Project Activities
The model parameter estimation algorithms for these extensions will be developed by the method of moments, the Monte Carlo EM algorithm approach, and the Bayesian HMC approach used in the software package Stan.
Key outcomes
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.
People and institutions involved
IES program contact(s)
Project contributors
Products and publications
- R package bspam GitHub repository:
- Shiny App:
- The project website has been designed for applied education researchers to use to navigate to install and use the R package bspam for our example data as well as for their own ORF assessment data.
Project website:
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.
Potgieter, C. J., Kamata, A., Kara, Y., & Qiao, X. (2025). Joint analysis of dispersed count-time data using a bivariate latent factor model. British Journal of Mathematical and Statistical Psychology.
Potgieter, C., Qiao, X., Kamata, A., & Kara, Y. (2024). Likelihood-based estimation of model-derived oral reading fluency. Journal of Educational Measurement. http://dx.doi.org/10.1111/jedm.12404
Qiao, X., Kamata, A., Kara, Y., Potgieter, C., & Nese, J. (2025). Beta-binomial model for count data: An application in estimating model-based oral reading fluency. Educational and Psychological Measurement. http://dx.doi.org/10.1177/00131644251335914
Qiao, X., Kamata, A., & Potgieter, N. (2023). Incorporating calibration errors in oral reading fluency scoring. British Journal of Mathematical and Statistical Psychology. http://doi.org/10.1111/bmsp.12348
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