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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
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 …
Multiple Imputation Of The Guessing Parameter In The Case Of Missing Data, Grant J. Orley
Multiple Imputation Of The Guessing Parameter In The Case Of Missing Data, Grant J. Orley
College of Education and Human Sciences: Dissertations, Theses, and Student Research
Missing data are a significant problem in testing. Research into strategies for dealing with it have yielded no clear consensus about the best approach to take. Accuracy of ability estimates, fairness and scoring transparency are affected by the choice of missing data handling technique. In this simulation study, we propose a technique of multiple imputation of the guessing parameter using both item difficulty and individual ability estimates. This approach was compared to several other popular strategies for imputing values, such as: treating the item as incorrect, imputing a guessing parameter of 0.5, proportion correct imputation, multiple imputation of responses, and …
The Effects Of Missing Data Treatment On Person Ability Estimates Using Irt Models, Sonia Mariel Suarez Enciso
The Effects Of Missing Data Treatment On Person Ability Estimates Using Irt Models, Sonia Mariel Suarez Enciso
College of Education and Human Sciences: Dissertations, Theses, and Student Research
Unplanned missing responses are common to surveys and tests including large scale assessments. There has been an ongoing debate on how missing responses should be handled and some approaches are preferred over others, especially in the context of the item response theory (IRT) models. In this context, examinees’ abilities are normally estimated with the missing responses generally ignored or treated as incorrect. Most of the studies that have explored the performance of missing data handling approaches have used simulated data. This study uses the SERCE (UNESCO, 2006) dataset and missingness pattern to evaluate the performance of three approaches: treating omitted …
Improving Irt Parameter Estimates With Small Sample Sizes: Evaluating The Efficacy Of A New Data Augmentation Technique, Brett P. Foley
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 …