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Item Response Theory, Parameter Recovery, Graded Response Model

Bahry, Louise M

Supervisor and department: Rogers, W. Todd Educational Psychology

Examining committee member and department: Cui, Ying Educational Psychology Varnhagan, Connie Psychology

Department: Department of Educational Psychology

Specialization: Measurement, Evaluation and Cognition

Date accepted: 2012-01-31T12:12:44Z

Graduation date: 2012-06

Degree: Master of Education

Degree level: Master's

Abstract: Item Response Theory IRT has been extensively used in educational research with large sample sizes and normally distributed traits. However, there are cases in which distributions are not normal, and research has shown that the estimation of parameters becomes problematic with non-normal data. This study investigates the effects of skewness on parameter estimation using the Graded Response Model GRM and MULTILOG. Three distribution types extreme and moderate skewness and a baseline condition i.e. normal and seven sample sizes from n = 100 to n = 3,000 were investigated using simulations. In keeping with previous findings, the extremely skewed distribution condition resulted in the poorest estimates regardless of sample size. In general, the accuracy of parameter estimation increased as sample size increased. For the normally distributed conditions, results suggest a minimum sample size of 750 for accurate estimation. Implications of these findings are discussed.

Language: English

DOI: doi:10.7939-R36H8X

Rights: Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.





Author: Bahry, Louise M

Source: https://era.library.ualberta.ca/


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University of Alberta Polytomous item response theory parameter recovery: An investigation of nonnormal distributions and small sample size by Louise Marie Bahry A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Education in Measurement, Evaluation and Cognition Department of Educational Psychology ©Louise Marie Bahry Spring 2012 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only.
Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the authors prior written permission. Running head: PIRT Parameter Recovery Abstract Item Response Theory (IRT) has been extensively used in educational research with large sample sizes and normally distributed traits.
However, there are cases in which distributions are not normal, and research has shown that the estimation of parameters becomes problematic with non-normal data.
This study investigates the effects of skewness on parameter estimation using the Graded Response Model (GRM) and MULTILOG.
Three distribution types (extreme and moderate skewness and a baseline condition (i.e.
normal) and seven sample sizes (from n = 100 to n = 3,000) were investigated using simulations.
In keeping with previous findings, the extremely skewed distribution condition resulted in the poorest estimates regardless of sample size.
In general, the accuracy of parameter estimation increased as sample size ...





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