Bayesian Hierarchical Model Results for Academic Progression and Student Retention

Bayesian hierarchical model results for academic progression, published by Moeketsi Mosia on figshare in March 2026. The dataset is 5.5 KB in size and is available under a CC-BY-4.0 license. It likely contains statistical outputs from a model analyzing student retention and advising effectiveness.

Use Cases

• Evaluating the effectiveness of academic advising interventions based on model results.
• Analyzing differential treatment effects on student retention across cohorts.
• Comparing academic orientation performance metrics between student groups.
• Applying Bayesian hierarchical modeling techniques to administrative educational data.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, indicating a compact and focused dataset.
• Model results are from a Bayesian hierarchical approach, a modern statistical method.

Limitations

• Row count and column definitions are unknown, requiring manual inspection after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely derived from administrative educational data, as suggested by platform tags.

Time Range

2024 academic periods, as indicated by platform tags.

Freshness

Last updated 2026-03-17 17:27:56; freshness should be verified.

Geography

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