Dr. Jason Bryer is currently an Assistant Professor and Associate Director in the Data Science and Information Systems department at the City University of New York. He is currently the Principal Investigator of the FIPSE ($3 million #P116F150077) and IES funded ($3.8 million R305A210269) Diagnostic Assessment and Achievement of College Skills (DAACS), which is a suite of technological and social supports designed to optimize student learning. Dr. Bryer’s other research interests include quasi-experimental designs with an emphasis on propensity score analysis, data systems to support formative assessment, and the use of open source software for conducting reproducible research. He is the author of over a dozen R packages, including three related to conducting propensity score analyses. When not crunching numbers, Jason is a wedding photographer and proud dad to three boys.
Ph.D. in Educational Psychology & Methodology, 2014
University at Albany
M.S. in Educational Psychology & Methodology, 2009
University at Albany
B.S. in Mathematics, 1999
The College of Saint Rose
An R package to interface with the Brickset.com API for getting data about LEGO sets
Visual Introduction to Maximum Likelihood Estimation
The Diagnostic Assessment and Achievement of College Skills is a diagnostic assessment designed to help students transition to college. DAACS provides personalized feedback about students’ strengths and weaknesses in terms of key academic and self-regulated learning skills, linking them to the resources to help them be successful students.
This project supports the implementaion of Positive Behavioral Interventions & Supports (PBIS) in New York State. PBIS is a systems approach to creating and maintaining positive school climates where teachers can teach and students can learn. This evidence-based framework emphasizes preventing school discipline problems.
An R package designed to help analyzing and visualizing Likert type items.
This package provides functions and a shiny application to simulate inter-rater reliability statistics based on various scoring and response models. The initial motivation for this package is to understand the relationship between percent rater agreement and intraclass correlation.
Propensity Score Analysis (PSA) is a statistical approach for estimating causal effects from observational studies. This project includes materials from workshops taught, an Shiny application for conducting PSA, and an early draft of a PSA book.
The ShinyQDA package is designed to assist researchers with the analysis of qualitative data.
Editable DataTables for shiny apps.
An R package to maintain data caches.
R Package and Shiny Application for the Analysis of Qualitative Data.
An R Package to Support Propensity Score Analysis.
Propensity score matching for non-binary treatments.
An R package to interface with the Integrated Postsecondary Education Data System.
Machine Learning Dashboard.
An R package for estimating and visualizing multilevel propensity score models.
An R package to interface with the National Assessment of Educational Progress (NAEP) restricted use databases. This includes access any analyzing data using the replicate weights and multiple plausible values.
A data-only R package for the 2009 Programme of International Student Assessment (PISA) conducted by Organisation for Economic Co-operation and Development (OECD).
An R package to interface with the Qualtrics.com survey system.
An R package to get Rural-Urban Commuting Area (RUCA) Codes from zip codes.
The sqlutils package provides a set of utility functions to help manage a library of structured query language (SQL) files.
An R package to create timeline figures.
I am currently teaching the following course at the City University of New York (CUNY) in the Master of Science in Data Science program:
R Package to support DATA606 is available here: https://github.com/jbryer/DATA606