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Matthew Naveiras


Research Area: Quantitative Methods

Representative Publications

Methodological Papers in Peer-Reviewed Journals  
  • Cho, S.-J., Shen, J., & Naveiras, M. (2019). Multilevel reliability measures of latent scores within an item response theory framework. Multivariate Behavioral Research, 54, 856-881.
  • Cho, S.-J., Naveiras, M., & Barton, E. E. (2021). Modeling multivariate count time series data with a vector Poisson log-normal additive model: Applications to testing intervention effects in single-case designs. Multivariate Behavioral Research.
  • Cho, S.-J., Watson, D. G., Jacobs, C., & Naveiras, M. (2021). A Markov mixed-effect multinomial logistic regression model for nominal repeated measures: An analysis on syntactic self-priming effects. Multivariate Behavioral Research.
  • Cho, S.-J., Brown-Schmidt, S., De Boeck, P., & Naveiras, M. (in press). Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic model. Psychological Methods. [Funding was supported by the National Science Foundation (SES 1851690); Code for parameter estimation and data visualization can be found in the paper; A tutorial on fitting a generalized additive logistic model using R is forthcoming.]
  • Cho, S.-J., De Boeck, P., Naveiras, M., & Ervin, H. (in press). Level-specific diagnostic plots, tests, and measures for random effects selection in multilevel linear models. Behavior Research Methods. [Funding was supported by the National Science Foundation (SES 1851690); Code for level-specific residual calculations, and diagnostic measures, plots, and tests can be found in the paper.]
  • Cho, S.-J., Preacher, K. J., Yaremych, H., Naveiras, M., Fuchs, D., & Fuchs, L. S. (in press.) Modeling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model. British Journal of Mathematical and Statistical Psychology. [Code for parameter estimation and data visualization can be found in the supplementary materials.]

Journal/Book Chapters

  • Brown-Schmidt, S., Cho, S.-J., De Boeck, P., & Naveiras, M. (2020). Statistical modeling of intensive categorical time series eye-tracking data using dynamic generalized linear mixed effect models with crossed random effects. A special issue of "Gazing toward the future: Advances in eye movement theory and applications", Psychology of learning and motivation series (Volume 73). [Funding was supported in part by the National Science Foundation (SES 1851690)]
  • Cho, S.-J., Brown-Schmidt, S., Naveiras, M., & De Boeck, P. (2020). A dynamic generalized mixed effect model for intensive binary temporal-spatio data from an eye tracking technique. In H. Jao & R.W. Lissitz (Eds.), Innovative psychometric modeling and methods (pp. 45-68). Charlotte, NC: Information Age Publishing.

Conference Presentations

  • Naveiras, M., & Cho, S.-J. (2020). Using auxiliary item information in the item parameter estimation of a graded response model. Poster presented at annual Meeting of National Council on Measurement in Education (NCME), virtual.
  • Naveiras, M., Cho, S.-J., & Shen, J. (2020). Multilevel reliability measures of latent scores within an item response theory framework. Paper presented at annual Meeting of National Council on Measurement in Education (NCME), virtual.
  • Naveiras, M., Cho, S.-J., De Boeck, P., & Brown-Schmidt, S. (2020). A dynamic tree-based item response model. Poster presented at the International Meeting of Psychometric Society, College Park, MD.
  • Brown-Schmidt, S., Naveiras, M., Cho, S.-J., & De Boeck, P. (2021). A dynamic tree-based item response model for visual world eye-tracking data. Paper presented at the 34th annual CUNY conference on human sentence processing.
  • Naveiras, M., Cho, S.-J., Goodwin, A.P., & Salas, J.A. (2021). Analysis of digital reading processes from multimodal time-series data using deep learning. Paper presented at annual Meeting of National Council on Measurement in Education (NCME), virtual.
  • Naveiras, M., Cho, S.-J., Goodwin, A., & Salas, J. (2022). Analyzing multimodal time-series digital reading-process and non-time-series data with recurrent neural networks. Annual meeting of National Council on Measurement in Education (NCME). San Diego, CA.
  • Goodwin, A., Cho, S.-J., Naveiras, M., & Salas, J. (2022). In-the-moment digital reading behaviors and links to comprehension for young adolescent learners. Annual meeting of the American Educational Research Association (AERA). San Diego, CA.

Web Materials

  • Naveiras, M., & Cho, S.-J. (2020). Tutorial: Fitting dynamic tree-based item response models to intensive polytomous time series eye-tracking data using R. https://naveirmd.github.io/IRTree-Tutorial/