Variance Explained in Student Exam Scores by Linear Mixed Model Effects

Featuring the percent variance in student exam scores explained by fixed and random effects from a primary linear mixed model (LMM) analysis. It is associated with research on active learning, first-generation student status, and exam performance. The dataset is 5.5 KB in size.

Use Cases

• Analyze the contribution of fixed effects, such as 'First Generation Status', to variance in exam performance within the LMM framework.
• Compare the variance explained by random effects versus fixed effects to understand student-level versus group-level influences on scores.
• Use the variance decomposition results to inform the design of future studies on 'Active Learning' or 'Exam Performance'.
• Integrate the variance percentages with other study data on 'Students Working Alone' or 'Faculty May Incorporate' to build a comprehensive model.

Strengths

• Data originates from a formal statistical analysis using a Linear Mixed Model, indicating a structured methodological approach.
• The dataset is shared under a permissive CC BY 4.0 license, facilitating reuse and distribution.
• Associated tags provide context, linking the data to specific research areas like 'Active Learning' and 'First Generation Status'.

Limitations

• The dataset is very small at 5.5 KB, suggesting it contains summary statistics rather than raw or granular data.
• No column definitions or sample data are provided, limiting understanding of the exact data structure and values.
• The specific sample size (number of students or observations) underlying the variance calculations is unknown.

Provenance

Source

Mallory A. Jackson via figshare.

Collection Method

Calculated from a primary Linear Mixed Model analysis of student exam scores.

Freshness

Last updated on 2026-03-20.