Premium
A New Statistic for Selecting the Smoothing Parameter for Polynomial Loglinear Equating Under the Random Groups Design
Author(s) -
Liu Chunyan,
Kolen Michael J.
Publication year - 2019
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12257
Subject(s) - equating , statistics , mathematics , log linear model , statistic , akaike information criterion , smoothing , selection (genetic algorithm) , type i and type ii errors , econometrics , linear model , computer science , artificial intelligence , rasch model
Smoothing is designed to yield smoother equating results that can reduce random equating error without introducing very much systematic error. The main objective of this study is to propose a new statistic and to compare its performance to the performance of the Akaike information criterion and likelihood ratio chi‐square difference statistics in selecting the smoothing parameter for polynomial loglinear equating under the random groups design. These model selection statistics were compared for four sample sizes (500, 1,000, 2,000, and 3,000) and eight simulated equating conditions, including both conditions where equating is not needed and conditions where equating is needed. The results suggest that all model selection statistics tend to improve the equating accuracy by reducing the total equating error. The new statistic tended to have less overall error than the other two methods.