MBA Admissions Criteria: An Empirical Investigation and Validation Study

Schwan, Edward S

doi:10.1080/08832323.1988.10117300

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Given that the goal of these analyses is to examine grades after the first year, this study was included in the analyses. A second study included in the meta-analyses examined GPA after at least one class was completed by a student (Schwan, 1988). In other words, students who dropped out were in this study's analyses.…”

Section: Criterion Measuresmentioning

confidence: 99%

A Meta-Analysis of the Predictive Validity of the Graduate Management Admission Test (GMAT) and Undergraduate Grade Point Average (UGPA) for Graduate Student Academic Performance.

Kuncel

Credé

Thomas

2007

AMLE

101

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Considerable debate both within and outside of academic circles surrounds the validity of standardized tests for predicting student performance in graduate business schools. This meta-analysis aggregates the existing literature on the validity of the two most heavily used predictors in business school admissions: the GMAT and undergraduate grade point average. Results based on over 402 independent samples across 64,583 students indicate that the GMAT is a superior predictor to UGPA and that the two combined yield a high level of validity for predicting student performance.

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Section: Criterion Measuresmentioning

confidence: 99%

A Meta-Analysis of the Predictive Validity of the Graduate Management Admission Test (GMAT) and Undergraduate Grade Point Average (UGPA) for Graduate Student Academic Performance.

Kuncel

Credé

Thomas

2007

AMLE

101

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“…Paolillo (1982) employed step-wise regression in his study and found that the applicant's junior and senior undergraduate grade point average was the first variable to enter into the equation. Schwan (1988) found GPA to be significantly correlated with GMAT score, undergraduate grade point average, and junior/senior grade point average among Murray State University MBA students. This research is to be able to make an effective prediction regarding the students' success in graduate education with Artificial Neural Networks (ANN).…”

Section: Introductionmentioning

confidence: 79%

“…The findings are quite consistent with other published research results. For example, Lee (2010), Naik& Rogotman (2009), Ibrahim & Rusli (2007), Schwan (1988), Amanatiadis, Mitsinis, & Maditinos (2014) According to the correlation results; the correlation between the average graduation and ALES score seemed to have had no significant correlation between the two data (r = 0.422, p> .05). This analysis was carried out with the students who received/were receiving graduate education between the years of 2006 -2013.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Prospective Mathematics Teachers’ Academic Success in Entering Graduate Education by Using Back-propagation Neural Network

Bahadır

2016

Journal of Education and Training Studies

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The purpose of this study is to examine a neural network based approach to predict achievement in graduate education for Elementary Mathematics prospective teachers. With the help of this study, it can be possible to make an effective prediction regarding the students' achievement in graduate education with Artificial Neural Networks (ANN). Two different neural networks are used for an effective prediction of the first network in which some core courses are taken by prospective Mathematics teachers in their first two years, including General Mathematics, Pure Mathematics, Analysis I, Analysis II, Geometry, Linear Algebra-I. The scores received from the above courses are used as an input for the back-propagation neural network (BPNN). Additionally, the scores of vocational core courses taken by third year students, including Analysis3, Special Teaching Methods 2, Elementary Number Theory, Algebra, Problem Solving, are used as the output of the BPNN. The second network uses the scores of all courses which are previously mentioned and uses them as an input of BPNN and also uses ALES (Academic Personnel and Postgraduate Education Entrance Exam) score. They are used as an output of the BPNN. Besides, the correlation analysis is conducted by using the average graduation and ALES scores components. Analytical results demonstrate that the BPNN model offers relatively accurate predictions for the student success in graduate education with a high average of accuracy (Neural Network1 is 77.125% and Neural Network2 is 68.5%). Another finding is that there is no significant correlation between the graduate average scores of candidates who qualified for a graduate education and their ALES scores. These results indicate that BPNN is a suitable tool to predict the academic success of all education majors. Student career advisors can use the ANN model to identify the students who have particular potential for graduate education, and this prediction model can help these students adjust their own teaching strategies, and provide guidance and support for their careers.

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“…Schwan (1988) found graduates' GPA (GGPA) to be significantly correlated to their GMAT score, undergraduate GPA, and junior/senior year GPA among Murray State University MBA students. Wongkhamdi and Seresangtakul (2010) compared discriminant analyses studies and ANN studies for their ability to predict student graduation outcomes.…”

Section: Neural Networkmentioning

confidence: 99%

Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers’ Academic Success upon Entering Graduate Education

2016

EDUC SCI-THEOR PRACT

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The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic Regression Analysis (LRA) and Artificial Neural Network (ANN) for predicting prospective mathematics teachers' academic success when they enter graduate education. A sample of 372 student profiles was used to train and test our model. The strength of the model can be measured through Logistic Regression Analysis (LRA). The average correct success rate of students for ANN was higher than LRA. The successful prediction rate of the back-propagation neural network (BPNN, or a common type of ANN was 93.02%, while the success of prediction of LRA was 90.75%.

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MBA Admissions Criteria: An Empirical Investigation and Validation Study

Cited by 6 publications

References 7 publications

A Meta-Analysis of the Predictive Validity of the Graduate Management Admission Test (GMAT) and Undergraduate Grade Point Average (UGPA) for Graduate Student Academic Performance.

A Meta-Analysis of the Predictive Validity of the Graduate Management Admission Test (GMAT) and Undergraduate Grade Point Average (UGPA) for Graduate Student Academic Performance.

Prediction of Prospective Mathematics Teachers’ Academic Success in Entering Graduate Education by Using Back-propagation Neural Network

Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers’ Academic Success upon Entering Graduate Education

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