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Selection Strategies for Univariate Loglinear Smoothing Models and Their Effect on Equating Function Accuracy
Author(s) -
Moses Tim,
Holland Paul W.
Publication year - 2009
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/j.1745-3984.2009.00075.x
Subject(s) - equating , univariate , statistics , selection (genetic algorithm) , log linear model , mathematics , econometrics , smoothing , function (biology) , model selection , multivariate statistics , linear model , computer science , artificial intelligence , evolutionary biology , biology , rasch model
In this study, we compared 12 statistical strategies proposed for selecting loglinear models for smoothing univariate test score distributions and for enhancing the stability of equipercentile equating functions. The major focus was on evaluating the effects of the selection strategies on equating function accuracy. Selection strategies’ influence on the estimation of cumulative test score distributions was also assessed. The results of this simulation study differentiate the selection strategies and define the situations where their use has the most important implications for equating function accuracy. The recommended strategy for estimating test score distributions and for equating is AIC minimization.