to better assess the performance of community colleges.

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Section: Existing Research On Institutional Graduation Ratesmentioning

confidence: 99%

Section: Dataset and Variablesmentioning

confidence: 99%

IS STUDENT-RIGHT-TO-KNOW ALL YOU SHOULD KNOW? An Analysis of Community College Graduation Rates

et al. 2006

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“…Raftery et al (1997) demonstrate using simulated data, where the true model is known, that BMA is better able to discern the true model specification than stepwise methods in the presence of uncertainty. Goenner and Snaith (2004) have also shown with respect to the analysis of graduation rates that this method provides a neutral way of dealing with variable selection and improves out of sample predictions relative to results based on a single model specification.…”

Section: Discussionmentioning

confidence: 92%

A Predictive Model of Inquiry to Enrollment

Goenner

Pauls

2006

Res High Educ

Self Cite

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The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an inquiry to our university such as by returning a request for information form, often provides far less information than is available from applicants. Despite this fact we find that characteristics of the student, as well as geographic and demographic data based on the student's zip code are significant predictors of enrollment. Accounting for uncertainty in our model's specification, we find that we are able to predict out of sample the enrollment decision of 89% of student inquiries. We also demonstrate how these findings can be used to improve marketing efforts.

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“…Although within the same theoretical framework, the variables employed can be operationalized in different ways, which makes the measurements used in the diverse studies vary from one study to another. Consequently, the results of the investigations may be different, both with regard to the effects considered as predictors and with regard to their meaning (Goenner & Snaith, 2004). Some of the studies are limited to specific settings, whose conditions are not generalizable (Nonis & Wright, 2003).…”

Section: Palabras Clave: Regresión Logística Versus Regresión Linealmentioning

confidence: 99%

Prediction of University Students' Academic Achievement by Linear and Logistic Models

Ayán

García

2008

Span. J. Psychol.

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University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns. Keywords: logistic versus linear regression, prediction, credits, academic achievement, advance in careerSe estudia el efecto de dos tipos de factores sobre el rendimiento de estudiantes universitarios: variables académicas de rendimiento previo y variables demográficas, mediante modelos lineales y logísticos. El principal objetivo del trabajo es comparar los resultados obtenidos con ambas técnicas estadísticas, para determinar cuál de ellos es más adecuado en términos de ajuste y capacidad predictiva cuando se pretende explicar y predecir el rendimiento académico, en función de variables de rendimiento previo y factores sociodemográficos. Como medida del rendimiento a predecir se empleó el avance en la carrera. Las hipótesis planteadas son: 1) El avance está directamente relacionado con las calificaciones en materias básicas de primer año y con variables demográficas y 2) Los modelos logísticos son más adecuados que los modelos lineales, ya que presentan mayor capacidad predictiva. Los resultados permiten confirmar la primera hipótesis en su primera parte, ya que el rendimiento previo está directa y significativamente asociado al avance en la carrera. Pero se cumple de forma parcial por lo que se refiere al efecto factores demográficos. Con respecto a la segunda hipótesis, la regresión logística mostró ser más adecuada que la lineal, pues arroja estimaciones más estables en relación con la presencia de patrones de mal ajuste. Palabras clave: regresión logística versus regresión lineal, predicción, créditos, rendimiento académico, avance en la carrera

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Accounting for Model Uncertainty in the Prediction of University Graduation Rates

Cited by 14 publications

References 19 publications

IS STUDENT-RIGHT-TO-KNOW ALL YOU SHOULD KNOW? An Analysis of Community College Graduation Rates

IS STUDENT-RIGHT-TO-KNOW ALL YOU SHOULD KNOW? An Analysis of Community College Graduation Rates

A Predictive Model of Inquiry to Enrollment

Prediction of University Students' Academic Achievement by Linear and Logistic Models

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