Shudong Wang, PhD
Shudong Wang focuses on product design and development, including psychometric development of MAP Reading Fluency and MAP Growth Spanish Reading. His research interests include adaptive testing and generalized linear mixed-model applications in educational measurement and psychometric research. Dr. Wang has published over 20 peer-reviewed journal articles and book chapters. He has presented at numerous conferences, including NCME, AERA, and CCSSO, on topics such as CAT item selection and quality of student scoring, effects of CAT designs on content coverage and efficiency, and accuracy of student ability estimations. Prior to joining NWEA in 2009, he was a senior psychometrician at ETS. Dr. Wang holds a PhD in educational research methodology from the University of Pittsburgh.
Publications by Shudong Wang
This technical report documents the processes and procedures employed by NWEA to build and support the MAP Reading Fluency assessment.
By: Shudong Wang
Products: MAP Reading Fluency
This technical report documents the processes and procedures employed by NWEA to build and support the Spanish MAP Growth Reading assessment.
This document presents the results of a calibration study conducted to investigate the possible consequences of replacing the English MAP Growth Math item parameters with the Spanish math item parameters from calibrating Spanish items using empirical data.
Effects of computerized adaptive test designs on content coverage and efficiency of reading comprehension test with passages aligned to Common Core State Standards
The major purpose of this paper is to investigate the effects of CAT test design and bank distribution on the content coverage and the efficiency of the tests.
By: Shudong Wang, Hong Jiao
Construct validity and measurement invariance of computerized adaptive testing: Application to Measures of Academic Progress (MAP) using confirmatory factor analysis
This study, using real data, provides empirical evidence of construct and invariance construct of MAP scales across grades at different academic calendars for 10 different states.
By: Shudong Wang, Marth S. McCall, Hong Jiao, Gregg Harris
Validation of longitudinal achievement constructs of vertically scaled computerised adaptive tests: a multiple-indicator, latent-growth modelling approach
The current investigative study uses a multiple-indicator, latent-growth modelling (MLGM) approach to examine the longitudinal achievement construct and its invariance for MAP Growth.
By: Shudong Wang, Hong Jiao, Liru Zhang
The effect of nonignorable missing data in computerized adaptive test on item fit statistics for polytomous item response models
These studies are conducted based on assumptions under regular conditions for fixed test forms, such as no missing responses and normal distribution of unidimensional ability for a population.
By: Shudong Wang, Hong Jiao