Introduction to Modern Modelling Methods: (The SAGE Quantitative Research Kit)

Using simple and direct language, this concise text provides practical guidance on a wide range of modeling methods and techniques for use with quantitative data. It covers: ·       2-level Multilevel Models ·       Structural Equation Modeling (SEM) ·       Longitudinal Modeling using multilevel and SEM techniques ·       Combining organizational and longitudinal models Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Table of Contents:
Clustering and Dependence: Our Entry into Multilevel Modeling Multilevel Modeling: A Conceptual Introduction Multilevel Model Building Steps and Example Introduction to Structural Equation Modeling Specification and Identification of Structural Equation Models Building Structural Equation Models Longitudinal Growth Curve Models in MLM and SEM An Applied Example of Growth Curve Modeling in MLM and SEM

About the Author :
D. Betsy McCoach, Ph.D., is professor of Research Methods, Measurement, and Evaluation in the Educational Psychology department at the University of Connecticut, where she teaches graduate courses in Structural Equation Modeling, Multilevel Modeling, Advances in Latent Variable Modeling, and Instrument Design. Dr. McCoach has co-authored over 100 peer-reviewed journal articles, book chapters, and books, including Instrument Design in the Affective Domain and Multilevel Modeling of Educational Data. In 2011, Dr. McCoach founded the Modern Modeling Methods conference. Dr. McCoach is co-Principal Investigator for the National Center for Research on Gifted Education and has served as Principal Investigator, co-Principal Investigator, and/or research methodologist for several other federally-funded research projects/grants. Dr. McCoach’s research interests include latent variable modeling, multilevel modeling, longitudinal modeling, instrument design, and gifted education. Dr. Dakota Cintron, Ph.D., is a postdoctoral scholar for the Evidence for Action (E4A) Methods Laboratory. Dr. Cintron’s research focuses on the application, development, and assessment of quantitative methods in the social and behavioral sciences. His areas of research interest include topics such as item response theory, latent variable and structural equation modeling, longitudinal data analysis, hierarchical linear modeling, and causal inference. Dr. Cintron earned his Ph.D. in Educational Psychology from the University of Connecticut. He has previously held research positions at the Institute for Health, Health Care Policy and Aging Research, the National Institute for Early Education Research, and New Visions for Public Schools.