Analyzing Student Success Outcome Variables in Higher Education Utilizing the Chi-Square Test of Independence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the past two decades student success measures such as student persistence, retention, and graduation rates have been a point of emphasis in higher education. These measures are often directly related to funding formulas for state public colleges and universities. Therefore, analyses of these data have become more critical to evaluating student success initiatives for faculty and administration at many institutions. However, while these data are often widely available there is very little higher education research on how they should be analyzed to assess student success initiatives, program evaluations, or teaching effectiveness at the institutional level.As student success outcome variables are categorical in nature, linear analyses of these data may prove rather difficult as a dependent variable without a significant amount of transformation. Therefore, the purpose of this article is to provide practitioners with a simple, yet powerful option for analyzing student success outcome variables utilizing the Chi-square test of independence. A case study approach was taken to illustrate how Chi-square can be used to specifically analyze the association between an experiential learning high impact practice and graduation rates among undergraduate students. This case was based on a results and interpretation perspective, rather than step-by-step instruction on how to perform the analysis itself.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it