Publication Date
2023
Document Type
Thesis
Committee Members
David LaHuis, Ph.D. (Committee Chair); Debra Steele-Johnson, Ph.D. (Committee Member); Joseph Houpt, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Item Response Trees are a type of item response model that incorporates information about conditional responding to items using a rooted tree graph structure. Researchers have used item response trees for common measurement tasks and for testing novel hypotheses. Previous simulation studies investigating item response trees either lack generalizability to the broad domain of their use or lack thorough investigation and reporting of the results. I conducted a simulation study to explore how sample size, test length, item characteristics, and tree structure affect both item and person parameter recovery for 1PL and 2PL models. The results suggested that, as with any item response model, item response tree models are unbiased. However, large samples and long test lengths are needed to minimize estimate uncertainty. Issues of sample size and test length are compounded by the conditional structure incorporated in item response tree models. In particular, the depth of the tree and low item endorsement can pose severe estimation issues when sample sizes are not large and test lengths are not long. I used posterior predictive simulations to provide the reader with a practical understanding of the limitations of item response trees in the context of item and personnel selection and prediction of external variables.
Page Count
217
Department or Program
Department of Psychology
Year Degree Awarded
2023
Copyright
Copyright 2023, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
ORCID ID
0000-0003-1638-1912
