The effect of nonignorable missing data in computerized adaptive test on item fit statistics for polytomous item response models
By: Shudong Wang, Hong Jiao
For both linear and adaptive tests, it is crucial to evaluate model-data fit because the goodness-of-fit of item response theory (IRT) models are relevant to any purpose of a test. To date, all item fit statistics are derived based on linear tests and almost all studies have been done in the context of linear testing. 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.See More
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
Topics: Item response theory
To avoid the subjectivity of having a single person evaluate a construct of interest (e.g., a student’s self-efficacy in school), multiple raters are often used. This study provides a model for estimating growth in the presence of multiple raters.
This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria, such as AIC, BIC.
By: Kang Xue, Laine Bradshaw
Topics: Measurement & scaling
This technical report documents the processes and procedures employed by NWEA to build and support the Spanish MAP Growth Reading assessment.
Comparability of MAP Growth tests administered through different technology and psychometric infrastructure: A Simulation study
This report presents the results of a mode comparability study conducted through simulations to evaluate how scores from MAP Growth administered on the constraint-based engine (CBE) compare to those administered on the current MAP Growth engine known as COLO.
Important educational policy decisions, like whether to shorten or extend the school year, often assume that growth in achievement is linear through the school year. This research examines this untested assumption using data from seven million students in kindergarten through 8th grade across the fall, winter, and spring of the 2016-17 school year.