R&D Hub

Published on Friday, December 3, 2021

Dr. David Kaplan’s study on Bayesian probabilistic forecasting with State NAEP data has been published

Dr. David Kaplan’s study on Bayesian probabilistic forecasting with State NAEP data has been published

This is the culmination of a Cooperative Agreement that was awarded to Dr. Kaplan in July 2019. Cooperative Agreements are one of the tools NAEP uses to advance its goals of keeping an accurate measure of student progress and advancing the field of large-scale assessment. Dr. Kaplan used this opportunity to establish a “proof-of-concept” of how NAEP data can inform policy through more than descriptive plots. In December 2020, he presented at the NAEP R&D Virtual Showcase about how data from NAEP state samples can be used to specify cross-state growth regressions as well as to build foreword looking Bayesian probabilistic predictive models to forecast policy-relevant predictors of educational outcomes. Dr. Kaplan expanded on the Bayesian forecasting potential of NAEP data in the study he published in Large-scale Assessment in Education in July 2021.  

Through the Cooperative Agreements program, NCES funded work leading to the possible expansion of the utility of NAEP and other large-scale assessments. The full-text of Dr. Kaplan’s study, “Bayesian probabilistic forecasting with large-scale educational trend data: A case study using NAEP,” can be found on Springer.

Comments (0)Number of views (509)
Print

More links

title of plugged in news

The Summer 2024 NAEP Data Training Workshop - Applications Open

04-12-2024

Applications are now open for the summer 2024 NAEP Data Training Workshop! This workshop is for quantitative researchers with strong statistical skills who are interested in conducting data analyses using NAEP data. For the first time, participants in this year's training will get an introduction to COVID data collections. Learn more here!

EdSurvey e-book now available!

02-14-2022

Analyzing NCES Data Using EdSurvey: A User's Guide is now available for input from the research community online here.  Check it out and give the team your feedback.

«April 2024»
MonTueWedThuFriSatSun
25262728293031
1234567
891011121314
15161718192021
22232425262728
293012345