Open Access. Powered by Scholars. Published by Universities.®

Education Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 4 of 4

Full-Text Articles in Education

Investigating The Fit Of The Generalized Graded Unfolding Model (Ggum) When Calibrated To Irt Generated Data From Dominance And Ideal Point Models, Abdulla Alzarouni Jul 2021

Investigating The Fit Of The Generalized Graded Unfolding Model (Ggum) When Calibrated To Irt Generated Data From Dominance And Ideal Point Models, Abdulla Alzarouni

College of Education and Human Sciences: Dissertations, Theses, and Student Research

The assessment of model fit in latent trait modelling, better known as item response theory (IRT), is an integral part of model testing if one is to make valid inferences about the estimated parameters and their properties based on the selected IRT model. Though important, the assessment of model fit has been less utilized in IRT research than it should. For example, there have been less research investigating fit for polytomous dominance models such the Graded Response Model (GRM), and to a lesser extent ideal point models such as the Generalized Graded Unfolding Models (GGUM), both in its dichotomous and …


Considerations In S-Χ2: Rest Score Or Summed Score, Priors, And Violations Of Normality, Christine E. Demars, Derek Sauder Apr 2019

Considerations In S-Χ2: Rest Score Or Summed Score, Priors, And Violations Of Normality, Christine E. Demars, Derek Sauder

Department of Graduate Psychology - Faculty Scholarship

The S-χ2 item fit index is one of the few item fit indices that appears to maintain accurate Type I error rates. This study explored grouping examinees by the rest score or summed score, prior distributions for the item parameters, and the shape of the ability distribution. Type I error was slightly closer to the nominal level for the total-score S-χ2 for the longest tests, but power was higher for the rest-score S-χ2 in every condition where power was < 1. Prior distributions reduced the proportion of estimates with extreme standard errors but slightly inflated the Type I error rates in some conditions. When the ability distribution was not normally distributed, integrating over an empirically-estimated distribution yielded Type I error rates closer to the nominal value than integrating over a normal distribution.


Improvements For Differential Functioning Of Items And Tests (Dfit): Investigating The Addition Of Reporting An Effect Size Measure And Power, Keith D. Wright May 2011

Improvements For Differential Functioning Of Items And Tests (Dfit): Investigating The Addition Of Reporting An Effect Size Measure And Power, Keith D. Wright

Educational Policy Studies Dissertations

Standardized testing has been part of the American educational system for decades. Controversy from the beginning has plagued standardized testing, is plaguing testing today, and will continue to be controversial. Given the current federal educational policies supporting increased standardized testing, psychometricians, educators and policy makers must seek ways to ensure that tests are not biased towards one group over another.

In measurement theory, if a test item behaves differently for two different groups of examinees, this test item is considered a differential functioning test item (DIF). Differential item functioning, often conceptualized in the context of item response theory (IRT) is …


Improving Irt Parameter Estimates With Small Sample Sizes: Evaluating The Efficacy Of A New Data Augmentation Technique, Brett P. Foley Jul 2010

Improving Irt Parameter Estimates With Small Sample Sizes: Evaluating The Efficacy Of A New Data Augmentation Technique, Brett P. Foley

College of Education and Human Sciences: Dissertations, Theses, and Student Research

The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different …