Educators lack the data necessary to pinpoint concerns and successes in schools. A focus on collecting student achievement data in the past ten years has increased the amount of information school districts have, but these data are insufficient. Successful organizations use extensive data analysis to guide decisions, but few K-12 districts have…

Autocorrelated residuals in longitudinal data are widely reported as common to longitudinal data. Yet few, if any, researchers modeling growth processes evaluate a priori whether their data have this feature. Sivo, Fan, and Witta (2005) found that not modeling autocorrelated residuals present in longitudinal data severely biases latent curve…

This report provides a detailed portrait of Hispanic and White academic achievement gaps and how students' performance has changed over time at both the national and state levels. The report presents achievement gaps using reading and mathematics assessment data from the National Assessment of Educational Progress (NAEP) for the 4th- and 8th-grade…

Reviews the role of exploratory data analysis (EDA) for spatial data mining and presents a case study addressing environmental risk assessments in New York State to illustrate the feasibility and usability of augmenting seriation for spatial data analysis. Describes augmentation with multimedia tools to understand relationships among spatial,…