Brandon L. Crawford, PhD

Assistant Professor of Applied Health Science


Curriculum vitae



Department of Applied Health Science

School of Public Health, Indiana University, Bloomington



The Development of a Standardized Effect Size for the SIBTEST Procedure


Journal article


James D. Weese, R. Turner, Allison J. Ames, Xinya Liang, Brandon L. Crawford
Journal of Experimental Education, 2022

Semantic Scholar DOI
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APA   Click to copy
Weese, J. D., Turner, R., Ames, A. J., Liang, X., & Crawford, B. L. (2022). The Development of a Standardized Effect Size for the SIBTEST Procedure. Journal of Experimental Education.


Chicago/Turabian   Click to copy
Weese, James D., R. Turner, Allison J. Ames, Xinya Liang, and Brandon L. Crawford. “The Development of a Standardized Effect Size for the SIBTEST Procedure.” Journal of Experimental Education (2022).


MLA   Click to copy
Weese, James D., et al. “The Development of a Standardized Effect Size for the SIBTEST Procedure.” Journal of Experimental Education, 2022.


BibTeX   Click to copy

@article{james2022a,
  title = {The Development of a Standardized Effect Size for the SIBTEST Procedure},
  year = {2022},
  journal = {Journal of Experimental Education},
  author = {Weese, James D. and Turner, R. and Ames, Allison J. and Liang, Xinya and Crawford, Brandon L.}
}

Abstract

In this study a standardized effect size was created for use with the SIBTEST procedure. Using this standardized effect size, a single set of heuristics was developed that are appropriate for data fitting different item response models (e.g., 2-parameter logistic, 3-parameter logistic). The standardized effect size rescales the raw beta-uni value using a pooled variation that incorporates the beta-uni inclusion factor. Although the heuristics for the standardized and unstandardized effect sizes provide similar true-positive and false-positive rates in most conditions, the standardized effect size provides higher true-positive rates for conditions where item response variability is smaller in proportion to raw score differences. Inflated false-positive rates were solely impacted by smaller sample sizes, whereas larger sample sizes improved true-positive rates. An empirical application is provided to demonstrate how the standardized effect size provides for a more consistent comparison across items with varying response distributions. This study lays the foundation for the utilization of a standardized effect size for both dichotomous and polytomous item response models using the suite of SIBTEST procedures.


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