The Effects of Selection Strategies for Bivariate Loglinear Smoothing Models on NEAT Equating Functions

In this study, eight statistical strategies were evaluated for selecting the parameterizations of loglinear models for smoothing the bivariate test score distributions used in nonequivalent groups with anchor test (NEAT) equating. Four of the strategies were based on significance tests of chi-square...

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Bibliographic Details
Published in:Journal of educational measurement 2010-03, Vol.47 (1), p.76-91
Main Authors: Moses, Tim, Holland, Paul W.
Format: Article
Language:English
Subjects:
Accuracy
Achievement tests
Bias
Criterion Referenced Tests
Data smoothing
Educational evaluation
Equated Scores
Evaluation
Parameterization
Parametric models
Population distributions
Population parameters
Sample size
Scores
Significance tests
Skewed distribution
Standard error
Statistical Analysis
Statistical Distributions
Statistical methods
Statistics
Test scores
Tests
Variability
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Summary:In this study, eight statistical strategies were evaluated for selecting the parameterizations of loglinear models for smoothing the bivariate test score distributions used in nonequivalent groups with anchor test (NEAT) equating. Four of the strategies were based on significance tests of chi-square statistics (Likelihood Ratio, Pearson, Freeman-Tukey, and Cressie-Read) and four additional strategies were based on different evaluations of the Likelihood Ratio Chi-Square statistic (Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, and an index traced to Goodman). The focus was the implications of the selection strategies' selection tendencies for the accuracy of chained and poststratification equating functions. The results differentiated the strategies in terms of their tendencies to select models with particular bivariate parameterizations and the implications of these tendencies for equating bias and variability.
ISSN:0022-0655
1745-3984
DOI:10.1111/j.1745-3984.2009.00100.x