Statistical Evaluation Model
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A Statistical Evaluation Model is a statistical model that analyzes evaluation data accounting for variance sources and dependency structures in assessment designs.
- AKA: Evaluation Statistical Model, Assessment Statistical Model, Statistical Assessment Model, Evaluation Analysis Model.
- Context:
- It can typically decompose Total Variance into component sources.
- It can typically handle Nested Structures in evaluation designs.
- It can often account for Rater Effects and Item Effects simultaneously.
- It can often provide Significance Tests for evaluation hypothesises.
- It can support Power Analysis for sample size determination.
- It can enable Effect Size Estimation beyond significance testing.
- It can facilitate Generalizability Studys for evaluation reliability.
- It can integrate with Bayesian Frameworks for uncertainty quantification.
- It can range from being a Simple Statistical Model to being a Complex Statistical Model, depending on its parameter count.
- It can range from being a Parametric Evaluation Model to being a Non-Parametric Evaluation Model, depending on its distribution assumption.
- It can range from being a Fixed Effects Model to being a Random Effects Model, depending on its effect treatment.
- It can range from being a Cross-Sectional Model to being a Longitudinal Model, depending on its temporal structure.
- ...
- Examples:
- Variance Component Models, such as:
- Regression-Based Models, such as:
- Specialized Evaluation Models, such as:
- ...
- Counter-Examples:
- Descriptive Statistics, which lacks model structure.
- Machine Learning Model, which emphasizes prediction.
- Theoretical Model, which lacks statistical formulation.
- See: Statistical Model, Mixed Effects Evaluation Model, Evaluation Design, Variance Analysis, Effect Size, Statistical Inference, Hypothesis Testing.