Introduction to Multilevel Modeling Techniques : MLM and SEM Approaches Using Mplus

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition:An expanded focus on the nature of different types of multilevel data structures (e.g., cross-section, longitudinal, cross-classified, etc.) for addressing specific research goalsVaried modelling methods for examining longitudinal data including random-effect and fixed-effect approachesExpanded coverage illustrating different model-building sequences and how to use results to identify possible model improvementsAn expanded set of applied examples used throughout the textUse of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available onlineThis is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.