Project Activities
People and institutions involved
IES program contact(s)
Products and publications
Bui, M. T., Potgieter, C. J., & Kamata, A. (2022). Penalized likelihood methods for modeling count data. Journal of Applied Statistics, 1-20.
Kara, Y., Kamata, A., Ozkeskin, E. E., Qiao, X., & Nese, J. F. (2023). Predicting oral reading fluency scores by between-word silence times using natural language processing and random forest algorithm. Psychological Test and Assessment Modeling, 65(2), 36-54.
Kara, Y., Kamata, A., Qiao, X., Potgieter, C. J., & Nese, J. F. (2023). Equating Oral Reading Fluency Scores: A Model-Based Approach. Educational and Psychological Measurement, 00131644221148122.
Related projects
Supplemental information
Co-Principal Investigators: Kara, Yusuf; Nese, Joseph; Potgieter, Cornelis
The primary product of this grant will be a Shiny app for rendering user-friendly the R code needed to estimate model parameters. The Shiny app will expand the capabilities of the previous software by providing better ORF score comparability both within and between students for better longitudinal and cross-sectional studies, as well as better estimates of measurement errors for ORF scores. The research team will also demonstrate the use of the new software on data collected for 150 passages as part of the work done for R305A140203 and develop web-based tutorials for supporting applied researchers who want to use the Shiny app.
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