<p>Hierarchical variance components.</p>

8,300 undergraduate student records from three entry cohorts at a South African public university, spanning the 2023–2024 academic periods. The dataset, created by Moeketsi Mosia and last updated in March 2026, contains administrative data used to analyze the effects of academic orientation and advising on retention and progression using Bayesian hierarchical multivariate models.

Use Cases

• Modeling the association between academic orientation performance and student retention based on the study's findings.
• Analyzing the interactive effects of academic advising utilization and orientation performance on academic progression.
• Applying Bayesian hierarchical multivariate modeling techniques to multi-cohort educational data as demonstrated in the study.
• Investigating the correlation between retention and progression outcomes in first-year university students.

Strengths

• Data covers 8,300 undergraduate students across three distinct entry cohorts.
• Analysis employs a Bayesian hierarchical multivariate model to account for cohort-level variation.
• Dataset is small (5.5 KB), facilitating quick download and initial exploration.

Limitations

• Row count and column-level documentation are unknown, requiring manual inspection after download.
• The dataset's small size (5.5 KB) indicates a very limited scope, likely containing only model results or aggregated statistics.
• Relationships described are associative, not causal, as explicitly stated in the description.

Provenance

Source

Moeketsi Mosia via figshare.

Collection Method

Administrative data from a South African public university.

Time Range

2023–2024 academic periods.

Freshness

Last updated 2026-03-17 17:27:57.

Geography

A South African public university.