Embedding item response theory (IRT) models within a multilevel modeling framework has been shown by many authors to allow better estimation of the relationships between predictor variables and IRT latent traits (Adams, Wilson, & Wu, 1997). A multilevel IRT model recently proposed by Kamata (1998, 2001) yields the additional benefit of being able to accommodate data that are collected in hierar...

Multilevel modeling is often used in the social sciences for analyzing data that has a hierarchical structure, e.g., students nested within schools. In an earlier study, we investigated the performance of various prediction rules for predicting a future observable within a hierarchical data set (Afshartous & de Leeuw, 2004). We apply the multilevel prediction approach to the NELS:88 educational...

Past statistical modeling of carbon dioxide (CO 2) emissions has primarily followed the reduced-form specification in the Environmental Kuznets Curve literature. This traditional approach relies on the assumption of a homogenous slope relationship between CO 2 emissions and economic growth across different geographical/political regions. This study uses a panel data comprised of the fifty U.S. ...

BACKGROUND Abdominal aortic aneurysm (AAA) growth is a complex process that is incompletely understood. Significant heterogeneity in growth trajectories between patients has led to difficulties in accurately modeling aneurysm growth across cohorts of patients. We set out to compare four models of aneurysm growth commonly used in the literature and confirm which best fits the patient data of our...

Multilevel organization of morphometric data (cells are "nested" within patients) requires special methods for studying correlations between karyometric features. The most distinct feature of these methods is that separate correlation (covariance) matrices are produced for every level in the hierarchy. In karyometric research, the cell-level (i.e., within-tumor) correlations seem to be of major...

The current study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data was collected on a large (n = 366) multiethnic sample over the course of fi...

Cognitive modeling of the visual concepts used by humans to classify images is a challenging task that first requires the characterization of images in terms of low-level or mid-level features that are salient to human visual perception. To complete the cognitive model, this characterization must then be correlated with higher-level concepts that are evoked in humans as they examine images. Thi...