Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random…

Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…

Researchers in the field of early adolescence interested in quantifying the environmental influences on a response variable of interest over time would use cluster sampling (i.e., obtaining repeated measures from students nested within classrooms and/or schools) to obtain the needed sample size. The resulting longitudinal data would be nested at…

Missing data (attrition and non-response) are a feature of most surveys especially longitudinal/panel studies. And many such studies now have multilevel designs and hence multilevel data structures. Recent advances in imputation methodology now offer social researchers opportunities to address issues of missing data in a statistically principled…

Paid employment is one of the most common extracurricular activities among full-time undergraduates, and an array of studies has attempted to measure its impact. Methodological concerns with the extant literature, however, make it difficult to draw reliable conclusions. Furthermore, the research on working college students has little to say about…

In this study, we analyzed data for the Illinois high school class of 2003 to determine the impact of dual credit participation on postsecondary attainment. We matched 8,095 dual credit participants to an equal number of nonparticipants within the same high school at the point of postsecondary enrollment using propensity scores calculated through…

Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying…

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and…

Using data from the 2006 Program for International Student Assessment (PISA), we explored nine aspects of science engagement (science self-efficacy, science self-concept, enjoyment of science, general interest in learning science, instrumental motivation for science, future-oriented science motivation, general value of science, personal value of…

Designing learning experiences that support the development of coherent understanding of complex scientific phenomena is challenging. We sought to identify analytics that can also guide such designs to support retention of coherent understanding. Based on prior research that distributing study of material over time supports retention, we explored…

Applied early adolescent researchers often sample students (Level 1) from within classrooms (Level 2) that are nested within schools (Level 3), resulting in data that requires multilevel modeling analysis to avoid Type 1 errors. Although several articles have been published to assist researchers with analyzing sample data nested at two levels, few…

Student enrollment in online courses has increased in the past 15 years and continues to grow. However, little is known about students' education experiences or online course outcomes. These are areas of particular interest to the Midwest Virtual Education Research Alliance, whose goal is to understand how to support student success in online…

How can illustrations motivate learners in multimedia learning? Which features make illustrations interesting? Beside the theoretical relevance of addressing these questions, these issues are practically relevant when instructional designers are to decide which features of illustrations can trigger situational interest irrespective of individual…

Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and…

We explored possible cognitive, behavioral, emotional, and physiological risk markers for sleep disturbance in children with autism spectrum disorders. Data from 1,583 children in the Autism Treatment Network were analyzed. Approximately 45 potential predictors were analyzed using hierarchical regression modeling. As medication could confound…