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.
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This 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.
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Ronald H. Heck is Professor of Education at the University of Hawai‘i at Mānoa. His areas of interest include organizational theory, policy, and quantitative research methods.
Scott L. Thomas is Professor and Dean of the College of Education and Social Services, University of Vermont. His specialties include sociology of education, policy, and quantitative research methods
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Hardback. Condition: New. By Ronald H. Heck and Scott Loring Thomas, this text treats the analysis of hierarchical data, such as students nested within schools, through two traditions: multilevel regression (MLM) and multilevel structural equation modeling with latent variables (SEM). It covers models for both univariate and multivariate outcomes, sets the two approaches side by side, and considers which suits a given research aim. Longitudinal data are handled with both random-effect and fixed-effect methods, two-level latent variable models are run in Mplus, and a separate chapter deals with data considerations in examining multilevel models. Seller Inventory # OA-260223-shelf-1-012
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