Beschreibung:
Kernel Equating (KE) is a powerful, modern and unified approach to test equating. It is based on a flexible family of equipercentile-like equating functions and contains the linear equating function as a special case. Any equipercentile equating method has five steps or parts. They are: 1) pre-smoothing; 2) estimation of the score-probabilities on the target population; 3) continuization; 4) computing and diagnosing the equating function; 5) computing the standard error of equating and related accuracy measures. KE brings these steps together in an organized whole rather than treating them as disparate problems.
This book will be of interest to statisticians working in the fieldsof educational testing and psychometrics. It is the first presentationof the kernel method in book form.
and Notation.- and Notation.- The Kernel Method of Test Equating: Theory.- Data Collection Designs.- Kernel Equating: Overview, Pre-smoothing, and Estimation of r and s.- Kernel Equating: Continuization and Equating.- Kernel Equating: The SEE and the SEED.- Kernel Equating versus Other Equating Methods.- The Kernel Method of Test Equating: Applications.- The Equivalent-Groups Design.- The Single-Group Design.- The Counterbalanced Design.- The NEAT Design: Chain Equating.- The NEAT Design: Post-Stratification Equating.