Open AccessApplication of Best Linear Prediction and Penalized Best Linear Prediction to ETS Tests
Author(s) -
Haberman Shelby J.
Publication year - 2020
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/ets2.12290
Subject(s) - linear prediction , linear model , best linear unbiased prediction , computer science , predictive modelling , linear regression , test (biology) , task (project management) , machine learning , best practice , statistics , econometrics , artificial intelligence , mathematics , engineering , selection (genetic algorithm) , paleontology , management , systems engineering , economics , biology
Best linear prediction (BLP) and penalized best linear prediction (PBLP) are techniques for combining sources of information to produce task scores, section scores, and composite test scores. The report examines issues to consider in operational implementation of BLP and PBLP in testing programs administered by ETS.