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.

Modeling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modeling and teacher value-added modeling are complex.

This study examined the utility of response time-based analyses in understanding the behavior of unmotivated test takers. For an adaptive achievement test, patterns of observed rapid-guessing behavior and item response accuracy were compared to the behavior expected under several types of models that have been proposed to represent unmotivated test taking behavior.

This study examined developmental trends in academic achievement gaps between poverty and race/ethnicity groups from school entry to middle school using two large longitudinal data sets. We used time-varying effect modeling (TVEM) to estimate how the associations among race/ethnicity, poverty status, and math and reading achievement vary across continuous age from age 5 to age 15.

Megan Kuhfeld shares surprising early findings from her work exploring patterns in students’ summer learning loss.

Megan Kuhfeld draws on data from the 3.4 million students who took the NWEA MAP Growth assessments to find that summer slide is common, but not inevitable. According to the data, the students who experienced the greatest loss were those who made the greatest gains during the previous school year.

his study describes a convergent validity analysis of the SEDA growth estimates in mathematics and English Language Arts (ELA) by comparing the SEDA estimates against estimates derived from NWEA’s MAP Growth assessments.