Non-Iterative Conditional Pairwise Estimation for the Rating Scale Model

Autor/inn/en	Elliott, Mark; Buttery, Paula
Titel	Non-Iterative Conditional Pairwise Estimation for the Rating Scale Model
Quelle	In: Educational and Psychological Measurement, 82 (2022) 5, S.989-1019 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Elliott, Mark)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211046253
Schlagwörter	Item Response Theory; Rating Scales; Computation; Simulation; Effect Size; Statistical Significance; Mathematics; Hypothesis Testing + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Rating-Skala; Simulation program; Simulationsprogramm; Mathematik; Hypothesenprüfung; Hypothesentest
Abstract	We investigate two non-iterative estimation procedures for Rasch models, the pair-wise estimation procedure (PAIR) and the Eigenvector method (EVM), and identify theoretical issues with EVM for rating scale model (RSM) threshold estimation. We develop a new procedure to resolve these issues--the conditional pairwise adjacent thresholds procedure (CPAT)--and test the methods using a large number of simulated datasets to compare the estimates against known generating parameters. We find support for our hypotheses, in particular that EVM threshold estimates suffer from theoretical issues which lead to biased estimates and that CPAT represents a means of resolving these issues. These findings are both statistically significant (p < 0.001) and of a large effect size. We conclude that CPAT deserves serious consideration as a conditional, computationally efficient approach to Rasch parameter estimation for the RSM. CPAT has particular potential for use in contexts where computational load may be an issue, such as systems with multiple online algorithms and large test banks with sparse data designs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01