|Advances in Longitudinal Methods in the Social and Behavioral Sciences
Jeffrey R. Harring & Gregory R. Hancock (Eds.)
2012, Information Age Publishing
This volume is a resource intended for advanced graduate students, faculty, and applied researchers interested in longitudinal data analysis, especially in the social and behavioral sciences. The chapters are written by established methodological researchers from diverse research domains such as psychology, biostatistics, educational statistics, psychometrics, and family sciences. Each chapter exposes the reader to some of the latest methodological developments and perspectives in the analysis of longitudinal data, and is written in a didactic tone that makes the content accessible to the broader research community. Part I, MODELING LONGITUDINAL DATA: EXAMINING FACETS OF CHANGE OR VARIABILITY. Part II, DRAWING VALID INFERENCES:
LONGITUDINAL DESIGN CONSIDERATIONS AND MODEL ASSUMPTIONS. Part III, THE ROLE OF MEASUREMENT IN MODELING WITHIN-SUBJECT AND BETWEEN-SUBJECT EFFECTS.
|Advances in Latent Variable Mixture Models
Gregory R. Hancock & Karen M. Samuelsen (Eds.)
2008, Information Age Publishing
This volume contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a “lay of the land” for latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems, deals with mixtures for data that are hierarchical in nature either due to the data’s sampling structure or to the repetition of measures (of varied types) over time. Part II, Models for Assessment and Diagnosis, addresses scenarios for making judgments about individuals’ state of knowledge or development, and about the instruments used for making such judgments. Finally, Part III, Challenges in Model Evaluation, focuses on some of the methodological issues associated with the selection of models most accurately representing the processes and populations under investigation.