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The Ohio State University

1. DiTrapani, John B. Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index.

Degree: PhD, Psychology, 2019, The Ohio State University

This dissertation introduces a new model evaluation tool - the RORME index - that can be used to select an item response model among competing alternatives. This criterion assesses the out-of-sample predictive performance of candidate models using a k-fold cross-validation procedure. The validity of RORME is evaluated with several simulation studies, which conclude that the proposed index performs well under a multitude of conditions. RORME often follows a similar selection pattern as the AIC; however, unlike the AIC or BIC, it does not rely on underlying modelassumptions or likelihood-based estimation. The RORME index is also flexible in how a researcher would prefer a model to be evaluated - the specic performance metric used to calculate RORME can be changed and investigations into local misfit (e.g. item- or person-specic mist) can be conducted.The RORME index is of particular interest when utilized to compare item response tree, or IRTree, models with non-IRTree alternative models. IRTree models are a particular application of the item response theory framework that allow interesting, novel research questions to be addressed. One of these novel explorations is assessing the most appropriate process that a respondent undertakes when responding to an item. This type of research question naturally requires comparing several candidate models and eventually endorsing the most appropriate alternative. Traditional model selection criteria, such as AIC or BIC, may not be appropriate for these types of model comparisons, since IRTree models require the underlying data to be transformed. In this project, the RORME index is developed and applied to directly compare IRTree and non-IRTree models. Simulation results suggest that the new metric can successfully determine the appropriate model when applied in this context, even when criteria like the AIC or BIC are invalid. Advisors/Committee Members: De Boeck, Paul (Advisor).

Subjects/Keywords: Quantitative Psychology; Item response theory; cross-validation; model selection; model fit; item response tree models; quantitative psychology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

DiTrapani, J. B. (2019). Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1555328378474406

Chicago Manual of Style (16th Edition):

DiTrapani, John B. “Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index.” 2019. Doctoral Dissertation, The Ohio State University. Accessed September 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555328378474406.

MLA Handbook (7th Edition):

DiTrapani, John B. “Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index.” 2019. Web. 21 Sep 2019.

Vancouver:

DiTrapani JB. Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2019 Sep 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555328378474406.

Council of Science Editors:

DiTrapani JB. Assessing the Absolute and Relative Performance of IRTrees Using Cross-Validation and the RORME Index. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555328378474406

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