Define the prediction target, intended decision, and prediction time point
Define the prediction target, intended decision, and prediction time point.
EDUCATION PREMIUM WORKFLOW · VERSION 1.0
Predict carefully.
Validate beyond the sample.
A reproducible prediction workflow that compares logistic regression, lasso, decision trees, and random forests while preventing school-level leakage and evaluating calibration, subgroup performance, uncertainty, and responsible use.
One-time purchase · Individual-use license · Secure digital delivery
Prevent leakageDefine the prediction target, intended decision, and prediction time point.
Validate by schoolSplit development and test samples by school so related cases do not leak across sets.
Review subgroup performanceFit preprocessing on development data and expose a deliberate leakage example.
WHAT IS INSIDE
A complete education-analysis workflow.
The package connects design-specific data audits, model choice, diagnostics, interpretation, and reporting in one reproducible analysis.
Split development and test samples by school so related cases do not leak across sets
Split development and test samples by school so related cases do not leak across sets.
Fit preprocessing on development data and expose a deliberate leakage example
Fit preprocessing on development data and expose a deliberate leakage example.
Compare a benchmark logistic model, lasso, decision tree, and random forest
Compare a benchmark logistic model, lasso, decision tree, and random forest.
Evaluate held-out discrimination, calibration, classification thresholds, and bootstrap uncertainty
Evaluate held-out discrimination, calibration, classification thresholds, and bootstrap uncertainty.
Review subgroup performance, dataset shift, feature importance limits, and responsible use
Review subgroup performance, dataset shift, feature importance limits, and responsible use.
DESIGNED FOR EDUCATION RESEARCH
Validation-first educational prediction.
This workflow is useful when:
- You want to predict an educational outcome for future or unseen cases.
- Students share schools or other units that should stay intact across validation splits.
- You need to compare interpretable baselines with machine-learning models.
- You need to discuss calibration, subgroup performance, and deployment limits.
This introductory workflow does not establish causal effects or authorize automated high-stakes decisions; prospective deployment needs governance, monitoring, and external validation.
PUBLIC PREVIEW
Review the rendered analysis.
The public preview shows output, figures, and interpretation without exposing source code or session information. The paid version includes the complete editable R Markdown source and all analysis code.
THE DOWNLOADABLE PACKAGE
Run the example.
Then adapt your study.
The release uses a documented synthetic education dataset. Run the complete example first, then adapt the clearly marked settings and models to your research design.
- Editable R Markdown source and rendered report
- Synthetic clustered education dataset with documented leakage
- School-level development and held-out test split
- Logistic, lasso, decision-tree, and random-forest comparisons
- Calibration, discrimination, thresholds, and bootstrap uncertainty
- Subgroup review and accessible prediction figures
- Start guide, reporting checklist, and individual-use license
The workflow supports analysis and documentation. The researcher remains responsible for selecting methods that fit the study and for reviewing conclusions with the dissertation advisor, committee, or a qualified statistician.
VERSION 1.0.0
Complete workflow.
$59 one time.
Pay securely and receive the version 1.0.0 ZIP with the editable analysis, synthetic data, documentation, and rendered report.
Checkout is temporarily unavailable. Please email hello@dissertationstatshelper.com for assistance.