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Bias and Precision of Continuous Norms Obtained Using Quantile Regression
Elise A V Crompvoets1,2, Jos Keuning2, Wilco H M Emons1
1Tilburg University, Tilburg, Netherlands.
Assessment
|June 3, 2020
Summary
Quantile regression offers a more precise method for continuous norming of test scores compared to traditional approaches. However, this method is most reliable when test score distributions approximate a normal shape.
Area of Science:
- Psychometrics
- Statistical modeling
Background:
- Continuous norming is essential for age-dependent test performance.
- Existing methods have restrictive assumptions, potentially causing bias.
Purpose of the Study:
- To evaluate quantile regression as a flexible alternative for continuous norming.
- To compare its bias and precision against traditional and mean regression norming.
Main Methods:
- Simulations were used to compare norming methods.
- Quantile regression, mean regression, and traditional norming were assessed.
- Covariates like age group, sample size, and score distributions were varied.
Main Results:
- Quantile regression demonstrated superior precision across most simulated conditions.
- Bias was observed in quantile regression when score distributions exhibited ceiling effects.
- Mean regression and traditional norming showed limitations in precision and flexibility.
Conclusions:
- Quantile regression is a promising norming method, especially for age-dependent tests.
- Its effectiveness is contingent on score distributions being approximately normal.
- It offers a more flexible and precise alternative to existing norming techniques.
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