Welcome to the Quantitative Methodology and Innovation (QMI).
QMI’s mission is to advance scientific discovery through multi-disciplinary collaboration on research design, data analysis, and innovation in quantitative methods and assessment. Please use the navigation bar to learn more about QMI’s mission, people, projects, and products.
FSU researchers partner with Harvard, MIT on $30M ‘Reach Every Reader’ project
The Florida Center for Reading Research and College of Communication and Information at Florida State University have announced a collaborative partnership with the Harvard Graduate School of Education and the Massachusetts Institute of Technology Integrated Learning Initiative to research improving early childhood literacy through personalized intervention. ...Read More
Conditional Longitudinal Relations of Elementary Literacy Skills to High School Reading Comprehension
July 2019The Journal of Learning Disabilities recently accepted a paper titled, “Conditional Longitudinal Relations of Elementary Literacy Skills to High School Reading Comprehension,” authored by Yaacov Petscher, Hugh Catts, and Emily Solari. The study explores the longitudinal development... Read More
Differential Co-Development of Vocabulary Knowledge and Reading Comprehension for Students with and without Learning Disabilities
A paper was recently accepted in the Journal of Educational Psychology entitled, ‘Differential Co-Development of Vocabulary Knowledge and Reading Comprehension for Students with and without Learning Disabilities.” This paper looked at the co-development of reading comprehension...Read More
An instrument to assess the multidimensional aspects of grief in response to the death of a loved one as it pertains to adolescents.
This application will generate a scatter plot with cut points from user input such as correlation, screen cut point, and outcome cut point. Additionally, a host of classification information is provided from these three inputs (assuming bivariate normality). This application will alsoprovide expected positive and negative predictive values based upon sensitivity, specificity, and the baserate of the problem.
This application uses IRT to find a student's ability score based on DELV-S responses input by the user.
This application allows the user to analyze their own longitudinal data. The submitted data is used to fit many different Latent Growth models and allows the user to conveniently compare the results.
This application graphically compares different reading fluency and reading comprehension scores. It also generates various model predictions based on those scores.
This application allows the user to analyze their own data by fitting various quantile regression models. The app also provides an easy way to customize plots and tables representing the model outputs. These outputs can be exported for furthur analysis and study.
This application will assist the user in choosing an academic reading screening tool that best fits their needs. The application will search through the current screeners advertised at charts.intensiveintervention.org and provide a checklist, based on user input, highlighting the screeners that fit the most criteria.