There has been increasing concern about the presence of disengaged test taking in international assessment programs and its implications for the validity of inferences made regarding a country’s level of educational attainment. In this paper, the author discusses six important insights yielded by 20 years of research on this and  implications for assessment programs.

This study investigated test-taking engagement on a large-scale state summative assessment. Overall, results of this study indicate that disengagement has a material impact on individual state summative test scores, though its impact on score aggregations may be relatively minor.

The more frequent collection of response time data is leading to an increased need for an understanding of how such data can be included in measurement models. Models for response time have been advanced, but relatively limited large-scale empirical investigations have been conducted. We take advantage of a large data set from the adaptive NWEA MAP Growth Reading Assessment to shed light on emergent features of response time behavior.

This paper describes a method for identifying partial engagement and provides validation evidence to support its use and interpretation. When test events indicate the presence of partial engagement, effort-moderated scores should be interpreted cautiously.

This visualization was developed to provide state-level insights into how students performed on MAP Growth in the 2020–2021 school year. Assessments are one indicator, among many, of the student impact from COVID-19. Our goal with this tool is to create visible data that informs academic recovery efforts that will be necessary in the 2022 school year and beyond.

This guide explains the analyses, statistics, terms, and data included in the NWEA state dashboard, Exploring the Educational Impacts of COVID-19.

To avoid the subjectivity of having a single person evaluate a construct of interest, multiple raters are often used. While a range of models to address measurement issues that arise when using multiple raters have been presented, few are available to estimate growth in the presence of multiple raters. This study provides a model that removes all but the shared perceptions of raters at a given timepoint then adds on a latent growth curve model across timepoints. Results indicate that the model shows promise for use by researchers who want to estimate growth based on longitudinal multi-rater data.