Predicting Performance of MBA Students: Comparing the Part-Time MBA Program and the One-Year Program.
Bibliographic record
Abstract
While predictor variables for success in MBA programs vary between schools, are they different within the same business school? At an AACSB-accredited school, although the curriculum and professors are essentially the same between the One-Year MBA and Part-Time MBA programs, the significant factors to predict success in each program are not. Results indicate significant factors to predict graduate performance for a One-Year MBA program include the GMAT-Verbal, undergraduate grade point average, and a Canadian factor. While the part-time program significant factors include GMAT-Verbal and undergraduate grade point average, they also include GMAT-Quantitative and age. These results favor using slightly different entrance criteria for each program, and the suggestion for faculty to consider the educational process differences between the two programs. ********** Graduate business programs continue to seek admission criteria that predict academic success. Studies indicate the need for each MBA program to individually determine the relationship among predictor variables and graduate level performance in its program [Wright and Palmer, 1997]; however, does the same curriculum delivered in a different framework require different predictor variables? Are there significant differences at the same school between a full-time MBA program and a part-time, evening program to warrant different entrance criteria? If the curriculum content and delivery process are the same, should the same incoming factors be considered for admission into each respective program or are there potentially other process differences that exist? In general, if both programs are delivered by the same professors that use similar materials and testing to deliver courses, do graduates achieve the same outcome level? These questions form the basis for our study: comparison of predictability for two MBA programs in the same school--a One-Year MBA program and a traditional, Part-Time, evening MBA program. Literature Review Business admissions use different processes ranging from review of undergraduate grade point average (GPA); transcript analysis that reviews the type of courses taken, trends and progress over time; level of analytical and quantitative skill required in current and past professions; recommendations; and the Graduate Management Admission Test (GMAT). Relevant admission factors to executive, full and Part-Time MBA programs around the world have been researched; however, the only conclusion that can be agreed upon is that GMAT and undergraduate GPA are significant factors to predicting MBA performance as measured by the graduate GPA [Wright & Palmer, 1994; Braunstein, 2002; Hecht et al., 1989; McClure et al., 1986; Paolillo, 1982; Wright and Palmer, 1997; Sireci & Talento-Miller, 2006]. Predictability, with only GMAT and undergraduate GPA as factors, is typically less than 19% of the graduate GPA [Wilson and Hardgrave, 1995], but when additional factors are considered, predictability as high as 36% for an Executive MBA program at Tulane in New Orleans, Louisiana has been reported [Arnold, Chakravarty and Balakrishnan, 1996]. Some studies favor GMAT as the stronger predictor over undergraduate GPA [Carver and King, 1994], while others favor undergraduate GPA as the stronger predictor over the GMAT [Yang and Lu, 2001]. In yet another study, the Graduate Records Exam (GRE) is a better predictor of performance than GMAT [Nilsson, 1995]. Other predictor variables are significant in some studies, but the results are not always replicated in others. The majority of studies focus on predicting exiting graduate GPA, although some attempt to model the first year performance. While GMAT and undergraduate GPA are always included in the models, other factors, such as GMAT--Verbal percentage, GMAT-Quantitative percentage, Junior/Senior GPA, length of time out of school, gender, age, undergraduate major, undergraduate institution, undergraduate major, gender, and work experience, have been tested and yield varying results as discussed below [Braunstein, 2002; Carver, Jr. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".